i a road network shortest path analysis: applying

i

A ROAD NETWORK SHORTEST PATH ANALYSIS: APPLYING TIME-VARYING

TRAVEL-TIME COSTS FOR EMERGENCY RESPONSE VEHICLE ROUTING,

DAVIS COUNTY, UTAH

A THESIS PRESENTED TO

THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES

IN CANDIDACY FOR THE DEGREE OF

MASTER OF SCIENCE

By

MICHAEL T. WINN

NORTHWEST MISSOURI STATE UNIVERSITY

MARYVILLE, MISSOURI

JANUARY, 2014

ii

A ROAD NETWORK SHORTEST PATH ANALYSIS

A Road Network Shortest Path Analysis: Applying Time-Varying

Travel-Time Costs for Emergency Response Vehicle Routing, Davis County, Utah

Michael T. Winn

Northwest Missouri State University

THESIS APPROVED

________________________________________________________________________

Thesis Advisor, Dr. Yi-Hwa Wu Date

________________________________________________________________________

Dr. Patricia Drews Date

________________________________________________________________________

Dr. Ming-Chih Hung Date

________________________________________________________________________

Dean of Graduate School, Dr. Gregory Haddock Date

iii

A Road Network Shortest Path Analysis

Abstract

Rapid emergency response to the scene of a traffic accident and transportation of

the injured to a medical facility is critical for saving lives. Traffic congestion is a major

problem in urban areas and Davis County, Utah is no exception. Traffic congestion can

disrupt emergency response, but dynamic network routing can offer solutions. A GIS can

be a useful tool for determining emergency vehicle response routing, and the application

of dynamic variables like historical traffic count data can help emergency response

vehicles avoid traffic congestion and improve response times.

This research examines a methodology where route solvers based on Dijkstra’s

shortest path algorithm in ArcGIS Network Analyst were utilized to identify the closest

ground emergency response unit (e.g., fire station) and hospital (e.g., trauma center) to

each incident and then solving the shortest path problem centered around emergency

response routing scenarios. Cost attributes or impedances, namely distance, free-flow

travel time and time-varying travel time originating from historical traffic data, were

applied to each routing scenario to determine the shortest, fastest, and best (optimal)

routes from an origin to a destination. The best route is defined as the route with the least

travel cost determined by the impedance applied.

Results were analyzed and compared. Findings based on these routing analyses

show that dynamic time-varying travel time derived from historical traffic count data can

iv

optimize emergency response routing, improve travel times and validate that dynamic

network routing can improve emergency response routing above static networks.

Although challenges and limitations existed in this research, it is believed that future

improvements through the incorporation of live traffic data using GPS technology and

traffic cams could greatly enhance this type of research and assist local public safety and

EMS agencies improve levels of service as population growth and subsequent traffic

congestion increases.

v

Table of Contents

Abstract ........................................................................................................................ iii

List of Figures ............................................................................................................. vii

List of Tables ................................................................................................................. x

Acknowledgments ....................................................................................................... xii

List of Abbreviations ................................................................................................. xiii

Chapter 1: Introduction .................................................................................................. 1

1.1 Research Background ....................................................................................... 1

1.2 Research Objectives .......................................................................................... 3

1.3 Study Area ........................................................................................................ 3

Chapter 2: Literature Review ......................................................................................... 8

2.1 Network Analysis ............................................................................................. 8

2.2 Shortest Path Analysis ...................................................................................... 9

2.3 Dijkstra’s Algorithm ....................................................................................... 10

2.4 Static and Dynamic Networks ........................................................................ 10

2.5 Traffic Congestion and Dynamic Emergency Response Routing .................. 12

2.6 Historical Traffic Profiles ............................................................................... 13

Chapter 3: Conceptual Framework and Methodology ................................................. 15

3.1 Data Sources ................................................................................................... 17

3.2 Data Preparation ............................................................................................. 18

3.2.1 Road Network Centerlines ...................................................................... 18

3.2.2 Road Classifications ................................................................................ 19

3.2.3 Historical Hourly Traffic Volume Data .................................................. 21

3.2.4 Grouping Historical Traffic Volume Data .............................................. 23

3.2.5 Historical Traffic Volume Profiles ......................................................... 25

3.2.6 Modeling Historical Traffic Data ............................................................ 33

3.2.7 Incorporating Historical Traffic Data ..................................................... 35

vi

3.3 Developing the Road Network Model ............................................................ 37

3.3.1 One Way Restrictions ............................................................................. 39

3.3.2 Global Turn Delays ................................................................................. 41

Chapter 4: Analysis and Results .................................................................................. 45

4.1 Routing Example for IN-1 .............................................................................. 50

4.1.1 IN-1: Closest Facility Analysis ............................................................... 50

4.1.2 IN-1: Route Analysis Scenario 1 ............................................................ 58


4.1.4 IN-1: Emergency Response Routing Review .......................................... 80

4.2 Routing Example for IN-2 .............................................................................. 84

4.2.1 IN-2: Closest Facility Analysis ............................................................... 84



4.2.4 IN-2: Emergency Response Routing Review ........................................ 109

4.3 Discussion of Results .................................................................................... 113

Chapter 5: Conclusion and Future Improvements ..................................................... 116

5.1 Conclusion .................................................................................................... 116

5.2 Limitations .................................................................................................... 117

5.3 Challenges and Solutions .............................................................................. 119

5.4 Future Improvements .................................................................................... 120

References .................................................................................................................. 122

vii

List of Figures

Figure 1. Study area, Davis County with Utah inset ....................................................... 4

Figure 2. Road network, Davis County, Utah ................................................................. 6

Figure 3. EMS facilities (ground units) and hospitals, Davis County, Utah .................. 7

Figure 4. Methodology flow chart ................................................................................. 16

Figure 5. Road network and the Urban Area Functional Classification system ............ 20

Figure 6. Geographic locations of the ATR sites ........................................................... 22

Figure 7. ATR site 0316 traffic volume profile - Tuesday average, April 2010 ............ 26

Figure 8. ATR site 0316 traffic volume profile – Saturday average, April 2010 .......... 26

Figure 9. ATR site 0316 traffic volume profile – Sunday average, April 2010 ............ 26

Figure 10. ‘DailyProfiles_Time_60min’ table: Profile 3 ............................................... 29

Figure 11. ‘DailyProfiles_Time_60min’ table: Profile 8 ............................................... 29

Figure 12. ‘DailyProfiles_Time_60min’ table: Profile 12 ............................................. 29







Figure 19. Network dataset properties associated with the historical traffic tables ....... 36

Figure 20. Assignment of network attributes ................................................................. 36

Figure 21. File geodatabase data model ......................................................................... 38

Figure 22. Correct one-way travel, from Incident 1 to Ogden Regional Medical

Center ........................................................................................................... 40

Figure 23. Incorrect one-way travel, from Incident 1 to Ogden Regional Medical

Center ........................................................................................................... 40

Figure 24. Turn categories available for various road types .......................................... 42

Figure 25. Global turn delay default settings ................................................................. 43

viii

Figure 26. Global turn delay customized settings .......................................................... 43

Figure 27. Example of routing scenarios S1 and S2 ...................................................... 46

Figure 28. Route analysis flowchart .............................................................................. 47

Figure 29. Analysis settings available for ‘Closest Facility’ solver .............................. 51

Figure 30. Analysis settings available settings for ‘Route’ solver ................................. 51

Figure 31. Routes from nearest ground unit to IN-1 applying DIST impedance .......... 54

Figure 32. Routes from nearest ground unit to IN-1 applying FFTT impedance .......... 55

Figure 33. Routes from nearest ground unit to IN-1 applying TVTT impedance ......... 55

Figure 34. Routes from IN-1 to nearest hospital applying DIST impedance ................ 56

Figure 35. Routes from IN-1 to nearest hospital applying FFTT impedance ................ 57

Figure 36. Routes from IN-1 to nearest hospital applying TVTT impedance ............... 58

Figure 37. IN-1, Scenario 1, Sunday travel time profile, TVTT impedance ................. 63

Figure 38. IN-1, Scenario 1, Tuesday travel time profile, TVTT impedance ................ 64

Figure 39. IN-1 Scenario 1, Route A ............................................................................. 65

Figure 40. IN-1 Scenario 1, Route B ............................................................................. 65

Figure 41. IN-1, Scenario 2, Sunday travel time profile, TVTT impedance ................. 73

Figure 42. IN-1, Scenario 2, Tuesday travel time profile, TVTT impedance ................ 74



Figure 45. IN-1 Scenario 2, Route C ............................................................................. 76

Figure 46. IN-1, combined scenarios, Sunday and Tuesday, DIST impedance ............ 81

Figure 47. IN-1, combined scenarios, Sunday and Tuesday, FFTT impedance ............ 81

Figure 48. IN-1, combined scenarios, Sunday, TVTT impedance ................................ 82

Figure 49. IN-1, combined scenarios, Tuesday, TVTT impedance ............................... 82

Figure 50. Routes from nearest ground unit to IN-2 applying DIST impedance ........... 85

Figure 51. Routes from nearest ground unit to IN-2 applying FFTT impedance ........... 86

Figure 52. Routes from nearest ground unit to IN-2 applying TVTT impedance .......... 87

ix

Figure 53. Routes from IN-2 to nearest hospital applying DIST impedance ................. 88

Figure 54. Routes from IN-2 to nearest hospital applying FFTT impedance ................. 89

Figure 55. Routes from IN-2 to nearest hospital applying TVTT impedance ................ 90

Figure 56. IN-2 Scenario 1, Sunday travel time profile, TVTT impedance ................... 93

Figure 57. IN-2 Scenario 1, Tuesday travel time profile, TVTT impedance .................. 94

Figure 58. IN-2 Scenario 1, Route A .............................................................................. 95

Figure 59. IN-2 Scenario 1, Route B .............................................................................. 95

Figure 60. IN-2 Scenario 2, Sunday travel time profile, TVTT impedance .................. 100

Figure 61. IN-2 Scenario 2, Tuesday travel time profile, TVTT impedance ................. 101



Figure 64. IN-2 Scenario 2, Route C ............................................................................. 103

Figure 65. IN-2 Scenario 2, Route D ............................................................................. 103

Figure 66. IN-2 Scenario 2, Route E.............................................................................. 104

Figure 67. IN-2, combined scenarios, Sunday and Tuesday, DIST impedance ............ 110

Figure 68. IN-2, combined scenarios, Sunday and Tuesday, FFTT impedance ............ 110

Figure 69. IN-2, combined scenarios, Sunday, TVTT impedance ................................ 111

Figure 70. IN-2, combined scenarios, Tuesday, TVTT impedance ............................... 111

x

List of Tables

Table 1. Urban Area Functional Classification system ................................................. 20

Table 2. ATR sites associated with the Functional Classification system ..................... 22

Table 3. April 2010 traffic volumes for ATR site 0316 ................................................ 23

Table 4. April 2010 traffic volumes for ATR site 0316, grouped ................................. 24

Table 5. ‘DailyProfiles_Time_60min’ file geodatabase table ....................................... 28

Table 6. Profile IDs from the ‘DailyProfiles_Time_60min’ table ................................. 28

Table 7. 'Project_Profiles' file geodatabase table ........................................................... 32

Table 8. 'ProjectArea' feature class attribute table ......................................................... 32

Table 9. ‘Global Turn Delay’ directions and penalty values in seconds ....................... 42

Table 10. Incident information from 2010 UDOT crash statistics ................................ 46

Table 11. Analysis settings for finding nearest ground unit to IN-1 ............................. 53

Table 12. Analysis settings for finding nearest hospital from IN-1 ............................... 53

Table 13. Results for finding nearest ground unit to IN-1 ............................................. 54

Table 14. Results for finding nearest hospital from IN-1 .............................................. 54

Table 15. Analysis settings used for S1 ......................................................................... 61

Table 16. Scenario 1, Sunday, Clinton FD to IN-1, DIST impedance .......................... 61

Table 17. Scenario 1, Tuesday, Clinton FD to IN-1, DIST impedance ......................... 61

Table 18. Scenario 1, Sunday, Clinton FD to IN-1, FFTT impedance .......................... 62

Table 19. Scenario 1, Tuesday, Clinton FD to IN-1, FFTT impedance ........................ 62

Table 20. Scenario 1, Sunday, Clinton FD to IN-1, TVTT impedance ......................... 63

Table 21. Scenario 1, Tuesday, Clinton FD to IN-1, TVTT impedance ........................ 64

Table 22. IN-1 Scenario 1, Sunday, comparison of cost impedance between Routes

A and B ........................................................................................................ 70

Table 23. Scenario 2, Sunday, IN-1 to Davis Hospital, DIST impedance ..................... 71

Table 24. Scenario 2, Tuesday, IN-1 to Davis Hospital, DIST impedance ................... 71

Table 25. Scenario 2, Sunday, IN-1 to Davis Hospital, FFTT impedance .................... 72

xi

Table 26. Scenario 2, Tuesday, IN-1 to Davis Hospital, FFTT impedance ................... 72

Table 27. Scenario 2, Sunday, IN-1 to Davis Hospital, TVTT impedance ................... 73

Table 28. Scenario 2, Tuesday, IN-1 to Davis Hospital, TVTT impedance .................. 74

Table 29. IN-1 Scenario 2, Tuesday, comparison of cost impedance between Routes

B and C ........................................................................................................ 79

Table 30. IN-1, combined scenarios, comparison of emergency response routes ......... 83

Table 31. Results for finding nearest ground unit to IN-2 .............................................. 84

Table 32. Results for finding nearest hospital from IN-2 ............................................... 84

Table 33. Scenario 1, Sunday, Kaysville FD to IN-2, DIST impedance ........................ 91

Table 34. Scenario 1, Tuesday, Kaysville FD to IN-2, DIST impedance....................... 91

Table 35. Scenario 1, Sunday, Kaysville FD to IN-2, FFTT impedance ........................ 92

Table 36. Scenario 1, Tuesday, Kaysville FD to IN-2, FFTT impedance ...................... 92

Table 37. Scenario 1, Sunday, Kaysville FD to IN-2, TVTT impedance ....................... 93

Table 38. Scenario 1, Tuesday, Kaysville FD to IN-2, TVTT impedance ..................... 94

Table 39. IN-2 Scenario 1, Tuesday, comparison of cost impedance between Routes

A and B ......................................................................................................... 97

Table 40. Scenario 2, Sunday, IN-2 to Davis Hospital, DIST impedance ...................... 98

Table 41. Scenario 2, Tuesday, IN-2 to Davis Hospital, DIST impedance .................... 98

Table 42. Scenario 2, Sunday, IN-2 to Davis Hospital, FFTT impedance ..................... 99

Table 43. Scenario 2, Tuesday, IN-2 to Davis Hospital, FFTT impedance .................... 99

Table 44. Scenario 2, Sunday, IN-2 to Davis Hospital, TVTT impedance ................... 100

Table 45. Scenario 2, Tuesday, IN-2 to Davis Hospital, TVTT impedance .................. 101

Table 46. IN-2 Scenario 2, Sunday, comparison of cost impedance between Routes

A, B, and C................................................................................................... 107

Table 47. IN-2 Scenario 2, Tuesday, summary of cost impedance between Routes

A, B, C, D, and E ......................................................................................... 108

Table 48. IN-2, combined scenarios, comparison of emergency response routes ......... 112

xii

Acknowledgements

I would first like to thank my thesis advisor, Dr. Yi-Hwa Wu, for her patience and

support throughout this research process. Her advice and understanding of the subject

matter was invaluable. I would like to thank my academic advisor, Dr. Patricia Drews,

who not only helped me with this research, but for over eight years guided and

encouraged me through the GIScience Master’s program. I would also like to thank Dr.

Ming-Chih Hung for his much appreciated assistance as well.

Other individuals and agencies I would like to acknowledge are Mike Price with

Entrada/San Juan, Inc. Nicolas Virgen, Scott Jones, Danielle Herrscher, and Brandi

Trujillo with the Utah Department of Transportation. Bert Granberg and his staff with

the Utah Automated Geographic Reference Center. Joshua Legler and Robert Jex with

the Utah Bureau of Emergency Medical Services. Mike King with the Hill Air Force

Base Fire Department and Patrick McDonald with the Layton City Fire Department. I

want to thank them for generously sharing information, their time, and their insight for

this research.

Lastly, I would like to thank my family for their patience and understanding over

the years. I would especially like to thank my wife Linda, for her love and support

during this long undertaking. Without her strength and encouragement, my educational

goals and this research would not have been possible.

xiii

List of Abbreviations

AGRC: Utah Automated Geographic Reference Center

ATR: Automatic Traffic Recorder

BEMS: Utah Bureau of Emergency Medical Services

DIST: Distance cost attribute or impedance

EMS: Emergency Medical Services

Esri: Environmental Systems Research Institute

FC: Functional Classification (Urban area functional classification system)

FFTT: Free-Flow Travel Time

FGDB: File Geodatabase

GIS: Geographic Information System

GIS-T: Geographic Information Systems for Transportation

GTD: Global Turn Delays

HAFB: Hill Air Force Base

NA: Esri Network Analyst

ND: Network Dataset

NHTSA: National Highway Traffic Safety Administration

TVTT: Time-Varying Travel Time

UDOT: Utah Department of Transportation

1

Chapter 1: Introduction

Emergency medical services (EMS) is a system that provides emergency medical

care. Once it is activated by an incident that causes serious illness or injury, the focus of

EMS is the emergency medical care and the patient(s). Another element of the EMS is

the ground or air transportation of the patient(s) to a hospital or trauma center (National

Highway Traffic Safety Administration Emergency Medical Services [NHTSA EMS]

2013). EMS response time is critical in emergency requests involving injury (Panahi and

Delavar 2009). Technological advances such as geographic information systems (GIS),

can allow emergency vehicles to reach patients more quickly (Wilde 2009), and

efficiency in routing emergency fire and medical vehicles to a traffic incident is critical

for saving lives (Cova 1999).

1.1 Research Background

A GIS can be used for many roles in emergency management. It is an effective

tool for determining emergency vehicle response routing and solving the emergency

vehicle shortest path routing problem (Alivand et al. 2008, Cova 1999, Panahi and

Delavar 2008). A shortest path algorithm applied to a routing problem in a transportation

network can calculate the path with minimal travel cost or least impedance from an origin

to a destination. Depending on the type of cost, the shortest path can be referred to as the

shortest, fastest, or most optimal path or route. There are several impedance factors that

can affect emergency services and vehicle response times. They include distance, travel

time, and traffic congestion as a result of variations in traffic flow related to the time of

2

day. Traffic congestion is a major problem in urban areas and can disrupt emergency

response (Panahi and Delavar 2008; 2009, Naqi et al. 2010).

In recent years, traffic congestion in Davis County, Utah has become more

problematic and widespread, thus affecting emergency response performance. Traffic

congestion will continue to be a concern as the region grows in population and

congestion increases (Utah Department of Transportation [UDOT] 2008, United States

Census Bureau 2012). East-west transportation is restricted by a narrow urban corridor

and many of the residents commute south to Salt Lake County. From 2000 to 2010,

Davis County experienced a population growth rate of 28.2% and an increase in housing

units by 31.6%, and the average population density per square mile increased by 30.7%

(United States Census Bureau 2012). With no signs of slowing population growth or

opportunities for employment, Davis County must plan for a variety of transportation

facilities such as roads and mass transit systems to accommodate the anticipated growth

(UDOT 2008).

This study selected Davis County, Utah as the case study area because of its

constricted, north/south orientated road system and traffic congestion. Using commercial

ready-to-use GIS software, a dynamic road network was created and a real-world

emergency response routing analysis was performed to determine the shortest, fastest,

and most optimal path or routes for emergency response vehicles by applying different

cost attributes or impedances. An analysis and comparison of the resulting emergency

vehicle routing scenarios was made to demonstrate how routes and travel times are

affected when these cost attributes are applied.

3

1.2 Research Objectives

The overall objective of this research was to observe if routes and response times

for emergency response vehicles change due to variations in traffic flow related to the

day (e.g., weekday or weekend) and the time of day (traffic congestion). Commonly used

shortest path algorithms were used to calculate the shortest, fastest, and the most optimal

path from an emergency response unit (e.g., fire station) to an incident (e.g., car crash)

then to a trauma center (e.g., hospital) by applying three cost attributes or impedances to

road network edges: distance, base travel time or free-flow travel time, and time-

dependent or time-varying travel time originating from historical traffic data. A major

component of this research was the application of historical traffic data. To perform this

analysis, traffic volume profiles based on Utah Department of Transportation (UDOT)

traffic count data were created and applied as a network cost attribute. Dynamic routing

based on cost attributes derived from historical travel-time data and applied to network

edges should help response vehicles avoid congested areas and improve travel times (Kok

et al. 2012, Panahi and Delavar 2009).

1.3 Study Area

Davis County was founded in 1850 and is situated in north central Utah (Figure

1). The Wasatch Range borders the east side of the county and the Great Salt Lake

borders the west side. Weber County is located to the north of Davis County with the

Weber River delineating part of the northern county line while Salt Lake County borders

on the south. Davis County has 15 incorporated cities and towns (Figure 1) and a total

population of 306,500 (United States Census Bureau 2012). Lands outside these

4

incorporated cities are primarily uninhabited wetlands, desert or mountainous areas. The

county seat is located in the city of Farmington which is located about mid-point in the

county. Davis County covers about 635 square miles with the Great Salt Lake occupying

more than half of this area. Hill Air Force Base (HAFB) is located entirely within the

northern part of the county and is the home of the Ogden Air Logistics Center (OALC)

which serves primarily as a repair facility for military aircraft (Davis County Emergency

Management Services 2009).

Figure 1. Study area, Davis County with Utah inset

5

An interstate highway (I-15) and a railroad system traverse the entire length of the

County and provide the only major access and egress route for the County. Davis County

contains 1,776 miles of roads mostly in the incorporated areas and includes 1,704 miles

of paved roads and 72 miles of dirt/4wd roads (Figure 2). There are 84 miles of federal

highways, 225 miles of state routes, 1,357 miles of local roads and 38 miles of access

ramps (Utah Automated Geographic Reference Center [Utah AGRC] 2012). It should be

noted that Figure 2 does not show the entire road network created for this research

project.

The study area is served by ten EMS agencies not including HAFB, four

designated emergency medical dispatch agencies and seventeen EMS facilities or ground

units not including HAFB (Utah AGRC 2012, Utah Bureau of Emergency Medical

Services [Utah BEMS] 2012a; b). There are four hospitals located in Davis County, two

of which are designated as resource hospitals that have emergency rooms staffed with

24/7 physicians (Figure 3). There are four Level I (highest level of care) trauma centers

located in the northern portion of Salt Lake County (Salt Lake City) within

approximately 8 miles of the southern border of Davis County and two Level II trauma

centers located in Ogden within 4 miles of the northern border of Davis County (Utah

AGRC 2012, Utah BEMS 2012c).

6

Figure 2. Road network, Davis County, Utah

7

Figure 3. EMS facilities (ground units) and hospitals, Davis County, Utah

8

Chapter 2: Literature Review

Geographic Information Systems for Transportation (GIS-T) represents one of the

most important application areas of GIS technology (Goodchild 2000). Shaw (2010)

referred to GIS-T as the application of information technology to the transportation

problem. Abkowitz et al. (1990) stated over two decades ago that the field of

transportation was inherently geographic and GIS was a technology with considerable

potential for achieving gains in efficiency and productivity for many transportation

applications.

2.1 Network Analysis

A background knowledge of a network can be beneficial to the understanding of

transportation network analysis. A network is essentially a set of lines known as

segments or edges connected or joined by a set of vertices known as nodes or junctions.

A GIS stores these edge and junction features with their attributes. Spatio-temporal

networks are networks whose topology and parameters change with time. These

networks are important to applications such as emergency traffic planning and route

finding (George et al. 2007).

Network analysis in GIS has its origins in the mathematical sub-disciplines of

graph theory and topology. An important association between graph theory and a

network is topology. Topological properties such as connectivity, coincidence, and

adjacency are key to network analysis. An important advantage of a GIS-based network

9

in contrast to graph theory is the geographic elements of shape or length. Length is

essential for calculating travel time (Curtin 2007).

The use of GIS for network analysis is essential for improving emergency

response routing based on travel time information (Alivand et al. 2008, Panahi and

Delavar 2008). Curtin (2007) thought network analysis was one of the most significant

research and application areas in GIScience while Sadeghi-Niarki et al. (2011) mentioned

network analysis is a powerful tool in the GIS environment for solving the optimal path

in a network.

2.2 Shortest Path Analysis

A shortest path problem is to find a path with minimum travel cost from one or

more origins to one or more destinations through a network (Lim and Kim 2005, Panahi

and Delavar 2008). Shortest path analysis is important because of its wide range of

applications in transportation (Lim and Kim 2005). Naqi et al. (2010) stated that the

shortest path helps calculate the most optimal route, and optimal routing is the process of

defining the best route to get from one location to another. The best route could be the

shortest or fastest depending on how it is defined.

The shortest path can be computed either for a given start time or to find the start

time and the path that leads to least travel time journeys. The classic shortest path

problem and finding the best route for vehicle routing in static road networks based on

Dijkstra’s algorithm has been examined extensively in the literature over the years

(Alazab et al. 2011, Alivand et al. 2008, Kim et al. 2005). George et al. (2007) claimed

that developing efficient algorithms for computing shortest paths in a time-varying spatial

network can be challenging.

10

2.3 Dijkstra’s Algorithm

Dijkstra’s algorithm or variations of it are the most commonly used route finding

algorithm for solving the shortest path (Sadeghi-Niaraki et al. 2011). Dijkstra's algorithm

is sometimes called the single-source shortest path because it solves the single-source

shortest-path problem on a weighted, directed graph (G = V, E) where

V is a set whose elements are called vertices (nodes, junctions, or intersections) and E is

a set of ordered pairs of vertices called directed edges (arcs or road segments). To find a

shortest path from a source s vertex or location to a destination location d, Dijkstra's

algorithm maintains a set S of vertices whose final shortest-path weights from the source

s have already been determined. Knowing that w is the edge weight, the edge is an

ordered pair (u, v) and assuming w (u, v) ≥ 0 for each edge (u, v) ϵ E, the algorithm

repeatedly selects the vertex u ϵ V – S with the minimum shortest-path estimate, adds u

to S, and relaxes all edges leaving u (Cormen et al. 2001, Puthuparampil 2007).

The commercial GIS software that was used to perform the route analysis for this

study is Esri ArcGIS Network Analyst. ArcGIS is suitable for this kind of research

because it is commercially available, and the Network Analyst extension is included in

the student edition of ArcGIS. The route solver in Network Analyst to determine the

shortest path is based on Dijkstra's algorithm (Karadimas et al. 2007).

2.4 Static and Dynamic Networks

A dynamic network differs from a static network in that travel time changes or

varies with respect to time. Variables used to store the cost of traversing across an edge

11

change with respect to time is a dynamic network. It is important to consider travel time

as a parameter for finding the optimal path in dynamic networks (Alivand et al. 2008).

Recent GIS data models related to GIS-T are basically static in nature. Static

information is not sufficient to estimate travel time, since it does not reflect dynamically

changing traffic conditions. Static information could lead to incorrect shortest paths;

however, if there is a way to obtain the cost in real-time and then apply a time-dependent

shortest path algorithm, it would result in a better solution for the shortest path (Panahi

and Delavar 2009). According to Nadi and Delavar (2003), most conventional GIS data

models are based on a static representation of reality and constrain GIS capabilities for

representation of dynamic information. GIS data models that can represent the dynamic

aspects of transportation challenges are needed to represent and analyze space-time

information (Shaw 2010). Static variables that could be assigned to a road edge or

junction might include distance, speed limits, free-flow travel time, number of lanes, turn

penalties, slope of the road, hierarchical classifications, etc. (Li and Lin 2003, Sadeghi-

Niaraki et al. 2011, Thirumalaivasan and Guruswamy 1997).

In contrast, travel time is considered dynamic due to traffic volume, and historical

traffic data applied to a network can approximate traffic congestion. Dynamic variables

known as costs or weights are time-dependent or time-varying travel times derived from

historical traffic data. Dynamic variables that could be assigned to a road edge or

junction might include weather variables or time-varying travel time derived from traffic

count data (Sadeghi-Niaraki et al. 2011, Thirumalaivasan and Guruswamy 1997). The

network analysis will better reflect actual traffic conditions occurring at various times

12

during the day when time-dependent variables are incorporated (Kok et al. 2012, Panahi

and Delavar 2009).

2.5 Traffic Congestion and Dynamic Emergency Response Routing

There are several factors that can affect emergency services and vehicle response

times. Variations in traffic flow or volume related to time of day is one of them. This is

referred to as traffic congestion. Traffic congestion can have several causes. Some are

predictable such as traffic during daily peak hours and some less predictable such as

weather or accidents. Delays caused by peak hour traffic congestion constitute the

majority of traffic congestion delays (Kok et al. 2012). Delays affecting response times

in emergency services caused by traffic congestion are considered dynamic because they

spread through a network and vary over time (Panahi and Delavar 2009, Riad et al.

2012).

”The increasing ubiquity and complexity of urban congestion combined with its

severe negative impacts suggests the need for new tools to analyze and predict congestion

patterns” like a GIS (Riad et al. 2012, Wu et al. 2001). A critical component in incident

or emergency response actions is to deploy appropriate response units to the incident

scene as quickly as possible (Huang and Pan 2007). According to Panahi and Delavar

(2008; 2009), the problem of traffic congestion in urban areas can influence the travel

times of emergency vehicles, but the development of dynamic routing can offer solutions.

A more recent study (Kamga et al. 2011) showed dynamic traffic models are particularly

appropriate for modeling highway incidents because the timing of incident occurrence,

management, recovery, and the use of alternate routes is critical to roadway performance

13

and driver behaviors. Haghani et al. (2003) argued the purpose of vehicle dispatching is

to minimize the total travel time in the system and that time-dependent shortest path

analysis is useful for the calculation of travel times and can help EMS dispatching and re-

routing by reducing response times and improve services. Dynamic shortest path routing

should improve emergency response times (Panahi and Delavar 2008; 2009).

2.6 Historical Traffic Profiles

Several methods are known to apply historical traffic data to a road network. One

approach is to compute travel times for each road segment, which are then stored as

attributes for each feature. Depending on the sampling rate, storage and duplication

issues can be a concern (Demiryurek et al. 2009, Esri 2012, George et al. 2007). Another

method is the use of historical traffic profiles often referred to as speed profiles that are

used to produce travel time estimates (Nannicini 2009, Park et al. 2005, TomTom 2012).

Historical traffic profiles can represent the value of travel time observed at the time

intervals of each link for a specific period of time in the past (Kim et al. 2007). The use

of traffic profiles can be useful because it is not realistic to have a road network

completely covered by traffic recorders, and they can reduce computation time and

database storage and improve data quality (Chien and Kuchipudi 2003, Shaw 2000). A

historical profile can be considered summary statistics such as mean/median travel time

for each time slice (e.g., 60 minutes) of a road segment which are observed for certain

past time periods (e.g., 30 days). For instance, if mean travel time is used as a historical

profile, it represents the average value of the observed edge travel times over certain past

time periods (Park et al. 2005).

14

Kim et al. (2005) examined the value of real-time traffic information such as

accidents, bad weather, traffic congestion, etc., to optimize vehicle routing in a dynamic

network. Real-time traffic information combined with historical traffic data can be used

to develop routing strategies that tend to improve both cost and service productivity

measures. According to Kok et al. (2012) and Panahi and Delavar (2009), historical

traffic data can realistically represent peak-hour traffic congestion and help emergency

vehicles avoid these congested areas and improve travel time.

15

Chapter 3: Conceptual Framework and Methodology

The scope of this research was to find out if time-varying travel times derived

from historical traffic data applied to road network edges would affect the response times

and routes of emergency vehicles within the study area. The use of distance and free-

flow travel time as cost attributes is common in static networks but may not reflect or be

sufficient to estimate travel time for emergency vehicle routing, since they do not reflect

dynamically changing traffic conditions (Panahi and Delavar 2009).

The overall approach and objective of this study were segmented into four parts

or elements for better understanding. The first part was to successfully develop a

functioning dynamic road network for the study area. Analyses without a well-built

functioning road network would be difficult to undertake. The second part was to

successfully convert historical traffic volume into time-varying travel time profiles that

would represent realistic travel times for different times of the day and for each day of the

week. This is in contrast to traditional methods for estimating travel times that are the

same, regardless of the time and day (TomTom 2012). The third part was to effectively

incorporate these historical traffic profiles to road edges that are applied in realistic

emergency response scenarios. The fourth part was to compare travel-time costs derived

from historical traffic data to cost attributes based on distance and free-flow travel time.

This can provide a good estimation of the performance of different congestion avoidance

strategies in a realistic setting (Kok et al. 2012, Panahi and Delavar 2009). This chapter

discusses the technical aspects of the research including an explanation of the data

16

sources and how the data was acquired, prepared and used. Figure 4 shows the general

methodology used for this research.

Build

Road ND

Road Feature

Class

Clip Roads to

Study Area

EMS, Hospital & Incident Feature Classes

Start

Traffic Profile

Tables

Network

Dataset

Create Network

Dataset

Clip to

Study Area

Configure Traffic

Profile Tables

Road ND &

Junctions

Acquire State

Roads Feature

Class

Acquire EMS,

Hospital & Crash

Statistics

Acquire Historical

Traffic Data

Specify Attributes

and Assign

Evaluators

Create Route

Analysis Layer

Compare &

Analyze Results

Road Network

File GDBCreate GDB

Apply Analysis

Settings

Created by:

Michael

Winn

Perform Route

Analysis

Scenario 1

Perform Route

Analysis

Scenario 2

Locate Incident,

Response Unit &

Hospital

Figure 4. Methodology flow chart

17

3.1 Data Sources

Most of the geographic datasets used for this research were obtained from the

Utah Automated Geographic Reference Center (Utah AGRC). Datasets from the Utah

AGRC included road and highway system centerline data, emergency response facilities

or units and hospital/trauma center locations. Additional data comprised state, county,

and municipal boundaries and other information to create the base maps used for this

study.

Incident data was obtained from UDOT. In accordance with the Government

Records Access Management Act (GRAMA), it was necessary to obtain written

permission to obtain this data and was received electronically (Jones 2013). Incident data

was from actual 2010 vehicle crash site locations within Davis County and included

statistical data about the crashes.

Historical traffic data was acquired from the UDOT website. Historical traffic

profile tables were available from Esri. All data was considered public domain and was

available for use at no cost. The spatial reference for all data except HAFB was UTM

Zone 12N NAD83. HAFB spatial reference was UTM Zone 12N WGS84.

18

3.2 Data Preparation

The road centerline data was obtained from the Utah AGRC. The Utah AGRC

created a functional road network called the Street Network Analysis dataset. This

dataset contained many attribute fields, some of which were not used while other fields

were added or modified to incorporate historical traffic and other functionalities.

Although the Utah AGRC continues to improve and maintain the routing capability and

connectivity of its road network centerline features, it was discovered at the beginning of

this research that additional work was needed to prepare the road network for analysis.

Edge directionality and connectivity were issues that needed to be addressed and fixed

for the network to function properly. Connectivity and directionality cannot be over-

emphasized and will be discussed in more detail in subsequent sections (Granberg 2011,

Utah AGRC 2012).

3.2.1 Road Network Centerlines

The road network centerline data was extracted from the statewide road dataset by

clipping to a polygon feature that encompassed the urbanized areas of Weber and Davis

counties. This area feature closely resembles the boundary represented in the Ogden-

Layton Urbanized Area Functional Class System map (UDOT 2012). The road network

used for this study actually covers the urbanized areas of both Davis and Weber counties.

It was necessary to extend the network into Weber County to accommodate travel to the

two Level II trauma centers situated in the Ogden area (Figure 3). The Level I hospitals

located in northern Salt Lake County are outside the scope of this study and the road

network ends at the southern border of Davis County.

19

3.2.2 Road Classifications

To include historical traffic data for this study, the classification of road segments

had to be accomplished. All road segments were classified and coded based on UDOT’s

Urban Area Functional Classification system (Figure 5). Adherence to this classification

system was closely followed except for a few modifications necessary to fit the study.

These modifications were made by disaggregating the Urban Principal Arterial

classification into several different categories (e.g., ramps and other freeways) and

aggregating urban local roads into the Urban Minor Collector classification (Federal

Highway Administration [FHWA] 1989, Nichol 2010, UDOT 2001; 2012). Table 1

shows a list of the functional classifications, their definitions and the number of road

segments associated with each classification. Functional classification (FC) codes 3, 5,

and 10 were aggregated under FC codes 11, 12, and 14, respectively.

20

Table 1. Urban Area Functional Classification system

Figure 5. Road network and the Urban Area Functional Classification system

FC Code Functional Classification Basic Functional Classification Definition Road Segments

3 Urban Principal Arterial - Interstate - Ramp Ramp feature (see FC Code 11) 192

5 Urban Principal Arterial - Other Freeways - Ramp Ramp feature (see FC Code 12) 20

10 Urban Principal Arterial - Other - Ramp Ramp feature (see FC Code 14) 42

11 Urban Principal Arterial - Interstate Interstates (e.g., I-15) 212

12 Urban Principal Arterial - Other Freeways Other Freeways (e.g., SR 67 Legacy Highway) 13

14 Urban Principal Arterial - OtherServes major activity centers. Majority of trips and

through traffic.330

16 Urban Minor ArterialTrips of moderate length, lower mobility than

primary arterials. 1,299

17 Urban Collector

Land access and circulation within and into

residential neighborhoods, commercial and

industrial areas. Collects from local streets and

channels to arterial system.

1,567

19 Urban Minor Collector

All routes not otherwise classified as

primary/principal arterials, minor arterials, or

collectors (e.g., urban local streets and roads).

24,297

27,972

21

3.2.3 Historical Hourly Traffic Volume Data

Traffic volume data is commonly referred to as traffic count or historical traffic

count data. It is considered historical because it is not real time data. This data

represents the number of vehicles passing a specific point or section of roadway for each

60 minute interval during a 24 hour period (UDOT 2010).

There are ninety-three Automatic Traffic Recorder (ATR) sites situated

throughout the state of Utah (UDOT 2010). Nine of these sites were used to collect

hourly traffic volume data for April 2010. April was preferred because it was thought it

might best represent typical traffic congestion in the study area. Weather conditions are

improving and normal workday traffic patterns are not interrupted by severe winter

weather conditions. School is in session and traffic patterns due to summer vacations,

furloughs or school recess are not affecting regular traffic patterns.

Of these nine ATR sites, five were chosen and matched to the Urban Area

Functional Classification system explained in Section 3.2.2. These ATR sites are

highlighted in Table 2 (0315, 0624, 0316, 0510, and 0601) with their associated

functional classification codes and location descriptions. In Table 2, four ATR sites

(0307, 0312, 0320 and 0609) were matched to rural area functional classifications (FC

Codes 1, 2, 6, and 7); however, no profiles were created because none of the road

segments were classified as rural. No ATR was found to represent FC Code 19. All nine

ATR sites are shown in Figure 6.

22

Table 2. ATR sites associated with the Functional Classification system

Figure 6. Geographic locations of the ATR sites

FC Code Functional Classification ATR Site Names Location County

1 Rural Principal Arterial - Interstate 0307 I 84 0.5 mile E of Mountain Green Int. MP 92.593 Morgan

2 Rural Principal Arterial - Other 0312 SR 6 4.5 miles SE of SR 89, Moark Jct. MP 182.390 Utah

3 Urban Principal Arterial - Interstate - Ramp 0315 Same as FC 11

5 Urban Principal Arterial - Other Freeways - Ramp 0624 Same as FC 12

6 Rural Minor Arterial 0320 SR 39 0.5 mile W of SR 158, Ogden Cyn. MP 13.243 Weber

7 Rural Major Collector 0609 SR 167 1.2 miles W of Mountain Green Int. MP 1.250 Morgan

10 Urban Principal Arterial - Other - Ramp 0316 Same as FC 14

11 Urban Principal Arterial - Interstate 0315 I 15 1.8 miles S of Lagoon Drive Int. MP 321.545 Davis

12 Urban Principal Arterial - Other Freeways 0624 SR 67 Legacy Highway MP 0.944 Davis

14 Urban Principal Arterial - Other 0316 SR 89 2 miles S of SR 193, Hillfield Road, Layton MP 402.695 Davis

16 Urban Minor Arterial 0510 SR 218 100 N 319 W, Smithfield MP 7.700 Cache

17 Urban Collector 0601 SR 92 American Fork Canyon W Toll Booth MP 7.873 Utah

19 Urban Minor Collector NA Represents all unclassified and 'Local Roads'

23

3.2.4 Grouping Historical Traffic Volume Data

The April 2010 hourly traffic volume data was grouped by weekdays and

weekends and averaged (Park et al. 2005). Weekday means Monday thru Friday, a total

of twenty-two days. Weekend means Saturday and Sunday, four days for each, a total of

8 days. There were 30 days total in April. Tables 3 and 4 show hourly traffic counts for

ATR site 0316. In Table 3, the hours are displayed along the top row and weekends are

highlighted.

Table 3. April 2010 traffic volumes for ATR site 0316

ATR Date 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0316 4/1/2010 238 116 82 97 221 670 1479 2333 2560 1766 1609 1670 1668 1776 1886 2401 2914 3379 2281 1424 1328 1119 744 363

0316 4/2/2010 233 129 67 94 180 499 1252 2117 2227 1783 1767 1888 1880 2011 2187 2492 2964 3130 2320 1594 1266 1151 933 638

0316 4/3/2010 400 239 144 102 118 197 432 673 1108 1144 1298 1478 1864 1713 1747 1719 1955 1970 1276 1020 1217 1252 906 538

0316 4/4/2010 333 213 142 89 95 152 278 397 597 818 1066 1169 1743 1619 1514 1639 2166 1790 1631 1729 1711 1184 676 306

0316 4/5/2010 183 104 73 101 201 658 1448 2252 2231 1686 1614 1674 1652 1755 1778 2127 2677 3181 1974 1270 859 686 520 289

0316 4/6/2010 193 114 87 88 194 627 1323 2044 2150 1576 1383 1538 1565 1523 1618 2002 2600 3009 2081 1288 1017 864 812 371

0316 4/7/2010 187 109 73 87 181 720 1447 2371 2403 1729 1543 1622 1737 1798 1820 2310 2853 3308 2264 1437 1208 1013 669 360

0316 4/8/2010 191 131 91 109 191 702 1438 2400 2420 1779 1669 1711 1760 1764 1918 2349 2948 3445 2404 1390 1252 1055 704 344

0316 4/9/2010 233 141 84 107 193 612 1301 2252 2133 1748 1706 1811 1865 1869 2057 2465 2960 3228 2158 1487 1186 1095 888 558

0316 4/10/2010 391 225 149 114 112 275 498 957 1263 1530 1512 1892 1886 2031 2003 2031 2239 2063 1845 1469 1251 1083 974 578

0316 4/11/2010 339 239 137 102 91 152 303 377 637 693 1034 989 1365 1216 1341 1400 1668 1553 1426 1295 1170 919 514 250

0316 4/12/2010 144 95 69 84 185 670 1612 2666 2459 1734 1655 1591 1647 1594 1909 2442 2885 3278 2266 1386 1088 893 521 264

0316 4/13/2010 184 106 65 95 191 722 1630 2508 2538 1755 1566 1562 1659 1733 1890 2512 2844 3413 2307 1457 1168 919 621 307

0316 4/14/2010 192 104 73 84 196 688 1682 2692 2524 1837 1643 1728 1727 1811 2092 2605 3090 3536 2523 1665 1295 940 621 588

0316 4/15/2010 237 122 87 112 190 705 1597 2589 2591 2028 1709 1869 1800 1852 2080 2752 3346 3707 2526 1614 1445 1103 733 342

0316 4/16/2010 227 128 86 95 182 592 1417 2393 2381 1944 1740 1881 1839 2226 2798 3287 3559 3626 2459 1551 1301 1208 881 620

0316 4/17/2010 352 227 122 102 138 354 524 988 1588 1716 1858 2035 2093 2216 2123 2132 2527 2126 1928 1583 1323 1183 880 671

0316 4/18/2010 391 269 128 109 85 147 317 445 765 740 1036 1076 1403 1399 1472 1547 1950 1632 1463 1459 1451 995 566 305

0316 4/19/2010 142 107 60 95 185 674 1620 2631 2534 1844 1550 1705 1794 1729 2022 2535 3013 3396 2406 1413 1276 875 511 273

0316 4/20/2010 196 115 62 104 171 706 1656 2576 2467 1816 1741 1722 1743 1908 2034 2619 3303 3490 2526 1521 1262 1011 602 302

0316 4/21/2010 177 122 81 89 199 721 1721 2468 2391 1759 1566 1629 1703 1783 2065 2563 2896 3314 2324 1433 1141 962 562 317

0316 4/22/2010 173 117 79 108 199 677 1530 2358 2288 1658 1526 1592 1652 1739 1943 2467 3126 3360 2421 1643 1236 1080 701 506

0316 4/23/2010 224 131 110 102 181 581 1464 2592 2104 1926 1667 1823 1891 2150 2393 2481 3216 3703 2506 1661 1384 1065 916 810

0316 4/24/2010 588 246 128 103 118 258 606 1059 1586 1827 1806 1928 2004 2129 2138 2158 2580 2326 1931 1500 1307 1294 988 589

0316 4/25/2010 433 256 204 101 99 178 322 460 909 1004 1095 1099 1497 1303 1435 1735 2138 1746 1688 1358 1205 962 607 662

0316 4/26/2010 190 83 77 103 213 687 1642 2460 2291 1608 1475 1546 1610 1620 1971 2413 2951 3426 2453 1409 1103 937 550 291

0316 4/27/2010 178 95 70 92 195 710 1658 2465 2344 1779 1599 1721 1690 1695 1881 2658 3024 3298 2415 1375 1128 972 600 314

0316 4/28/2010 193 118 86 94 197 714 1591 2271 2259 1601 1370 1551 1524 1631 1825 2409 2770 3275 2296 1425 1226 847 538 344

0316 4/29/2010 207 148 84 94 178 685 1540 2334 2189 1621 1413 1568 1602 1564 1910 2519 2841 3316 2118 1414 1068 1029 692 342

0316 4/30/2010 197 140 89 107 179 573 1339 2095 2135 1677 1702 1599 1682 1901 2097 2436 3029 3330 2410 1574 1163 1108 847 846

24

Table 4 shows the hourly traffic counts grouped by weekdays and the weekend.

Averages were calculated for each hourly time slice throughout the day. Because Sunday

and Tuesday will be analyzed for all routing examples and scenarios, the highlighted

rows represent the four Tuesdays in April. Tuesday averages are shown below the 22-

day average to show a comparison between aggregated 22-day weekday averages and the

4-day Tuesday averages. Examples of tables and profiles associated with ATR and

traffic volume data was limited to ATR site 0316 to conserve space.

Table 4. April 2010 traffic volumes for ATR Site 0316, grouped

Weekday (M-F) 22-Day and Tuesday 4-Day Average

ATR Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0316 4/1/2010 238 116 82 97 221 670 1479 2333 2560 1766 1609 1670 1668 1776 1886 2401 2914 3379 2281 1424 1328 1119 744 363

0316 4/2/2010 233 129 67 94 180 499 1252 2117 2227 1783 1767 1888 1880 2011 2187 2492 2964 3130 2320 1594 1266 1151 933 638

0316 4/5/2010 183 104 73 101 201 658 1448 2252 2231 1686 1614 1674 1652 1755 1778 2127 2677 3181 1974 1270 859 686 520 289

0316 4/6/2010 193 114 87 88 194 627 1323 2044 2150 1576 1383 1538 1565 1523 1618 2002 2600 3009 2081 1288 1017 864 812 371

0316 4/7/2010 187 109 73 87 181 720 1447 2371 2403 1729 1543 1622 1737 1798 1820 2310 2853 3308 2264 1437 1208 1013 669 360

0316 4/8/2010 191 131 91 109 191 702 1438 2400 2420 1779 1669 1711 1760 1764 1918 2349 2948 3445 2404 1390 1252 1055 704 344

0316 4/9/2010 233 141 84 107 193 612 1301 2252 2133 1748 1706 1811 1865 1869 2057 2465 2960 3228 2158 1487 1186 1095 888 558

0316 4/12/2010 144 95 69 84 185 670 1612 2666 2459 1734 1655 1591 1647 1594 1909 2442 2885 3278 2266 1386 1088 893 521 264

0316 4/13/2010 184 106 65 95 191 722 1630 2508 2538 1755 1566 1562 1659 1733 1890 2512 2844 3413 2307 1457 1168 919 621 307

0316 4/14/2010 192 104 73 84 196 688 1682 2692 2524 1837 1643 1728 1727 1811 2092 2605 3090 3536 2523 1665 1295 940 621 588

0316 4/15/2010 237 122 87 112 190 705 1597 2589 2591 2028 1709 1869 1800 1852 2080 2752 3346 3707 2526 1614 1445 1103 733 342

0316 4/16/2010 227 128 86 95 182 592 1417 2393 2381 1944 1740 1881 1839 2226 2798 3287 3559 3626 2459 1551 1301 1208 881 620

0316 4/19/2010 142 107 60 95 185 674 1620 2631 2534 1844 1550 1705 1794 1729 2022 2535 3013 3396 2406 1413 1276 875 511 273

0316 4/20/2010 196 115 62 104 171 706 1656 2576 2467 1816 1741 1722 1743 1908 2034 2619 3303 3490 2526 1521 1262 1011 602 302

0316 4/21/2010 177 122 81 89 199 721 1721 2468 2391 1759 1566 1629 1703 1783 2065 2563 2896 3314 2324 1433 1141 962 562 317

0316 4/22/2010 173 117 79 108 199 677 1530 2358 2288 1658 1526 1592 1652 1739 1943 2467 3126 3360 2421 1643 1236 1080 701 506

0316 4/23/2010 224 131 110 102 181 581 1464 2592 2104 1926 1667 1823 1891 2150 2393 2481 3216 3703 2506 1661 1384 1065 916 810

0316 4/26/2010 190 83 77 103 213 687 1642 2460 2291 1608 1475 1546 1610 1620 1971 2413 2951 3426 2453 1409 1103 937 550 291

0316 4/27/2010 178 95 70 92 195 710 1658 2465 2344 1779 1599 1721 1690 1695 1881 2658 3024 3298 2415 1375 1128 972 600 314

0316 4/28/2010 193 118 86 94 197 714 1591 2271 2259 1601 1370 1551 1524 1631 1825 2409 2770 3275 2296 1425 1226 847 538 344

0316 4/29/2010 207 148 84 94 178 685 1540 2334 2189 1621 1413 1568 1602 1564 1910 2519 2841 3316 2118 1414 1068 1029 692 342

0316 4/30/2010 197 140 89 107 179 573 1339 2095 2135 1677 1702 1599 1682 1901 2097 2436 3029 3330 2410 1574 1163 1108 847 846

22-Day Avg 196 117 79 97 191 663 1518 2403 2346 1757 1601 1682 1713 1792 2008 2493 2991 3370 2338 1474 1200 997 689 427

TU 4-Day Avg 188 108 71 95 188 691 1567 2398 2375 1732 1572 1636 1664 1715 1856 2448 2943 3303 2332 1410 1144 942 659 324

Saturday Average (4 days)

ATR Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0316 4/3/2010 400 239 144 102 118 197 432 673 1108 1144 1298 1478 1864 1713 1747 1719 1955 1970 1276 1020 1217 1252 906 538

0316 4/10/2010 391 225 149 114 112 275 498 957 1263 1530 1512 1892 1886 2031 2003 2031 2239 2063 1845 1469 1251 1083 974 578

0316 4/17/2010 352 227 122 102 138 354 524 988 1588 1716 1858 2035 2093 2216 2123 2132 2527 2126 1928 1583 1323 1183 880 671

0316 4/24/2010 588 246 128 103 118 258 606 1059 1586 1827 1806 1928 2004 2129 2138 2158 2580 2326 1931 1500 1307 1294 988 589

4-Day Avg 433 234 136 105 122 271 515 919 1386 1554 1619 1833 1962 2022 2003 2010 2325 2121 1745 1393 1275 1203 937 594

Sunday Average (4 days)

ATR Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0316 4/4/2010 333 213 142 89 95 152 278 397 597 818 1066 1169 1743 1619 1514 1639 2166 1790 1631 1729 1711 1184 676 306

0316 4/11/2010 339 239 137 102 91 152 303 377 637 693 1034 989 1365 1216 1341 1400 1668 1553 1426 1295 1170 919 514 250

0316 4/18/2010 391 269 128 109 85 147 317 445 765 740 1036 1076 1403 1399 1472 1547 1950 1632 1463 1459 1451 995 566 305

0316 4/25/2010 433 256 204 101 99 178 322 460 909 1004 1095 1099 1497 1303 1435 1735 2138 1746 1688 1358 1205 962 607 662

4-Day Avg 374 244 153 100 93 157 305 420 727 814 1058 1083 1502 1384 1441 1580 1981 1680 1552 1460 1384 1015 591 381

25

3.2.5 Historical Traffic Volume Profiles

The historical traffic volume profiles were created based on the weekday and

weekend averages for the five ATR sites explained in Section 3.2.3 and displayed in

Table 2. Three profiles related to ATR site 0316 are shown in Figures 7, 8, and 9.

Figure 7 represents the profile from the 4-day Tuesday averages found in Table 4. For

comparison, the red dashed line in Figure 7 represents the 22-day weekday traffic count

averages. There is little noticeable difference between the profiles. Figure 8 and Figure

9 illustrate the profile from the 4-day weekend (Saturday and Sunday, respectively)

traffic count averages.

Esri has provided a free-flow traffic profiles table for simulating time-dependent

traffic condition (Esri 2012). There were 98 records with 5 minutes intervals in the

profiles table originally created for San Francisco areas (Esri 2012). Each record has a

unique identifier or number and stores the free-flow scale factor for each time interval.

However, in dynamic network analysis, the shorter the time interval is, the more

computational power required to run a dynamic network analysis. Therefore, to reduce

the computation complexity and to accommodate UDOT traffic volume data, this study

converted the Esri 5-minutes free-flow traffic profiles into hourly free-flow traffic

profiles and created the ‘DailyProfiles_Time_60min’ table (shown in Table 5). The table

stores the free-flow scale factors or multipliers for each 60 minute time interval or time

slice during a 24 hour day. This is 24 equal time intervals represented by 24 fields. The

profile numbers are listed in the ‘ProfileID’ field. Because of the number of fields in the

‘DailyProfiles_Time_60min’ table, the field names were shortened and some fields were

omitted.

26

Figure 7. ATR site 0316 traffic volume profile - Tuesday average, April 2010

Figure 8. ATR site 0316 traffic volume profile – Saturday average, April 2010

Figure 9. ATR site 0316 traffic volume profile – Sunday average, April 2010

27

The traffic volume profiles in this study (created based on the weekday and

weekend averages for the five ATR sites) were visually matched to the free-flow traffic

profiles created from the ‘DailyProfiles_Time_60min’ table (Table 5) by comparing the

profile or graph lines and choosing the profile with the best fit. It should be noted that

the method of visually comparing profiles is subjective and can introduce bias. Of the 98

free-flow traffic profiles found in the ‘DailyProfiles_Time_60min’ table (Table 5), there

are nine free-flow traffic profiles (‘ProfileID’ 3, 8, 12, 14, 21, 91, 92, 96, and 98) as

shown in Figures 10 through 18, respectively, matched to the traffic volume profiles

created from ATR sites 0315, 0624, 0316, 0510, and 0601 (Table 2). The three free-flow

traffic profiles that matched closest to the traffic volume profiles associated with ATR

site 0316 shown in Figures 7, 8 and 9 were profiles 91, 14 and 3. These free-flow traffic

profiles can be viewed in Figures 15, 13 and 10, respectively.

Table 6 shows how the nine free-flow traffic profiles (shown in Figures 10

through 18) are arranged and correspond to the nine road functional classifications and

the days of the week. The nine ‘ProfileID’ free-flow traffic profile numbers are

organized and stored in the ‘Project_Profiles’ table (Table 7) and correspond to the daily

traffic pattern of each road segment. The fields, ‘Profile_1' through ‘Profile_7’, in the

‘Project_Profiles’ table are populated with ‘ProfileID’ numbers and match to the same

profile numbers found in the ‘DailyProfiles_Time_60min’ table. The ‘Profile_1’ field

shows the ‘ProfileID’ of Sunday free-flow traffic profile; ‘Profile_7 field represents the

‘ProfileID’ of Saturday free-flow traffic profile; ‘Profile_2’ through ‘Profile_6’ fields are

for Monday through Friday. Therefore, there is a ‘ProfileID’ for each day of the week for

28

all 27, 972 road segments or records. Table 8 represents the ‘ProjectArea’ feature class.

Each record represents a road segment.

Table 5. ‘DailyProfiles_Time_60min’ file geodatabase table

Table 6. Profile IDs from the ‘DailyProfiles_Time_60min’ table

FC Code Functional Class SUN MON TUE WED THR FRI SAT Notes

3 Urban Principal Arterial - Interstate - Ramp 8 98 98 98 98 98 92 Same as FC Code 11

5 Urban Principal Arterial - Other Freeways - Ramp 12 91 91 91 91 91 12 Same as FC Code 12

10 Urban Principal Arterial - Other - Ramp 3 91 91 91 91 91 14 Same as FC Code 14

11 Urban Principal Arterial - Interstate 8 98 98 98 98 98 92

12 Urban Principal Arterial - Other Freeways 12 91 91 91 91 91 12

14 Urban Principal Arterial - Other 3 91 91 91 91 91 14

16 Urban Minor Arterial 96 21 21 21 21 21 8

17 Urban Collector 12 3 3 3 3 3 3

19 Urban Minor Collector 8 98 98 98 98 98 92

29

Figure 10. ‘DailyProfiles_Time_60min’ table: Profile 3



30




31




32

Table 7. 'Project_Profiles' file geodatabase table

Table 8. 'ProjectArea' feature class attribute table

OBJECTID LENGTH_MI FC_CODE EdgeFCID EdgeFID FreeFlowMi Profile_1 Profile_2 Profile_3 Profile_4 Profile_5 Profile_6 Profile_7

1 0.051704 16 53 1 0.077556 96 21 21 21 21 21 8

17 0.063222 14 53 17 0.094832 3 91 91 91 91 91 14

18 0.329915 11 53 18 0.304537 8 98 98 98 98 98 92

20 0.079990 17 53 20 0.119984 12 3 3 3 3 3 3

23 0.035119 19 53 23 0.052679 8 98 98 98 98 98 92

51 0.045459 3 53 51 0.109103 8 98 98 98 98 98 92

3493 0.107606 10 53 3493 0.161408 3 91 91 91 91 91 14

14446 0.230291 5 53 14446 0.345437 12 91 91 91 91 91 12

14449 0.453260 12 53 14449 0.494466 12 91 91 91 91 91 12

27972 27972

13 of 21 total fields

Field Name Data Type 27, 972 total records

OBJECTID Object ID

LENGTH_MI Double

FC_CODE Short Integer

FUNCTIONAL_CLASS Text

Shape_Length Double

EdgeFCID Long Integer

EdgeFID Long Integer

EdgeFrmPos Double

EdgeToPos Double

FreeFlowMi Double

Profile_1 Long Integer







Val_Dir Short Integer

SPFREEFLOW Short Integer

SPWEEKDAY Short Integer

SPWEEKEND Short Integer 21 of 21 total fields

OBJECTID SPD_LMT ONE_WAY MINUTES LENGTH_MI FC_CODE FT_Min TF_Min OneWay Shape_Len

1 40 0 0.077556 0.051704 16 0.077556 0.077556 83.209915

2 40 0 0.097135 0.064757 16 0.097135 0.097135 104.216068

3 40 0 0.061795 0.041197 16 0.061795 0.061795 66.300000

4 40 1 0.064878 0.043252 16 0.064878 0.064878 FT 69.607615

5 40 0 0.031018 0.020678 16 0.031018 0.031018 33.278655

27968 40 0 0.097976 0.065317 16 0.097976 0.097976 105.118241

27969 40 0 0.099292 0.066195 16 0.099292 0.099292 106.530051

27970 40 0 0.021112 0.014075 16 0.021112 0.021112 22.651280

27971 55 0 0.141260 0.129488 14 0.141260 0.141260 208.391454

27972 40 0 0.070704 0.047136 16 0.070704 0.070704 75.858100

10 of 85 total fields

Field Name Data Type 27972 total records

OBJECTID Object ID

LABEL Text

SPD_LMT Short Integer

ONE_WAY Short Integer

MINUTES Double

LENGTH_MI Double

FC_CODE Short Integer

FUNCTIONAL_CLASS Text

FT_Minutes Double

TF_Minutes Double

FT_WeekdayMinutes Double

TF_WeekdayMinutes Double

FT_WeekendMinutes Double

TF_WeekendMinutes Double

OneWay Text

Shape_Length Double 16 of 85 total fields

33

3.2.6 Modeling Historical Traffic Data

Historical traffic data is at the heart of this research and is essential for creating a

dynamic road network that will represent peak-hour traffic congestion and assist first

responders to avoid these congested areas and improve travel time. The approach to

modeling historical data for this study has its origins in the private sector by industry

leaders who provide navigation products and location-based services (LBS) to the general

public and other vendors and partners (Esri 2013a, Tele Atlas 2009, TomTom 2012).

Instead of storing historical traffic data for each individual road segment, related tables

are used to store and represent the changes in travel time throughout the day (Esri 2012).

Two tables work in conjunction with the ‘ProjectArea’ feature class that stores the road

segment features (Table 8). These are the ‘DailyProfiles_Time_60min’ and

‘Project_Profiles’ tables that are discussed in Section 3.2.5 and represented in Tables 5

and 7, respectively.

Each road segment in the ‘ProjectArea’ feature class has a unique identifier. Each

record in the ‘DailyProfiles_Time_60min’ table where the free-flow multipliers are

stored, also has a unique identifier or ‘ProfileID’ for each record or traffic profile. The

‘Project_Profiles’ table stores the free-flow travel time and the ‘ProfileID’ that best

represents traffic for each day of the week and for each road segment. This table joins

the road segments in the ‘ProjectArea’ feature class to the various traffic profiles in the

‘DailyProfiles_Time_60min’ table through a unique identifier found in the ‘EdgeFID’

field that correlates to the ‘ObjectID’ field in the ‘ProjectArea’ feature class (Esri 2012).

Other values are stored in ‘ProjectArea’ and will be discussed in the following sections.

34

All these network sources are required for historical traffic data to work in the

network dataset. When a road segment in the ‘ProjectArea’ feature class is related to a

traffic profile in the ‘DailyProfiles_Time_60min’ table by the ‘Project_Profiles’ join

table, the travel time for any 60 minute time slice on a given day is calculated. This

calculation is based on the free-flow travel time value stored in the ‘Project_Profiles’

table and the free-flow multiplier value associated with the ‘ProfileID’ in the

‘DailyProfiles_Time_60min’ table.

Example: If a road segment with an ‘ObjectID’ of 20 in the ‘ProjectArea’ feature class

(not shown in Table 8) is related to a record in the ‘Project_Profiles’ table with an

‘EdgeFID’ of 20 (Table 7) and has a ‘ProfileID’ value of 3 for Tuesday, the free-flow

travel time (‘FreeFlowMi’) in minutes is 0.119984. The expected travel time at 1800

(Figure 10) for Profile 3 will be calculated by multiplying the road segment free-flow

travel time (0.119984) by the profile's free-flow multiplier or time factor value of

1.051520 (see Table 5 at 1800 for ‘ProfileID’ 3).

35

3.2.7 Incorporating Historical Traffic Data

After the historical traffic tables were configured and populated correctly, they

were incorporated into the network dataset. This is completed during the network

creation but prior to the building process. Figure 19 shows the properties associated with

the historical traffic tables and how the ‘DailyProfiles_Time_60min’ and

‘Project_Profiles’ join tables are configured. Note that the ‘First Time Slice’ is set to

4:00 am and the ‘Last Time Slice’ is set to 10:00 pm because the free-flow multiplier

value from 10:00 pm to 4:00 am is 1.

The location where network cost attributes are applied to road network edges is

shown in Figure 20. The distance cost is displayed as ‘Length’ and corresponds to the

‘LENGTH_MI’ field in the ‘Project_Profiles’ table in Table 7 and the ‘ProjectArea’

feature class in Table 8. The free-flow travel time cost is displayed as ‘MINUTES’ and

corresponds to the 'FreeFlowMi’ field in the ‘Project_Profiles’ table in Table 7 and to the

‘MINUTES’ field in the ‘ProjectArea’ feature class in Table 8. The time-varying travel

time cost is a calculated value based on historical traffic data and is displayed as

‘TravelTime’ in Figure 20. Other costs and descriptors shown in Figure 20 were

assigned values but are not used in this analysis. ‘Oneway’ restrictions will be explained

in Section 3.3.1. Global turns will be explained in Section 3.3.2.

36

Figure 19. Network Dataset properties associated with the historical traffic tables

Figure 20. Assignment of network attributes

37

3.3 Developing the Road Network Model

Esri ArcGIS Network Analyst was used to create a dynamic road network model

and spatio-temporal database for incorporating historical traffic data and performing the

shortest path analysis. The road network model is considered dynamic in the sense that

cost attributes such as travel time change with respect to time. The database is

considered spatio-temporal in the sense it has spatial, non-spatial and temporal

characteristics such as location, attribute and time (Shaw 2000). ArcGIS is suitable for

this kind of research because it is commercially available and the Network Analyst

extension is included in the student edition of ArcGIS. Network Analyst provides the

functionality to incorporate historical traffic data and model the time-dependent costs of

traveling the network.

The term Network Dataset (ND) is important to the understanding of how a road

network is modeled and functions in Network Analyst. It is defined by Esri as a

collection of topologically connected network elements (e.g., edges, junctions, and turns)

that are derived from network sources (e.g. feature classes) and used to represent a road

network. Each network element is associated with a collection of network attributes

(e.g., cost, descriptor, hierarchy, and restriction). When any analysis is performed in

Network Analyst, it is performed on a network dataset (Esri 2013b). This term is used to

when describing road network features.

Several steps were required to create the road network dataset. The first step was

to create a file geodatabase (FGDB) as a repository for all network related elements and

feature classes including the traffic profile tables. The network dataset was created in a

feature dataset to maintain topology and spatial reference. In a geodatabase-based

38

network dataset, all feature classes participating as sources in a network are stored in a

feature dataset (Esri 2013b). Figure 21 shows a view of the file geodatabase data model.

Although it is not necessary, a relationship class was created between the

‘ProjectArea’ feature class and the ‘Project_Profiles’ table. This made the process of

editing road network features faster and simpler to manage. The records and unique

identifiers in the ‘ProjectArea’ feature class and in the ‘Project_Profiles’ table should be

identical. The final step prior to performing the analysis was to build the network

dataset. Building the network dataset is the process of creating network elements,

establishing connectivity and assigning network values (Esri 2013c).

Figure 21. File geodatabase data model

39

3.3.1 One Way Restrictions

One Way restrictions are applied to limit travel on one way roads and avoid

routing irregularities. There are 184 miles of one way road segments in the road network

comprised mostly of highways and ramps. All road segments were digitized in the

‘from-to’ (FT) direction. If the ‘OneWay’ field in the ‘ProjectArea’ feature class was

populated with FT, it means travel was only allowed in the digitized direction of the road

segments. One Way restrictions can be set to ‘Prohibit’, ‘Avoid’, or ‘Prefer’ for one way

road segments (Esri 2013d). All one way roads segments are restricted and set to

‘Prohibit’. The ‘Prefer’ and ‘Avoid’ parameters were not used because they were

considered subjective and would bias the analysis.

Example: Figure 22 shows a route from Incident 1 to Ogden Regional Medical Center

with the One Way restriction on. The correct ramps and lanes were traveled for I-84.

Figure 23 shows the route from Incident 1 to Ogden Regional Medical Center with the

One Way restriction off. Notice the incorrect ramps and lanes for I-84 were traveled.

40

Figure 22. Correct one-way travel, from Incident 1 to Ogden Regional Medical Center

Figure 23. Incorrect one-way travel, from Incident 1 to Ogden Regional Medical Center

41

3.3.2 Global Turns

Global turn delays are used as a kind of cost attribute to improve travel time

estimates by delaying movements from one road segment to another. These delays are

also referred to as turn penalties. There are four types of turn directions used in the

study: straight, reverse, right and left turn. Global turn delays are not intended to be as

accurate as the turn feature class model of applying turn penalties (Esri 2013e). Global

turns were applied to the free-flow travel time and time-varying travel time cost

attributes. They are not available for use with the distance cost attribute.

If road hierarchies were applied, more turn directions would be available for use.

Because road hierarchies are not used, all roads are considered local roads and the

numbers of turn directions to choose from were reduced. This made the application of

turn delays simpler but less exact. The default Esri turn penalty values associated with

the turn directions and descriptions in Figure 24 were not considered suitable for this

study area. Averaging the default turn penalty seconds for each turn category shown in

Figure 24 produces a more representative turn penalty value for modeling emergency

response vehicle turn movements. Table 9 show the directions and penalties in seconds

used to model the turn delays. The applied values for each turn category were derived by

averaging the seconds shown in Figure 24.

Example: There are 4 left turns with the following default Esri values; 2, 10, 5 and 8

seconds. The average is 6 seconds. The default global turn delay values that are applied

in this study are shown in Figure 25. The calculated values are listed in Figure 26. The

default values for turn angles were used.

42

Table 9. Global turn delay directions and penalty values in seconds

Figure 24. Turn categories available for various road types

Direction Description Seconds (default) Seconds (applied)

Straight From Local to Local Road Across No Roads 0 0

Straight From Local to Local Road Across Local Road 2 4

Reverse From Local To Local Road 3 7

Right Turn From Local To Local Road 2 3

Left Turn From Local To Local Road 2 6

43

Figure 25. Global turn delay default settings

Figure 26. Global turn delay customized settings

44

In general, ambulance operators are allowed some privileges when responding to

an incident; however, safety is their number one priority. Operators are responsible for

the safe operation of the response vehicle at all times, including compliance with all

traffic laws. Usually emergency vehicles are prohibited from exceeding the posted speed

limit when approaching and crossing an intersection with the right-of-way, and they must

come to a complete stop before proceeding through a controlled intersection or using the

opposing traffic lanes to approach an intersection (International Association of Fire

Chiefs [IAFC] 2013, McDonald 2013).

In addition to safety concerns, vehicle size and maneuverability were taken into

account when assigning turn penalty values. Emergency response vehicles are larger and

more challenging to drive when negotiating turns than smaller vehicles. When making

turns or negotiating curves too fast, an ambulance could be susceptible to losing control

or even overturning due to its size and box shaped design. At a minimum, equipment,

patients, and medical personnel working with patients during transport could be tossed

about or injured. Caution with or without lights and sirens is important and will take a

few seconds longer when negotiating turns. Additional factors might include weather,

road conditions, and intersection sizes. Based on these policies and other factors

mentioned, averaging turn penalty second values is thought to be a reasonable attempt to

model emergency response routing more realistically (McDonald 2013).

45

Chapter 4: Analysis and Results

This analysis comprises two routing examples centered on two discrete vehicle

accident locations selected from 2010 UDOT crash data (shown in Table 10 as IN-1 and

IN-2). Each example comprises two scenarios. The first scenario, which will be referred

to as S1, represents an ambulance on an emergency call from a ground emergency

response unit (e.g., fire station) to the scene of a traffic incident (e.g., car crash). The

second scenario, which will be referred to as S2, represents an ambulance leaving the

scene of the accident transporting the victim(s) to the nearest hospital or trauma center.

Figure 27 shows an example routing solution for scenarios 1 and 2.

The ‘Closest Facility’ solver in Network Analyst was used to locate the nearest

ground emergency response unit and hospital to each incident. The ‘Route’ solver in

Network Analyst was used to find the shortest path between two locations using a

distance-based cost attribute, the fastest route using a time-based cost attribute known as

the free-flow travel time, and the optimal route using a time-varying cost attribute based

on historical traffic data.

For both routing scenarios, similarities and differences between route directions,

distances, and travel times generated from each cost attribute are compared and analyzed.

Emergency response routing based on cost attributes derived from historical travel-time

data and applied to network edges should assist emergency response vehicles to avoid

congested areas (Kok et al. 2012, Panahi and Delavar 2009). Figure 28 shows the

general process of the routing analysis for both routing scenarios in each example.

46

Table 10. Incident data from 2010 UDOT crash statistic

Figure 27. Example of routing scenarios S1 and S2

Incident Crash ID Junction Type Crash Severity Location

IN-1 10369590 4-Leg Intersection Non-Incapacitating Injury 2000W, at 1800 N

IN-2 10364031 4-Leg Intersection Non-Incapacitating Injury Boynton at Fairfield Rd

47

Network

Dataset

Identify Incident

Location

Compare &

Analyze

ResultsCreated by:

Michael

Winn

Locate Nearest

Ground Unit with

‘Closest Facility’

Solver

Locate Nearest

Hospital with

‘Closest Facility’

Solver

Nearest

Hospital

Nearest

Ground Unit

Solve shortest path

with ‘Route’ Solver

Run 1 (R1)

DIST

Run 2 (R2)

FFTT

Run 3 (R3)

TVTT

Apply Analysis

Settings

Apply Analysis

Settings

Apply Analysis

Settings

Scenario 1

(S1)

Scenario 2

(S2)

Compare &

Analyze

Results

Solve shortest path

with ‘Route’ Solver

Run 1 (R1)

DIST

Run 2 (R2)

FFTT

Run 3 (R3)

TVTT

Apply Analysis

Settings

Figure 28. Route analysis flowchart

48

For all routing examples (IN-1, IN-2), S1 and S2 are comprised of three routing

runs. The first run (R1) uses a distance cost attribute. The distance refers to the length in

miles of each road segment or edge in the network. This cost attribute or impedance will

be referred to as DIST.

The second run (R2) uses a travel time cost attribute. This travel time cost

represents a static shortest path calculation with no major impedances or cost other than

the base travel time for each road segment or edge. The base travel time is considered

fixed and proportional to the length of a road segment (Demiryurek et al. 2010). This

impedance is also known as the free-flow travel time or FFTT which is derived from the

free-flow speed. The FFTT speed is the speed a vehicle travels when it is not impeded by

other traffic movement. This is typically the posted speed limit but can be defined as five

miles per hour greater than the posted speed limit (Esri 2012, FHWA 2013). The

equation used to calculate the FFTT in minutes for each road segment is shown in

Equation 4.1.

Road Segment Length in Miles * (60 / Speed Limit in Miles per Hour)

Equation 4.1

The third run (R3) uses historical traffic data to model time-varying costs of

traveling on the network. Time-varying or time-dependent travel time costs are used to

find the best route from an origin to a destination. For this analysis, time-varying travel

time is referred to as TVTT. TVTT is what makes the road network considered dynamic.

How historical traffic data is modeled and incorporated into this analysis was explained

in Sections 3.2.6 and 3.2.7.

49

Sunday and Tuesday are analyzed for all routing examples and scenarios. The

start times for each routing scenario were run at the top of the hour (e.g., 0700, 0800,

etc.) for a 24 hour period. Sunday was selected to best represent weekend traffic and

Tuesday was selected to best represent weekday traffic. These selections were based on

grouping days by weekdays and weekends. Niemeier et al. (2002) claimed that “It is

well accepted that temporal profiles of daily traffic volumes tend to be similar across

certain days and time periods. For instance, the typical traffic pattern seen on Tuesday is

often very similar to the traffic pattern seen on Wednesday and Thursday. Saturday and

Sunday tend to have similar traffic patterns, whereas the patterns on Monday and Friday

are usually unique”. Some liberties were taken with these selections. Two days were

selected for analysis to reduce the size of the study.

As explained in Section 3.3.2, global turn delays are only available for use with

the FFTT and TVTT impedances. When executing the ‘Route’ solver in Network

Analyst, all three cost attributes (DIST, FFTT, TVTT) run and generate results, but only

the specified impedance is used to optimize the solution. For example, when utilizing

DIST as impedance, the ‘Route’ solver will produce the best route for the specified

impedance, which is the shortest distance route. The route run results will generate three

attribute fields. The ‘DIST (mi)’ field represents the distance or total length of the route

in miles. The ‘FFTT (min)’ field represents the free-flow travel time in decimal minutes

for the specified time interval of the route without the additional travel-time costs that

would normally be added when FFTT and TVTT impedances are used to optimize the

solution. This is because global turn restrictions are not available when the DIST

impedance is used. The ‘TVTT (min)’ field represents the time-varying travel time in

50

decimal minutes for the specified time interval of the route without the additional travel-

time costs for the same reasons as explained for the ‘FFTT (min)’ field.

When the ‘Closest Facility’ solver is used, no start or end time attribute fields are

generated. When the ‘Route’ solver is used, start and end time attribute fields are

generated when FFTT and TVTT impedances are applied. However, no start or end time

attribute fields are generated when the DIST impedance is used.

Figure 29 shows the analysis settings that are available for the ‘Closest Facility’

solver. Figure 30 shows the analysis settings that are available for the ‘Route’ solver.

When the DIST and FFTT impedances are applied, time settings were used but were not

necessary. These time settings are named ‘Use Time’ in ‘Closest Facility’ solver and

‘Use Start Time’ in ‘Route’ solver. For instance, if route runs were performed using the

DIST and FFTT cost attributes every hour for 24 hours, the distance and travel time

values would be the same. Changes only occur when using time setting and the TVTT

impedance. This is required in order to apply historical traffic data. Only the impedance

applied to the route run is used to optimize the solution. For instance, if the TVTT

attribute is used as the cost attribute, DIST and FFTT costs can still be accumulated and

reported to assist in the analysis but the path is actually calculated based on the TVTT

(Esri 2013f).

4.1 Route Example for IN-1

4.1.1 IN-1: Closest Facility Analysis

Incident 1 (IN-1) is located in Clinton at the intersection of 200W, at 1800N

(Table 10). After the incident location was identified, the ‘Closest Facility’ solver was

51

Figure 29. Analysis settings available for ‘Closest Facility’ solver

Figure 30. Analysis settings available for ‘Route’ solver

52

used to locate the nearest ground unit and hospital/trauma center. The analysis settings

for each cost attribute used to find the nearest ground unit are shown in Table 11. The

analysis settings for each cost attribute used to find the nearest hospital are shown in

Table 12. The only difference in the settings between Tables 11 and 12 is in the ‘Travel

From’ field. The ‘Facility to Incident’ setting was used to find the nearest ground unit to

IN-1, and the ‘Incident to Facility’ setting was used to find the nearest hospital from IN-

1.

The same methodology was used to determine the nearest ground unit and

hospital to IN-1. The DIST, FFTT and TVTT impedances were applied in both

instances. Although distance should determine the shortest route, it was believed that

using the FFTT and TVTT cost attributes would validate that the shortest routes were

also the routes with the least travel time. In other words, if two hospitals were close in

total distance from the same incident, TVTT could determine that during a time of heavy

traffic congestion, the travel time to the closer hospital could be greater than the travel

time to the farther hospital.

All route runs were run for Tuesday at 1700. After previously examining the

TVTT values for a 24 hour period of time, the 1700 to 1800 time slice proved to have the

greatest TVTT in both cases. Table 13 shows the results of runs applying DIST, FFTT

and TVTT impedances to determine the nearest ground unit to IN-1. Table 14 shows the

results of runs applying DIST, FFTT and TVTT impedances to determine nearest hospital

from IN-1. When observing route run results in Tables 13 and 14, the accumulated

values are shown in italicized red font and are for reference and comparison only. The

bolded values are the values based on the applied impedance. The same settings were

53

applied to all routing examples and scenarios. Changes in routes are shown in Tables 13

and 14 under the ‘Run/Route’ field, and the ‘Figure’ field indicates the corresponding

figure showing the route changes. The ‘Run/Route’ field is used to identify the route

runs. Figures 31 through 36 show the routes associated with the cost attribute used.

For Table 13, run routes R1/A, R2/A, and R3/A indicate the shortest, fastest and

optimal routes, respectively for finding the nearest ground unit to IN-1. For Table 14,

run routes R1/A, R2/B, and R3/C indicate the shortest, fastest and optimal routes,

respectively for finding the nearest hospital from IN-1. As a result of these run routes

and applying DIST, FFTT and TVTT as impedances, it was determined the closest

ground unit to IN-1 is Clinton Fire Department and the closest hospital from IN-1 is

Davis Hospital.

Table 11. Analysis settings for finding nearest ground unit to IN-1

Table 12. Analysis settings for finding nearest hospital from IN-1

Impedance Use Time Usage Time of Day Day of Week Facilities to Find

DIST Yes Start time 1700 SUN & TUE 3

FFTT Yes Start time 1700 SUN & TUE 3

TVTT Yes Start time 1700 SUN & TUE 3

Impedance Trave From U-Turns OneWay

DIST Facility to Incident Allowed Prohibited

FFTT Facility to Incident Allowed Prohibited

TVTT Facility to Incident Allowed Prohibited

Impedance Use Time Usage Time of Day Day of Week Facilities to Find

DIST Yes Start time 1700 SUN & TUE 3

FFTT Yes Start time 1700 SUN & TUE 3

TVTT Yes Start time 1700 SUN & TUE 3

Impedance Trave From U-Turns OneWay

DIST Incident to Facility Allowed Prohibited

FFTT Incident to Facility Allowed Prohibited

TVTT Incident to Facility Allowed Prohibited

54

Table 13. Results for finding nearest ground unit to IN-1

Table 14. Results for finding nearest hospital from IN-1

Figure 31. Routes from nearest ground unit to IN-1 applying DIST impedance

Cost Origin-Destination Run/Route Day Time DIST (mi) FFTT (min) TVTT (min) Figure

DIST Clinton FD to IN-1 R1/A TU 1700 0.887 1.330 1.759 31

DIST Sunset FD to IN-1 R1/A TU 1700 1.922 2.882 3.423 31

DIST N. Davis FD West Pt to IN-1 R1/A TU 1700 2.705 5.228 7.750 31

FFTT Clinton FD to IN-1 R2/A TU 1700 0.887 1.747 2.176 32

FFTT Sunset FD to IN-1 R2/A TU 1700 1.922 4.249 4.790 32

FFTT N. Davis FD West Pt to IN-1 R2/B TU 1700 2.714 5.090 6.369 32

TVTT Clinton FD to IN-1 R3/A TU 1700 0.887 1.747 2.176 33

TVTT Sunset FD to IN-1 R3/A TU 1700 1.922 4.249 4.790 33

TVTT N. Davis FD West Pt to IN-1 R3/C TU 1700 2.715 5.692 6.885 33

Cost Origin-Destination Run/Route Day Time DIST (mi) FFTT (min) TVTT (min) Figure

DIST IN-1 to Davis R1/A TU 1700 6.260 11.505 16.702 34

DIST IN-1 to Ogden Regional R1/A TU 1700 8.812 16.503 28.633 34

DIST IN-1 to McKay Dee R1/A TU 1700 8.932 15.583 25.452 34

FFTT IN-1 to Davis R2/B TU 1700 6.362 10.579 19.310 35

FFTT IN-1 to Ogden Regional R2/B TU 1700 8.977 16.424 27.936 35

FFTT IN-1 to McKay Dee R2/B TU 1700 8.979 18.469 24.478 35

TVTT IN-1 to Davis R3/C TU 1700 6.339 13.106 17.358 36

TVTT IN-1 to McKay Dee R3/B TU 1700 8.979 18.469 24.478 36

TVTT IN-1 to Ogden Regional R3/C TU 1700 9.150 20.199 25.955 36

55

Figure 32. Routes from nearest ground unit to IN-1 applying FFTT impedance

Figure 33. Routes from nearest ground unit to IN-1 applying TVTT impedance

56

Figure 34. Routes from IN-1 to nearest hospital applying DIST impedance

57

Figure 35. Routes from IN-1 to nearest hospital applying FFTT impedance

58

Figure 36. Routes from IN-1 to nearest hospital applying TVTT impedance

4.1.2 IN-1: Route Analysis Scenario 1

Scenario 1 (S1) is the route run and analysis from the Clinton Fire Department to

IN-1, which illustrates an ambulance on an emergency run from Clinton Fire Department

to IN-1. The analysis settings for each cost attribute used for S1 are shown in Table 15.

59

The results from S1 are divided into three sections for examination. The first section

describes the tables and figures associated with each route analysis run. The second

section explains the findings. The third section discusses the effects of the time-varying

travel time as impedance for network analysis.

Description

Six tables and four figures were created based on these runs. Tables 16 and 17

show the results of runs from Clinton FD to IN-1 applying the DIST impedance for

Sunday and Tuesday, respectively. The DIST impedance was used to optimize the

solution. The ‘DIST (mi)’ field shows the path distance expressed as the total length of

the route in miles. The ‘FFTT (min)’ field shows the accumulated free-flow travel time

value in decimal minutes. The ‘TVTT (min)’ field shows the accumulated time-varying

travel time value in decimal minutes. The values that are italicized and highlighted in red

were used for comparison purposes only and were not used to optimize the solution.

The DIST impedance route run is considered a static network analysis since the

path distance does not change through time. Therefore, Tables 16 and 17 show one

record representing all 24 time intervals. The ‘FFTT (min)’ field represents the

accumulated free-flow travel time for the route results and the ‘TVTT (min)’ field shows

the accumulated TVTT value calculated for 1700 (5:00 pm) only. Both ‘FFTT (min)’

and ‘TVTT (min)’ fields are generated without global turns delays since the global turn

restriction is not available while applying DIST as impedance.

Tables 18 and 19 show the results of runs from Clinton FD to IN-1 applying the

FFTT impedance for Sunday and Tuesday, respectively. The FFTT impedance was used

60

to optimize the solution. The FFTT impedance is considered a static network analysis

since the free-flow travel time of each road segment does not change through time.

Therefore, Tables 18 and 19 have the same value in ‘FFTT (min)’ field throughout the

run. The ‘TVTT (min)’ field shows the accumulated TVTT value calculated for the route

in each corresponding time interval.

All tables have a ‘Route’ field that represents the path created for each impedance

analysis. Though the route results (shown in the ‘Route’ field) from both DIST and

FFTT impedance runs are the same, the values in ‘FFTT (min)’ field are different when

comparing Table 16 to Table 18 and Table 17 to Table 19. The FFTT values in Tables

18 and 19 are greater than those in Tables 16 and 17. This is because global turn delays

(Section 3.3.2) were used in the FFTT impedance runs but cannot be used in the DIST

impedance runs. Start and end times are not generated when DIST is used as the

impedance but they are generated when FFTT is used as the impedance. Global turn

delays are used and reflected in the ‘FFTT (min)’ values, but they are not reflected in the

elapsed run times found in the ‘EndTime (hms)’ field. In other words, the FFTT values

will not be the same as the end times. If global turn delays were not used, these times

would be the same.

Tables 20 and 21 show the results of runs from Clinton FD to IN-1 applying the

TVTT impedance for Sunday and Tuesday, respectively. The TVTT impedance was

used to optimize the solution. Figures 37 and 38 show the travel time profiles associated

with Tables 20 and 21, respectively. They represent the TVTT when historical traffic

data is applied. Both the FFTT and TVTT values are generated with global turn delays;

therefore, they are different from those shown in Tables 16 and 17.

61

It is important to note that when TVTT is applied as impedance, the optimal route

choice (shown in the ‘Route’ field) might be varied in different time slices. Table 20

shows that there are two optimal routes choices, Route A and Route B, in the ‘Route’

field, generated by the ‘Route’ solver based on the time, day, and impedance applied.

Route A (Figure 39) is the optimal solution for Sunday from 0000 (midnight) to 1000

(10:00 am) and from 2000 (8:00 pm) to 2400 (midnight), but Route B (Figure 40) is the

optimal solution for Sunday from 1000 (10:00 am) to 2000 (8:00 pm) when TVTT is

used for impedance. The values in the ‘TVTT (min)’ field represents the accumulated

travel time of the optimal route in each time interval. The values in the ‘DIST (mi)’ and

‘FFTT (min)’ fields are adjusted corresponding to the change of route. The values in

‘DIST (mile)’ field represents the path distance in miles of the selected optimal route

(Route A or B), and the values in ‘FFTT (min)’ field represents the free-flow travel time

of the decimal minutes of the selected optimal route (Route A or B).

Table 15. Analysis settings used for S1

Table 16. Scenario 1, Sunday, Clinton FD to IN-1, DIST impedance

Table 17. Scenario 1, Tuesday, Clinton FD to IN-1, DIST impedance

Impedance Use Start Time Time of Day Day of Week Use Time Windows

DIST Yes 0000 to 2300 SUN & TUE No

FFTT Yes 0000 to 2300 SUN & TUE No

TVTT Yes 0000 to 2300 SUN & TUE No

Impedance Reorder Stops U-Turns OneWay

DIST No Allowed Prohibited

FFTT No Allowed Prohibited

TVTT No Allowed Prohibited

Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)

A Clinton FD to IN-1 0.887 1.330 2.242


A Clinton FD to IN-1 0.887 1.330 1.759

62

Table 18. Scenario 1, Sunday, Clinton FD to IN-1, FFTT impedance

Table 19. Scenario 1, Tuesday, Clinton FD to IN-1, FFTT impedance

Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)

A Clinton FD to IN-1 0.887 1.747 1.747 0:00:00 0:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 1:00:00 1:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 2:00:00 2:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 3:00:00 3:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 4:00:00 4:01:20

A Clinton FD to IN-1 0.887 1.747 1.751 5:00:00 5:01:20

A Clinton FD to IN-1 0.887 1.747 1.759 6:00:00 6:01:20

A Clinton FD to IN-1 0.887 1.747 1.780 7:00:00 7:01:20

A Clinton FD to IN-1 0.887 1.747 1.867 8:00:00 8:01:20

A Clinton FD to IN-1 0.887 1.747 2.060 9:00:00 9:01:20

A Clinton FD to IN-1 0.887 1.747 2.317 10:00:00 10:01:20

A Clinton FD to IN-1 0.887 1.747 2.581 11:00:00 11:01:20

A Clinton FD to IN-1 0.887 1.747 2.777 12:00:00 12:01:20

A Clinton FD to IN-1 0.887 1.747 2.829 13:00:00 13:01:20

A Clinton FD to IN-1 0.887 1.747 2.820 14:00:00 14:01:20

A Clinton FD to IN-1 0.887 1.747 2.785 15:00:00 15:01:20

A Clinton FD to IN-1 0.887 1.747 2.720 16:00:00 16:01:20

A Clinton FD to IN-1 0.887 1.747 2.659 17:00:00 17:01:20

A Clinton FD to IN-1 0.887 1.747 2.523 18:00:00 18:01:20

A Clinton FD to IN-1 0.887 1.747 2.328 19:00:00 19:01:20

A Clinton FD to IN-1 0.887 1.747 2.163 20:00:00 20:01:20

A Clinton FD to IN-1 0.887 1.747 1.871 21:00:00 21:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 22:00:00 22:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 23:00:00 23:01:20


A Clinton FD to IN-1 0.887 1.747 2.036 0:00:00 0:01:20

A Clinton FD to IN-1 0.887 1.747 2.036 1:00:00 1:01:20

A Clinton FD to IN-1 0.887 1.747 2.036 2:00:00 2:01:20

A Clinton FD to IN-1 0.887 1.747 2.036 3:00:00 3:01:20

A Clinton FD to IN-1 0.887 1.747 2.036 4:00:00 4:01:20

A Clinton FD to IN-1 0.887 1.747 2.042 5:00:00 5:01:20

A Clinton FD to IN-1 0.887 1.747 2.045 6:00:00 6:01:20

A Clinton FD to IN-1 0.887 1.747 2.083 7:00:00 7:01:20

A Clinton FD to IN-1 0.887 1.747 2.157 8:00:00 8:01:20

A Clinton FD to IN-1 0.887 1.747 2.162 9:00:00 9:01:20

A Clinton FD to IN-1 0.887 1.747 2.144 10:00:00 10:01:20

A Clinton FD to IN-1 0.887 1.747 2.147 11:00:00 11:01:20

A Clinton FD to IN-1 0.887 1.747 2.142 12:00:00 12:01:20

A Clinton FD to IN-1 0.887 1.747 2.138 13:00:00 13:01:20

A Clinton FD to IN-1 0.887 1.747 2.142 14:00:00 14:01:20

A Clinton FD to IN-1 0.887 1.747 2.158 15:00:00 15:01:20

A Clinton FD to IN-1 0.887 1.747 2.169 16:00:00 16:01:20

A Clinton FD to IN-1 0.887 1.747 2.176 17:00:00 17:01:20

A Clinton FD to IN-1 0.887 1.747 2.156 18:00:00 18:01:20

A Clinton FD to IN-1 0.887 1.747 2.117 19:00:00 19:01:20

A Clinton FD to IN-1 0.887 1.747 2.088 20:00:00 20:01:20

A Clinton FD to IN-1 0.887 1.747 2.053 21:00:00 21:01:20

A Clinton FD to IN-1 0.887 1.747 2.036 22:00:00 22:01:20

A Clinton FD to IN-1 0.887 1.747 2.036 23:00:00 23:01:20

63

Table 20. Scenario 1, Sunday, Clinton FD to IN-1, TVTT impedance

Figure 37. IN-1 Scenario 1, Sunday travel time profile, TVTT impedance


A Clinton FD to IN-1 0.887 1.747 1.747 0:00:00 0:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 1:00:00 1:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 2:00:00 2:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 3:00:00 3:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 4:00:00 4:01:20

A Clinton FD to IN-1 0.887 1.747 1.751 5:00:00 5:01:20

A Clinton FD to IN-1 0.887 1.747 1.759 6:00:00 6:01:21

A Clinton FD to IN-1 0.887 1.747 1.780 7:00:00 7:01:22

A Clinton FD to IN-1 0.887 1.747 1.867 8:00:00 8:01:27

A Clinton FD to IN-1 0.887 1.747 2.060 9:00:00 9:01:39

B Clinton FD to IN-1 1.136 2.304 2.352 10:00:00 10:01:45

B Clinton FD to IN-1 1.136 2.304 2.373 11:00:00 11:01:46

B Clinton FD to IN-1 1.136 2.304 2.390 12:00:00 12:01:47

B Clinton FD to IN-1 1.136 2.304 2.404 13:00:00 13:01:48

B Clinton FD to IN-1 1.136 2.304 2.417 14:00:00 14:01:49

B Clinton FD to IN-1 1.136 2.304 2.428 15:00:00 15:01:50

B Clinton FD to IN-1 1.136 2.304 2.439 16:00:00 16:01:50

B Clinton FD to IN-1 1.136 2.304 2.458 17:00:00 17:01:51

B Clinton FD to IN-1 1.136 2.304 2.460 18:00:00 18:01:52

B Clinton FD to IN-1 1.136 2.304 2.431 19:00:00 19:01:50

A Clinton FD to IN-1 0.887 1.747 2.163 20:00:00 20:01:45

A Clinton FD to IN-1 0.887 1.747 1.871 21:00:00 21:01:27

A Clinton FD to IN-1 0.887 1.747 1.747 22:00:00 22:01:20

A Clinton FD to IN-1 0.887 1.747 1.747 23:00:00 23:01:20

64

Table 21. Scenario 1, Tuesday, Clinton FD to IN-1, TVTT impedance

Figure 38. IN-1 Scenario 1, Tuesday travel time profile, TVTT impedance


A Clinton FD to IN-1 0.887 1.747 2.036 0:00:00 0:01:37

A Clinton FD to IN-1 0.887 1.747 2.036 1:00:00 1:01:37

A Clinton FD to IN-1 0.887 1.747 2.036 2:00:00 2:01:37

A Clinton FD to IN-1 0.887 1.747 2.036 3:00:00 3:01:37

A Clinton FD to IN-1 0.887 1.747 2.036 4:00:00 4:01:37

A Clinton FD to IN-1 0.887 1.747 2.042 5:00:00 5:01:38

A Clinton FD to IN-1 0.887 1.747 2.045 6:00:00 6:01:38

A Clinton FD to IN-1 0.887 1.747 2.083 7:00:00 7:01:40

A Clinton FD to IN-1 0.887 1.747 2.157 8:00:00 8:01:44

A Clinton FD to IN-1 0.887 1.747 2.162 9:00:00 9:01:45

A Clinton FD to IN-1 0.887 1.747 2.144 10:00:00 10:01:44

A Clinton FD to IN-1 0.887 1.747 2.147 11:00:00 11:01:44

A Clinton FD to IN-1 0.887 1.747 2.142 12:00:00 12:01:43

A Clinton FD to IN-1 0.887 1.747 2.138 13:00:00 13:01:43

A Clinton FD to IN-1 0.887 1.747 2.142 14:00:00 14:01:43

A Clinton FD to IN-1 0.887 1.747 2.158 15:00:00 15:01:45

A Clinton FD to IN-1 0.887 1.747 2.169 16:00:00 16:01:45

A Clinton FD to IN-1 0.887 1.747 2.176 17:00:00 17:01:46

A Clinton FD to IN-1 0.887 1.747 2.156 18:00:00 18:01:44

A Clinton FD to IN-1 0.887 1.747 2.117 19:00:00 19:01:42

A Clinton FD to IN-1 0.887 1.747 2.088 20:00:00 20:01:40

A Clinton FD to IN-1 0.887 1.747 2.053 21:00:00 21:01:38

A Clinton FD to IN-1 0.887 1.747 2.036 22:00:00 22:01:37

A Clinton FD to IN-1 0.887 1.747 2.036 23:00:00 23:01:37

65

Figure 39. IN-1 Scenario 1, Route A

Figure 40. IN-1 Scenario 1, Route B

66

Findings

Based on the results found in Tables 16 and 17, the total distance of Route A

(Figure 39) for Sunday and Tuesday was 0.887 miles. This value is based on the DIST

impedance and represents the shortest path from Clinton FD to IN-1 for Sunday and

Tuesday. No route changes were observed based on the use of the DIST cost attribute.

When only a distance-based cost attribute is used for impedance, the result is the shortest

path between the origin and destination.

Based on the results found in Tables 18 and 19, where FFTT was used as

impedance, the total FFTT for each run was 1.747 minutes for Sunday and Tuesday. The

total distance for each run or Route A was 0.887 miles. This is the sum of all road

segments or edges associated with the route. When FFTT is used as the impedance,

historical traffic data is not used to optimize the solution; the values in the ‘TVTT (min)’

field were just calculated for comparison. No variations in DIST, FFTT, or routes were

observed based on runs for Sunday and Tuesday. Note that the DIST values are the same

as those in Tables 16 and 17 but the FFTT values are not. The difference between 1.330

value found in Tables 16 and 17 and 1.747 value found in Tables 18 and 19 is because of

the application of global turn delays (Section 3.3.2). If global turn delays were not

applied, the FFTT values in Table 18 and 19 would be 1.330, a difference of 0.417

minutes. This is a good example why accumulated values must be compared cautiously.

When only a FFTT cost attribute is used for impedance, the result is the fastest route

between the origin and the destination. In this instance, it is also the shortest route

because the total distance is the same as those distances found in Tables 16 and 17 when

the DIST impedance is applied.

67

The impedance used to create Tables 20 and 21 was the TVTT cost attribute for

Sunday and Tuesday, respectively. TVTT is derived from historical traffic data. For

Sunday (Table 20), the TVTTs for 17 of 24 time intervals are shown to vary with time.

From the time intervals 0000 (midnight) to 0400 (4:00 am) and 2200 (10:00 pm) to 2300

(11:00 pm), the travel-time values are identical (1.747 minutes). These values are exactly

the same as the free-flow travel times (shown in the ‘FFTT (min)’ field) associated with

lighter traffic patterns of late evening and early morning hours on a Sunday. TVTT

values between the time intervals 0500 (5:00 am) and 2100 (9:00 pm) vary based on

Sunday time-of-day traffic patterns. Traffic congestion is believed to be the primary

reason.

The different values in the ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 20 are

due to a route change. This change occurs between the time intervals 1000 (10:00 am)

and 1900 (7:00 pm), represented as Route B in the ‘Route’ field and highlighted in

orange. The total distance for the route associated with Route A (Figure 39) is 0.887

miles which is the same as the DIST values in Tables 16 through 19. The distance value

increased slightly (0.249 miles) to 1.136 miles due to the change from Route A to Route

B (Figure 40). It indicates that Route A has a shorter distance than Route B, but it has a

longer travel time when time-varying travel times are used for impedance. Based on

Tables 16, 18 and 20, Route A would be considered as the shortest and fastest route for

Sunday traffic patterns and the optimal route for Sunday between midnight to 10:00 am

and from 8:00 pm to midnight. Route B would be considered as the optimal route for

Sunday from 10:00 am to 8:00 pm.

68

Table 21 shows the TVTT for Tuesday; the TVTTs for 17 of 24 time intervals are

shown to vary with time. From the time intervals 0000 (midnight) to 0400 (4:00 am) and

2200 (10:00 pm) to 2300 (11:00 pm), the travel-time values are identical (2.036 minutes).

These values are close to the free-flow travel times (shown in the ‘FFTT (min)’ field)

associated with lighter traffic patterns of late evening and early morning hours on a

Tuesday. TVTT values between the time intervals 0500 (5:00 am) and 2100 (9:00 pm)

vary, however, based on Tuesday time-of-day traffic patterns. Traffic congestion is

believed to be the primary reason.

The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 21 do not indicate a route

change. Route A shown in the ‘Route’ field is constant throughout the day. The total

distance for the path associated with Route A (Figure 39) is 0.887 miles. This distance is

the same as the DIST values in Tables 16 through 19. When Tables 19 and 21 for

Tuesday are compared, the values in the ‘DIST (mi)’, ‘FFTT (min)’ and ‘TVTT (min)’

fields are the same. Although the TVTT values in Table 21 vary with time between the

time intervals 0500 (5:00 am) and 2100 (9:00 pm), they do not change enough to generate

a new route. Based on Tables 17, 19 and 21, Route A would be considered as the

shortest, fastest and most optimal route for Tuesday traffic patterns.

Discussion

Although travel distance and travel time generated by applying TVTT impedance

sometimes increased due to traffic congestion, previous research (Alazab et al. 2011,

Chien and Kuchipudi 2003, Wu et al. 2001) has demonstrated that the travel times and

routes generated within a dynamic network are still considered as more realistic than the

69

ones in a static network environment. For instance, in Route Analysis Scenario 1 for IN-

1, Route B would be considered a more realistic optimal route than Route A during the

hours of 1000 (10:00am) and 2000 (8:00 pm) for Sunday traffic pattern.

Table 22 compares the travel times for Route A and Route B for Sunday during

the hours of 1000 (10:00am) to 2000 (8:00 pm) in order to validate the assumption that

applying TVTT will yield a more optimal routing solution when compared to DIST or

FFTT. Columns ‘A-I’, ‘A-II’, and ‘A-III’ are the values in the ‘DIST (mi)’, ‘FFTT

(min)’, and ‘TVTT (min)’ fields, respectively, from Table 18. Though the route choices

from Table 18 were based on FFTT as the impedance and generated Route A as the

fastest route, the values in the ‘TVTT (min)’ field were generated by applying TVTT as

impedance, which represents the accumulated time-varying travel time for Route A in

each time interval. The values in the ‘DIST (mi)’ field were generated by applying DIST

as impedance, which represents the total lengths of the road segments in Route A.

Columns ‘B-I’, ‘B-II’, and ‘B-III’ are the values in the ‘DIST (mi)’, ‘FFTT

(min)’, and ‘TVTT (min)’ fields, respectively, from Table 20 when TVTT was applied as

the impedance and generated Route B as the optimal route. The value in the ‘DIST (mi)’

represents the total lengths of Route B. The values in the ‘FFTT (min)’ field were

generated by applying FFTT as impedance, which represents the accumulated free-flow

travel time for Route B. Columns ‘A-IV’ and ‘B-IV’ are multipliers or free-flow factors

derived from Tables 18 and 20, respectively. These free-flow factors are ratios,

calculated by dividing TVTT by FFTT (TVTT/FFTT). The lower the value of the free-

flow factor means the travel time is closer to the free-flow travel time with less traffic

congestion.

70

Table 22. IN-1 Scenario 1, Sunday, comparison of cost impedance between

Routes A and B

Comparing the DIST and FFTT value between Routes A and B within a static

network environment, Route A is a better choice with shorter distance (Column ‘A-I’ vs.

Column ‘B-I’) and less free-flow travel time (Column ‘A-II’ vs. Column ‘B-II’). When

considering a dynamic network environment with time-varying travel time, Route B is a

more optimal choice with lower travel time (Column ‘B-III’ vs. Column ‘A-III’) for

Sunday during the hours of 1000 (10:00 am) and 2000 (8:00 pm). Two exceptions take

place at the time intervals 1000 (10:00 am) and 1900 (7:00 pm); Route A has less travel

time than Route B. However, when comparing Columns ‘A-IV’ and ‘B-IV’, Route B has

a lower free-flow factor than Route A, which means there is less traffic in Route B than

in Route A. Therefore, for the hours from 10:00 am to 11:00 am, and from 7:00 pm to

8:00 pm, Route B could be considered a better or more reliable route than Route A, but

not more optimal.

A-I A-II A-III A-IV B-I B-II B-III B-IV

From (hrs) To (hrs) DIST (mi) FFTT (min) TVTT (min)Free-flow

FactorDIST (mi) FFTT (min) TVTT (min)

Free-flow

Factor

1000 1100 0.887 1.747 2.317 1.326 1.136 2.304 2.352 1.021

1100 1200 0.887 1.747 2.581 1.478 1.136 2.304 2.373 1.030

1200 1300 0.887 1.747 2.777 1.590 1.136 2.304 2.390 1.037

1300 1400 0.887 1.747 2.829 1.619 1.136 2.304 2.404 1.043

1400 1500 0.887 1.747 2.820 1.614 1.136 2.304 2.417 1.049

1500 1600 0.887 1.747 2.785 1.594 1.136 2.304 2.428 1.054

1600 1700 0.887 1.747 2.720 1.557 1.136 2.304 2.439 1.059

1700 1800 0.887 1.747 2.659 1.552 1.136 2.304 2.458 1.067

1800 1900 0.887 1.747 2.523 1.444 1.136 2.304 2.460 1.068

1900 2000 0.887 1.747 2.328 1.333 1.136 2.304 2.431 1.055

Route A Route B

71


Scenario 2 is the route run and analysis from the IN-1 to Davis Hospital. S2

represents an ambulance on an emergency run from IN-1 to Davis Hospital. The analysis

settings window is shown in Figure 30. The analysis settings for each cost attribute used

for S2 are the same as those used for S1 and are shown in Table 15.

Description

Six tables and five figures were created based on these runs. Tables 23 and 24

show the results of runs from IN-1 to Davis Hospital applying the DIST impedance for

Sunday and Tuesday, respectively. Similar to Tables 16 and 17, the ‘TVTT (min)’ field

in these tables show the accumulated TVTT value calculated for 1700 (5:00 pm) only.

Tables 25 and 26 show the results of runs from IN-1 to Davis Hospital applying

the FFTT impedance for Sunday and Tuesday, respectively. Tables 27 and 28 show the

results of runs from IN-1 to Davis Hospital applying the TVTT impedance for Sunday

and Tuesday, respectively. Figures 41 and 42 show the travel time profiles associated

with Tables 27 and 28, respectively. They represent the TVTT when historical traffic

data is applied. Routes A (Figure 43), B (Figure 44) and C (Figure 45) represent the

routes generated by the ‘Route’ solver based on the time, day and impedance applied.

Table 23. Scenario 2, Sunday, IN-1 to Davis Hospital, DIST impedance

Table 24. Scenario 2, Tuesday, IN-1 to Davis Hospital, DIST impedance


A IN-1 to Davis Hospital 6.260 11.505 15.081



72

Table 25. Scenario 2, Sunday, IN-1 to Davis Hospital, FFTT impedance

Table 26. Scenario 2, Tuesday, IN-1 to Davis Hospital, FFTT impedance


B IN-1 to Davis Hospital 6.362 10.579 10.579 0:00:00 0:08:23

















































73

Table 27. Scenario 2, Sunday, IN-1 to Davis Hospital, TVTT impedance



























74

Table 28. Scenario 2, Tuesday, IN-1 to Davis Hospital, TVTT impedance











C IN-1 to Davis Hospital 6.339 13.106 17.195 8:00:00 8:13:42
















75



76

Figure 45. IN-1 Scenario 2, Route C

Findings


(Figure 43) for Sunday and Tuesday was 6.260 miles. The value was based on the DIST

impedance and represents the shortest path from IN-1 to Davis Hospital for Sunday and

Tuesday. No route changes were observed based on the use of the DIST impedance.

Based on the results found in Tables 25 and 26, where FFTT was used as

impedance, the total FFTT for each run was 10.579 minutes for Sunday and Tuesday.

The total length for each run or Route B (Figure 44) was 6.362 miles. The difference in

length between Route A in Table 23 and Route B in Table 25 was 0.102 miles or 1.6%.

The difference in travel time between Route A and B was 0.926 minutes or 8.0%. The

use of FFTT as an impedance triggered the change from Route A in Table 23 to Route B

77

in Table 25 with relatively small differences in the path length and travel time. It was

also observed that Route B makes use of a more direct route taking advantage of

Interstate 15 (I-15) with greater speed limits when compared to Route A. No variations

in DIST or FFTT were observed based on runs for Sunday and Tuesday.


Sunday and Tuesday, respectively. Table 27 shows the TVTT for Sunday; the TVTTs for

17 of 24 time intervals are shown to vary with time. From the time intervals 0000

(midnight) to 0400 (4:00 am) and 2200 (10:00 pm) to 2300 (11:00 pm), the travel-time

values are identical (10.579 minutes) as free-flow travel times in the ‘FFTT (min)’ field.

These values illustrate lighter traffic patterns of late evening and early morning hours on

a Sunday. TVTT values between the time intervals 0500 (5:00 am) and 2100 (9:00 pm)

vary based on Sunday time-of-day traffic patterns.

The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 27 do not indicate a route

change. Route B shown in the ‘Route’ field is constant throughout the day. The total

distance for Route B is 6.362 miles. This distance is the same as the DIST values in

Tables 25 and 26. When Tables 25 and 27 for Sunday are compared, the values in the

‘DIST (mi)’, ‘FFTT (min)’ and ‘TVTT (min)’ fields are the same. Although the TVTT

values in Table 27 vary with time between the time intervals 0500 (5:00 am) and 2100

(9:00 pm), they do not change enough to generate a new route. Route B, based on the

TVTT impedance for Sunday, would be considered as the optimal route. Based on

Tables 23, 25 and 27, Route A would be considered as the shortest path and Route B

would be considered as the fastest and most optimal route for Sunday traffic patterns.

78

For Tuesday (Table 28), the TTVTs for 17 of 24 time intervals are shown to vary

with time. From the time intervals 0000 (midnight) to 0400 (4:00 am) and 2200 (10:00

pm) to 2300 (11:00 pm), the travel-time values are identical (12.945 minutes). These

values are close to the free-flow travel times (shown in the ‘FFTT (min)’ field) associated

with lighter traffic patterns of late evening and early morning hours on a Tuesday. TVTT


Tuesday time-of-day traffic patterns.

The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 28 indicate a route change.

This change occurs between the time intervals 0800 (8:00 am) and 1900 (7:00 pm)

denoted by Route B and Route C (Figure 45) in the ‘Route’ field. The total distance for

the route associated with Route B is 6.362 miles. This distance is the same as the DIST

values in Tables 25 through 27. The distance value decreased slightly (-0.023 miles) to

6.339 miles due to the change from Route B to Route C. These changes are based on

increased day time traffic congestion. Based on Tables 24, 26 and 28, Route A would be

considered as the shortest path and Route B would be considered as the fastest route for

Tuesday traffic patterns. Route B would also be considered as the most optimal route

between midnight and 8:00 am and from 8:00 pm to midnight, but Route C is the most

optimal route from 8:00 am to 8:00 pm for Tuesday.

Discussion

Table 29 compares travel times for Route B and Route C for Tuesday during the

hours between 8:00 am and 8:00 pm to validate that applying TVTT will yield a more

optimal routing solution. Columns ‘B-I’, ‘B-II’, and ‘B-III’ are the values in the ‘DIST

79

(mi)’, ‘FFTT (min)’, and ‘TVTT (min)’ fields, respectively, from Table 26. Columns ‘C-

I’, ‘C-II’, and ‘C-III’ are the values in the ‘DIST (mi)’, ‘FFTT (min)’, and ‘TVTT (min)’

fields, respectively, from Table 28. Columns ‘B-IV’ and ‘C-IV’ are the free-flow factors

derived from Tables 26 and 28, respectively.

Comparing the DIST and FFTT values between Routes B and C within a static

network environment, Route B is the better solution for free-flow travel times, but Route

C has shorter travel distance. When considering a dynamic network environment with

time-varying travel time, Route C is a more optimal choice with lower travel time for

Tuesday during the hours of 0800 (8:00 am) to 2000 (8:00 pm). One exception takes

place at the time interval 1900 (7:00 pm), Route B requires less travel time than Route C.

However, when compare the Columns ‘B-IV’ and ‘C-IV’, Route C has a lower free-flow

factor than Route B, which means there is less traffic in Route C than in Route B.

Therefore, for the hours between 7:00 pm and 8:00 pm, Route C could be considered a

better or more reliable route than Route B, but not more optimal.

Table 29. IN-1 Scenario 2, Tuesday, comparison of cost impedance between

Routes B and C

BI B-II B-III B-IV C-I C-II C-III C-IV



Free-flow

Factor

0800 0900 6.362 10.579 18.149 1.716 6.339 13.106 17.195 1.312

0900 1000 6.362 10.579 17.819 1.684 6.339 13.106 17.185 1.311

1000 1100 6.362 10.579 17.254 1.631 6.339 13.106 17.071 1.303

1100 1200 6.362 10.579 17.308 1.636 6.339 13.106 17.090 1.304

1200 1300 6.362 10.579 17.503 1.655 6.339 13.106 17.083 1.303

1300 1400 6.362 10.579 17.520 1.656 6.339 13.106 17.069 1.302

1400 1500 6.362 10.579 17.732 1.676 6.339 13.106 17.101 1.305

1500 1600 6.362 10.579 18.304 1.730 6.339 13.106 17.211 1.313

1600 1700 6.362 10.579 18.856 1.782 6.339 13.106 17.294 1.320

1700 1800 6.362 10.579 19.310 1.825 6.339 13.106 17.358 1.324

1800 1900 6.362 10.579 18.426 1.745 6.339 13.106 17.215 1.314

1900 2000 6.362 10.579 16.608 1.570 6.339 13.106 16.918 1.291

Route CRoute B

80

4.1.4 IN-1: Emergency Response Routing Review

In review, four maps and one table were created showing the combined results of

Scenarios 1 and 2. For each map, the dashed red line represents the emergency response

route from Clinton FD (origin) to IN-1 (destination), and the blue dashed line represents

the emergency response route from IN-1 (origin) to Davis Hospital (destination). For

comparison purposes, each route was run at 1700 (5:00 pm) for Sunday and Tuesday.

Figure 46 shows the shortest route from Clinton FD to IN-1 (S1, Route A) and

from IN-1 to Davis Hospital (S2, Route A) when the static cost attribute DIST was

applied as impedance. The results were the same for Sunday and Tuesday. No route

change was observed between Sunday and Tuesday runs. Figure 47 illustrates the fastest

route from Clinton FD to IN-1 (S1, Route A) and from IN-1 to Davis Hospital (S2, Route

B) when the static cost attribute FFTT was applied as impedance. The results were the

same for Sunday and Tuesday. No route change was observed between Sunday and

Tuesday runs. In this instance, the fastest route from Clinton FD to IN-1 (Route A) is

also the shortest route.

The optimal routes generated by the dynamic cost attribute TVTT as impedance

are shown in Figures 48 and 49. Route changes were observed between the Sunday and

Tuesday runs due to the application of historical traffic data representing traffic

congestion. Figure 48 shows the dynamic optimal route from Clinton FD to IN-1 (S1,

Route B) and from IN-1 to Davis Hospital (S2, Route B); these paths are considered as

the most optimal routes from each origin to each destination on 5:00 pm, Sunday. In this

instance, the optimal route from IN-1 to Davis Hospital (Route B) is also the fastest

route.

81

Figure 46. IN-1, combined scenarios, Sunday and Tuesday, DIST impedance

Figure 47. IN-1, combined scenarios, Sunday and Tuesday, FFTT impedance

82

Figure 48. IN-1, combined scenarios, Sunday, TVTT impedance

Figure 49. IN-1, combined scenarios, Tuesday, TVTT impedance

83

Figure 49 shows the dynamic optimal route from Clinton FD to IN-1 (S1, Route

A) and from IN-1 to Davis Hospital (S2, Route C); these paths are considered as the most

optimal routes from each origin to each destination on 5:00 pm, Sunday. In this instance,

the optimal route from Clinton FD to IN-1 (Route A) is also the shortest route.

Table 30 shows the distances and travel times associated with each route

generated for routing example IN-1, and the routes are displayed in Figures 46 through

49. This table can be used to analyze the values associated with each route. When

observing route run results, the bolded values are based on the applied impedance that

was used to optimize the solution. The accumulated values are shown in italicized red

font and are for reference and comparison only. As previously mentioned, it is important

to note that differences in travel times can occur because of the application of global turn

delays (Sections 3.3.2 and 4.1.2 IN-1).

Table 30. IN-1, combined scenarios, comparison of emergency response routes

Cost Day StartTime (h) Scenario Route Origin-Destination Dist (mi) FFTT (min) TTVT (min) Figure

DIST SU 1700 S1 A Clinton FD to IN-1 0.887 1.330 2.242 46

DIST SU 1700 S2 A IN-1 to Davis Hospital 6.260 11.505 15.081 46

DIST TU 1700 S1 A Clinton FD to IN-1 0.887 1.330 1.759 46

DIST TU 1700 S2 A IN-1 to Davis Hospital 6.260 11.505 16.702 46

FFTT SU 1700 S1 A Clinton FD to IN-1 0.887 1.747 2.659 47

FFTT SU 1700 S2 B IN-1 to Davis Hospital 6.362 10.579 11.268 47

FFTT TU 1700 S1 A Clinton FD to IN-1 0.887 1.747 2.176 47

FFTT TU 1700 S2 B IN-1 to Davis Hospital 6.362 10.579 19.310 47

TVTT SU 1700 S1 B Clinton FD to IN-1 1.136 2.304 2.458 48

TVTT SU 1700 S2 B IN-1 to Davis Hospital 6.362 10.579 11.268 48

TVTT TU 1700 S1 A Clinton FD to IN-1 0.887 1.747 2.176 49

TVTT TU 1700 S2 C IN-1 to Davis Hospital 6.339 13.106 17.358 49

84

4.2 Route Example for IN-2

4.2.1 IN-2: Closest Facility Analysis

Incident 2 (IN-2) is located in Kaysville at the intersection of Boynton and

Fairfield Roads (Table 10). The same methodology and analysis settings used in 4.1.1

IN-1: Closest Facility Analysis were applied to this routing example. As a result of these

runs and applying DIST, FFTT and TVTT as impedances, it was determined the closest

ground unit to IN-2 is Kaysville Fire Department, and the closest hospital from IN-2 is

Davis Hospital. Tables 31 and 32 indicate runs 1A, 2A and 3A are the shortest, fastest

and optimal routes, respectively. Figures 50 through 55 show the routes associated with

the cost attribute used.

Table 31. Results for finding nearest ground unit to IN-2

Table 32. Results for finding nearest hospital from IN-2

Run Cost Origin-Destination Route Day Time DIST (mi) FFTT (min) TVTT (min) Figure

1A DIST Kaysville FD to IN-2 A TU 1700 1.038 1.900 3.277 50

1B DIST Layton FD No. 53 to IN-2 A TU 1700 1.888 3.338 6.480 50

1C DIST Layton FD No. 52 to IN-2 A TU 1700 3.985 5.978 8.639 50

2A FFTT Kaysville FD to IN-2 A TU 1700 1.038 2.517 3.894 51

2B FFTT Layton FD No. 53 to IN-2 B TU 1700 1.953 4.013 4.839 51

2C FFTT Layton FD No. 52 to IN-2 A TU 1700 3.985 8.011 10.673 51

2A TVTT Kaysville FD to IN-2 B TU 1700 1.277 2.879 3.431 52

2B TVTT Layton FD No. 53 to IN-2 B TU 1700 1.953 4.013 4.839 52

2C TVTT Layton FD No. 52 to IN-2 A TU 1700 3.985 8.011 10.673 52

Run Cost Origin-Destination Route Day Time DIST (mi) FFTT (min) TVTT (min) Figure

1A DIST IN-2 to Davis Hospital A TU 1700 5.055 7.620 15.095 53

1B DIST IN-2 to McKay Dee A TU 1700 11.464 15.855 31.475 53

1C DIST IN-2 to Ogden Regional A TU 1700 11.551 15.509 30.022 53

2A FFTT IN-2 to Davis Hospital B TU 1700 5.895 7.696 18.733 54

2B FFTT IN-2 to Ogden Regional B TU 1700 12.130 17.774 28.431 54

2C FFTT IN-2 to McKay Dee B TU 1700 11.987 17.936 29.039 54

2A TVTT IN-2 to Davis Hospital C TU 1700 5.338 10.538 13.485 55

2B TVTT IN-2 to Ogden Regional C TU 1700 12.659 20.547 29.147 55

2C TVTT IN-2 to McKay Dee C TU 1700 12.515 20.709 29.755 55

85

Figure 50. Routes from nearest ground unit to IN-2 applying DIST impedance

86

Figure 51. Routes from nearest ground unit to IN-2 applying FFTT impedance

87

Figure 52. Routes from nearest ground unit to IN-2 applying TVTT impedance

88

Figure 53. Routes from IN-2 to nearest hospital applying DIST impedance

89

Figure 54. Routes from IN-2 to nearest hospital applying FFTT impedance

90

Figure 55. Routes from IN-2 to nearest hospital applying TVTT impedance

91


Scenario 1 is the route run and analysis from Kaysville Fire Department to IN-2.

S1 represents an ambulance on an emergency run from Kaysville Fire Department to IN-

2. The same methodology and analysis settings used in 4.1.2 IN-1: Route Analysis

Scenario 1 were applied to this route analysis.

Description

Six tables and four figures were created based on these runs. Tables 33 and 34

show the results of runs from Kaysville FD to IN-2 applying the DIST impedance for


in these tables shows the accumulated TVTT value calculated for 1700 (5:00 pm) only.

Tables 35 and 36 show the results of runs from Kaysville FD to IN-2 applying the

FFTT impedance for Sunday and Tuesday, respectively. Tables 37 and 38 show the

results of runs from Kaysville FD to IN-2 applying the TVTT impedance for Sunday and

Tuesday, respectively. Figures 56 and 57 show the travel time profiles associated with

Tables 37 and 38, respectively. Routes A and B are displayed in Figures 58 and 59,

respectively, and represent the routes generated by the ‘Route’ solver based on the time,

day and impedance applied.

Table 33. Scenario 1, Sunday, Kaysville FD to IN-2, DIST impedance

Table 34. Scenario 1, Tuesday, Kaysville FD to IN-2, DIST impedance


A Kaysville FD to IN-2 1.038 1.900 3.145


A Kaysville FD to IN-2 1.038 1.900 3.277

92

Table 35. Scenario 1, Sunday, Kaysville FD to IN-2, FFTT impedance

Table 36. Scenario 1, Tuesday, Kaysville FD to IN-2, FFTT impedance


A Kaysville FD to IN-2 1.038 2.517 2.517 0:00:00 0:01:54

















































93

Table 37. Scenario 1, Sunday, Kaysville FD to IN-2, TVTT impedance



























94

Table 38. Scenario 1, Tuesday, Kaysville FD to IN-2, TVTT impedance










B Kaysville FD to IN-2 1.277 2.879 3.246 7:00:00 7:02:25

















95



96

Findings


(Figure 58) for Sunday and Tuesday was 1.038 miles. Based on the results found in

Tables 35 and 36, where FFTT was used as impedance, the total FFTT for each run was

the same at 2.517 minutes for Sunday and Tuesday. The total length for each run or

Route A (Figure 58) was 1.038 miles. No variations in DIST, FFTT, or routes were

observed based on runs for Sunday and Tuesday. In this instance, the fastest route is also

the shortest route from Kaysville FD to IN-2 (S1, Route A).


Sunday and Tuesday, respectively. For Sunday (Table 37), the TVTTs for 17 of 24 time

intervals are shown to vary with time. From the time intervals 0000 (midnight) to 0400

(4:00 am) and 2200 (10:00 pm) to 2300 (11:00 pm), the travel-time values are identical

(2.517 minutes). The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 37 do not indicate a

route change. Based on Tables 33, 35 and 37, Route A would be considered the shortest,

fastest, and most optimal route for Sunday traffic patterns.

For Tuesday (Table 38), the TVTTs for 17 of 24 time intervals are shown to vary


pm) to 2300 (11:00 pm), the travel-time values are identical (2.709 minutes). The ‘DIST

(mi)’ and ‘FFTT (min)’ fields in Table 38 indicate a route change. This change occurs

between the time intervals 0700 (7:00 am) and 2000 (8:00 pm), represented as Route B

(Figure 59) in the ‘Route’ field and highlighted in orange. The distance value increased

slightly (0.239 miles) to 1.277 miles due to the change from Route A to Route B. Based

on Tables 34, 36 and 38, Route A would be considered the shortest and fastest route for

97

Tuesday traffic patterns. Route A would also be considered as the most optimal route

from midnight to 7:00 am and from 9:00 pm to midnight, but Route B is the most optimal

route from 7:00 am to 9:00 pm for Tuesday.

Discussion

Table 39 compares travel times for Route A and Route B for Tuesday during the

hours between 7:00 am and 9:00 pm. Route A is a better choice with shorter distance and

less free-flow travel time when comparing the static DIST and FFTT values. Route B is

a more optimal choice with lower travel time for Tuesday during the hours of 7000 (7:00

am) and 2100 (9:00 pm). Two exceptions take place at the time intervals 0700 (7:00 am)

and 2000 (8:00 pm), when Route A has less travel time than Route B, but Route B has a

lower free-flow factor with less traffic than Route A. Therefore, for the hours from 7:00

am to 8:00 am, and from 8:00 pm to 9:00 pm, Route B could be considered a better or

more reliable route than Route A, but not more optimal.

Table 39. IN-2 Scenario 1, Tuesday, comparison of cost impedance between

Routes A and B

A-I A-II A-III A-IV B-I B-II B-III B-IV



Free-flow

Factor

0700 0800 1.038 2.517 3.148 1.251 1.277 2.879 3.246 1.127

0800 0900 1.038 2.517 3.686 1.464 1.277 2.879 3.394 1.179

0900 1000 1.038 2.517 3.639 1.446 1.277 2.879 3.402 1.182

1000 1100 1.038 2.517 3.527 1.401 1.277 2.879 3.366 1.169

1100 1200 1.038 2.517 3.540 1.406 1.277 2.879 3.374 1.172

1200 1300 1.038 2.517 3.564 1.416 1.277 2.879 3.362 1.168

1300 1400 1.038 2.517 3.562 1.415 1.277 2.879 3.354 1.165

1400 1500 1.038 2.517 3.600 1.430 1.277 2.879 3.362 1.168

1500 1600 1.038 2.517 3.712 1.475 1.277 2.879 3.396 1.180

1600 1700 1.038 2.517 3.813 1.515 1.277 2.879 3.416 1.187

1700 1800 1.038 2.517 3.894 1.547 1.277 2.879 3.431 1.192

1800 1900 1.038 2.517 3.735 1.484 1.277 2.879 3.392 1.178

1900 2000 1.038 2.517 3.391 1.347 1.277 2.879 3.313 1.151

2000 2100 1.038 2.517 3.090 1.228 1.277 2.879 3.256 1.131

Route A Route B

98


Scenario 2 is the route run and analysis from IN-2 to Davis Hospital. S2

represents an ambulance on an emergency run from IN-2 to Davis Hospital. The same

methodology and analysis settings used in 4.1.3 IN-1: Route Analysis Scenario 2 were

applied to this route analysis.

Description

Six tables and seven figures were created based on these runs. Tables 40 and 41

show the results of runs from IN-2 to Davis Hospital applying the DIST impedance for


in these tables show the accumulated TVTT value calculated for 1700 (5:00 pm) only.

Tables 42 and 43 show the results of runs from IN-2 to Davis Hospital applying

the FFTT impedance for Sunday and Tuesday, respectively. Tables 44 and 45 show the

results of runs from IN-2 to Davis Hospital applying the TVTT impedance for Sunday

and Tuesday, respectively. Figures 60 and 61 show the travel time profiles associated

with Tables 44 and 45, respectively. Routes A (Figures 62), B (Figures 63), C (Figures

64), D (Figures 65), and E (Figures 66) represent the routes generated by the ‘Route’

solver based on the time, day and impedance applied.

Table 40. Scenario 2, Sunday, IN-2 to Davis Hospital, DIST impedance

Table 41. Scenario 2, Tuesday, IN-2 to Davis Hospital, DIST impedance





99

Table 42. Scenario 2, Sunday, IN-2 to Davis Hospital, FFTT impedance

Table 43. Scenario 2, Tuesday, IN-2 to Davis Hospital, FFTT impedance



















































100

Table 44. Scenario 2, Sunday, IN-2 to Davis Hospital, TVTT impedance



























101

Table 45. Scenario 2, Tuesday, IN-2 to Davis Hospital, TVTT impedance










D IN-2 to Davis Hospital 5.524 10.734 12.429 7:00:00 7:10:05

E IN-2 to Davis Hospital 5.338 10.538 13.279 8:00:00 8:10:51
















102



103

Figure 64. IN-2 Scenario 2, Route C

Figure 65. IN-2 Scenario 2, Route D

104

Figure 66. IN-2 Scenario 2, Route E

Findings


(Figure 62) for Sunday and Tuesday was 5.055 miles. No route changes were observed

based on the use of the DIST impedance. Based on the results found in Tables 42 and 43,

where FFTT was used as impedance, the total FFTT for each run was 7.696 minutes for

Sunday and Tuesday. The total length for each run or Route B (Figure 63) was 5.895

miles. No variations in DIST, FFTT, or routes were observed based on runs for Sunday

and Tuesday. The use of FFTT as an impedance triggered the change from Route A in

Tables 40 and 41 to Route B in Table 42 and 43. It was also observed that Route B takes

more advantage of Interstate 15 (I-15) when compared to Route A.

105


Sunday and Tuesday, respectively. Table 44 shows the TVTT for Sunday; the TVTTs for

17 of 24 time intervals are shown to vary with time. From the time intervals 0000

(midnight) to 0400 (4:00 am) and 2200 (10:00 pm) to 2300 (11:00 pm), the travel-time

values are identical (8.288 minutes) and close to the corresponding FFTT values. TVTT


Sunday time-of-day traffic patterns. The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table

44 indicate a route change. This change occurs between the time interval 0900 (9:00 am)

and 2100 (9:00 pm) denoted by Route C (Figure 64) in the ‘Route’ field. The distance

value decreased slightly (-0.209 miles) to 5.686 miles due to the change from Route B to

Route C.

For Tuesday (Table 45), the TTVTs for 17 of 24 time intervals are shown to vary


pm) to 2300 (11:00 pm), the travel-time values are identical (8.417 minutes). TVTT

values between the intervals 0500 (5:00 am) and 2100 (9:00 pm) vary based on Tuesday

time-of-day traffic patterns. The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 45

indicate multiple route changes. Several changes occur between the time intervals 0700

(7:00 am) and 2000 (8:00 pm) denoted by Route D (Figures 65) and Route E (Figures

66) in the ‘Route’ field. The total distance for the route associated with Route C is 5.686

miles. The distance value decreased slightly (-0.162 miles) to 5.524 miles due to the

change from Route C to Route D. The distance value decreased even more (-0.348

miles) to 5.338 miles due to the change from Route C to Route E. The difference in

distance between Route D and Route E is 0.186 miles.

106

Discussion

Table 46 compares travel times for Routes A, B, and C for Sunday during the

hours between 9:00 am and 10:00 pm to validate that applying TVTT will yield a more

optimal routing solution. Column ‘A-I’ is the value in the ‘DIST (mi)’ field from Table

40. Columns ‘B-I’, ‘B-II’, and ‘B-III’ are the values in the ‘DIST (mi)’, ‘FFTT (min)’,

and ‘TVTT (min)’ fields, respectively, from Table 42. Columns ‘C-I’, ‘C-II’, and ‘C-III’

are the values in the ‘DIST (mi)’, ‘FFTT (min)’, and ‘TVTT (min)’ fields, respectively,

from Table 44. Columns ‘B-IV’ and ‘C-IV’ are the free-flow factors derived from Tables

40 and 42, respectively.

Comparing the DIST and FFTT values between Routes A, B, and C within a static

network environment, Route A (Figure 62) is the best solution for the shortest distance,

and Route C (Figure 64) seems to be the best solution for free-flow travel times (7.412

minutes). However, from the route analysis applying FFTT as impedance (Table 42),

ArcGIS Network Analyst Route Solver generated Route B (Figure 63) as the fastest route

based on static free-flow travel time (7.696 minutes). According to Esri (2013g), “the

best route can be defined as the route that has the lowest impedance, where the

impedance is chosen by the users.” Therefore, Route B should be the fastest route based

on the static free-flow time (FFTT impedance) from IN-2 to Davis Hospital. There

should not be any other route with less free-flow time. Table 40 shows Route A with less

free-flow travel time (7.620 minutes) due to DIST impedance route analysis, which does

not consider the global turn restriction. Route C was generated by applying TVTT as

impedance as the optimal route during the hours between 9:00 am and 10:00 pm within a

dynamic network environment, but its FFTT value (7.412 minutes) is way less than

107

Routes A and B, which raises the question of which route is actually the fastest route.

This inconsistent route analysis was re-run several times through different versions of

ArcGIS Network Analyst (9.3, 10, and 10.) to ensure no human error in parameter inputs,

but all re-runs produced the exact same results. Route C based on TVTT impedance has

less free-flow travel time than Route B generated through FFTT impedance. According

to Esri (2013f), the users “can accumulate any number of impedance attributes in a route

analysis, but accumulated attributes don’t play a role in computing the path along the

network.” The FFTT values in Table 44 is just accumulated free-flow times attributes, so

it might not be a reliable result for the fastest route. Without further investigation on

ArcGIS Network Analyst shortest path algorithm, the fastest route can’t be determined

for IN-2 Scenario 2 for Sunday traffic pattern.

Comparing TVTT and Free-flow Factor between Routes B and C with a dynamic

network environment with time-varying travel time, Route C is the optimal route during

the hours between 9:00 am and 10:00 pm. Route C requires less travel time than Route B

(TVTT values) and has lower free-flow factors in each time interval shown in Table 46.

Table 46. IN-2 Scenario 2, Sunday, comparison of cost impedance between

Routes A, B, and C

Route A

A-I B-I B-II B-III B-IV C-I C-II C-III C-IV

From

(hrs)

To

(hrs)DIST (mi) DIST (mi) FFTT (min) TVTT (min)

Free-flow


Free-flow

Factor

0900 1000 5.055 5.895 7.696 9.081 1.180 5.686 7.412 8.672 1.170

1000 1100 5.055 5.895 7.696 9.574 1.244 5.686 7.412 9.038 1.219

1100 1200 5.055 5.895 7.696 10.040 1.305 5.686 7.412 9.366 1.264

1200 1300 5.055 5.895 7.696 10.333 1.343 5.686 7.412 9.548 1.288

1300 1400 5.055 5.895 7.696 10.389 1.350 5.686 7.412 9.571 1.291

1400 1500 5.055 5.895 7.696 10.376 1.348 5.686 7.412 9.563 1.290

1500 1600 5.055 5.895 7.696 10.332 1.343 5.686 7.412 9.541 1.287

1600 1700 5.055 5.895 7.696 10.213 1.327 5.686 7.412 9.455 1.276

1700 1800 5.055 5.895 7.696 10.076 1.309 5.686 7.412 9.346 1.261

1800 1900 5.055 5.895 7.696 9.798 1.273 5.686 7.412 9.132 1.232

1900 2000 5.055 5.895 7.696 9.413 1.223 5.686 7.412 8.842 1.193

2000 2100 5.055 5.895 7.696 9.131 1.186 5.686 7.412 8.647 1.167

2100 2200 5.055 5.895 7.696 8.566 1.113 5.686 7.412 8.226 1.110

Route B Route C

108

Table 47 is the summary report for the shortest, fastest, and optimal route from

IN-2 to Davis Hospital for Tuesday traffic pattern. Route A (Figure 62) is the shortest

route (Table 41) with the travel distance as 5.055 miles. Route B (Figure 63) can be

considered as the fastest route (7.696 minutes) while applying FFTT as impedance (Table

43). However, Route C (Figure 64) based on TVTT as impedance (Table 45) has less

free-flow travel time (7.412 minutes) than Route B. Without further investigation, the

fastest route can’t be determined for IN-2 Scenario 2 for Tuesday traffic pattern. Route C

(Figure 64) is the optimal route during the hours from midnight to 7:00 am, and from

9:00 pm to midnight (Table 45). Route D (Figure 65) is the optimal route during the

hours from 7:00 am to 8:00 am, from 1:00 pm to 2:00 pm, and from 7:00 pm to 9:00 pm

(Table 45). Route E (Figure 66) is the optimal route during the hours from 8:00 am to

1:00 pm, and from 2:00 pm to 7:00 pm (Table 45).

Table 47. IN-2 Scenario 2, Tuesday, summary of cost impedance between Routes A, B, C,

D, and E

Routes DIST (mi) FFTT (min) TVTT (min) Remarks

Route A 5.055 7.620 Shortest route

Route B 5.895 7.696 8.871-18.733Fastest route based on FFTT

impedance

Route C 5.686 7.412 8.417-9.310Optimal route between time intervals

0000-0600 and 2100-2300

Route D 5.524 10.734 12.429-12.788Optimal route in time intervals 0700,

1300, 1900, and 2000

Route E 5.338 10.538 13.122-13.485Optimal route between time intervals

0800-1200 and 1400-1800

109

4.2.4 IN-2: Emergency Response Routing Review

In review, four maps and one table were created showing the combined results of

Scenarios 1 and 2. For each map, the dashed red line represents the emergency response

route from Kaysville FD (origin) to IN-2 (destination) and the blue dashed line represents

the emergency response route from IN-2 (origin) to Davis Hospital (destination). For

comparison purposes, each route was run at 1700 (5:00 pm) for Sunday and Tuesday.

Figure 67 shows the shortest route from Kaysville FD to IN-2 (S1, Route A) and

from IN-2 to Davis Hospital (S2, Route A) when the static cost attribute DIST was

applied as impedance. The results were the same for Sunday and Tuesday. No route

change was observed between Sunday and Tuesday runs. Figure 68 illustrates the fastest

route from Kaysville FD to IN-2 (S1, Route A) and from IN-2 to Davis Hospital (S2,

Route B) when the static cost attribute FFTT was applied as impedance. In this instance,

the fastest route from Kaysville FD to IN-2 (Route A) is also the shortest route.

However, the fastest route from IN-2 to Davis Hospital (Route B) might not be a reliable

result as discussed in 4.2.3.

The optimal routes generated by the dynamic cost attribute TVTT as impedance

are shown in Figures 69 and 70. Route changes were observed between and during the

Sunday and Tuesday runs due to the application of historical traffic data representing

traffic congestion. Figure 69 shows the dynamic optimal path from Kaysville FD to IN-2

(S1, Route A) and from IN-2 to Davis Hospital (S2, Route C). These paths are

considered the most optimal routes from each origin to each destination on 5:00 pm,

Sunday. In this instance, the optimal route from Kaysville FD to IN-2 (Route A) is also

the shortest and fastest route.

110

Figure 67. IN-2, combined scenarios, Sunday and Tuesday, DIST impedance

Figure 68. IN-2, combined scenarios, Sunday and Tuesday, FFTT impedance

111

Figure 69. IN-2, combined scenarios, Sunday, TVTT impedance

Figure 70. IN-2, combined scenarios, Tuesday, TVTT impedance

112

Figure 70 shows the dynamic optimal route from the path from Kaysville FD to

IN-2 (S1, Route B) and from IN-2 to Davis Hospital (S2, Route E) on 5:00 pm, Tuesday.

S2, Route D is an additional route change that represents the optimal route on 7:00 pm,

Tuesday from IN-2 to Davis Hospital. These paths are considered the most optimal

routes from each origin to each destination on Tuesday for their specified time intervals.

Table 48 shows the distances and travel times associated with each route

generated for routing example IN-2 and are displayed in Figures 67 through 70. This

table can be used to analyze the values associated with each route. When observing route

run results, the bolded values are based on the applied impedance that was used to

optimize the solution. The accumulated values are shown in italicized red font and are

for reference and comparison only.

Table 48. IN-2, combined scenarios, comparison of emergency response routes

Cost Day StartTime (h) Scenario Route Origin-Destination Dist (mi) FFTT (min) TTVT (min) Figure

DIST SU 1700 S1 A Kaysville FD to IN-2 1.038 1.900 3.145 67

DIST SU 1700 S2 A IN-2 to Davis Hospital 5.055 7.620 11.938 67

DIST TU 1700 S1 A Kaysville FD to IN-2 1.038 1.900 3.277 67

DIST TU 1700 S2 A IN-2 to Davis Hospital 5.055 7.620 15.095 67

FFTT SU 1700 S1 A Kaysville FD to IN-2 1.038 2.517 3.762 68

FFTT SU 1700 S2 B IN-2 to Davis Hospital 5.895 7.696 10.076 68

FFTT TU 1700 S1 A Kaysville FD to IN-2 1.038 2.517 3.894 68

FFTT TU 1700 S2 B IN-2 to Davis Hospital 5.895 7.696 18.733 68

TVTT SU 1700 S1 A Kaysville FD to IN-2 1.038 2.517 3.762 69

TVTT SU 1700 S2 C IN-2 to Davis Hospital 5.686 7.412 9.346 69

TVTT TU 1700 S1 B Kaysville FD to IN-2 1.277 2.879 3.431 70

TVTT TU 1300 S2 D IN-2 to Davis Hospital 5.524 10.734 13.021 70

TVTT TU 1700 S2 E IN-2 to Davis Hospital 5.338 10.538 13.485 70

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4.3 Discussion of Results

On the whole, the results seemed to agree with the expectations and meet the

objective of the study. The DIST impedance generated the shortest path with no regard

to travel time. The FFTT impedance generated the quickest or fastest path, and the

TVTT generated the best or most optimal path by applying historical traffic data.

There are three apparent inconsistent outcomes in the analysis results. First is the

travel time calculation while employing the ‘Start Time’ option in the analysis setting for

ArcGIS Network Analyst ‘Route’ solver (Figure 30). Theoretically, the results of ‘End

Time’ should be the sum of ‘Start Time’ and the travel time in the specified time interval,

but the results from this study showed different outcomes. See Table 18 (FFTT

impedance) as an example. In the time interval from 0200 (2:00 am) to 0300 (3:00 am),

the travel time is 1.747 decimal minutes or 00:01:45 (hms), therefore, the ‘End Time’

should be 2:01:45 (hms) instead of 2:01:20 (hms). This inconsistency can be observed

throughout the entire study. With further investigation, it was discovered that the ‘End

Time’ was calculated by the travel time (for both FFTT and TVTT) without global turn

delays. The ‘FFTT (min)’ field in Table 16 represents the accumulated free-flow travel

time for the same route shown in Table 18 without global turn delays. The travel time is

1.330 decimal minutes or 00:01:20 (hms), which is exactly the same elapsed time from

‘StartTime (hms)’ to ‘EndTime (hms)’ shown in Table 18.

The second inconsistent outcome is the determination of the best route while

applying TVTT as impedance. According to Esri (2013g), the best route is the result

with the lowest impedance. See Table 22 as an example. In the time intervals 1000

(10:00 am) to 1100 (11:00 am) and 1900 (7:00 pm) to 2000 (8:00 pm), Route B is the

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best route generated from TVTT impedance (Table 20), but Route A, generated from

FFTT impedance (Table 18), has a lower TVTT value than Route B. The explanation

can be made that Route B has a lower free-flow factor than Route A, but based on Esri’s

(2013g) document, the best route should be determined by the user’s specified

impedance, which is TVTT, not the free-flow factor.

The third inconsistent outcome is the accumulated impedance values generated

when a particular impedance was not used to optimize the route analysis. See Table 42

as an example. The fastest route from IN-2 to Davis Hospital, while applying FFTT

impedance, is Route B with a free-flow travel time of 7.696 minutes. However, applying

the TVTT impedance generated an optimal route, Route C, for the time intervals between

0900 (9:00 am) and 2100 (9:00 pm), (Table 44). The accumulated FFTT value for Route

C is less than Route B’s free-flow travel time. If the calculations of other accumulated

impedances through TVTT route analysis (such as DIST and FFTT from Table 44) are

correct, then Route C should be the best route results from FFTT route analysis not Route

B.

Even with these three inconsistent outcomes, this project still demonstrates that

the routes and response times for emergency response vehicles could change due to

variations in traffic flow related to the day (e.g., weekday or weekend) and the time of

day (traffic congestion). The shortest route might not be the most efficient path for

emergency vehicles. Although emergency vehicle routing can at times exceed the normal

speed limit, FFTT impedance route analysis can also serve as the surrogate of road class

(generally roads with multiple lanes have higher speed limits, which makes it easier for

emergency vehicles to pass other vehicles), which is a factor when considering traffic

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conditions and the necessity of passing other vehicles. Traffic conditions are not static;

they are dependent on the time and day. TVTT impedance route analysis could provide a

more realistic simulation than DIST and FFTT impedance route analysis. The optimal

route from IN-2 to Davis Hospital (4.2.3) changed based on the time of the day (Table

45). A decrease in travel time by a few minutes might not be significant for normal

traffic, but when considering emergency vehicle routing, it can be a matter of life and

death.

Although a fundamental aim of this study was to illustrate how a dynamic

network is preferred over a static network when applied to emergency response routing,

this research was nevertheless theoretic in nature. Regardless of how accurate the

network data is, or how many variables, restrictions, and impedances were applied to

generate the most realistic and best path, decisions made by an experienced emergency

response vehicle driver in real time under real traffic scenarios will always outweigh a

computer generated routing model. However, dynamic emergency response routing as

shown in this research can be valuable for generating preliminary routes from an origin to

a destination then modified by an experienced emergency response vehicle driver as the

situation demands.

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Chapter 5: Conclusion and Future Improvements

5.1 Conclusion

The objective of this research was to observe, record, and analyze changes to routes

and travel/response-times of emergency vehicles due to variations in traffic flow related to

traffic congestion on certain days of the week and times of day. It was believed that

dynamic routing based on cost attributes derived from historical travel-time data and

applied to network edges could help response vehicles avoid congested areas and improve

travel times (Kok et al. 2012, Panahi and Delavar 2009). As mentioned in the literature

review, because travel congestion affects the travel time of emergency vehicles and

increases response times, time-dependent variables derived from traffic count data could

realistically represent peak-hour traffic congestion and help emergency vehicles avoid

these congested areas and improve travel time (Kok et al. 2012, Panahi and Delavar

2009).

The results of this analysis indicate that when the DIST impedance was used by the

‘Route’ solver, it generated the shortest path between the origin and the destination in both

scenarios. When the FFTT impedance was applied, it generated the quickest or fastest

route. When the TVTT impedance was used, it generated the best or most optimal path

under realistic traffic conditions.

The project was overall a success and the research objectives were met. This

project was able to utilize the shortest path algorithms in Esri’s Network Analyst to

calculate the shortest, fastest, and the most optimal routes by applying various cost

attributes or impedances to practical vehicle emergency response scenarios. Differences in

117

route directions, travel times, and distances were observed and analyzed based on these

impedances and the findings were discussed in detail explaining the results.

5.2 Limitations

Six noticeable limitations associated with this research are discussed in this

section. The lack of experience in the creation and application of traffic profiles was one

limitation. Scarcity of literature about the origin of and how free-flow multipliers are

generated and incorporated into a spatio-temporal database and the actual implementation

of traffic profiles was another limitation. The main source of information on the creation

and use of traffic profiles was from Esri. Other literature did not detail the making of

traffic profiles. Several inquiries to private corporations and government organizations

for clarification were not very successful. Answers to questions that would be helpful

include: What is the origin and background of historical traffic profiles? What

methodology is used to create the free-flow factors or multipliers? Is there a scientific

approach for relating traffic volume profiles created from ATR site data to free-flow

traffic profiles stored in the ‘DailyProfiles_Time_60min’ table?

Another issue that limited the study was the coarseness or resolution of the

historical traffic data. UDOT traffic volume data was only available in 60 minute time

intervals. The original Esri free-flow traffic profile (‘DailyProfiles’) table was available

in 5 minute time slices. Modifications had to be made to accommodate UDOT traffic

volume data and generate the ‘DailyProfiles_Time_60min’ table used in this research. A

loss in granularity resulted from this modification. It is believed that the precision and

correctness of travel times and routes would be improved and better represent traffic

118

conditions using smaller time intervals, however, there would be a downside. For

research purposes it would increase the number of runs per 24 hour period from 24 to

288. This would impact the size and configuration of the tables and increase the work

load associated with executing the runs and the route analysis.

Road segment classification was another limitation and concern. The

methodology used to select and match ATR sites to the Urban Area Functional

Classification system was based on limited information and guidance. It is unclear if the

methodology used in this research was the most suitable approach. Questions that

surfaced were: Is one classification system preferred over another when creating a

transportation network? Is there a better or perhaps a more systematic approach to the

classification of road segments? Is there a better process to match ATR sites to a

classification system?

The study was also limited in the sense that certain dynamic variables that would

have improved the network and routing scenarios were not used due to time, availability

and the complexity of implementation. Examples include seasonal weather conditions,

road conditions, number of lanes, slope, etc.

The final limitation was the lack of transparency in Esri’s shortest path algorithm.

Esri (2013g) maintains the best route is determined based on the lowest impedance.

While applying TVTT as impedance in this study, there were several exceptions where

the new route’s TVTT was higher than the route based on FFTT, although the free-flow

factor values were lower. These results are inconsistent with Esri’s (2013g) statement.

Examples can be found in Table 22, time intervals 1000 (10:00 am) to 1100 (11:00 am)

and 1900 (7:00 pm) to 2000 (8:00 pm); Table 29, time interval 1900 (7:00 pm) to 2000

119

(8:00 pm); and Table 39, time intervals 0700 (7:00 am) to 0800 (8:00 am) and 2000 (8:00

pm) to 2100 (9:00 pm). Another inconsistent result is the fastest path from IN-2 to Davis

Hospital. The TVTT route analysis generated a route (Route C, Figure 64) with a lower

free-flow travel time (Tables 44 and 45) than the solution (Route B, Figure 63) produced

from FFTT route analysis (Tables 42 and 43). These inconsistences cannot be explained

without further investigation of Esri’s shortest path algorithm. However, there is

insufficient documentation from Esri to describe how Dijkstra’s algorithm was

implemented in ArcGIS Network Analyst.

5.3 Challenges and Solutions

One challenge both in time and complexity was the preparation and maintenance

of the road network dataset. As explained in Section 3.2, additional work was needed to

prepare the road network for analysis. Preparation included directionality, connectivity

and adding one way restrictions to limit travel on one way roads and avoid routing

irregularities. Routes overshooting an expected ramp, going the wrong way on a

freeway, entering or exiting the wrong way on a ramp or overshooting an entrance into

the hospital because of junction and road segment errors were a few challenges that

needed to be addressed.

The solutions to these challenges required hours of editing road edges, junctions,

and associated attribute fields for the network to function properly. More experience

might have made this process easier and less time consuming. Identifying an error or

irregularity, repairing it through digitization or re-attribution, rebuilding the network

dataset and testing was the general pattern. For instance, after a road segment was added,

120

deleted or edited in some manner such as merging or splitting segments, certain fields

had to be recalculated. Some field attributes also had to be copied to the

‘Project_Profiles’ join table so the historical traffic data would function correctly. If a

speed limit was changed in the ‘ProjectArea’ feature class, the same change had to be

made in the ‘Project_Profiles’ join table. Travel times also had to be re-calculated. After

these changes, the network dataset had to be rebuilt. To aid in the process, a relationship

class was created between the ‘ProjectArea’ feature class and the ‘Project_Profiles’ join

table and proved very useful. The relationship class is mentioned in Section 3.3 and one

way restrictions are explained in Section 3.3.1.

5.4 Future Improvements

This research has shown how a GIS was used to solve a shortest path problem

with respect to emergency vehicle response routing. Certain network attributes and

attribute values were omitted or not used to their fullest potential for this research. It was

not practical nor was this research meant to cover all aspects of network analysis.

Several future improvements could make the road network and subsequent analysis more

functional and realistic. In actuality, improvements to a road network and shortest path

are boundless. A continuation of this research might include the following

improvements:

1. Explore the feasibility of incorporating average annual daily traffic (AADT),

vehicle miles traveled (VMT), peak hourly volume (PHV), or other measures

of traffic capacity as alternatives ways to model traffic congestion.

2. Apply elevations or Z values to highway and other overpasses.

3. Incorporate slope values especially on the mountain front benches.

121

4. Improve road classifications and incorporate road hierarchy.

5. Incorporate traffic lanes.

6. Incorporate more specified ‘restricted turns’ modeled from a turn feature class

versus the generalized use of global turn delays.

7. Incorporate barriers and other restrictions to resemble areas of road construction,

traffic calming measures, weather conditions, etc.

8. Fine tune the use of one way restrictions.

9. Explore and compare other route solvers available in Esri Network Analyst.

10. Compare results to real world emergency response call data.

One additional future improvement might be to expand this study and develop an

efficient low-cost web-based emergency response routing system that can incorporate

real-time or live traffic data based on using GPS technology. This system could be used

by local EMS dispatch agencies to improve response times for not only lower level

medical priority dispatches but for higher level emergency situations or disasters that can

affect large areas and cause significantly more casualties.

122

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i a road network shortest path analysis: applying

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