an intersection traffic data collection device utilizing logging

AN INTERSECTION TRAFFIC DATA COLLECTION DEVICE

UTILIZING LOGGING CAPABILITIES OF TRAFFIC CONTROLLERS

AND CURRENT TRAFFIC SENSORS

Final Report

November 2008

UI Budget KLK134

NIATT Report Number N08-13

Prepared by

National Institute for Advanced Transportation Technology

University of Idaho

Ahmed Abdel-Rahim

Brian k. Johnson

DISCLAIMER

The contents of this report reflect the views of the authors,

who are responsible for the facts and the accuracy of the

information presented herein. This document is disseminated

under the sponsorship of the Department of Transportation,

University Transportation Centers Program, in the interest of

information exchange. The U.S. Government assumes no

liability for the contents or use thereof.

1. Report No. 2. Government Accession

No.

3. Recipient’s Catalog No.

4. Title and Subtitle

An Intersection Traffic Data Collection Device Utilizing Logging

Capabilities of Traffic Controllers and Current Traffic Sensors

5. Report Date

November 2008

6. Performing Organization Code

KLK134

7. Author(s)

Abdel-Rahim, Dr. Ahmed; Johnson, Dr. Brian

8. Performing Organization

Report No. N08-13

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

National Institute for Advanced Transportation Technology

University of Idaho

PO Box 440901; 115 Engineering Physics Building

Moscow, ID 83844-0901

11. Contract or Grant No.

DTRS98-G-0027

12. Sponsoring Agency Name and Address

US Department of Transportation

Research and Special Programs Administration

400 7th Street SW

Washington, DC 20509-0001

13. Type of Report and Period

Covered

Final Report: November

2006 – August 2008

14. Sponsoring Agency Code

USDOT/RSPA/DIR-1

15. Supplementary Notes:

16. Abstract

The project presents a high-resolution data logging device that can be used in real-time traffic monitoring at signalized

intersections. The data logging device can be connected to traffic cabinets using different connection modes. The data

logging device logs the status of all input and output communication channels and updates their status continuously. This

data can be accessed remotely through an Ethernet port over IP based communication. The data logging device presented in

this project provides an opportunity for high-resolution real-time performance monitoring of intersection operations.

The project presents two applications in which the data logging device was used to monitor intersection performance. In the

first application, the device was used to plot continuous time-occupancy and signal indication graphs for different

movements. Such plots provide system operators with the information needed to assess the efficiency of phase operations

and to continuously monitor the level of green time utilization for different phases. Two applications to demonstrate how

the data logging device can be used to monitor intersection operations are presented in this report. The first application is

microscopic time-occupancy and signal indication status plots for different movements. Such plots provide system operators

with the information needed to assess the efficiency of phase operations and to continuously monitor the level of green time

utilization for each phase. The research project examined the validity of using this microscopic detector occupancy and

signal indication status data to obtain traffic counts, identify heavy vehicles in the traffic stream, and determine the

percentage of stopped and non-stopped vehicles. The second application is macroscopic in nature and is intended to show

how the data logging device can be used to estimate average values of different performance measures based on detector

and signal indication status information. The delay and speed results estimated using the proposed approach are compared to

speed and delay data obtained from a VISSIM microscopic simulation model. The comparisons show that the data logger

device can reliably and accurately estimate average delay and speed values for signalized intersection approaches using

detector occupancy and signal indication data.

17. Key Words

Traffic data, traffic signal controllers, signalized

intersections

18. Distribution Statement

Unrestricted; Document is available to the public through the

National Technical Information Service; Springfield, VT.

19. Security Classif. (of

this report)

Unclassified

20. Security Classif. (of

this page)

Unclassified

21. No. of

Pages

51

22. Price

…

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

An Intersection Data Collection Device Utilizing Logging Capabilities . . . i

TABLE OF CONTENTS

TABLE OF CONTENTS ........................................................................................................... I

LIST OF TABLES .................................................................................................................... II

LIST OF FIGURES .................................................................................................................. II

1. INTRODUCTION ............................................................................................................ 1

1.1 BACKGROUND ............................................................................................................ 1

1.2 PROJECT OVERVIEW AND OBJECTIVES ....................................................................... 2

1.3 REPORT ORGANIZATION ............................................................................................. 3

2. REAL-TIME MONITORING OF CONTROLLER OPERATION ................................. 4

2.1 OVERVIEW ................................................................................................................. 4

2.2 DATA LOGGING DEVICE: COMPONENTS AND COMMUNICATION ARCHITECTURE ........ 4

2.3 TRAFFIC CABINETS: AN OVERVIEW ........................................................................... 5

2.4 CONNECTING THE DATA LOGGING DEVICE TO DIFFERENT CABINET ASSEMBLIES ...... 8

2.5 DATA LOGGING DEVICE OUTPUT FILES .................................................................... 12

2.6 MOES REPORTING CAPABILITIES OF TRAFFIC CONTROL SOFTWARE: STATE OF THE

PRACTICE ....................................................................................................................... 14

3. ESTIMATING DELAY AND SPEED USING DETECTOR DATA ............................. 16

3.1 DEFINITIONS ............................................................................................................ 16

3.2 MOE ESTIMATION APPROACH ................................................................................. 17

3.2.1 Estimating Average Delay using Detector Data .............................................. 18

3.2.2 Estimating Average Speed using Detector Data .............................................. 20

4. RESEARCH METHODOLOGY AND EXPERIMENTAL DESIGN ........................... 22

4.1 PROPOSED DELAY ESTIMATION METHOD ................................................................. 22

4.2 PROPOSED SPEED ESTIMATION APPROACH ............................................................... 25

4.3 HARDWARE-IN-THE-LOOP SIMULATION MODEL ....................................................... 27

5. ANALYSIS AND RESULTS .......................................................................................... 30

5.1 INTRODUCTION......................................................................................................... 30

5.2 MICROSCOPIC TIME-OCCUPANCY AND SIGNAL INDICATION PLOTS .......................... 30

5.2.1 Estimation of Vehicle Count and Vehicle Type ................................................. 33

5.2.2 Estimation of Stopped and Non-Stopped Vehicles ........................................... 38

5.3 DELAY AND SPEED ESTIMATION ............................................................................... 39

An Intersection Data Collection Device Utilizing Logging Capabilities . . . ii

5.3.1 Delay Estimation – Webster Formulation (Method1) ...................................... 39

5.3.2 Delay Estimation – Method 2 .......................................................................... 42

5. 3.3 Speed Estimation ............................................................................................. 44

5.4 SUMMARY ................................................................................................................ 46

6. CONCLUSIONS AND FURTHER RESEARCH .......................................................... 47

6.1 SUMMARY ................................................................................................................ 47

6.2 CONCLUSIONS .......................................................................................................... 48

6.3 FURTHER RESEARCH ................................................................................................ 48

7. REFERENCES ............................................................................................................... 50

LIST OF TABLES

TABLE 1: MOES REPORTING CAPABILITIES OF DIFFERENT CONTROL SOFTWARE PACKAGES

................................................................................................................................... 15

LIST OF FIGURES

FIGURE 1 DATA LOGGING DEVICE COMPONENTS ................................................................ 5

FIGURE 2 NEMA TS2 TYPE 1 TRAFFIC CONTROL CABINET ................................................. 6

FIGURE 3 NEMA TS2 TYPE 2 TRAFFIC CONTROL CABINET ................................................. 7

FIGURE 4 PROPOSED DATA LOGGING DEVICE CONNECTION TO NEMA TS1 CABINETS ....... 9

FIGURE 5 TWO PROPOSED DATA LOGGING DEVICE CONNECTION OPTIONS TO TS2 TYPE 1

CABINET .................................................................................................................... 10

FIGURE 6 DATA LOGGING DEVICE PROPOSED CONNECTION TO TS2 TYPE 2 CABINET ........11

FIGURE 7 SAMPLE OF DATA LOGGING DEVICE OUTPUT FILES ............................................ 13

FIGURE 8 DELAY COMPONENTS AT A SIGNALIZED INTERSECTION APPROACH .................... 17

FIGURE 9 TIME DISTANCE DIAGRAM AT A SINGLE SIGNAL (SKABARDONIS ET AL., 2005) .. 24

FIGURE 10 ASSUMED FLOW DENSITY DIAGRAM (SKABARDONIS ET AL., 2005) ................. 25

FIGURE 11 HARDWARE-IN-THE-LOOP SIMULATION MODEL ............................................... 28

FIGURE 12 VISSIM SIMULATION NETWORK ...................................................................... 29

FIGURE 13 EXAMPLES OF TIME-OCCUPANCY PLOTS FOR TWO CONFLICTING PHASES ........ 31

FIGURE 14 ESTIMATED BOUNDARY LINES OF TIME-OCCUPANCY BETWEEN CARS AND HVS

(1ST

VEHICLE IN THE QUEUE) ...................................................................................... 34


An Intersection Data Collection Device Utilizing Logging Capabilities . . . iii

(2ND

VEHICLE IN THE QUEUE) ..................................................................................... 35


(3RD



(4TH



(5TH



(6TH


FIGURE 20 ESTIMATED BOUNDARY VALUES OF OCCUPANCY TIME FOR CARS AND HVS .. 37

FIGURE 21 A SAMPLE OF DISCHARGE TIME-HEADWAY IN A CYCLE ................................... 39

FIGURE 22 COMPARISON OF SIMULATED AND ESTIMATED DELAY (METHOD 1) ................. 40

FIGURE 23 MEAN ABSOLUTE ERROR AND MEAN ABSOLUTE PERCENT ERROR - DELAY

ESTIMATION (METHOD 1) .......................................................................................... 41

FIGURE 24 COMPARISON OF SIMULATED AND ESTIMATED DELAY ESTIMATION (METHOD 2)

................................................................................................................................... 42

FIGURE 25 MEAN ABSOLUTE ERROR AND MEAN PERCENT ABSOLUTE ERROR - DELAY

ESTIMATION (METHOD 2) .......................................................................................... 43

FIGURE 26 COMPARISON OF SIMULATED AND ESTIMATED SPEED ESTIMATION .................. 44

FIGURE 27 MEAN ABSOLUTE ERROR AND MEAN PERCENT ABSOLUTE ERROR OF SPEED

ESTIMATION ............................................................................................................... 45

An Intersection Data Collection Device Utilizing Logging Capabilities . . . 1

1. INTRODUCTION

1.1 Background

Delay and speed are the primary measures of effectiveness (MOEs) used to evaluate the

performance of traffic signal systems. Delay and speed values, measured in the field, are

extremely valuable to system operators because they provide accurate information

regarding the quality of service for different movements at signalized intersections.

Several methods have been developed and employed to measure delay in the field.

However, most of these methods rely on manually collected traffic counts and require

intensive data collection efforts. Detector and signal indication information, available in

the traffic controller and cabinet infrastructure, can be effectively used to estimate delay

and speed values as well as other signalized intersections MOEs.

Real-time monitoring of traffic signal system operations can also be accomplished

through the central or closed loop software that communicates with traffic controllers in

the field through a network of communication devices. These control software tools use

detector and signal status data to estimate different performance measures such as

detector occupancy, volume, delay, speed, and the level of green-time utilization for each

movement and for the intersection. There are a few significant issues with this approach.

First, most of these control software tools report only average values over a specific time

interval that ranges from one minute to fifteen minutes. This is a huge limitation when

one wants to evaluate second-by-second performance of either the control logic or

detection technology used. Second, the only data available are those explicitly collected

by the vendor of the closed loop or central control software. In addition, these data are

typically not easily accessible and may require complicated direct database access within

the software. Finally, the type and accuracy of the measures obtained are highly

dependent on the detection configuration used in the intersection.


1.2 Project Overview and Objectives

This research project presents an alternative approach to achieve real-time monitoring of

signalized intersections operations using instrumentation at each signalized intersection

cabinet. A logging device is used to collect high-resolution detector and signal status

data. The data logging device can be embedded in the cabinets and connected to the input

and output (I/O) communication channels. The device logs the status of each I/O channel

for every time interval, which can be as low as ten milliseconds (0.01 second), and stores

it into a data file. This data file can be remotely accessed through an Internet Protocol

(IP) based communication. This device allows for real-time high-resolution data

collection and monitoring of signalized intersections operations independent of the

control software used and, thus, has several potential advantages. The data items that can

be monitored and reported are not limited by what data items are collected or by the

frequency at which the vendor of the closed loop or central system software collects

them. The interface device can be accessed from the district office over any

communication channel available in the field. The proposed data collection device has

several potential advantages:

1. The data items that can be monitored are not limited by what data items are polled

by or the frequency they are polled, by the vendor of the closed loop or central

system software, if any.

2. Intelligent data acquisition devices can be embedded in the signal cabinet that

execute data tabulation logic and are accessible via web browsers over an IP

based communication.

3. The system will be completely isolated from the ITD operations and will not

impact the operation of the ITD signal systems.

Two applications to demonstrate how the data logging device can be used to monitor

intersection operations are presented in this report. The first application is microscopic

time-occupancy and signal indication status plots for different movements. Such plots

provide system operators with the information needed to assess the efficiency of phase

operations and to continuously monitor the level of green time utilization for each phase.

The research project examined the validity of using this microscopic detector occupancy


and signal indication status data to obtain traffic counts, identify heavy vehicles in the

traffic stream, and determine the percentage of stopped and non-stopped vehicles. The

second application is macroscopic in nature and is intended to show how the data logging

device can be used to estimate average values of different performance measures based

on detector and signal indication status information. The delay and speed results

estimated using the proposed approach are compared to speed and delay data obtained

from a VISSIM microscopic simulation model. The comparisons show that the data

logger device can reliably and accurately estimate average delay and speed values for

signalized intersection approaches using detector occupancy and signal indication data.

This project has the followings four objectives: 1) review and document the state of the

practice of real-time monitoring of traffic signal system operations; 2) test the validity of

using the data logging device to monitor and report the status of different I/O channels; 3)

develop a procedure to use detector occupancy and signal indication data, reported by the

data logging device, to estimate approach and intersection performance measures; and 4)

validate the procedures to estimate performance measures and test their accuracy using a

hardware-in-the-loop simulation model.

1.3 Report Organization

This report is organized in six chapters. After the introduction, chapter 2 presents a

background on real-time monitoring of controller operation and the state of the practice

in traffic signal systems real-time monitoring. Chapter 3 includes a review of different

methods used to estimate speed and delay based on detector data. Chapter 4 covers the

analysis methodology and experimental design. Chapter 5 documents the results of the

analysis. Finally, chapter 6 presents the conclusions and proposed ideas for future

research.


2. REAL-TIME MONITORING OF CONTROLLER OPERATION

2.1 Overview

In integrated traffic signal systems, real-time monitoring can be accomplished through

the central or closed loop software that provides control decisions and continuously

communicates with the traffic controllers and cabinets. The type and quality of the MOEs

reported depend on the control software and on the configuration of the detection system

used in the field. The only MOEs available are those explicitly collected by the vendor of

the closed loop or central system. In addition, only average values are usually collected

and reported. Furthermore, data retrieved by the closed loop or central system are not

typically accessible to users. For traffic signal systems that have no control software,

real-time monitoring can only be done by accessing the MOEs collected and stored in the

traffic controller. System operators have to access these controllers and manually

download this data every time. This is a huge limitation considering the limited resources

available for operators of such small traffic signal systems.

An alternative to achieve real-time monitoring is to use instrumentation at each cabinet

that is connected to actuator and detector signals. The intelligent data acquisition device

provides real-time high-resolution data logging and performance monitoring for

signalized intersections. It can be embedded in the signal cabinet and executes data

tabulation logic and writes the status of all I/O channels to a data file that is remotely

accessible through IP based communication. The data that can be monitored are not

limited by what data are collected or by the frequency at which they are collected by the

closed loop, central system software, or traffic controllers.

2.2 Data Logging Device: Components and Communication Architecture

Figure 1 shows the proposed data logging instrumentation and its major components.

This instrumentation is based upon the “Opto 22” family of ultimate I/O brains (item 10

in Figure 1) and “SNAP IDC 5” modules (4 channel10-24 VDC inputs- item 3 in Figure

1).


Figure 1: Data logging device components.

2.3 Traffic Cabinets: An Overview

A traffic cabinet is essentially a platform within which modular components can be added

to serve a variety of applications at the intersection. It provides the communications

infrastructure between the various subsystems, as well as a system to monitor their

operation. Further, the cabinet provides power supplies suitable for the various electronic

subassemblies mounted throughout the cabinet.

Cabinet assemblies consist of a controller cabinet, controller unit, back panel,

malfunction management unit, bus interface unit, switches, and connectors. The National

Electrical Manufacturers Association (NEMA) family of cabinets include: NEMA TS 1,

NEMA TS2 Type 1, and NEMA TS2 Type 2 cabinets. NEMA TS1 cabinets include a

controller along with the conflict monitor, detectors’ connection matrix, load switches,

other peripheral equipment, and the necessary internal wiring. NEMA TS2 standard

defines two types of controllers and cabinet architectures, the TS2 Type 1 and TS2 Type

2. The NEMA TS2 controller assembly is nearly identical to the TS 1. The two primary


differences are the change in controller unit and the conflict monitor being replaced by a

malfunction management unit (MMU). The NEMA TS2 Type 1 cabinet is unique in the

sense that it uses a RS-485/SDLC data link connection to the peripheral devices, with a

separate power connector. The TS2 Type 2 provides the same connectors as the TS1 but

includes the data link connector. The TS2 cabinet also uses a bus interface unit (BIU) for

communication between the various control components and detectors. The BIU provides

simplification in cabinet wiring as well as flexibility and power. The TS2 assembly

contains a shelf-mounted power supply unit that provides the appropriate power to each

of the controller devices. The detectors in the TS2 cabinet are rack-mounted. The TS2

standard defines advanced traffic signal operations, such as coordination and preemption,

and developed standards for pre-timed operations and advanced cabinet monitoring and

diagnostics. Details of NEMA TS2 Type 1 and NEMA TS2 Type 2 cabinets are shown in

Figure 2 and Figure 3, respectively.

Figure 2: NEMA TS2 Type 1 traffic control cabinet.


Figure 3: NEMA TS2 Type 2 traffic control cabinet.

In terms of communication with the traffic controllers, the NEMA TS1 and NEMA TS2

Type 2 standards use the four connectors A, B, C and D on the front of the controller.

The “A” connector provides power to the controller as well as inputs and outputs with the

cabinet. The “B” and “C” connectors provide various inputs and outputs for control. The

A, B, and C connector pin outs are standardized by NEMA and are interchangeable

among all manufacturers. Each connector is different, preventing cables from being

inserted in the wrong connection port. The “D” connector provides communication,

preemption, and expanded detection capabilities that are used in more advanced systems.

Typical controllers have eight available detection inputs. The D connector provides input

for eight additional detectors. The D connector pin out is not standardized by NEMA;

therefore, it may not be interchangeable.

In NEMA TS2 cabinets, the BIU links the controller to the cabinet input/output (I/O)

elements. It can also be used as a detector interface device. The BIU is responsible for

controlling load switches, receiving and isolating pedestrian calls, analyzing detector

faults, time-stamping detector calls, and providing detector resets. By design, the BIU is


free of operator controls. The BIU performs its I/O functions based upon a pre-wired card

rack address. The MMU is a more advanced device, not only monitoring all of the

conflict voltages, but also communicating with the controller, providing an additional

element of monitoring. The type-16 MMU is usually used in a NEMA TS2 standard

cabinet that monitors up to 16 traffic signal channels for conflicting inputs, improper

sequencing, incorrect timing, and invalid signal voltage levels. The MMU is also capable

of operating in older TS1 type cabinets and is compatible with 12-channel conflict

monitor units conforming to the TS 1 standard. All connectors, indicators, and operator

controls are located on the front panel of the MMU. Channel and control input signals

and relay output connections are made through two connectors. Indicators on the front of

the MMU provide status and fault information. The MMU performs continuous

diagnostic tests during all operating modes.

2.4 Connecting the Data Logging Device to Different Cabinet Assemblies

In a standard NEMA TS1 style cabinet, the connections to the controller are made

through the connection matrix on the back panel of the cabinet. These terminals are the

only available connection points for the data logging device. The proposed connection is

shown in Figure 4. The data logging device cables should have non-locking fork

terminals that can be connected to the matrix. The connection is done by loosening the

screws on the back panel then connecting the data logging device cable terminals. This

should not interfere with the cabinet operations and should not cause any malfunction

within the cabinet.


Figure 4: Proposed data logging device connection to NEMA TS1 cabinets.

In a NEMA TS2 Type 1 style cabinet, the data logging device connection is rather

challenging as the cabinet assembly does not have a connection matrix. The controller

communicates with the cabinet using a serial connection through the cabinet’s BIUs. The

communication link from the controller uses the RS-485 serial communication format or

synchronous data link control (SDLC) in combination with the NEMA standard TS2

command frames. There are two possible connection options. The first option is to

connect via the data logging device to the terminals on the back panel of the cabinet. The

second option is to connect the cabinets’ BIUs that are hardwired to this back panel. This

will likely require cooperation with the cabinet vendors as details of BIU wiring

mechanism are needed. The first mode of connection is represented uses a solid line and

the second mode is represented uses dashed lines in Figure 5.

CMU

DLD

Load Switch Detector Auxiliary

Devices

Controller

A B C D

Connection Matrix MS Connector


Figure 5: Two proposed data logging device connection options to TS2 Type 1

cabinet.

As shown in Figure 6a and 6b, there are two options to connect the data logging device in

a NEMA TS2 Type 2 style cabinet since this cabinet combines standards for the TS1 and

TS2 Type 1. The first is to connect the device to the connection matrix on the back panel,

like that of the NEMA TS1 cabinet. The second is through a serial connection through

either the cabinet’s back panel or the BIUs similar to that for TS2 Type1 cabinets.

Controller

MMU

DLD

SD

LC

Load Switch BIU BIU Detector BIU Auxiliary

Devices

Back Panel

SDLC


a. Connection Via the Back Panel and/or BIUs

b. Connection Via Connection Matrix

Figure 6: Data logging device proposed connection to TS2 Type 2 cabinet.

MMU

DLD

Connection Matrix

BIU Detector BIU Auxiliary

Devices

Controller

A B C D

MS Connector

Load Switch

MMU

DLD

New

SD

LC

Connection Matrix

BIU Detector BIU Auxiliary

Devices

Controller

A B C D

MS Connector

Load Switch

Back Panel

SDLC

BIU


2.5 Data Logging Device Output Files

The data logging device monitors and records the communication exchanged between the

detector and the controller and between the controller and signal heads. It also records

any other special calls sent to the controller such as pre-emption calls. In essence, the

device monitors activities in all input and output communication channels to and from the

controllers. In each sampling interval, it scans the status of all input/output channels and

records the state of each channel (on or off). The data are then stored in a log file which

can be accessed through the Ethernet port. The sampling interval for data logging can be

as small as 10ms. However, since most cabinets update the communication channel status

every 300 ms, a resolution time ranging from 300 ms to 1000 ms can more easily be used

in traffic signal system monitoring applications.

Data recorded by the data logging device include date, time, and the status of each

communication channel on the sampling interval. Figure 7 shows a sample of the data

logging device files for the status of detector and signal indication I/O communication

channels. A value of “-1” represents when the communication channel is “On”; a value of

“0” represents when the communication channel is “Off.”

Figure 7a shows the status of different vehicle detectors using a 100 ms resolution.

Detector occupancy and vehicle count can be directly calculated from these raw detector

data principally based on the discontinuity distribution of occupancy time followed by

un-occupancy time. Figure 7b shows the signal indication status for different phases. The

average cycle length and the duration of green, red, and yellow intervals can be directly

calculated from the raw signal state and timing data.


a. Status of Detector Input Channels

b. Status of Signal Indication Output Channels

Figure 7: Sample of data logging device output files.


2.6 MOEs Reporting Capabilities of Traffic Control Software: State of the Practice

Performance measures reporting capabilities for different controller and control software

tools were reviewed and documented in this section. Two controller software tools:

Econolite (used in Econolite TS2 controllers) and Nextphase (used in 170 and 2070 type

controllers) and two centralized control software packages QuicNet/4 and IconsTM

are

reviewed. Their MOEs reporting capabilities are listed in Table 1.


Table 1: MOEs Reporting Capabilities of Different Control Software Packages

MOEs

Reporting

Controller Software Centralized control Software

Econolite Nextphase QuicNet Icons TM

Volume When system detector is

enabled, volume is

reported in the detector

events logs.

Reports actual counts

of the detector during

the most recent

reporting period for all

detectors

Reports

volume counts

for system

detectors only.

Reports actual counts of

the detector during the

most recent reporting

period for all detectors.

Reports volume for each

link

Delay Average delay

based on

detector

occupancy

Average delay for each

link based on detector

occupancy

Speed Speed are calculated

based on average vehicle

and detector length

(single speed detector); or

based on effective

distance between the

leading edges of start and

end detectors (speed trap

length) and the time (used

in two-detector).

Reports average speed

for system detectors

only. Speed samples

are registered at the

end of each actuation.

It is shown in system

detector status of

submenu of Status

Reports

average speed

for system

detectors only.

Calculated speed value

using a measured

volume and occupancy

in a specific time period,

it depends on detection

zone length and vehicle

length. Reports average

link speed.

Occupancy When system detector is

enabled, occupancy is

reported in the detector

events logs.

Reports the percentage

time each detector was

occupied during the


period for all

detectors.

Reports

average

occupancy for

system

detectors only.

Reports detector

occupancy during the


period for all detectors.

Reports volume for each

link

Green Split Actual green split is

reported. It is calculated

via phase split minuses

clearance time.

Reports minimum

green split, nominal

green split and

maximum green split

for each phase.

Provides real

time split

monitoring

Reports real-time green

split display for each link

Time-Space

Diagram

Displays a

real-time space

diagram.

Shows green, yellow

and red times and

progression of vehicle


3. ESTIMATING DELAY AND SPEED USING DETECTOR DATA

3.1 Definitions

Control delay at a signalized intersection approach is defined by the Highway Capacity

Manual (HCM2000) as “the additional travel time experienced by a vehicle affected by

intersection control, relative to conditions where the vehicle is unaffected by intersection

control.” The following definitions of delay and speed are used in this project (Figure 8).

Control delay ( td ) is the portion of the total delay attributed to traffic signal

operation for signalized intersections. Control delay includes four components:

1) initial deceleration delay ( dd ), 2) queue move-up time, 3) stopped delay ( sd ),

and 4) acceleration delay ( ad ).

Approach delay (apd ) includes stopped time, but also includes the time lost

when a vehicle decelerates from its original speed to a stop, as well as

accelerating from the stop back to its original speed.

Stopped delay ( sd ) is the time that a vehicle is stopped while waiting to pass

through the intersection. It includes only the time that a vehicle is actually

stopped waiting at the red signal.

Deceleration delay ( dd ) is defined as the time needed by a vehicle to reduce its

speed.

Acceleration delay ( ad ) is defined as the time taken by a vehicle to resume its

desired speed from a stop. This delay can begin before or at the stop bar,

depending on the vehicle’s queue position. The acceleration delay consists of

two components: acceleration before the stop bar ( 1ad ) and acceleration after

the stop bar ( 2ad ).


Queue delay (qd ) is defined in HCM as the delay experienced by queued

vehicles. This delay consists of two control delay components sd and 1ad .

Figure 8: Delay components at a signalized intersection approach.

The following speed definitions are used in this study:

Average Speed(s) is the summation of the instantaneous or spot-measured speeds

at a specific location of vehicles divided by the number of vehicles observed.

Average Running Speed (sr) is defined as the length of the segment divided by the

average running time of vehicles to traverse the segment. "Running time" includes

only time that vehicles spend in motion.

Average Travel Speed (st) is defined as the length of the segment divided by the

average travel time of vehicles traversing the segment, including all stopped delay

times.

3.2 MOE Estimation Approach

A variety of methods to estimate different MOEs for an intersection approach have been

developed using data collected through loop or video detectors. The following sections


document several delay and speed estimation methods using detector and signal

indication data.

3.2.1 Estimating Average Delay using Detector Data

Skabardonis et al. (2005) proposed an analytical model to estimate the total control delay

of an intersection approach in real-time based on flow, occupancy measurements, and

signal status data. The model is based on the kinematic wave theory that considers the

temporal and spatial formation of the queue and the assumption of a linear flow-density

relationship. This delay was considered as the sum of 1) the delay because of a traffic

signal, 2) the delay because of the queue, and 3) the over-saturation delay. The detectors

were placed approximately 300 feet upstream of the intersection stop-line, and detector

data were collected and stored every 30 seconds. The model was applied in two arterial

sites, and the predicted results were compared with the simulated data from COSSIM and

the field data. This project uses this method and it will be described in detail in chapter 4.

Liu et al. (2005) proposed a method to estimate average stopped delay of an intersection

approach using flow measurements and arrival timings from the two loop detectors at the

beginning and end points of the approach segment. Hellinga et al. (2000) proposed a

regression-based approach to estimate the total control delay of an intersection approach

using occupancy data from detectors located at different distances relative to the

approach’s stopbar. Loop detectors were modeled at four different locations (5, 30, 100

and 250 m upstream from the stopbar), and three different data aggregation intervals (100

seconds, 300 seconds, 900 seconds) were considered. Simulation models were used to

generate data needed to calibrate the regression model parameters. These regression

models were then used to estimate the average delay under different traffic volume

conditions based on detector occupancy data. One major limitation of this method is that

it did not consider signal timing parameters, such as average cycle length and green time

to cycle length ratio, which could greatly influence delay.

Li et al. (2008) proposed a formulation for average control delay estimation by cycle for

signalized intersections. The delay is expressed as a function of saturation flow rate, start

of green indication, lost time, duration of green interval for each cycle, queue clearance


time, arrival count, and free flow travel time from the advance loop to the stopbar, as

shown in Equation (1).

j

j

jCQ

j

LostLost

c

ct

FF

t

ct

jFFjjj

CQ

jj

CQ

j

TtA

ctTtAcTgtTgt

d

1

1

11

1

1

)(

)()()]1(2)()][([2

(1)

where

d j = the average control delay for cycle j;

µ

tCQj = the clearance queue time for cycle j;

g j = the green start time for cycle j;

T Lost = the lost time;

c j = the end time for cycle j;

c j-1

= the end time for cycle j-1;

A(t) =the instant arrival count at advance loops at time t;

T FF

= the free flow travel time from advance loop to the stop line.

Kebab et. al (2007) proposed to estimate the total control delay by collecting individual

vehicle’s timestamps at three locations along the intersection approach. The three data

collection points are: 1) at a point beyond the maximum queue length, 2) at a point where

the turning movements are fully developed, and 3) at the approach’s stopbar. The delay is

treated as the sum of the differences between the actual and free flow travel time at two

segments among the three points. Tung (2007) applied this method to estimate field delay

using video detection. The results of his study showed that the automated delay

measurement procedure produces accurate and reliable delay estimates. When compared


against delay values measured using the procedure proposed in the Highway Capacity

Manual, the proposed automated delay measurements produced more accurate results.

Lin et al. (2004) proposed an approach to estimate the control delay at signalized

intersections. The approach reduces the delay at each intersection, a non-negative

continuous variable, into two distinctive states, a state of zero-delay and a state of

nominal delay, coupled with a one-step probability transition matrix that relates the delay

to a vehicle to its delay at the adjacent upstream intersection. The calibration of the

parameters in the one-step probability transition matrix is based on the flow level, the

flow composition, and the degree of signal coordination along the path of a trip.

3.2.2 Estimating Average Speed using Detector Data

Son et al. (1998) classified the vehicles in a cycle into two categories according to

discharge headways: vehicles in the queue with saturation flow headways and vehicles

after the dissipation of the queue with departure headways equal to arrival headways. The

average speeds for the two types of vehicles can be calculated principally using detector

occupancy and vehicle count data. Finally, on the basis of the two speeds and signal

timing, the average speed for each cycle is estimated. This method is used as the speed

estimation method in this project and will be described in detail in chapter 4.

Zhang (1999) proposed a model to estimate the average speed for arterial traffic by

combining two speeds: one is estimated based on the approach’s volume/capacity ratio

and the other is based on volume counts and detector occupancy data. Weighting factors

are chosen to combine the two speeds. The average speed for each approach is a weighted

average of the two speeds. Weighting factors are determined and calibrated based on field

measurements of speed and according to the traffic volume level on the approach.

Wang et al. (2000) proposed a simplified equation to estimate arterial speed by isolating

the effect of speed variance. They conducted a study on the speed variance for different

volume levels and found that the variance is inversely proportional to volume levels.

They also found high correlation between speed variance and the mean effective vehicle

length. They established a log regression model to improve the accuracy of speed

estimation based on detectors occupancy data. Zhang et al. (2006) applied the catastrophe


theory to estimate average speed for the relationship among the traffic variables

associated with speed, occupancy, and flow. These variables could be extrapolated from

the data obtained from a single loop detector using three simple linear transformations.

Bermejo et al. (2003) used the extended Kalman filter method to linearize the

measurement equation of a general Kalman Filter model for estimating average speed

based on detector data. There are two phases in this method: the time update phase is

operated to “predict” a new state, and the measurement update phase is operated to

“correct” any new state. Lucas et al. (2004) proposed an approach to estimate average

speed on arterial based on second-by-second data from upstream and downstream

detectors. The detector data are first used to identify platoons of vehicles and then a

matching algorithm compares the platoons identified at the upstream and downstream

locations. The average speed estimate is based on the travel time of the median vehicles

in the platoons as determined at both the upstream and downstream locations.

Sun et al. (1999) proposed a model to estimate average speed using single loop inductive

waveforms. This model uses signal processing and statistical methods to extract speeds

and involves two main procedures. The first is the extraction of the vehicle slew rate from

the inductive vehicle waveform signal from the detector. The second is the estimation of

the vehicle speed based on slew rate of each vehicle. While this method yielded high

accurate results, it requires special instrumentation for each detector and is highly

sensitive to the accuracy of the detector’s inductive signal.


4. RESEARCH METHODOLOGY AND EXPERIMENTAL DESIGN

4.1 Proposed Delay Estimation Method

Two different delay estimation methods are used in this project. The first is based on the

Webster delay equation (Liu, et al., 2005), a commonly used model to determine the

average control delay of an intersection approach. The Webster delay equation has three

terms. The first term presents the average delay for a particular approach assuming

uniform arrivals at a fixed-time signal-controlled intersection and can be easily derived

using deterministic queuing theory. The second term is added to account for random

arrivals. The third term is subtracted from the first two terms and varies from zero to a

value equal to the second term. The Webster delay equation is given as follows:

)52(2/1

2

22

)(65.0)1(2)1(2

)1(x

q

C

xq

xCD (2)

where

D = Average control delay (seconds/vehicle);

C = signal cycle length (seconds);

x = degree of saturation;

q = volume (vps); and

λ= effective green proportion.

Parameters C and λ are obtained from the signal timing data. Volume, q, is directly

obtained from the detector measurement. The degree of saturation, x, should be

calculated based on detector occupancy and signal timing data. A stop-line detector is

required to collect flow and occupancy measurement.

The second delay estimation method examined in this study uses the analytical model

proposed by Skabardonis and Geroliminis (2005). In this model, the delay is the sum of


two types of delay: delay caused by the traffic signal and delay caused by the queue

present in the intersection approach.

The first part of the model assumes that each vehicle has no interaction with other

vehicles in the traffic stream. Under this assumption, all queued vehicles are considered

stopped at the stop line (vertical queue). The delay, (d (t)), of a single vehicle as a

function of arriving time, t, is given by Equation (3):

a

f

d

f ut

uTrtd

22)( (3)

where

r = the effective red time;

T = the driver’s reaction time;

uf = free flow speed;

γd = the vehicle’s deceleration rate;

γa = the vehicle’s acceleration rate; and

t = the time a vehicle starts to decelerate.

The parameters for γd, γa, uf and T are assumed constant. Their values are determined

according to the Institute of Transportation Engineers (ITE) guidelines. The effective red

time, “r”, is directly obtained from signal timing data reported in the data logging device

output files. The parameters for uf and t are obtained from detector measurements.

The delay in the second part of the Skabardonis and Geroliminis model is the result of the

queue present at the traffic signal approach. It is estimated based on the kinematic wave

theory considering the temporal and spatial formation of the queue and assuming a

relationship between linear flow and density. The queue delay, dq, of the n-th vehicle

arriving at the signal from the beginning of the red time is the sum of three types of

delays (dq1, dq2, dq3) as illustrated in Figure 9.


Figure 9: Time distance diagram at a single signal (Skabardonis et al., 2005).

The delay values can be determined using the following equations:

f

sqmq

u

LNnd )1),(min(1

(4)

w

sqmqqmq

u

LNNNnd ))),,( (max (min2

(5)

w

LNnd s

qq )1),(min(3

(6)

where

Ls = the effective length of a stopped vehicle;

uw = speed of the shockwave;

w = congested wave speed;

Nqm = number of the maximum queue; and

Nq = number of the maximum back of the queue.


The effective length of a stopped vehicle, Ls, is assumed constant using the reciprocal of

the jam density (kj). The parameters, uw and w, are obtained from flow and occupancy

measurements based on an assumed linear flow-density relationship shown in Figure 10.

Figure 10: Assumed flow density diagram (Skabardonis et al., 2005).

The parameters of Nqm and Nq can be calculated using Equations (7) and (8).

wf

wf

qmuu

uurL

s

qm

qmL

LN (7)

w

w

quw

wurL

s

q

qL

LN (8)

4.2 Proposed Speed Estimation Approach

The average approach speed is estimated using the model proposed by Son and Oh

(1998). Vehicles in each cycle are classified into two categories: vehicles in the queue

with saturation flow headways and vehicles arriving and departing after the dissipation of

the queue with arrival headways. The average speed of a cycle (Vcycle) is determined using

the following equation:


AS

ANAsNS

cycleNN

NvNvtR

l

V

)1(0

(9)

where

t0 = the time of the first vehicle’s arrival during red time;

R = the red time;

vNS = the average speed of vehicles discharging at the saturation flow rate;

vNA = the average speed of vehicles discharging at the arrival flow rate;

NS = the number of vehicles crossing the stop line with saturation headway in a

cycle;

NA= the number of vehicles crossing the stop line with arrival headway in a cycle;

l = the average length of the sum of vehicles and detectors.

Red time, R, is directly obtained from signal timing data reported in the data logging

device output file. The parameters, t0, NS, NA and NC, are obtained from detector data. The

parameter, vNA , can be calculated using the following equations:

iocc

it

lv

)( (10)

A

N

i

i

NAN

v

v

A

1 (11)

vNS is calculated using the equation,


A

NA

cycleocc

s

NS

Nv

ltRT

lNv

)()( 0

(12)

In the three equations,

Vi = the speed of vehicle i;

(Tocc) cycle = the total occupancy time for a cycle; and

(tocc)i = the occupancy time of vehicle i.

The parameters, (Tocc) cycle and (tocc)i , are obtained directly from detector data.

4.3 Hardware-in-the-Loop Simulation Model

The data logging device was tested and validated in the lab using a hardware-in-the-loop

simulation model, in which the control of the intersection in the simulation model was

done by an actual traffic controller. A controller interface device (CID) was used to

facilitate the information exchange between the microscopic simulation model and an

actual traffic controller. Detector actuation information was sent from the simulation

model to the controller. Signal status information was sent back from the controller to the

simulation model. This data exchange was done in every simulation time step. In this

experiment, VISSIM microscopic simulation was used along with a NEMA TS2 traffic

controller. The data logging device was connected to the controller through an interface

connected to the A, B, C, and D connectors in the traffic controller. The data flow in the

hardware-in-the-loop simulation model used in the analysis is shown in Figure 11. The

simulation time step was set to 100 ms (0.1 second).


Figure 11: Hardware-in-the-loop simulation model.

The intersection used in the analysis is an isolated intersection located in the city of

Moscow, Idaho. The VISSIM simulation network is shown in Figure 12. This

intersection was run using standard eight-phase NEMA operation. Data presented in this

project focused on the eastbound approach through traffic (phase 4). The approach has

two lanes with a stop bar and an advanced detector placed approximately 180 feet

upstream of the intersection stop bar. Each simulation ran for a total simulation time of

20 minutes; data were collected for the last 15 minutes only. The average value of five

runs was used for each case.

The measure of effectiveness (MOE) chosen for this experiment was average delay and

speed of the eastbound through movement. The objective of the hardware-in-the-loop

simulation model experiment was to determine whether the average delay and speed for

the approach can be estimated with an acceptable level of accuracy using the high-

resolution data logging device output data.

PC

VISSIM

Controller

CID

DLD

Detector

actuations

Detector actuations

Phase status Phase status

Detecto

r actuatio

ns

Ph

ase status

Record data


Figure 12: VISSIM simulation network.


5. ANALYSIS AND RESULTS

5.1 Introduction

This chapter presents the results and analysis of two applications to demonstrate how the

data logging device can be used to monitor intersection operations. The first application

uses microscopic time occupancy plots for different detectors. Validity of using detector

occupancy and signal indication date to estimate vehicle count, vehicle type, and the

numbers of stopped and non-stopped vehicles is examined as part of this analysis. The

second application is macroscopic and involves using the characteristics of detector

occupancy, headway, and signal indications to estimate average delay and speed values

using the methods identified in chapter 3. Delay and speed values reported by the

VISSIM hardware-in-the-loop simulation model were assumed to be the true delay and

speed values. They were compared against values estimated using the data logging device

output files. Two measures - mean absolute error and mean absolute percent error, were

used to compare the accuracy of the estimated measurements to true values.

5.2 Microscopic Time-Occupancy and Signal Indication Plots

Figure 13 shows an example of continuous time-occupancy plots for two detectors

located on the stop bar of an intersection approach. Signal indication for the approach is

shown along the x-axis. The plots are updated at 300ms intervals, a typical rate for a

standard cabinet to update detector and signal status information. These plots can provide

system operators with useful information regarding the efficiency of the phase operations.

Information such as average detector un-occupancy time and green time utilization can be

obtained directly for these graphs.


Figure 13: Examples of time-occupancy plots for two conflicting phases.

1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295

Time (0.3s)

Detector Occupancy (Phase 4)

Red Green Yellow

Phase 3(Green & Yellow)

1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295

Time (0.3s)


Red Green Yellow


1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295 309 323

Time (0.3s)


Red Green Yellow



Figure 13(Cont.): Examples of time-occupancy plots for two conflicting phases.

1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295 309 323

Time (0.3s)


Red Green Yellow


1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281

Time (0.3s)


Red Green Yellow


1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281

Time (0.3s)


Red Green Yellow



5.2.1 Estimation of Vehicle Count and Vehicle Type

A set of time-occupancy data over 130 cycles was analyzed to estimate the vehicle count

and vehicle type. Although the data were gathered with different flow rates, the time

occupancy generally reached a relative constant state after the sixth vehicle in the queue.

So in the following analysis, the characteristics of time occupancy from the first to the

sixth vehicle in the queue are analyzed. The occupancy data were grouped on the basis of

vehicle queue position. Various characteristics were observed as follows:

The occupancy time for the first vehicle was greater than that for the following

vehicles because it included the driver’s reaction time calculated from the

beginning of the green interval to the beginning of the successive un-occupancy

time.

The distributions of time occupancy tended to gradually scatter from the first

vehicle to the sixth. For example, time occupancy of the first vehicle fluctuated

within a much smaller range than the time occupancy of the second or third

vehicle. The standard deviation of the time occupancy decreases as the vehicle

queue position increased.

The time occupancy values for few vehicles (outliers) were significantly greater

than for the others in each group.

The data became distinctly discontinuous for the 5th

and 6th

vehicles; during very

low rates, no vehicles were in these locations.

The traffic of the study approach included two types of vehicles, passenger cars and

heavy vehicles (HV). The boundary lines for each group were predicted and drawn with a

line of black dashes shown in Figure 14 through Figure 19, in which the points for heavy

vehicle are distinguished from those of cars with circles. The areas between cars and HVs

could be distinctively separated by a horizontal line (boundary line) as shown in Figures

14, 15, 18 and 19. The occupancy for passenger cars is defined below the line; the

occupancy for trucks occurs above it. However, in Figure 16 and 17, the areas for car and

trucks are not distinctly divided by a horizontal line since the occupancy values of some


cars are greater than those of heavy vehicles. The basic rule used to identify the

occupancy boundary between two vehicle types was to set the upper time limit of car

occupancy. In Figure 14 through 19, the points highlighted with a circle denote

occupancy time for HVs and all other point denotes occupancy time for car; the dash line

denotes estimated boundary line of time-occupancy between car & HV; the point

highlighted with dashes and a circle denotes a vehicle estimated in error.

Figure 14: Estimated boundary lines of time-occupancy between cars and HVs (1st

vehicle in the queue).

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 20 40 60 80 100 120 140

Occu

pa

ncy (

s)

Vehicle

Occupancy Time of the First Queued Vehicle


Figure 15: Estimated boundary lines of time-occupancy between cars and HVs (2nd


Figure 16: Estimated boundary lines of time-occupancy between cars and HVs (3rd


0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 20 40 60 80 100 120 140

Occu

pa

ncy (

s)

Vehicle

Occupancy Time of the Second Queued Vehicle

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 20 40 60 80 100 120 140

Occu

pa

ncy (

s)

Vehicle

Occupancy Time of the Third Queued Vehicle


Figure 17: Estimated boundary lines of time-occupancy between cars and HVs (4th




0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 20 40 60 80 100 120 140

Occu

pa

ncy (

s)

Vehicle

Occupancy Time of the Fourth Queued Vehicle

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 20 40 60 80 100 120 140

Occu

pa

ncy (

s)

Vehicle

Occupancy Time of the Fifth Queued Vehicle




Figure 20: Estimated boundary values of occupancy time for cars and HVs.

The HV occupancy area includes some passenger car points for the 3rd

and 4th

vehicle

figures. The car points in the HV area may have been caused by two vehicles occupying

the same detector and hence being detected as a single vehicle with a large value of

occupancy time. Though this situation affected the accuracy of vehicle count and type,

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 20 40 60 80 100 120 140

Occu

pa

ncy (

s)

Vehicle

Occupancy Time of the Sixth Queued Vehicle

3.5

2.0 1.9 1.8 1.7 1.6

0

1

2

3

4

5

6

1 2 3 4 5 6

Occu

pa

ncy (

s)

Queued Vehicle Position

Estimated Occupancy Boundary Values for Car & HV

Heavy Vehicle

Passage Car


the data suggested that the possibility of the error’s occurrence had a small impact on the

accuracy of estimation. Using a higher frequency data update rate (less than 300 ms) will

certainly improve the quality of traffic volume counts and HV identification output. The

occupancy boundary values for cars and HVs are summarized in Figure 20.

5.2.2 Estimation of Stopped and Non-Stopped Vehicles

The numbers of stopped and non-stopping vehicles in a cycle are two major parameters in

the proposed delay and speed estimation methods. They were determined based on the

size and distribution of the discharge time-headways and the status of the signal

indication for the phase. The headway between vehicles was computed on the basis of the

number of times the front bumpers of vehicles passed over the upstream end of the

detector. In general, the distribution of time headway in a cycle by vehicle number

showed the following tendency: the headways tended to gradually decrease and then start

to increase abruptly once the queue fully dissipates. The first increased values were

considered the boundary between the last stopped vehicle and the first non-stopped

vehicle. A typical sample of the headways in a cycle is shown in Figure 21, with the first

non-stopped vehicle being the 9th

one. In addition, another characteristic that can be used

to determine the first non-stopped vehicle is the of time-occupancy value. This value

depends on the length of the stopbar detector used and the free flow speed on the

approach. For the approach used in this analysis, it was determined that the time

occupancy for non-stopped may be equal to or less than 0.5 second. This characteristic

was also used to identify the first non-stopped vehicle in the traffic stream.


Figure 21: A sample of discharge time-headway in a cycle.

5.3 Delay and Speed Estimation

Delay and speed values reported by the VISSIM simulation model were compared

against values estimated using the data logging device output files and following the

procedure described in chapter 3.The delay and speed values of each cycle were

calculated. Cycle-delays were then aggregated over the 15-minute simulation time.

Absolute error and percent absolute error values for each simulation run were used to

compare the accuracy of the estimated measurements to true values obtained from

VISSIM simulation output. Flow rates used in the experiments ranged from 400 vph to

800 vph.

5.3.1 Delay Estimation – Webster Formulation (Method1)

Figure 22 shows the comparison of the true delay (simulated) and delay values estimated

using the Webster formulation over 25 runs. Simulation runs with five different traffic

volume levels were performed: 400 vph, 500 vph, 600 vph, 700 vph, and 800 vph. Five

20-minute VISSIM simulation runs were conducted for each traffic flow rate using

different random number seeds to account for traffic variability. The first 5 minutes of the

simulation run was considered a start-up initialization period. Output from the last 15

minutes of the simulation runs were included in the analysis.

2.4

2.0 2.0 1.91.7

1.5 1.2

2.8

3.2

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1 2 3 4 5 6 7 8 9

He

ad

wa

y (

s)

Vehicle Pairs

Discharge Time-Headway (10 Runs)


Figure 22: Comparison of simulated and estimated delay (method 1).

The accuracy of estimated delays was analyzed using the mean absolute error and mean

absolute percent error shown in Figure 23a and Figure 23b. The results indicate that the

maximum absolute errors for flow rates of 400 to 700 vph are less than 5.0

seconds/vehicle and that the average error value is approximately 2.0 seconds/vehicle.

The maximum percent errors are less than 20.0 percent with an average value of

approximately 6.0 percent. The error increased significantly as demand increased to 800

vph (near or over saturation). This is expected as Webster equation is primarily used to

determine delay values for under-saturated conditions (volume to capacity ration of less

than 0.8).

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

De

lay (

s)

Simulation Run

Delay Estimation ( Method1) Estimated

Simulated

400vph

500vph600vph

700vph800vph


a. Absolute Error

b. Percent Absolute Error

Figure 23: Mean absolute error and mean absolute percent error - delay estimation

(method 1).

0

4

8

12

16

400 500 600 700 800

Err

or

(se

c/v

eh)

Volume (vph)

15-Minute Absolute Error (Average Delay)

Ave.

Max.

Min.

0

8

16

24

32

400 500 600 700 800

Err

or

Perc

ent

Volume (vph)

15-Minute Absolute %Error (Average Delay)

Ave.

Max.

Min.


5.3.2 Delay Estimation – Method 2

The second method to estimate delay in this study is based on the analytical model

proposed by Skabardonis and Geroliminis (2005). One limitation of this method is that it

requires the maximum queue length not to extend to the upstream detector location. In

this study, the detector was placed approximately 180 feet upstream of the stop bar.

Initial investigatory simulation runs showed that, when the traffic volume exceeds 500

vph, the maximum queue length exceeded the upstream detector location. Accordingly,

only three traffic flow levels: 300 vph, 400 vph and 500 vph were used in the analysis. As

in the previous experiment, five 20-minute runs were simulated for each flow rate with

different random number seed numbers. A sample of comparisons of simulated and

estimated average delay is presented in Figure 24.

Figure 24: Comparison of simulated and estimated delay estimation (method 2).

The maximum absolute error for 300 vph is less than 1.0 second/vehicle. The maximum

error values for volumes of 400 vph and 500 vph are less than 2.0 seconds/vehicle

(Figure 25a). All percent absolute errors are within 6.5 percent (Figure 25b). This

approach predicts delay with higher accuracy than the Webster method.

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

De

lay (

s)

Simulation Run

Delay Estimation (Method 2 )

Simulated

Estimated

300vph

400vph

500vph


a. Mean Absolute Error

b. Mean Absolute Percent Error

Figure 25: Mean absolute error and mean percent absolute error - delay estimation

(method 2).

0.0

0.6

1.2

1.8

2.4

300 400 500

Err

or

(se

c/v

eh)

Volume (vph)

15-Minute Absolute Error (Average Delay)

Ave.

Max.

Min.

0

2

4

6

8

300 400 500

Err

or

Pe

rce

nt

Volume (vph)

15-Minute Absolute %Error (Average Delay)

Ave.

Max.

Min.


5. 3.3 Speed Estimation

The speed estimated in this study is the average spot-measured speeds at the approach

stopbar. The required data were obtained from the stopbar detectors. The mean speed of

all stop and non-stop vehicles in a cycle was estimated, and then all cycle-speeds are

aggregated into 15-minute intervals. Figure 26 compares the true speed values

(simulated) and the speed values estimated using the data logging device output files

following the procedures described in section 4.2.

Figure 26: Comparison of simulated and estimated speed estimation.

The accuracy of the estimated speed was analyzed using the mean absolute error and

mean percent absolute error. The results are shown in Figure 27a and Figure 27b. The

most accurate predicted value appeared at flow rate of 600 vph, with a maximum error of

only 0.7 feet/second/vehicle. As the volume increases or decreases, the error of estimated

speed tends to increase. In all cases, the maximum difference between the predicted and

the simulated speed is always less than 2.0 feet/second/vehicle, with the average values

ranging from 0.5 feet/second/vehicle to 1.2 feet/second/vehicle. The mean percent

absolute errors for all cases are less than 7.0 percent.

0

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Sp

ee

d (

ft/s

ec)

Simulation Run

Speed EstimationEstimated

Simulated

400vph500vph

600vph700vph

800vph


a. Mean Absolute Error

b. Mean Absolute Percent Error

Figure 27: Mean absolute error and mean percent absolute error of speed

estimation.

0.0

0.5

1.0

1.5

2.0

400 500 600 700 800

Err

or

(ft/

se

c/v

eh)

Volume (vph)

15-Minute Absolute Error (Average Speed)

Ave.

Max.

Min.

0

3

6

9

12

400 500 600 700 800

Err

or

Perc

ent

Volume (vph)

15 - Minute Absolute %Error (Average Speed)

Ave.Max.Min.


5.4 Summary

Comparing the estimated range and values of errors for average delay and speed

values over different traffic flow rates, the minimum error appears to happen at

moderate traffic flow conditions (flow rates 600 vph). The error increases

proportionately as flow rates increase. When the flow rate reaches or exceeds the

capacity (saturation or oversaturation condition), the error of predicted delay and

speed values greatly increase.

Under unsaturated flow conditions, the maximum delay error is approximately 5.0

seconds/vehicle, and the maximum speed error is approximately 2.0 feet/second.

The average delay error is approximately 2.0 seconds/vehicle, and the average

speed error is approximately 1.0 feet/second. From an operational perspective,

these variations are within acceptable ranges. These values show that average

delay and speed values can be accurately estimated using the high-resolution

output data reported by the data logger device.

Comparing the two delay estimation methods, the second method provided delay

estimations with higher accuracy level. However, it can be used only for low and

moderate traffic flow rates when the maximum queue length does not exceed the

upstream detector location.


6. CONCLUSIONS AND FURTHER RESEARCH

6.1 Summary

The project presents a high-resolution data logging device that can be used in real-time

traffic monitoring at signalized intersections. The data logging device can be connected

to traffic cabinets using different connection modes. It records the communication

exchanged between the detector and the controller and between the controller and signal

heads. It can also record any other special calls such as preemption. The data logging

device main function is to log and store the status of all input and output communication

channels at every time step, which can be as small as 10 milliseconds. The data logging

device can be accessed remotely through an Ethernet port over IP based communication

protocols. It provides an opportunity for high-resolution real-time performance

monitoring of intersection operations.

The project presents two applications in which the data logging device was used to

monitor signalized intersections performance.

In the first application, the device was used to plot continuous time-occupancy

and signal indication graphs for different movements. Such plots provide system

operators with the information needed to assess the efficiency of phase operations

and to continuously monitor the level of green time utilization for different

phases.

In the second application, the data logging device was used to estimate average

delay and speed values for signalized intersection approaches using detector

occupancy and signal indication data with a high level of accuracy.

The data logging device was tested and validated in the lab using a hardware-in-the-loop

simulation model. The simulation data were collected in this study from an isolated

intersection in the city of Moscow, Idaho. Webster delay equation and the analytical

model proposed by Skabardonis and Geroliminis (2005) were used to estimate the

average delay values for signalized intersection approaches. The speed estimation method

proposed by Son and Oh (1998) was used to estimate average speed values for each


approach. The accuracy of the estimated average delay and speed values were compared

against true delay and speed values obtained through microscopic simulation. The error

analysis was conducted using two measures: mean absolute error and mean absolute

percent error values.

6.2 Conclusions

Based on the results of the error analysis presented in this project, the data logging device

output can be used to accurately estimate average delay and speed values for signalized

intersection approaches. Results from the analysis validated the following four research

hypotheses:

the data logging device monitors and reports high-resolution detector and signal

indication status data that can be used to provide accurate detector occupancy and

signal-indication plots,

data reported by the data logging device can be used to estimate traffic counts

with a high degree of accuracy,

data reported by the data logging device can be used to identify heavy vehicles

(HVs) in the traffic stream,

data reported by the data logging device can be used to estimate average delay

and speed values for each approach with an acceptable level of accuracy.

6.3 Further Research

Future research tasks should involve field study at an isolated intersection to show

how the data logging device can be used to monitor intersection operation. The

data logging connection to NEMA TS2 Type 1 family of cabinets should be

developed, tested, and validated in the field.

Data reported by the data logging device can be applied to estimate other

performance measures, such as queue length, travel time, and mean stopped time.

The sampling interval of data logging has a significantly impact on the accuracy

of performance measures estimation. A best sampling interval should be

determined.


A real-time performance measurement system based on the data logging device

can be developed to collect, store, and analyze data, not only for an isolated

intersection, but for a network of signalized intersections.


7. REFERENCES

Bermejo, B. C. and Tiglao, N. C. C. (2003). “Interfacing the Advanced Traveler

Information System with Inductive Loop Detector Technology.” Proceedings of the

Eastern Asia Society for Transportation Studies, Vol.4, Fukuoka, Japan, 1106-1121.

Hellinga, B. and Gudapati, R. (2000). “Estimating Link Travel Times from Different

Data Sources for Use in ATMS and ATIS.” Proceedings of the ITE District 1 and

District 7 Joint Annual Conference, Niagara Falls, Ontario.

Highway Capacity Manual (HCM 2000), Transportation Research Board, National

Research Council, Washington D. C.

Li, M., Zou, Z.J., Bu, F.P. and Zhang, W.B. (2008). “Application of vehicle-

infrastructure integration (VII) data on real-time arterial performance measurements.”

Transportation Research Board 87th Annual Meeting (CD-ROM), Transportation

Research Board, Washington, D.C.

Lin, W. H., Kulkarni, A. and Mirchandani, P. (2004). “Short-term arterial travel time

prediction for advanced traveler information systems.” Intelligent Transportation

Systems, Vol. 8, 143-154.

Liu, H., van Zuylen, H. J., van Lint H., Chen Y., and Zhang K. (2005). “Prediction of

urban travel times with intersection delays.” Proceedings of the 8th International IEEE

Conference on Intelligent Transportation Systems, Vienna, Austria, 402- 407.

Liu, H., Song, J. and Yin, Y. (2005). “A Simplified Method for Estimation of Back of

Queue and Its Application in Macroscopic Simulation.” Proceedings of IEEE Intelligent

Vehicle Symposium, Las Vegas, USA, 662-668.

Lucas, D. E., Mirchandani, P. B. and Verma, N. (2004). “Online travel time estimation

without vehicle identification.” Transportation Research Record, Vol. 1867, 193-201.

Noyce, D.A. and Barlow, J.M. (2003). “Interfacing Accessible Pedestrian Signals (APS)

with Traffic Signal Control Equipment.” U.S. Access Board, Washington, D.C., 45-51.

http://trb.metapress.com/content/120399/


Shea, M. “NEMA TS2 standard & technology.” <www.baltometro.org/ITS/TSF05-

5NEMA.pdf> (Aug. 11, 2008).

Skabardonis, A. and Geroliminis, N. (2005). “Real-time estimation of travel times on

signalized arterials.” Proceedings of the 16th International Symposium on Transportation

and Traffic Theory, College Park, Maryland, 387-406.

Son, Y. and Oh, J. (1998). “Estimating arterial link travel times using loop detector

output.” The 5th

ITS World Congress, Seoul, Korea.

Sun, C. and Ritchie, S. G. (1999). “Individual vehicle speed estimation using single loop

inductive waveforms.” <http://repositories.cdlib.org/its/path/papers/UCB-ITS-PWP-99-

14> (Aug. 23, 2008).

Tung, L.W. (2007). “Automated measurement of approach delay at signalized

intersections – a vehicle-event based approach.” MS thesis, University of Idaho,

Moscow, ID.

Wang, Y. and Nihan, N. (2000). “Freeway traffic speed estimation with single-loop

outputs.” Transportation Research Record, Vol. 1727, 120-126.

Zhang, H. M. (1999). “Link-Journey-Speed model for arterial traffic.” Transportation

Research Record, Vol. 1676, 109-115.

Zhang, Y., Ye, Z., and Xie, Y. (2006). Texas A&M University, College Station, Texas.

“Accurate speed estimation using single loop detector data.” Texas Transportation

Institute, College Station, Texas, 21-29.

http://www.baltometro.org/ITS/TSF05-5NEMA.pdf

http://www.baltometro.org/ITS/TSF05-5NEMA.pdf

http://repositories.cdlib.org/its/path/papers/UCB-ITS-PWP-99-14

http://repositories.cdlib.org/its/path/papers/UCB-ITS-PWP-99-14


http://trb.metapress.com/content/?Author=H.+M.+Zhang

http://trb.metapress.com/content/a4v835v086355222/?p=3439f40c81d84170b9203f77583234ef&pi=16



an intersection traffic data collection device utilizing logging

Documents