delineating high crash locations with the use of gis
TRANSCRIPT
SUNIL GYAWALI
PLANNING WITH GIS
DELINEATING HIGH CRASH LOCATIONS WITH THE USE OF GIS
OBJECTIVE
• To delineate the high crash locations in GIS map
• To do the exploratory analysis about the relationships between crash characteristics (frequency, severity) with causal factors using GIS maps.
• To develop a model showing the relationship of frequency of crash with these causal factors.
LITERATURE REVIEW
LITERATURES
GENERAL TRANSPORTATION
· Planning, design, safety, operation, environmental management, construction managemnt, right of way managemnt, etc.
· Data integration, cash referencing, mapping and spatial analysis capabilities of GIS made it useful for transportation.
HIGH CRASH LOCATIONS
· Crash referencing, querrying, mapping
· Critical crash rate
· Model can Be used to forecast crash.
· Integration of GIS with Crash Analysis Tool (Example :CARE)
DATA SOURCES
• City of Lincoln
Data Type Source Institution URL
Annual Average Daily Traffic (AADT)
City of Lincoln, Public Works Department Engineering Services Div.Traffic Operation Section
http://www.lincoln.ne.gov/city/pworks/engine/traffic/adtv/pdf/map/city2010.pdf
High Crash Data 2010 Crash Study, City of Lincoln, Prepared by
ITERIS
http://www.lincoln.ne.gov/city/pworks/engine/crash/pdf/2010-crash-report.pdf
GIS Road Shape FileCity of Lincoln and Lancaster County Geographic
Information Systems
http://www.lincoln.ne.gov/city/pworks/engine/crash/pdf/2010-crash-report.pdf
DATA SOURCES
AADT
CITY REPORT
CRASH DATA
1= F+I, 2= PDO, 3=NRTotal No. of Crashes = 937
2771322976141016717458106951286632
PPROPERTY DAMAGE ONLY CRASH
26242220181614121086420
PDO
22
2120
19
1817
16
1514
13
1211
10
9
87
6
54
3
21
0
878121377612118695257363515645
FATAL AND INJURY CRASHES
26242220181614121086420
F+I
13
12
11
10
9
8
7
6
5
4
3
2
1
0
CRASH DATA
Count: 68Minimum: 3Maximum: 55Sum: 937Mean: 13.779412Standard Deviation:
10.285406
Count: 68Minimum: 1Maximum: 13Sum: 303Mean: 4.45Standard Deviation: 3.20
Count: 68Minimum: 0Maximum: 22Sum: 387Mean: 5.69Standard Deviation: 4.35
Count: 68Minimum: 0Maximum: 20Sum: 247Mean: 3.63Standard Deviation: 3.88
METHODOLOGY
1.GIS Files (Road Data)
Source: City of Lincoln and Lancaster County
Geographic Information Systems)
1. High Crash DataSource: 2010 Crash Study,
City of Lincoln
2. Annual Average Daily Traffic (AADT) Information
Source: City of Lincoln, Public Works Department Engineering Services Div.Traffic Operation Section
ARC GIS 1. Geo-Coding
2. Joining Tables3. Selection methods
Modelling
OUTPUT 1
Different GIS Maps Relating Crash frequency, Crash Characteristics and
Crash Causal Factors
OUTPUT 2
Crash Frequency Poisson Model
Major MinorMin 15550.00 2500.00Max 75500.00 65100.00
Average 47875.56 27905.77
Minimum Speed Maximum Speed Mean of Total STD. Dev.
20 70 43.12 11.24
Major MinorMin 0.03 0.04Max 0.76 0.59
Average 0.21 0.21
MODELLING
Code Decsription
M_AADT Total AADT on the approaches of major street segement.
C_AADT Total AADT on the approaches of minor street segement.
M_SPEED Average speed on major street segment
C_SPEED Average speed on minor street segment
M_L Length of major street segment
C-L Length of minor street segment
CONTROL 0= Stop Control, 1= Signal Control
Independent Variables
MODELLING
Model Equation: Log (Number of crashes on High Crash Locations) = 0.125 *10-4 *M_AADT+0.864*10-5 * C_AADT + 0.054*M_SPEED -1.565 *M-L +0.506* CONTROL
DISCUSSION ON THE RESULTS•AADT of the major street approach is positively affecting the number of crashes. It can be said that the number of crashes will increase with the increment in AADT of the major street approach. This is in harmony with the observation in GIS Map.
•AADT of the minor street approach is positively affecting the number of crashes. It can be said that the number of crashes will increase with the increment in AADT of the minor street approach. This is in harmony with the observation in GIS Map.
•The average speed of major street segment is positively affecting the number of crashes. It can be said that the number of crashes will increase with the increment in the average speed of major street segment.
•The length of major street segment is negatively affecting the number of crashes. It can be said that the number of crashes will decrease with the increment in the length of major street segment.
•The presence of signal as the control of intersection is affecting the number of crashes positively. It can be said that the number of crashes will increase when there is signal control than stop control. This is in harmony with the observation in GIS Map.
CONCLUSION• High Crash Locations were delineated in the GIS Maps.
• Some of the inferences obtained from GIS Maps about the relationship between frequency of crash and causal factors were harmonious.
• Statistical modeling showed POISITIVE REALTIONSHIP between crash frequency and
AADT Speed Signal Control
• Statistical modeling showed NEGATIVE REALTIONSHIP between crash frequency and
Length of Major Street Segment Length
Thank You !!