Development of Accident Prediction Models for the
Highways of Thailand
Lalita Thakali
Transportation Engineering
Outline of Presentation








Statements of Problem
Objective
Methodology
Preliminary analysis
Model development
Identification of hazardous location
Conclusion
Recommendation
Statement of Problems
 In
2002 the economic losses due to road accidents was estimated to be in
approximately 115932 million baht, or 2.13% of the GDP
 18% of the annual road accidents occurs in highway of Thailand.
(The annual report (2005) of the Bureau of Traffic Safety )
Year Accidents Fatalities Injuries
 Trend of accident in highways of
Thailand
 Budget allocation for road safety for
highways of Thailand
Property Damage
(THB)
2001
15,341
2,212
12,712
352,851,000
2002
15,066
2,265
13,285
445,236,000
2003
15,171
2,023
12,984
464,248,000
2004
18,547
2,324
18,381
425,623,000
2005
16,287
2,169
15,300
405,248,000
Year
2002
2003
2004
2005
Budget (in Million Baht)
1,400.000
1,400.000
1,770.000
1,644.999
Causes of Accidents in Thailand
63%
Human
5% 1% 20%
4%
3%
Vehicle
6%
Road & Environment
How to Address Road Safety Problem
By Accident Modeling
1
Descriptive model
2
Predictive model
3
Risk model
4
Accident consequences model
Objective of Study
1
Identify existing accident characteristic
2
To develop a generalized accident prediction models
for highways using different statistical techniques.
3
To identify hazardous locations
Methodology
Literatures Review
•DoH historical Accident Data
•DoH traffic data
•Metrological data
•Video data
Site Selection
Data Collection
Monthly accident data (λij)
•Accident
•Fatality
•Injury
•Property damage
Explanatory Variables
(Xij)
Homogenous Section
l1
l2
ln-1
ln
i= 1,2….n
Preliminary data analysis
(Characteristic of accident &
severities
Identification of
possible Variables
Site Selection
2. Why route no 4 in Ratcha Buri
& Nakhonpathom
1. Why route no 4
Year
Accidents
(highways)
Accidents
(major route
1,2,3,4)
Accidents
(route 4)
% of
accidents
(major
route)
% of accidents
(route 4 w.r.t.
major routes)
Year
Accident
Fatalit
y
Injury
Property
Damage
2001
29.9
15.1
10.4
15.6
2001
15341
3228
800
21.04
24.783
2002
23.5
10.0
8.6
13.8
2002
15066
3142
869
20.85
27.658
2003
19.0
5.0
5.2
7.3
2003
15171
2982
949
19.66
31.824
2004
34.4
11.4
24.0
10.3
2004
18547
3534
993
19.05
28.098
2005
29.6
10.0
14.9
7.3
2006
29.2
3.0
22.3
5.9
Average
27.59
9.10
14.24
10.04
2005
16287
3016
861
18.52
28.548
2006
10597
2077
552
19.60
26.577
Average
15168
2997
837
19.79
27.91
3. Relatively high no of AADT count stations
27.59% of accident occurs in selected
site, where it covers only 8.56% of
Total road length of route no 4
Study Area
Nakhonpathom
Ratcha Buri
Route no 4
Total length = 117.93 km
Methodology
Literatures Review
•DoH historical Accident Data
•DoH traffic data
•Metrological data
•Video data
Site Selection
Data Collection
Monthly accident data (λij)
•Accident
•Fatality
•Injury
•Property damage
Explanatory Variables
(Xij)
Homogenous Section
l1
l2
ln-1
ln
i= 1,2….n
Preliminary data analysis
(Characteristic of accident &
severities
Identification of
possible Variables
Data Collection
1.
2.
3.
4.
Traffic


DoH historical Accident Data
DoH traffic data
Metrological data
Video data
AADT (2)
% of heavy vehicle (2)
Weather
 Rainfall (3)
Calendar
 Month
Accident
Geometric
2001- 2006










Total accident
Fatality
Injury
Property damage
DOH (1)
No of lane (4,1))
Types of median (4,1)
Shoulders available (4)
No of curves (4)
No of intersection (4)
No of access (4)
Data: Year 2001 to 2006
Methodology
Literatures Review
•DoH historical Accident Data
•DoH traffic data
•Metrological data
•Video data
Site Selection
Data Collection
Monthly accident data (λij)
•Accident
•Fatality
•Injury
•Property damage
Explanatory Variables
(Xij)
Homogenous Section
l1
l2
ln-1
ln
i= 1,2….n
Preliminary data analysis
(Characteristic of accident &
severities
Identification of possible
Variables
Accident Rate (MVK)
Fatality rate - 3.08 per MVK
Countries
Canada
France
Germany
Italy
UK
USA
Bahrain
Egypt
Oman
Yemen
0.01
0.02
0.02
0.01
0.01
0.001
0.002
0.44
0.04
0.11
Average Fatality rate in this study area is much higher
than the rate in other countries. seven times greater than
that of Egypt
Objective1
Causes of Accident
S.N Types of Causes
Total %
1
Maximum speed limit
1051
76.10
2
3
4
5
Maximum speed limit + others
Improper passing
Improper passing +others
Failure to yield right
Failure to temporary stop, slow
down, turn
Disregarding traffic signal
marking
Vehicle defective
Drunkeness
Sleepy
Others
31
47
4
1
2.24
3.40
0.29
0.07
1
0.07
7
0.51
24
1
8
206
1.74
0.07
0.58
14.92
1381
100
6
7
8
9
A
B
Total
The exceeding of max speed is mostly due to the humanvehicle and its interaction with the geometric features of
the road- this could be addressed in the model with the
inclusion of geometric variables
Objective1
Location of Accident
45%
78%
67%
73%
•Accident
•Fatality
•Injury
•Property damage
55%
22%
33%
27%
intersection
Segment
Weather related Accident
Vehicle involvement
Weather
Accident
%
Fatality
%
Injury
%
PD
%
Clear
Fog
Rain
Other
Total
1018
1
125
237
1381
74
0
9
17
100
59
0
0
5
64
92
0
0
8
100
421
0
40
187
648
65
0
6
29
100
14295
22
1654
398
16369
87
0
10
2
100
Note: PD= Property Damage (1000 baht)
Surface
Condition
Dry
Dirty
Wet
Other
Total
Accident
990
1
125
265
1381
%
Fatality
%
Injury
%
PD
%
72
0
9
19
100
59
0
0
5
64
92
0
0
8
100
394
0
40
214
648
61
0.00
7
33
100
14045
27
1628
669
16369
86
0
10
4
100
Vehicles
Total %
Pedestrian
21
1.04
Bicycle
3
0.15
Tricycle
2
0.10
Motorcycle
392
19.34
Trimotcycle
480
23.68
Passenger car
605
29.85
Light bus
59
2.91
Light truck
98
4.83
Heavy vehicle (HV)
Heavy bus
218
10.75
Medium
truck
Heavy truck
Farm vehicle
1
92
56
0.05
4.54
2.76
HV only 16% of total number of accidents while it
represents 22.39% of total traffic volume
Objective1
Accident distribution based on month
Objective1
Methodology
Literatures Review
•DoH historical Accident Data
•DoH traffic data
•Metrological data
•Video data
Monthly accident data (λij)
•Accident
•Fatality
•Injury
•Property damage
Site Selection
Data Collection
Explanatory Variables
(Xij)
Homogenous Section
l1
l2
ln-1
ln
i= 1,2….n
Preliminary data analysis
(Characteristic of accident &
severities
Identification of
possible Variables
Variables (per month)
Independent
Dependent
Variables
Total Mean
Std
Units
Accident
1220
1.22
1.89
Number
Fatality
61
0.06
0.33
Person
Variables
AADT
PD
578
0.54
1.68
15596 15.66 50.05
Person
Thousand
baht
%
Lane
Number
Number/km
Intersection(I’)
Number/km
Curve (C’)
Number/km
AADT and lane is highly correlated, so lane has
been excluded
Data from 2001- 2005 was used in model development
Objective 2
Median
(MD)
Category
Divided (1)
Undivided
(0)
km
Access (A’)
Rain (R)
Variables
Number (*1000)
HV
Length
Injury
Units
•Literatures
•Preliminary analysis
•Data availability
mm
No (1)
Shoulder
(S)
Yes (0)
Others (1)
Month (M)
April (0)
Addition of variables
Forward selection
Pearson Correlation
Variables
Accident Fatality
Injury
PD
AADT
(*1000)
HV
Length
(km)
Lane
A
I
C
R
Dependent Variables
Accident
1
Fatality
Injury
0.217
1
PD
0.65
0.386
0.251
0.189
1
0.201
AADT
HV
0.488
0.083
0.132
0.006
0.258
0.071
0.149
0.035
0.109
0.461
0.117
0.238
0.363
0.899
0.379
-0.294
0.536
0.145
0.487
0.075
0.276
0.54
0.019
0.028
-0.048 0.014
-0.126
0.141
-0.012
0.083
0.128
0.113
-0.025
-0.078
-0.077 0.002
0.052
-0.05
0.172
0.084
0.004
Length
Lane
A
I
C
R
1
Independent Variables
0.295
1
0.118 0.335
1
-0.148 -0.07
0.005
1
0.587
0.249
-0.023
1
0.229
1
0.367
1
0.247
0.357 0.724
1
0.067
0.053
-0.02 1
0.002 0.019
Pearson Correlation
Variables
Accident
Fatality
Injury
AADT
(*1000)
PD
HV
(%)
Length
(km)
A'
I'
C'
R
Dependent Variables
Accident
1
Fatality
0.217
1
Injury
0.650
0.251
1
PD
0.386
0.189
0.201
1
Independent Variables
AADT
0.487
0.132
0.258
0.295
1
HV
0.090
0.008
0.074
0.122
0.341
1
Length
0.149
0.035
0.109
-0.005
-0.148
-0.066
1
A'
0.382
0.123
0.375
0.043
0.387
0.222
-0.243
1
I'
-0.196
-0.031
-0.131
-0.097
-0.183
-0.276
-0.402
0.100
1
C'
-0.074
-0.023
-0.051
-0.156
-0.158
-0.442
-0.229 -0.168
0.699
R
-0.025
-0.078
-0.077
0.002
0.052
-0.005
-0.023
0.021
1
-0.002 -0.024
1
Methodology cont.
Forward selection of variables
Yes
Model development
•GLM- Poisson regression
•GLM- NB regression
E(λ) = exp∑βjXij
λ = accident per month
βj = parameter coefficient
Xi = explanatory variable
Is included variable
significant? And is the
goodness of fit better?
If yes
•Continue to include
If not
•Exclude the variable
Any explanatory
variables remaining?
No
Selection of model (Poisson or NB)
•Accident Data 2006
•(Visual validation)
Identification of hazardous location
Generalized Linear Model ?
Accident
Modeling
Descriptive
model
Empirical
Bayes
Predictive
model
Fuzzy Logic
Risk
model
Multivariate
Accident consequences
Model
Artificial Neural
Network
Linear Model
Normal dis of accident with
constant mean & variance
GLM
Only Poisson /Negative Binomial regression
model- poisson trial
Accident- Normally follows poisson trial
rather than binomial trial
Objective 2
Generalized Linear Model cont.
Link Function
E(λ) =μ= ∑βiXi
Linear model
λ (i, t) = e∑βjXij
η= ∑βiXi Generalized Linear Model

Link function used gives non negative value which comply with
nature of accident.

Parameter is estimated by max likelihood method unlike OLS
method
.
Objective 2
Significance & Goodness of tests
Estimation of parameters β
Significance of parameters
Maximum log likelihood method. SPSS (16)
= standard deviation
W = Wald value
95% confident interval
Discuss about the goodness of fit
Tests
Log
likelihood
(LR) test.
Formula
Criteria
Purpose
P value >0.05
To select Step
AIC
Less the value better is
the selected step model
“
BIC
“
“
Greater the value better
is the model
To select either
Poisson or NB
Deviance
Total explained
variation (R2D)
Objective 2
“
“
Variables
/goodness of
fit
Constant
Steps
1
2
3
4
5
6
7
8
9
10
11
0.203
(0.000)
- 0.656
(0.000)
- 1.13
(0.000)
-1.426
(0.000)
-1.019
(0.000)
-1.147
(0.000)
-1.132
(0.000)
-851
(0.000)
-739
(0.000)
-1.235
(0.000)
-1.189
(0.000)
0.018
(0.000)
0.019
(0.000)
0.016
(0.000)
0.017
(0.000)
0.017
(0.000)
0.016
(0.000)
0.016
(0.000)
0.016
(0.000)
0.015
(0.000)
0.015
(0.000)
0.063
(0.000)
0.078
(0.000)
0.118
(0.000)
0.079
(0.000)
0.125
(0.000)
-0.021
(0.000)
0.08
(0.000)
0.126
(0.000)
-0.025
(0.000)
0.233
(0.083)
0.101
(0.000)
0.101(0.0
00)
-0.024
(0.000)
0.101
(0.000)
0.101
(0.000)
-0.024
(0.000)
0.097
(0.00)
0.101
(0.000)
-0.025
(0.000)
0.112
(0.00)
0.103
(0.000)
-0.017
(0.004)
0.112
(0.000)
0.103
(0.000)
-0.017
(0.004)
0.639
(0.000)
0.639
(0.000)
-0.348
(0.000)
0.601
(0.000)
-0.348
(0.000)
-0.055
(0.380)
0.769
(0.000)
-0.348
(0.000)
0.769
(0.000)
-0.338
(0.000)
0.298
(0.020)
0.294
(0.020)
AADT
Length
Access
HV
Median
Shoulder
Month
Intersection
Detail forward selection procedure
for Accident model (Poisson)
Curve
Rain
0.0 (0.059)
Deviance
2281
1719
1610
1491
1474
1471
1415
1392
1391
1382
1378
Pearson-Chi
2886
1866
1724
1561
1531
1529
1533
1490
1490
1481
1479
LL
AIC
BIC
-1796
3593
3598
0
(0.000)
-1515
3034
3044
562
(0.000)
-1460
2927
2942
670
(0.000)
-1401
2809
2829
790
(0.000)
-1392
2795
2819
806
(0.000)
-1391
2794
2823
810
(0.000)
-1363
2738
2767
865
(0.000)
-1351
2716
2750
889
(0.000)
-1351
2717
2756
890
(0.000)
-1346
2708
2748
899
(0.000)
-1344
2707
2751
903
(0.000)
0.25
0.29
0.35
0.35
0.35
0.38
0.39
0.39
0.39
0.39
LR ratio
R2 D
Accident
Variables/Goodness
Tests
Selected Step
Constant
Poisson
(1)
10
-1.235
(0.000)
Fatality
Negative
Binomial
(2)
10
-1.162 (0.027)
Injury
Property Damage (PD)*1000 baht
Poisson
(3)
Negative
Binomial
(4)
Poisson
(5)
Negative
Binomial
(6)
11
-2.855
(0.000)
11
- 2.576
(0.000)
11
-2.153
(0.00)
11
-1.911
(0.000)
Poisson
(7)
Negative Binomial
(8)
10
10
-0.449 (0.00)
2.100 (0.000)
AADT (1000)
0.015 (0.000)
0.014 (0.000)
0.016 (0.000)
0.017 (0.00)
0.015 (0.000)
0.013 (0.00)
0.018 (0.00)
0.031 (0.00)
length (km)
0.112 (0.00)
0.121 (0.000)
0.102 (0.002)
0.093 (0.003)
0.148 (0.000)
0.155 (0.00)
0.087 (0.00)
-0.056 (0.055)
Access (per km)
0.103 (0.000)
0.115 (0.000)
0.097 (0.017)
0.104 (029)
0.210 (0.000)
0.224 (0.00)
-0.115 (0.00)
-0.21 (0.000)
-0.017
(0.004)
- 0.017 (0.053)
HV (%)
Median
Shoulder
Month
-0.009 (0.00)
-1.054
(0.030)
- 1.217
(0.008)
-0.506
(0.003)
0.769 (0.000)
0.884 (0.00)
0.963 (0.030)
0.791 (0.042)
0.561 (0.001)
-0.348
(0.000)
- 0.552
(0.001)
-0.916
(0.000)
- 0.906
(0.011)
-0.714
(0.000)
-0.654
(0.004)
0.767
(0.0017)
-0.814 (0.00)
Intersection (per km)
Curve (per km)
0.298 (0.020)
0.333 (0.013)
0.43 (0.005)
1.086 (0.00)
-0.206 (0.00)
0.589 (0.00)
-0.342 (0.00)
-1.427 (0.00)
-0.33 (0.004)
841
841
36730
36730
3182
3182
0.381 (0.037)
Deviance
Scaled Deviance
1382
1382
739
739
Pearson Chi-Square
1481
751
1567
1480
3494
2459
96300
9800
1481
-1346
2708
2748
899
(0.000)
0.39
751
-1332
2680
2719
386
(0.000)
0.34
1567
-211
441
485
72
(0.000)
0.18
1480
-200
417
456
66
(0.000)
0.19
3494
-980
1978
2023
726
(0.000)
0.33
2459
-793
1604
1648
410
(0.000)
0.32
96300
-19300
38620
38670
23509
(0.000)
0.39
9800
-3092
6198
6232
1350
(0.000)
0.29
Scaled Pearson
LL
AIC
BIC
LR ratio
R2D
-0.002
(0.000)
1459
1459
1.567 (0.00)
-0.003
(0.012)
329
329
Rain (mm)
- 0.005
(0.016)
267
267
1.783 (0.00)
-0.003 (0.00)
Prediction Models
Accident
Fatality
Injury
Property Damage
Unit: per month
Objective 2
Multiplier Factors
Annual Average Daily Traffic
Objective 2
Percent of heavy vehicle
The factor is computed for its changes in
magnitude of each predicting variables while
considering all the other variables to be constant
Length
No of Access per km
Multiplier factors cont.
Median
Objective 2
Intersection
Shoulder
Intersection
Multiplier factors cont.
No of Curve per km
Rain fall
Objective 2
Methodology cont.
Forward selection of variables
Yes
Model development
•GLM- Poisson regression
•GLM- NB regression
E(λ) = exp∑βjXij
λ = accident per month
βj = parameter coefficient
Xi = explanatory variable
Is included variable
significant? And is the
goodness of fit better?
If yes
•Continue to include
If not
•Exclude the variable
Any explanatory
variables remaining?
No
Selection of model (Poisson or NB)
•Accident Data 2006
•(Visual validation)
Identification of hazardous location
Comparative study : Actual vs Model prediction
 Total road section of 26.98 km
 Road section divided into constant
length of 2km, with few less then 2 km.
Predicting
Variables
Mean
Standard Deviation
Critical Frequency
Actual
Model
Actual
Model
Actual
Model
Accident
0.42
1.65
0.76
1.20
1.18
2.85
Fatality
0.01
0.11
0.07
0.12
0.07
0.23
Injury
0.33
0.94
0.74
1.32
1.06
2.26
PD
1.83
22.79
7.20
23.13
9.03
45.92
Visual validation
Predicting
Variables
Mean
Standard Deviation
Critical Rate
Actual
Model
Actual
Model
Actual
Model
Accident
3.51
13.63
6.48
10.21
9.99
23.84
Fatality
0.07
0.94
0.54
1.01
0.62
1.95
Injury
2.78
7.86
6.24
11.33
9.02
19.19
PD
14.77
183.38
57.08
182.15
71.85
365.52
Hazardous Locations for accident
Control
Section
201.1.1
201.1.2
201.2.1
201.2.2
201.2.3
201.2.4
201.2.5
201.2.6
202.1.1
202.1.2
202.1.3
202.1.4
202.1.5
202.2.1
Chainage
From
To
26+420
27+700
29+700
31+700
33+700
35+700
37+700
39+700
41+700
27+700
29+700
31+700
33+700
35+700
37+700
39+700
41+700
43+830
45+830
47+830
49+830
51+830
43+830
45+830
47+830
49+830
51+830
53+830
AADT
Length
(km)
A’
HV
MD
S
I’
C’
117.187
117.187
117.187
117.187
117.187
117.187
117.187
117.187
126.068
126.068
126.068
126.068
126.068
126.068
1.28
2
2
2
2
2
2
2
2.13
2
2
2
2
1.57
12.5
12
5
4
3.5
2.5
3
3
3.28
2.5
2
3
2
1.28
36.1
36.1
36.1
36.1
36.1
36.1
36.1
36.1
25.14
25.14
25.14
25.14
25.14
25.14
1
1
1
1
1
1
1
1
1
1
1
1
1
1
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
Yes
0
1
1
0.5
0.5
1
0.5
0
1.406
1
2
1
0.5
1.276
0
0.5
1
0.5
1
0.5
0.5
0
1
0.5
0.5
0.5
0.5
0.319
Chainage
Control
Section
Objective 3
Month
AADT
From
To
Length
(km)
Hazardous
location
Frequency
Rate
201.1.1
Jan- Dec
26+420 27+700
117.2
1.28
Yes
Yes
201.1.2
Jan- Dec
27+700 29+700
117.2
2
Yes
Yes
202.1.1
April
41+700 43+830
126.1
2.13
Yes
Yes
Hazardous Locations for accident
April
Nakhonpathom
Jan- Dec
Objective 3
Conclusions
Characteristic of Accidents
 Accident trend is highly dependent on the exposure factors (MVK).
76% of accidents - exceeding of speed limit.
Light vehicles have comparatively greater influence to the accidents than the HV.
April has higher trend of accident and its severity than in rest of the months.
Model Development
Total Explained variation (%)
S.N Variables
Poisson
Negative
Binomial
1 Accident
39
34
2 Fatality
18
19
3 Injury
33
32
4 Property
39
29
Damage
Significant
variables
AADT (1,2,3,4)
Length (1,2,3,4)
Access per km (1,2,3,4)
HV % (1,4)
Median (2,3)
Shoulder (1,2,3,4
Month (1,2,4)
Intersection per km (4)
Curve per km (1,3,4)
Rainfall (2,3)
Conclusions cont.
•Total explanatory variation is not surprising as data excludes detail station of traffic
count, detail geometric data like lane width, shoulder width and the human
behaviors. Comparable with to Caliendo et al. (2007).
•The variables on the different severity of accident comply with the preliminary
analysis. i.e. methodology implemented for the model formulation is appropriate
one.
Identification of hazardous location
Using the accident prediction models as the tool for the identification of hazardous
section, the road sections with high traffic volume, high number of curves per km
and absence of shoulder were found to be hazardous.
Recommendations
From preliminary analysis & the models
accident is prominent in April. Hence,
more instant safety measures would be taken to reduce the numbers of accidents during
this period which would safe both huge life and economic losses.
Accident is enhanced by the light vehicles as depicted in the result. Traffic
management enforcing the rules and regulation would be implemented such as provision
of separate lanes.
The developed accident prediction models would be integrated with the GIS tools
and develop interface that would explicitly present the hazardous road sections.
Future Researches
Develop separate accident prediction models for intersection.
Develop model with inclusion of more detail geometric data like width of lane, width shoulder,
speed limit etc.
Recommendations cont.
Separate accident prediction model, such as for vehicle to vehicle collision, vehicle turn over
etc.
Real time crash prediction model would be developed for the link and intersection provided the
data availability is real time.
The real time crash prediction would be integrated with the simulation package in the network
for the traffic assignment with the safety factor with addition to the delay factors.
The real time traffic model would be integrated with GIS or Google earth to display the risk of
particular section.