Accident risk simulation

Toshio Yoshii
Ehime Univ.
Leeds University, 10 July 2014
Accident Risk Simulation
1

1992 graduate from Department of Civil
Eng., The Univ. of Tokyo
1994 Master degree
1999 Ph.D supervised by Prof. Kuwahara
1994-1999. Research associate of
The Univ. of Tokyo
1999-2003. Associate professor of
Kochi Univ. of Technology
2003-2010. Associate professor of
Kyoto Univ. with Prof. Kitamura
2010- Professor of
Ehime Univ.
2
CV

3
Researches
1. Traffic control
- Dynamic network traffic simulation
SOUND(1995): Simulation On Urban expressway Networks with
Dynamic route choice
Meso-scopic simulation based on Block Density Method (Cell
Transmission Model)
About 20 years before,
I visited LEEDS for getting a information about CONTRAM,SATURN
and DRACULA.
In order to develop a simulation model, we have to solve various
issues…

1. Traffic control
- Demand estimation
4
Researches
T. Yoshii，M. Kuwahara：Estimation of a Time Dependent OD Matrix
from Traffic Counts Using Dynamic Traffic Simulation，Proceedings
of the 8th WCTR Vol.2，pp.163-174，1998.7.

1. Traffic control
- Demand estimation
- Dynamic information provision
5
Researches
T. Yoshii & M. Kuwahara：An Evaluation method on Effects of
Dynamic Traffic Information, The 7th Annual World Congress on
Intelligent Transport Systems 00’ Torino, CD-ROM, 2000.11.

1. Traffic control
- Demand estimation
- Traffic flow/ Network theory
6
Researches
Y. Shiomi, T. Yoshii and R. Kitamura: Platoon-based traffic flow model
for estimating breakdown probability at single-lane expressway
bottlenecks, Transportation Research Part B: Methodological,
Volume 45, Issue 9, pp.1314-1330, 2011.11
T. Yoshii, M. Kuwahara and K. Kumagai: A theory on dynamic system
optimal assignment, Proceedings of the Third International
Symposium on Transportation Network Reliability, CD-ROM, 2007.7.

1. Traffic control
- Demand estimation
- Driver’s behavior
7
Researches
R. Kitamura and T. Yoshii: Rationality and heterogeneity in taxi driver
decision: An application of a stochastic-process model of taxi
behavior. In H.S. Mahmassani (ed.) Transportation and Traffic
Theory: Flow, Dynamics and Human Interaction, Elsevier, Oxford,
pp.609-628，2005.7
Now, I am investigating the driver’s route choice behavior when they
will get the information about traffic accident risk.

1. Traffic control
- Demand estimation
- MFD
8
Researches
Toshio Yoshii, Yuji Yonezawa & Ryuichi Kitamura: Evaluation of an
Area Metering Control Method Using the Macroscopic Fundamental
Diagram, The 12th World Conference on Transport Research, Lisbon,
Portugal, July 11-15, 2010.7.

1. Traffic control
- Demand estimation
- MFD
- Traffic safety
9
Researches
Toshio Yoshii and Yuki Takayama: Development of a Traffic Accident
Simulation Model on Urban Expressway Networks, OPTIMUM 2013
– International Symposium on Recent Advances in Transport
Modelling, Kingscliff, Australia, 2013.4

1. Traffic control
- Demand estimation
- MFD
- Traffic safety
2. Others
- Traffic guide signs
10
Researches
T. Yoshii: Symbolization of Intersections using Alphabet Signs,
Proceedings of Workshop on Transportation Researches for Urban
Safety, CD-ROM, 2008.12.
By using these guide signs for route
guidance, drivers can find the intersections
earlier where they should make a turn.

1. Traffic control
- Demand estimation
- MFD
- Traffic safety
2. Others
- Traffic guide signs
- Demand estimation model of first-aid transportation service
- etc.
11
Researches
Today, I will show you about the research on Accident Risk
Simulation,
which consists of two parts,
- network traffic simulation model
- accident risk estimation model

12
Traffic Control Measures
(ramp metering, signal control, etc.)
Traffic States(Q,K,V)
can change
Predicted by
network traffic simulation

13
Traffic Control Measures
(ramp metering, signal control, etc.)
Traffic Accident Risk
(likelihood)
Geometric Design
(road alignment, merging/diverging, etc)
Road Environment
(precipitation, etc)
can change
Accident risk simulation is developed, which can
estimate the likelihood of occurrence of traffic accidents
considering the traffic states.
After developing the Accident Risk Simulation,
it must be useful for carrying out effective traffic
control measures.

14
Traffic Accident Risk
Geometric Design
(road alignment, merging/diverging, etc)
Road Environment
(precipitation, etc)
Accident Risk Estimation Model
The traffic states at each
link can be estimated by
previous traffic simulations.
Accident risk estimation model should be
established in order to develop the accidenr risk
simulation.

Accident Risk Estimation Model
Rij：Traffic accident risk for accident type j on
state category i [/108 veh*km]
αij,βijk：parameters
xk：factors
15
Linear regression model
nijnijijijij xxxR βββα ++++= ...2211
What is the state categories ?
For example,
3 time mean speed : [1-29km/h] , [30-59km/h] , [60km/h-]
2 gradient : [>=+5%] , [<+5%])
3 road section : [merging], [Toll plaza], [others]
→ 3*2*3=18 categories of the states

16
Data Analysis
Study road network
(Hanshin Expressway)
CBD of Osaka
- Traffic counts
(volume, time mean occupancy, time mean speed)
are observed by 10 detectors at every 5 min.
- Accident record
(accident type, place, occurrence time, etc)
includes 747 accidents in total from 2006 to 2008
- Weather record
provides hourly precipitation around the study area
- Road alignment
(gradient, radius of curvature and Geometric Design merging/diverging, toll plaza)
are determined every 100m
3 accident types
- Rear-ender collision
- Minor collision
- Own-crash accident
12km

17
Estimation Results(rear-ender collision)
説明変数等偏回帰係数 t値 P値
低速度ダミー 587.3***
30.03 0.000
中速度ダミー 163.4***
15.55 0.000
下り勾配・平坦ダミー 39.6*** 2.50 0.000
分流部手前 48.7* -2.60 0.062
料金所ダミー 113.2***
1.92 0.000
データ数 5061
R2
0.23
修正R2
0.23
***　
有意水準1% **　
有意水準5% *　
有意水準10%
Traffic accident risk becomes higher in lower
speed flow.
Coefficient t-value prob.
speed D ( < 30km/h)
speed D (30-60km/h)
grade D ( < 0.5%)
upstream diverging D
toll plaza
Samples
R2
adjusted R2
***1%significant **5%significant *10%
1.87

18
低速度ダミー 88.3*** 9.39 0.000
直線・緩カーブダミー 9.5***
3.10 0.002
急カーブダミー 15.0** 2.41 0.016
合流部奥ダミー 35.0*** 3.01 0.003
合流部ダミー 37.8***
3.59 0.000
料金所ダミー 239.6***
15.97 0.000
データ数 5061
R2
0.08
修正R2
0.08
***　
有意水準5% *　
有意水準10%
speed D ( < 30km/h)
curve D ( r > 500m)
curve D ( r < 500m)
downstream merging D
merging D
toll plaza D
Traffic accident risk becomes higher in lower
speed flow and at toll plaza.
Estimation Results(minor collision)
Samples
R2
adjusted R2

19
降雨ダミー 34.6*** 4.77 0.000
急カーブダミー 30.3***
6.42 0.000
合流部ダミー 44.8***
5.97 0.000
合流部手前ダミー 26.0***
2.96 0.003
データ数 5059
R2
0.03
修正R2
0.03
***　
有意水準5% *　
有意水準10%
rainfall D
curve ( r < 500m)
merging D
upstream merging D
Traffic accident risk becomes higher in case
of rain and at merging section.
Estimation Results(own-crash accident )
Samples
R2
adjusted R2
From these 3 results, “Rainfall” significantly
only affects the traffic accident risk for own-
crash accident.

Evaluation of a Ramp Metering Control
20
Average Speed
at each Link
9:00 – 9:05 a.m.
NO control with control
You can see the traffic improvement by carrying out
the control.

Evaluation of a Ramp Metering Control
21
Average Speed
at each Link
9:00 – 9:05 a.m.
NO control with control
Accident Risk
at each Link
You can see the improvement on traffic accident risk
by carrying out the control.
In addition to these results the simulation can
estimates the accident risks at each links.
Traffic Accident Simulation Model estimates traffic
accident risk at each link at each time interval, which
shows expected number of accident occurring at a link
per 108 veh*kms.

22
The Next Study
This study established the accident risk simulation
which includes the accident risk estimation model.
However,
The traffic risk estimation model includes only SPEED.
At the next model, the experimental variables determined by
TRAFFIC STATES(Q,K) are included.
When the simulation estimates the traffic accident risks, it
has to use the aggregated data.
Because the accident risk estimation model uses the
experimental variables determined by aggregated data such
as 5min. average speed.

Traffic States
 Traffic states must appear on or near the Fundamental
Diagram on the Q-K plane.
2100
1800
1500
1200
900
600
300
交通流率(台/h)
交通密度(台/km)
10 20 30 40 50 60 70 80 900
23
Q
K
Flowrate[veh/h]
Density[veh/km]
Free
Flow
Congested
Flow
Fundamental
Diagram

This research uses the aggregated data, which is 5min.
average of detector data.
In such an aggregated data, at the time interval when the
traffic state is changing, traffic states can appear far from
the Fundamental Diagram.
2100
1800
1500
1200
900
600
300
交通流率(台/h)
10 20 30 40 50 60 70 80 900
24
Q
K
Flowrate[veh/h]
Density[veh/km]
Transition of the Traffic State
Free
Flow
Congested
Flow
Fundamental
Diagram

accident
space
0
B.N.
25
accident
congestion
consider the aggregating time interval of observation
by traffic sensors.
Free Cong. Cong. Free
About these two time intervals, both Free Flow and
Congested Flow are observed.
time
1 2 3 4 5 6

 The traffic states has been changed in the time interval at
the observed section, from Free to Congested.
26
accident
space
0
B.N.
accident
congestion
Free
Cong.
time

2100
1800
1500
1200
900
600
300
交通流率(台/h)
10 20 30 40 50 60 70 80 900
 When the traffic states has been changed during the
aggregated time interval, the traffic state appear far from
the Fundamental Diagram.
27
accident
space
0
B.N.
accident
congestion
Free
Cong.
Q
K
Flowrate[veh/h]
Density[veh/km]
Free
Cong.
The traffic states in this region are named as
“Mixed flow”, which indicates the transition of the traffic
state between Free Flow and Congested Flow.
Mixed Flow

2100
1800
1500
1200
900
600
300
交通流率(台/h)
10 20 30 40 50 60 70 80 900
Flowrate[veh/h]
Density[veh/km]
Heterogeneous Mixed Flow
 Heterogeneous mixed flow appears under the situation
that the lane traffic states have heterogeneity.
Congested flow
Free flow
28
Q
K
Free Cong.
 The traffic state far from the Fundamental Diagram
appears.

Mixed Flow
 2 types of the Mixed Flow is established.
2100
1800
1500
1200
900
600
300
交通流率(台/h)
10 20 30 40 50 60 70 80 900
29
Q
K
Flowrate[veh/h]
Density[veh/km]
TMF : Transitional Mixed Flow
HMF : Heterogeneous Mixed flow
Mixed Flow
It is hard to distinguish these 2 Mixed flow
perfectly, TMF and HMF.

Impacts on the Accident Risk
 Impacts of the 2 types of the Mixed Flow on the traffic
accident risk is investigated using actual data.
30
Loop
2-lanes
 Study network
Hanshin Expressway.
In this analysis,
・Loop section
The mixed flow must include the HMF because
it has 4 lanes and it has higher share of
weaving section.
・2-lanes section
Almost of the mixed flow must be considered
as TMF because it has only 2 lanes.

Results ～Rear-ender Collision
31
～1 accidents/108
veh・km
1～100 accidents/108
veh・km
100～1,000 accidents/108
veh・km
1,000～ accidents/108
veh・km
Loop
(Heterogeneous)
2-lanes
(Transitional)
Flowrate[veh/h]
Density[veh/km]
2100
1800
1500
1200
900
600
300
10 20 30 40 50 60 70 80 900
Mix
Free Congestion
2100
1800
1500
1200
900
600
300
10 20 30 40 50 60 70 80 900
Flowrate[veh/h]
Density[veh/km]
Mix
Free CongestionCongested Congested
Mixed Mixed
 The risks of Mixed Flow and Congested Flow are higher
than Free flow in both section.
 The risks of Mixed Flow on the loop section are much
higher than that of on the 2-lanes section.
These results imply HMF has higher risk than that of TMF.

32
Summary
This study
- established the 3rd traffic flow state, “Mixed Flow” .
- did a comparison analysis to evaluate the relationship
between accident risks and these 3 traffic states.
⇒ Generally, the risks of the Mixed Flow are higher than
that of Free Flow state.
⇒ The risks of Heterogeneous Mixed Flow are higher than
those of Transitional Mixed Flow.

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