This document outlines Moinul Hossain's research on predicting crash risks on freeways. It discusses the problem of road accidents globally and in various countries. It identifies human, vehicle, and road/environmental factors that contribute to crashes. Accident prediction models are presented as a potential solution, including static, dynamic, and real-time models. However, prior studies have limitations in integrating different data sources and comparing model performance. Hossain's research aims to address these issues by identifying crash precursors that explain real-time crash probability and comparing prediction abilities of static, dynamic, and real-time models.

1. IPISE Int. Communication,
Understanding and Predicting
Crash Risks on Freeways
January 23, 2008
Moinul Hossain
Dept. of Built Environment
Tokyo Institute of Technology (TIT)
Supervised By:
Dr. YASUNORI MUROMACHI

2. Outline
Road Accidents – How Big is the Problem?
Components of Road Accidents
Safe Roads and Accident Prediction
Accident Prediction Models – Progress
Problems and My Research Objectives
Methodology
Expected Outcome
Me, Myself and Road Safety

3. Facts about Global Road Safety
• 1.2 million die a year
• 20-50 million more are
injured or disabled
• 11th leading cause of
death
• account for 2.1% of all
deaths globally

4. In Thailand, every day approximately 50 Thai
people who leave home to work, school, shop,
temple/church/mosque, social gathering never
return home because of road accidents.
Source: Yordphol TANABORIBOON

5. Road Traffic Fatality Rate
in Several Countries in 1998
Fatality Rate
(per 100,000 population)
50
Thailand Japan France German Sweden Britain Canada USA
40
30.4
30
20
14.4 15.4
9.5 10.0
8.6
10 6.0 6.1
0

6. What are the
Causes of
Accidents ?

7. Components of Crash
Components of Crash
Human
HUMAN
57%
6% 27%
3%
2% 1%
3% Road &
Vehicle Environment
Source: Treat et al. (1980)
Human
65%
VEHICLE 4% 24%
1%
2% 1%
2%
Road &
ROAD & Vehicle Environment
ENVIRONMENT Source: Sabey (1980)

8. A Thai Experience ...
A Thai Experience ...
• Numerical Share Remains almost
Identical to the Previous Studies
• 20% of the Human Factors were
found to be directly related to Road
& Environment
• 75% of the Human Factors were
Directly or Indirectly related to Road
& Environment
Source: Thailand Accident Research Center (2007)

9. Pre-crash, Crash, Post-crash
Pre-crash, Crash, Post-crash
Factors
Factors
Road &
Human Vehicle
Environment
Information Roadworthiness Road design & layout
Attitude Lighting Speed Limit
Impairment Braking Pedestrian Facilities
Enforcement Handling
Speed Management
Use of Restraints Occupant Restraints Crash protective roadside
Impairment Other Safety Devices objects
Crash-protective design
First-aid skill Ease of access Rescue facilities
Access to medics Fire risk Congestion
Source: WHO world Report on Road Traffic Injury, 2004

10. What solution can be offered
to improve the safety
problems within a short time
span ?

11. Accident Prediction Model
(APM)
A Potential Solution
“Mathematical Models Capable of Predicting
the Safety Hazards of Roads based on Traffic
Flow Variables and Other Physical and/or
Environmental Variables.”

12. Benefits of Accident Prediction Models
Identify Factors and Establishing relationships
between crashes and Explanatory Variables
Eliminate Black-Spots from Future Road Projects
Conduct Cost-Benefit Analysis for New and Existing
Road Projects
Understanding Change in Traffic Parameters in Pre &
Post Crash Situations
Predict Accidents in Real-time and Facilitate
Proactive Safety Measures
A POWERFUL TOOL WITH MULTIFARIOUS BENEFITS ...

13. APMs - Classification
Will not have any change in probability of accidents
based on the change in time, lighting conditions,
rainfall or other environmental and seasonal
ct a S
parameters that may vary in different parts of a day
i t
and different parts of the year.
Will demonstrate varying probability of accident
based on time and environmental parameters,
however, will not depend on the input of real
time traffic data.
c m ny D
i a
Will take real time traffic data as an input to the model
and calculate the risk of accidents in real time – a pro
active approach.
ae R

14. Static Models
Variables
• Road Geometry (no. of lanes,
Shoulders, Divider, Super
Elevation, Curvature, etc.)
Model
• Pavement Characteristics
• Generalized Linear Modeling
• Type of Road
(Poisson / Negative Binomial)
• Severity of Accident
• Bayesian Approach (Recent)
• AADT
• % of Heavy Vehicles, etc.

15. Dynamic Models
Variables Model
• Road Geometry (no. of lanes, • Time Series Analysis (Calender
Shoulders, Divider, Super Data Analysis)
Elevation, Curvature, etc.)
• Pavement Characteristics
• Type of Road
• Severity of Accident
• AADT
• % of Heavy Vehicles, etc.
• Seasonal Input (e.g., Rainfall)

16. Comments
Static Dynamic
• Based on Historical Data • Based on Historical Data
• Concentrated Towards • Concentrated Towards
Physical Characteristics Seasonal Variation
• Ignores Seasonal Variation • Includes Trends and Weather
• Explicit Theoretical Foundation Characteristics
• Bayesian Approach provides • Explicit Theoretical Foundation
superior outcome for low • Some Studies suggest that it
sample size and low sample can provide better outcome
mean than the Regression Models
• Data requirements are • A Suitable Alternative
relatively low approach when AADT, % HV,
etc. are not readily available

17. Real-time Models
Why is a Real-time Model Necessary???
• Likelihood of a crash or crash
probability is vastly affected by the
short term turbulence of traffic flow
How is it Different???
• Data Requirements are Different
• A Proactive Measure
• Can be linked with Real-time
Prevention is Better than Cure... Accident Detection and ATIS

18. Real-time Models
Variables Data & Collection Method
• Traffic Related Variables • Real-time Traffic Data
(Traffic Flow, Speed, Variation of Collected with Single-Loop
Speed in Lanes, Standard Detector
Deviation of Speed and Volume, • Accident Data Collected from
Vehicle Occupancy, Mean Appropriate Authority
Speed, Density, Speed
• Weather Data Collected from
Difference between Upstream
Meteorological Department
and Downstream, etc.)
• Seasonal Input (e.g., Rainfall)
Prominent Studies
Oh et al, (2000) Golob et al, (2002)
Lee et al, (2002) Abdel-Aty et al, (2004)
Garber et al, (2006)

19. PROBLEMS
• Interaction among Traffic, Geometric
and Environmental Parameters were
not studied
• Loop detectors proved to be inefficient
in providing good data for the pre and
post crash situations
• Different sources of data created a
time lag between the actual time of
accident and reported time of accident
• No comparison was made between the
performance of Real-time and non-
Real-time models

20. Research Demand and Questions
Research Questions
 What are the crash precursors or variables that can
explain the probability of accidents in real time?
 Can a traffic condition in relation to environmental
conditions be anticipated as dangerous enough to lead
to crash? If yes then how?
 What are the performance variations among a
static, dynamic and a real time accident prediction
models?

21. Research Purpose
The purpose of this research study is to
understand the dynamics of road
accidents from traffic engineering
perspective through historical and pre and
post crash condition study and devise a
way to improve the roads as well as driving
conditions to make drivers less susceptible
to mistakes.

22. Research Objectives
 Understand relationships of speed, flow, density
and environmental variables (e.g., weather, time of
day, etc.) during pre and post crash conditions and
develop a real time accident prediction model
thereby.
 Develop static and dynamic accident prediction
models as well for the same study area and compare
the prediction capabilities of these three models.

23. Work Flow Diagram
Literature Review
(Prev. Study, Stat., TE, Tech.)
Selection of Study Area
Data Collection
(Historical, Meteorological, Real
Time – 3 to 4 months)
Model Development
Performance Measure and
Comparison

25. STUDY AREA
STUDY AREA
Study Area
Study Area
• Country : Thailand
• Highway : Route No. 9
• Length of Section : 6-10 Km
Data Sources
Data Sources
• Historical : Dept. of Highways
• Geometry : Dept. of Highways
• Weather : Dept. of Meteorology
• Real-time : Self

26. REQUIREMENTS...
• Equipment:
– Video Cameras: 7-10, for 3-4 Months
– Autoscope: 1, for 3-4 Months
– Rain gauge: 7-10, for 3-4 Months
Cooperation:
– TARC
– DOH
– DOHP

27. Limitations...
The Model will not consider
intersections or Entrances or Exits
A Highly Accident prone section
will be selected
Data will be collected for a short
period of Time
Land Use Data will not be
Considered
The Model will not be Tested in
Real-time situation

28. Prepared by Yordphol T., AIT, 1 Dec 2005
Thank You
Working for road safety means not working under 'lime light' ...
even not being paid attention by other people.
- Late Professor Yordphol TANABORIBOON.
First Manager of Thailand Accident Research Center

29. Diagrams can be DECEPTIVE . . .
•High Concentration on
Human Factors by the
Decision Makers
•Improvement through
Human Factors demands
uncertain Time-limit
A Substantial Portion of
•the Human Factors can be eliminated with better
driving Condition
•Concentration of Road and Environment may
remain unattended.

30. The Million Dollar Question:
Academic and Practical Contribution...
Academic Contribution Practical Contribution
•Understand relationships • Develop a proactive
between crash and traffic accident Warning system
variables considering • Calculate Cost of
environmental variations
Accident or Delay with
•Comparison among Static, Higher accuracy
Dynamic and Real-time • Use VMS and VSL with
Accident Prediction Models
higher effectiveness
• A New method of
developing real-time
accident prediction models
(hopefully...)

31. Me, Myself
and
Road Safety

32. Projects, Reports and Trainings (in Road Safety Only)
Projects
 Team Leader, Nationwide Real-time Vehicle Tracking System, Monico Limited,
Bangladesh. July 2007 – September 2007. (For Profit Project).
 Research Associate, Thailand Accident Research Center (TARC), Thailand. June 2006 –
July 2007.
 System Analyst, Provincial Road Safety Management System (PRSS), Donor: Thai Health
Promotion Foundation, Thailand. June 2006 – December 2008.
 Student Assistant, Thailand Accident Research Center,Thailand. June 2005 – May 2006.
Project Reports
 Project Reports and Business Plan of Thailand Accidents Research Center, Thailand
 Application manual of Provincial Road Safety Management System (PRSS), Thailand
Training
 Lecturer, Road Safety and IT, The Third Country Training on Injury Surveillance (TCTP),
November 14, 2006, Khon Kaen Hospital, Khon Kaen, Thailand. Donor: JICA.

33. Publications (in Road Safety Only...)
Thesis, Master of Engineering
 Application of Data Mining in Road Safety, AIT Thesis, 2006, Thailand.
Research Papers
 Motorcycle Accidents, Alcohol Intoxication and Futile Helmet Legislation in Thailand: How
Long Must We Tolerate. Journal of the Eastern Asian Society for Transportation Studies, Vol. 7,
China, October 2007.
 Medical Investigation of Motorcycle Accidents in Thailand. Journal of the Eastern Asian
Society
for Transportation Studies, Vol. 7, p. 147-162, , China, October 2007
 Efficacy of Safety Helmets for the Thai Motorcyclists. The 3rd National Transport Conference:
Sustainable and Safe Transportation Systems (SSTS), Khon Kaen University, 24 November,
2006,
Khon Kaen, Thailand. Paper ID: TS-08.
 Probability of Survival (PS): An Alternative Severity Assessment Approach in Road Safety,
Proceedings of International Conference of Road Safety in Developing Countries, Accident
Research Center, Bangladesh University of Engineering and Technology, 22-24 August, 2006,
Dhaka,
Bangladesh.
 A Framework of Injury Surveillance (IS) Database for the Developing Countries: A Thai
Experience from the Road Safety Perspective, Proceedings of International Conference of
Road
Safety in Developing Countries, Accident Research Center, Bangladesh University of
Engineering
and Technology, 22-24 August, 2006, Dhaka, Bangladesh.
 A Study on Pedestrian Accidents Based on the Injury Surveillance (IS) Data: Thailand’s Case,

34. Before September 11, 2001 September 11, 2001

35. Benefits of Accident Prediction Models
Identify Factors and Conduct Cost-Benefit
Establishing relationships Analysis for New and
between crashes and Existing Road Projects
Explanatory Variables
Understanding Change Predict Accidents
in Traffic Parameters in APM in Real-time and
Facilitate Proactive
Pre & Post Crash
Situations Safety Measures
May be used as an Predict Seasonal Eliminate Black-
input in Road and Env. Variation Spots from Future
Maintenance Projects of Accidents Road Projects
A POWERFUL TOOL WITH MULTIFARIOUS BENEFITS ...

36. Previous Models at a Glance

37. Research Demand and Questions
Demand:
 Traffic and environmental data require to be
obtained in a more accurate way
 Time of accident need to be detected
accurately
Research Questions
 What are the crash precursors or variables that can
explain the probability of accidents in real time?
 Can a traffic condition in relation to environmental
conditions be anticipated as dangerous enough to lead
to crash? If yes then how?
 What are the performance variations among a
static, dynamic and a real time accident prediction
models?