1. Department of Civil Engineering University of Washington Quantitative Safety Analysis for Intersections on Washington State Two-lane Rural Highways Master’s Thesis Defense Ngan Ha Nguyen 8/15/2007

2. 2 Overview  Introduction  Study Routes and Data  Methodology  Data Analysis  Accident Risk Modeling  Conclusions and Recommendations

3. 3 Average Comprehensive Cost by Injury Severity  Improving traffic safety is an important task Introduction: Traffic Accidents  Traffic accidents are leading causes of death  Huge economic loss to the society Leading Causes of U-I Deaths, U.S., 1969-2005

4. 4 Introduction: National Statistics  Rural fatal accident rate is more than twice as high as urban fatal accident rate

5. 5 Introduction: National Statistics  More than 1 death per hour in accidents at intersections

6. 6 Introduction: Washington State Stats  4.5% increase in total accidents from 2004 to 2005 Fatal and Disabling Accidents 56% 44% Total annual VMT. 25% 75% Two-lane rural highways Others

7. 7 Introduction: Objective  Analyze causal factors of intersection accidents  Identify cost-effective solutions for intersection safety improvements

8. 8 Overview  Introduction  Study Routes and Data  Methodology  Data Analysis  Accident Risk Modeling  Conclusions and Recommendations

9. 9 Study Routes and Data : Collecting  Three sources: Highway Safety Information System (HSIS) WSDOT Office of Information Technology WSDOT online tool, State Route Web (SRWeb)  Six years data ( 1999 -2004) Roadway data Accident data Traffic data Intersection data  141 state routes

10. 10 Study Routes and Data : preliminary steps  Focus on 3-legged and 4-legged intersections  Classify manually based on SRWeb.  Link intersection file to roadway files: Roadway characteristic file, Curvature file Gradient file  Complicated process  not applicable for all 141 state routes  select six representative study routes

11. 11 Study Routes and Data : six study routes  Two criteria Route length Geographic location and spatial alignment

12. 12 Overview  Introduction  Study Routes and Data  Methodology  Data Analysis  Accident Risk Modeling  Conclusions and Recommendations

13. 13 Methodology: Data Organization  Intersection approach section: XsXs XsXs Increasing milepost direction Increasing approach Decreasing approach

14. 14 Methodology: Data Organization  Determining “intersection section” by using “Stopping Sight Distance” (SSD): V = Approach speed, fps ( feet per second) t = Perception/reaction time ( typically 1 sec) d = Constant deceleration rate, fps 2 (feet per second square) t = 1 sec d =10 ft/sec 2

15. 15 Methodology: Data Organization  Entity-Relationship (E/R) Diagram  Microsoft SQL Server are used to manage and query data

16. 16 Methodology: Hypothesis testing  Test whether a variable has a significant impact on accident rate T-test  testing variable has 2 groups F-test (ANOVA)  testing variable has more than 2 groups

17. 17 Methodology: Modeling  Nature of accident data: Discrete Non-negative Randomly distribute  Poisson model λ i is the expected accident frequency X i is a vector of explanatory variables β is a vector of estimable coefficient

18. 18  Over-dispersion problem: mean not equal variance  Negative binomial model:  Over-dispersion parameter : select between Poisson model and negative binomial model Methodology: Modeling EXP(εi) is a gamma-distributed error term with mean 1 and variance α 2

19. 19 Methodology: Modeling  Parameters estimation using log-likelihood functions: Poisson model Negative binomial model n i : number of accident happened during 6 consecutive study years λ i :expected accident frequency in 6 years  : over-dispersion parameter

20. 20 Methodology: Modeling  Goodness of Fit: The likelihood ratio test statistic is Sum of model deviances The ρ-statistic

21. 21 Overview  Introduction  Study Routes and Data  Methodology  Data Analysis  Accident Risk Modeling  Conclusions and Recommendations

22. 22 Data Analysis: Preliminary Analysis

23. 23 Data Analysis: Statistical Analysis t-test

24. 24 Data Analysis: Statistical Analysis t-test

25. 25 Data Analysis: Statistical Analysis F-test

26. 26 Data Analysis: Statistical Analysis F-test

27. 27 Overview  Introduction  Study Routes and Data  Methodology  Data Analysis  Accident Risk Modeling  Conclusions and Recommendations

28. 28 All-type Accident Risk Modeling  Negative binomial model applied  Over-dispersion parameter is significant  Model:

29. 29 All-type Accident Risk Modeling  Result:

30. 30 All-type Accident Risk Modeling  Goodness of fit:

31. 31 Strike-At-Angle Accident Risk Modeling  Negative binomial model applied  Over-dispersion parameter is significant  Model:

32. 32 Strike-At-Angle Accident Risk Modeling  Result:

33. 33 Strike-At-Angle Accident Risk Modeling  Goodness of fit

34. 34 Overview  Introduction  Data Processing  Methodology  Data Analysis  Accident Risk Modeling  Conclusions and Recommendations

35. 35 Conclusions: 1. Reduce speed limit at the intersection 2. Put more signage ahead of the intersections 3. Increase shoulder width (greater than 6 feet) around the intersection area 4. Keep the shoulder width consistent along the intersection sections 5. Decrease the degree of curvature at the intersection locations 6. Decrease the slopes (less than 5%) along the intersection area

36. 36 Recommendations  Negative binomial model is chosen over Poisson model for modeling accident frequency  Before-and-after studies on safety at intersections that have traffic control device or feature illumination installed are needed  More data: Crossing roads Human activity Detailed intersection layout

37. 37 Ngan Ha Nguyen nganhanguyen@gmail.com