1. Prioritization Techniques to Evaluate Sites for Improving Winter Safety in Iowa Mohammad Shaheed Zach Hans Konstantina Gkritza Inya Nlenanya Neal Hawkins 2013 Mid-Continent Transportation Research Symposium Ames, Iowa 16 th August, 2013 1

2. Acknowledgement Tina H. Greenfield (Iowa DOT) Annette Dunn (Iowa DOT) Bob Younie (Iowa DOT) Mid-America Transportation Center (MATC) 2

3. Presentation Outline Background Objective Methodology Analysis Results Conclusions 3

4. Background Winter-weather related crashes –Hundreds of fatalities/injuries on Iowa’s roadways –About 1/3 of the total crashes occurred during 2004/2005 to 2011/12 winter seasons –Half of the rural interstate crashes related to weather Transportation agencies spend millions for winter weather maintenance and operations No systematic method to identify critical winter weather crash locations 4

5. Objective Identify candidate locations for improving winter weather safety in Iowa –Using winter weather crash data from 2002-2009 –For different types of roadways –Using different metrics for prioritization 5

6. Methodology Evaluation of Winter-weather prone sites based on –Crash density –Crash proportion –Crash severity –Combined metric –Standard deviation based analysis –Moving average analysis 6

7. Crash Density Primarily crash frequency per mile per year Derived for each road segment –Dividing total crash frequency by total road segment and number of years Normalized Crash density –Each individual density divided by the maximum value –For common type of roadway 7

8. Crash Proportion Derived for each road segment –Total winter crashes divided by the total crashes on a road segment for the analysis period –Already normalized with a maximum value of 1.0 –No further normalization necessary 8

9. Crash Severity Takes severity into consideration A total score assigned to each road network Normalized score used for each road segment 9

10. Combined Metric Computed the total score –Normalized scores each given a one-third (1/3) weight Less possibility of misrepresentations of winter crash experience for a single road segment 10

11. Categorization of Each metric Each metric categorized within the appropriate road type and analysis period The derive ranges include five categories 11

12. Standard Deviation(STDEV) Based Analysis Standard deviation of the metrics were computed –For each road type and analysis period Divide the total score by the standard deviation –To examine if the combined metric was within a certain range of the standard deviation Rank the segments based on the values 12

13. Moving Average Analysis Moving average of the combined metric –3 mile road segment –Conducted on I-29, I-35, I-80, I-380, US-20 –To consider the spatial proximity of the road segments in the ranking process 13

14. Results: Crash Density (Freeway) 14

15. Results: Crash Density STDEV based Analysis 15

16. Results: Crash Proportion (Freeway) 16

17. Results: Crash Proportion STDEV based Analysis 17

18. Results: Crash Severity (Freeway) 18

19. Results: Crash Severity STDEV based Analysis 19

20. Results: Combined Metric (Two-Lane) 20

21. Results: STDEV Based Combined Metric (Freeway) 21

22. Results: STD Based Combined Metric (I-35 N) 22

23. Results: Standard Deviation Based Combined Metric (I-80 E) 23

24. Results: STD Based Combined Metric (Two Lane) 24

25. Results: STDEV Based Combined Metric (Two Lane) 25

26. Ranking Based on Combined Metric (Two Lane) 26

27. Results: Moving Average Analysis 27

28. Results: Moving Average Analysis 28

29. Results: Moving Average Analysis (I-35 N) 29

30. Results: Moving Average Analysis (I-80 E) 30

31. Conclusions Results can be helpful to identify problematic locations during winter weather conditions Useful for screening road networks for winter safety improvement Moving average analysis was performed to take into account the spatial proximity of the road segments Combined metric is useful to offset the limitations of each individual metric 31

32. Future Research Research underway to develop safety performance functions for winter weather related crashes –Incorporation of winter weather conditions –Empirical-Bayes adjusted crash frequency can be used to screen road networks 32