1. Portland Crash Severity Analysis for 2013-2014
Department of Civil Engineering
Semester Project Oral Presentation
Portland Crash Severity Analysis for
CEE 5190 / 6190 – GIS for Civil Engineers
April 25, 2017
Students:
Hossein Nasr-Isfahani
Sohrab Mamdoohi
Ibrahim Ahmed
Instructor:
Dr. Horsburgh

2. Introduction Crashes are causing serious social and economic losses
In 2015, 410 fatal crashes and 28,647 non-fatal injury crashes were reported in the State of Oregon.
Many studies tried to describe factors which make crashes more probable and to model their relationship(Kaplan, Sigal et. Al (2014)).
Contributing Factor
Weather
Road Geometry
Road Surface Condition
Lighting Condition
Type of Crash …
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

3. Data City of Portland boundaries shapefile
Crash dataset for 2013 and 2014 in the State of Oregon
Downloaded from the Oregon Department of Transportation (ODOT) website.
Containing several features:
Time
Location
Crash Type
Road Surface Condition
Lighting Condition
Number of Fatalities …
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

4. Objectives Determination of areas with high density of crashes
Predicting for future
Crash severity modeling
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

5. To address this problem:
Extraction of crashes happened in the City of Portland
Number of crashes in 2013: 11226
Number of crashes in 2014: 11176
Data categorization based on:
Month
Season
Crash Types (Pedestrian, Pedal Cyclists, and Fixed Objects)
Road Surface Condition (Dry, Wet, Sown, Ice)
Determination of the areas with the high density of crashes
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

6. The primary software used: ArcMap 10.4.1
Main Tools Used:
Selection (Select by Location and attribute)
Kernel Density
Raster Calculator
Microsoft Excel: Creation of Plots
SAS Studio: Modeling
The main tools provided in arc map that were used in this work are:
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

7. Separated the crash data per month for Portland City
Selection by month
Separated the crash data per month for Portland City
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

8. Selection by season Winter ( December, January, and February) Spring
(March, April, and May)
Summer
( June, July, and August)
Fall
(September, October, and November)
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

9. Kernel Density
Calculate density of the feature in a neighborhood around these features
The surface value is highest at the location of the point and diminishes with increasing distance from the point until it reachs Zero at the search radius
The diameter of the circle is defined = 10m
Max Surface Value
Min Surface Value
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

10. Raster Calculation
Define Threshold for the high densities values = 160
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

11. Raster Calculation
Add Raster Calculation of Summer 2013 and Summer 2014, etc.
Occurred two years
Occurred one year
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

12. Total Crashes with Regards to Season
Highest number of crashes: Fall
0.44% increase in total number
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

13. Total Crashes with Regards to Season
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

14. Crash Density in Summer 2014
Majority of crashes: along the highways
I-05 and I-405
For instance, in summer 2014, the majority of crashes happened along the highways which provide an enhance corridor for people to drive with higher velocity.
Kernel Density function was used to build density maps based on our crash points. The density maps are created for each years separately, and also for each season. In all seasons, we observed a different pattern for crash distribution within the city; however, some main roads have constantly spotted to host the greatest number of crashes. For instance I-5 and I-405 are where crashes are highly concentrated.

15. Comparison Between Seasonal Crash Patterns
A great proportion of crashes do not take place every year
The most hazardous places are along the highways and intersections
To make a comparison between seasonal crash patterns, similar approach to H. Lloyd et. al was used [2]. Using raster calculations, locations were identified that have the density greater than or equal to 160. This number was approximately our middle category highest value which seems to be a good breaking point for determination of hazardous area. Unlike H. Lloyd et. al who used their last category as their high density level, in this paper due to lowering the radios which increases the accuracy, the mentioned approach was used. A similar symbology was uses for each density raster. Our maximum density range was determined by selecting the middle category to be the exclusive lower bound. Raster calculator was used to create a raster whose cells get the values of 1 if they experience a density within maximum density range and zero otherwise. Using raster calculator, the summation of two raster was possible. The outcome marks places that possess the same pattern in two successive years with the value of 2 and the remaining with ones and zeroes. This process was used for each of four season for 2013 and In this process we tried to minimize the effect of randomness which is intrinsic part of crashes.
Maps of our analysis showing comparisons between two years, either totally or seasonally, can be found in Appendix A. From these maps, it can be concluded that a great proportion of crashes doesn’t take place every years. Seasonal comparison maps also indicate the same things. These maps point out that the most dangerous places are along the highways, intersections of main roads with other low volume ones, and intersections with at least one angle more than 90 degree. During past years, ODOT is trying to exploit ITS to make roads safer.

16. Crashes Involving Pedestrian
Highest number of crashes: Winter
Lowest number of crashes : Summer
17% increase in total number
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

17. Crashes Involving Pedestrian
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

18. Density Map for Pedestrian Crashes in 2014
Majority of crashes: Portland Downtown
For instance, in summer 2014, the majority of crashes happened along the highways which provide an enhance corridor for people to drive with higher velocity.
Kernel Density function was used to build density maps based on our crash points. The density maps are created for each years separately, and also for each season. In all seasons, we observed a different pattern for crash distribution within the city; however, some main roads have constantly spotted to host the greatest number of crashes. For instance I-5 and I-405 are where crashes are highly concentrated.

19. Crashes Involving Pedal Cyclists
. Then, using select by location, the crash data for the City of Portland was extracted from that of the State of Oregon
Highest number of crashes: Summer
Lowest number of crashes: Winter
2.4% decrease from 2013 to 2014
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

20. Density Map for Pedal Cyclists Crashes in 2014
Majority of crashes: East Portland
More bike facilities
For instance, in summer 2014, the majority of crashes happened along the highways which provide an enhance corridor for people to drive with higher velocity.
Kernel Density function was used to build density maps based on our crash points. The density maps are created for each years separately, and also for each season. In all seasons, we observed a different pattern for crash distribution within the city; however, some main roads have constantly spotted to host the greatest number of crashes. For instance I-5 and I-405 are where crashes are highly concentrated.

21. Crashes with Fixed Objects
. Then, using select by location, the crash data for the City of Portland was extracted from that of the State of Oregon
Highest number of crashes: Winter
9% decrease from 2013 to 2014
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

22. Crashes Categorized by Road Surface Condition
It seems that the number of crashes in snow or ice condition must be higher; however, since there are little snow and cold periods in Portland, the number of crashes are almost unnoticeable for these categories. Additionally, people in harsh situation try to drive more safely. Maps showing a comparison between wet and dry category for 2013 and 2014 can be seen in Appendix B.
Highest number of crashes: Dry
Lowest number of crashes: Snow
In harsh situation, people try to drive more safely
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

23. Time Series Analysis Using the data for years from 2011 to 2014
Through SAS’s ARIMA Procedure
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

24. Time Series Analysis After fitting time series model Introduction Data
Procedure
ArcMap Tools
Results
Conclusion

25. Time Series Analysis Comparison among 2013, 2014, and 2015
Introduction
Data
Procedure
ArcMap Tools
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Conclusion

26. Crash Severity Analysis
Our response variable is:
Using Logistic Regression Method
𝑌 =1 𝑖𝑓 𝑡ℎ𝑒𝑟𝑒 𝑒𝑥𝑖𝑠𝑡 𝑓𝑎𝑡𝑎𝑙𝑖𝑡𝑖𝑒𝑠 𝑜𝑟 𝑖𝑛𝑗𝑢𝑟𝑖𝑒𝑠. 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Type 3 Analysis of Effects
Effect DF
Wald Chi-Square
Pr > ChiSq
Direction from Intersection 9
0.0292
Crash Type 16
<.0001
Collition Type 8
Weather Condition 7
0.0019
Alcohol Involved Flag 1
8.4963
0.0036
Speed Involved Flag
9.9079
0.0016
Hit and Run Flag
0.0003
Introduction
Data
Procedure
ArcMap Tools
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Conclusion

27. Conclusion
Attempting to investigate some of contributing factors to crashes
Using the time series approach to predict for the future
Modeling crash severity
Introduction
Data
Procedure
ArcMap Tools
Results
Conclusion

28. Questions?