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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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 2014. 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.

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

Crashes Involving Pedestrian Introduction Data Procedure ArcMap Tools Results Conclusion

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.

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

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.

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

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

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

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

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

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 18.5619 0.0292 Crash Type 16 83.0073 <.0001 Collition Type 8 52.8431 Weather Condition 7 22.7337 0.0019 Alcohol Involved Flag 1 8.4963 0.0036 Speed Involved Flag 9.9079 0.0016 Hit and Run Flag 13.1507 0.0003 Introduction Data Procedure ArcMap Tools Results Conclusion

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

Questions?