An Exploration of Bicycle-Motor Vehicle Crash Types and Causes ( Portland-Metro, Oregon)

1. An Exploration of Bicycle-Motor Vehicle Crash Types and Causes ( Portland-Metro, Oregon) Sam Monsef, EIT March 2013 Sam.monsef@ch2m.com

2. Outline Introduction Literature Review Data Source Crash Analysis Application of the HSM Predictive Method Summary of Findings Future Work

3. Introduction • While gasoline prices are rising and urban areas are growing… • Bicycling is becoming a more attractive mode of transportation and a promising transportation alternative • Affordable • Good for health • Good for environment • Some times could be the fastest mode of transportation • Bicyclists are more likely to be involved in a severe or fatal crash Introduction Literature Review Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

4. Introduction • Some Facts • Health • By 2017, Portland, Oregon residents will have saved $64 million in health care costs thanks to bicycling (Gotsochi, T, 2011) • Bike commuters report lower stress, more relaxed than car commuters (Appleton, M, 2011) • Environment • Philadelphia reports 47,450 tons of CO2 reduced (estimated daily bicycle trips=260,000 miles) Introduction Literature Review Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

5. Introduction • Some Facts • Safety • National Highway Traffic Safety Administration (NHTSA) • 2665 Bicycle-motor vehicle (BMV) fatal crashes between 2007-2010 • 41 fatal crashes in Oregon (approximately 2% of nations total crashes) • 1.82 pedal cyclist fatalities per million population in Oregon Introduction Literature Review Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

6. Introduction Research Objective • Explore bicycle crashes to find: • Crash trends • Major crash types and their cause • Hot spots • Review the Highway Safety Manual (HSM) to determine how well calibrated models estimate BMV crashes at intersections Introduction Literature Review Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

7. Literature Review • Major focus areas: • Severity of injuries resulting from BMV crashes • Bicycle safety tools • Possible future research areas: • Evaluation of different treatments • Prediction models Introduction LiteratureReview Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

8. Literature Review Severity of Injuries Resulting from BMV Crashes • Regression models e.g. (MNL, MXL, OP) • Factors contributing to severe injuries (i.e., fatal, incapacitating injury) • Alcohol consumption was a robust factor (Kwigzile et al.) • Elder bicyclists tend to have higher risk of severe injury (Bil et al.) • Speed (Kim et al.) • Intersections (Wang et al.) Introduction LiteratureReview Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

9. Literature Review Bicycle safety tools • PBCAT • FHWA & NCHSRC (March 2000) • Bicycle and Pedestrian crash analysis • Series of questions to develop a unique crash type (Cross and Fisher 1977) • Second version (June 2006) • Countermeasures for each crash type Introduction LiteratureReview Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

10. Literature Review Bicycle safety tools • Bicycle RSA Guidelines and Prompt Lists • FHWA (2012) • Identify potential safety issues • Possible countermeasures • Prompt list includes 12 major areas Introduction LiteratureReview Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

11. Literature Review FHWA Bicycle Road Safety Audit Prompt List (www.fhwa.dot.gov) Introduction LiteratureReview Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

12. Literature Review Bicycle safety tools • BIKESAFE • Bicycle Countermeasure Selection System (FHWA 2006) • Provides information about improving bicycle safety • Two interactive matrices • Objective Matrix • Crash Matrix Introduction LiteratureReview Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

13. Data Source • Bicycle Crash Data • Oregon Department of Transportation (ODOT) • Crash data between 2007-2010 (Most recent) • GIS Data • ODOT ( Geocoded bicycle crashes) • Metro (Road geometry, traffic information) • Regional Land Use and Information System (RLIS) Introduction Literature Review DataSource Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

14. Data Source • Limitations • Unreported Crashes • Only bicycle motor vehicle crashes are included • Bicycle collisions with obstacles in the street are not included • Bicycle-only falls are not included • Collision with other cyclists are not included • Bicycle Usage data Introduction Literature Review DataSource Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work

15. Crash Analysis Why crash analysis? • Evaluate performance of a facility • Improve safety by identifying crash patterns, reduce number of crashes • Adopt suitable countermeasures • Common safety practice What is crash analysis? • Method to identify important factors that result in a collision • Locate hot spots • Categorize crash trends Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

16. Crash Analysis • Methods for Crash Analysis: • Data Analysis • E.g. (Crash frequency, time and day of crash, age and gender, environmental conditions) • Spatial Analysis • E.g. (Hot spots, density) Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

17. Crash Analysis • Oregon BMV Crashes (2007-2010) • 3163 BMV Crashes • 41 Fatalities • 243 Incapacitating Injury Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

18. Crash Analysis • Forecasting Bicycle Trips in Oregon • Used a methodology from the Durham Comprehensive Bicycle Transportation Plan, 2006 Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

19. Crash Analysis • Portland-Metro BMV Crashes (2007-2010) • 1602 BMV Crashes • 14 Fatal Crashes • 127 Incapacitating Injuries Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

20. Crash Analysis • Portland-Metro Serious BMV Crashes (2007-2010) • 1602 BMV Crashes • 14 Fatalities • 127 Incapacitating Injuries Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

21. Crash Analysis FAT: Fatal INJ A: Incapacitating Injury INJ B: Non-Incapacitating Injury INJ C: Possible Injury PDO: Property Damage Only Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

22. Crash Analysis BMV Crashes by Month of the Year (Portland-Metro 2007-2010) Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

23. Crash Analysis BMV Crashes Risk Ratio by Month of the Year (Portland-Metro 2007-2010) • Bicycle counts obtained for Hawthorn bridge • Assumed these volumes generally replicate overall metro area volumes • Calculated risk ratio by (%crashes/%volume) • Values greater than 1=higher risk of crashes Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

24. Crash Analysis BMV Crashes by Day of the Week and Time of Day (Portland-Metro 2007-2010) Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

25. Crash Analysis BMV Crashes Risk Ratio by Day of the Week and Time of Day (Portland-Metro 2007-2010) Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

26. Crash Analysis BMV Crashes by Age and Gender (Portland-Metro 2007-2010) Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

27. Crash Analysis BMV Crashes by Environmental Condition (Portland-Metro 2007-2010) Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

28. Crash Analysis • Top 5 Highest BMV Crashes by City • Portland • Gresham • Hillsboro • Beaverton • Tigard Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

29. Crash Analysis • Top 5 Highest BMV Crashes by Neighborhood • Buckman • Downtown • Hillsboro • Hosford-Abernethy • Lloyd District Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

30. Crash Analysis • High Risk Segments and Intersections • Segments • NW Broadway St • SE Hawthorne Blvd • NE Sandy Blvd • SW Madison St • SE Division St • Intersections • SW Capital Hwy and Vermont • NE Broadway and Williams • NW Broadway and Couch St • N Broadway and Wheeler • NW Lovejoy and 9th Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

31. Crash Analysis BMV Crashes by Crash Cause Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

32. Crash Analysis • BMV Crashes by Crash Type • Important to study the actual direction of impact • Recommend best countermeasure • ODOT reports: • Crash type • Crash cause and event • Direction of movement • Crash typing based on available information • Many unknown information from data • Cross and Fisher (36 crash types in 7 groups) Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

33. Crash Analysis BMV Crashes by Crash Type (Portland-Metro 2007-2010) Before break down: Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

34. Crash Analysis BMV Crashes by Crash Type (Portland-Metro 2007-2010) After break down: Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

35. Crash Analysis BMV Crashes by Crash Type (Portland-Metro 2007-2010) After break down: Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

36. Crash Analysis BMV Crashes by Crash Type (Portland-Metro 2007-2010) Left Cross Right Hook Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

37. Crash Analysis BMV Crashes by Road Characteristics • 33% of BMV intersection related crashes were fatal crashes (NHTSA 2010) • 70% of BMV crashes were intersection related (Portland-Metro2007-2010) • Approximately 65% of BMV intersection crashes were serious crashes Introduction Literature Review Data Source CrashAnalysis Application of HSM Predictive Method Summary of Findings Future Work

38. Application of HSM Predictive Method The Highway Safety Manual • The thought process started in January 1999 • Felt the need of having a document to quantitatively estimate “safety” • 1st version published in 2010 as an AASHTO document • Provides tools to conduct quantitative safety analysis Goal Reduce crashes and crash severity through the comparison and evaluation of alternative treatments and design of roadways Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

39. Application of HSM Predictive Method Why Predict Crashes? • What happens if we • Add a bike signal? • Add a bike lane? • Widen the shoulder? • Add a roundabout? • How will these changes effect safety? • Example: Roundabout • 48% reduction in all crashes (FHWA) • 78% reduction in a fatal/injury crashes (FHWA) Average Economic Cost (National Safety Council 2010) Death = $1,410,000 Nonfatal Disable Injury = $70,200 Property Damage Crash (including non-disabled injuries) = $8,900 Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

40. Define Roadway Limits Select first or next year of evaluation Sum all sites and year Define period of study Is there an alternative design, treatment or forecast AADT to be evaluated? Select and apply SPF • Determine AADT and availability of crash for every year in the period of interest Apply CMF’s Compare and evaluate results Apply a calibration factor Determine Geometric Conditions Is there another year? YES • Divide roadway into individual roadway segment And intersections Apply Site-specific EB method (if applicable) Assign observed crash site (if applicable) YES Is there another site? Select a roadway segment or intersection Apply project-level EB method (if applicable) Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

41. Application of HSM Predictive Method Part C - Predictive Method • BMV intersection crashes were the primary focus Investigate • Where: Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

42. Application of HSM Predictive Method Part C - Predictive Method • The number of vehicle-bicycle collisions per year for an intersection is estimated as: • Where: (Dixon, Monsere, Xie, & Gladhill 2012) Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

43. Application of HSM Predictive Method Methodology • 26 random intersections • Chosen intersections experienced at least 1 BMV crash Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

44. Application of HSM Predictive Method Methodology • Additional information needed • AADT • Geometric conditions, for example: • Number of approaches with left-turn lanes • Type of left-turn signal phasing • Presence of intersection red light cameras • Number of bus stops within 1000 ft of the intersection • Number of schools within 1000 ft of the intersection Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

45. Application of HSM Predictive Method Methodology • Google earth was used to collect some of the mentioned information • GIS was used to find number of bus stops, schools and alcohol establishment Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

46. Application of HSM Predictive Method Methodology • Annual Average Daily Traffic (AADT) • Powell Blvd (AADT available online, ODOT website) • Used GIS to find AADT for the areas of interest • Metro developed a shapefile in 2005 which had attributes such as ADT, capacity, and v/c ratio • Used growth factor to estimate AADTs for years 2007-2010 • Gresham's Automatic Traffic Recorder (ATR) Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

47. Application of HSM Predictive Method Methodology Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

48. Application of HSM Predictive Method Methodology Introduction Literature Review Data Source Crash Analysis ApplicationofHSMPredictiveMethod Summary of Findings Future Work

49. Application of HSM Predictive Method • All predictions were close to zero • Highest prediction: • 0.5 crash in 4 years • R-Square approximately zero • Conclusion: • No relationship between predicted and observed crashes Why? Introduction Literature Review Data Source CrashAnalysisApplication of HSM Predictive Method Discussion Future Work

50. Summary of Findings The HSM • HSM focused more on motorized vehicles • Ped and bike crashes account for only a portion of total vehicle crashes • Regression models built from 3 main states: (California, Maine and Washington) • Even though “Fbike” factor was locally calibrated, it does not appear to be robust to estimate BMV crashes as a proportion Introduction Literature Review Data Source Crash Analysis Application of HSM Predictive Method Summary of Findings Future Work