Bicycle Crash Analysis and Review of Trends City of Chicago, 2005 to 2010 Final Report Siim Sööt, Ph.D. Lu Gan Piyushimita (Vonu) Thakuriah, Ph.D. - Project Lead University of Illinois at Chicago Melody Geraci Patrick Knapp Active Transportation Alliance Charlie Short Chicago Department of Transportation November, 2012 This report was generously funded through a grant from the Illinois Department of Transportation, and by the City of Chicago.
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iii Disclaimer The analysis and views presented in this report are the sole responsibility of the authors. Acknowledgements The research team is indebted to the help given by, Lori Midden of the Illinois Department of Transportation, Parry Frank of the Chicago Metropolitan Agency for Planning, Tracie Smith of the Children s Memorial Hospital, Chicago, and Chrystal Price of the American College of Surgeons. We are grateful to William Vassilakis, formerly at the University of Illinois at Chicago and Dr. Caitlin Cottrill, Postdoctoral Research Associate at Massachusetts Institute of Technology National University of Singapore for their help with the project.
iv Table of Contents Chapter 1: Key Findings... 1 Chapter 2: Introduction, Report Objectives and Organization of the Report... 3 2.1: Introduction... 3 2.2: Objectives of the Report... 3 2.3: Organization of the Report... 4 Chapter 3: Background and Overview of the Bicycling Environment... 5 3.1: Trends in Bicycle Use... 5 3.2: National Trends in Bicycle Safety... 6 3.3: Bicycle Safety in Chicago... 7 3.3.1: Chicago Bicycle Safety Trends... 7 3.3.2: Chicago Bicycle Crashes Compared with Crashes for Other Modes... 9 3.4: Chicago versus Suburban Chicago and the Rest of Illinois... 10 3.5: Peer City Comparison of Cycling to Work and Crashes... 12 3.6: Comparison with Other Large Cities... 15 Chapter 4: Characteristics of Cyclists Involved in Crashes... 18 4.1: Bicycle Use and Safety Trends by Age and Gender... 18 4.2: Education Level of Cyclists... 25 Chapter 5: Vehicles and Operators Involved in Bicycle Crashes... 26 5.1: Alcohol and Bicycle Crashes... 26 5.1.1: Blood Alcohol Content of Motorists... 26 5.1.2: Blood Alcohol Content of Cyclists... 27 5.1.3: Hit-and-Run Crashes... 28 5.2: Age and Gender of Motorists... 29 5.2.1: Age of Motorists... 29 5.2.2: Gender of Motorists Involved in Bicycle Crashes... 29 5.3: Vehicle Type and Use... 30 5.4: Driver and Vehicle Maneuvers... 33
v 5.5: Dooring... 35 5.6: Bicyclist Activity... 36 5.6.1: Bicyclist Action... 36 5.6.2: Bicyclist Location... 38 5.6.3: Bicyclist Helmet Use... 39 Chapter 6: Environmental Factors and Road Conditions... 40 6.1: Environmental Factors during Crashes... 40 6.1.1: Weather-Related Factors... 40 6.1.2: Light Conditions... 41 6.1.3: Weather-Related Road Surface... 42 6.2: Roadway Environment... 42 6.2.1: Relation to Intersections... 42 6.2.2: Road Defects... 46 6.2.3: Roadway Type and Number of Lanes... 46 6.2.4: Roadway Classification... 48 6.2.5: Traffic Signal Control... 49 6.3: Work Zones... 52 Chapter 7: Temporal Distributions of Crashes... 53 7.1: Crashes by Quarter... 53 7.2: Bicycle Crashes by Month... 54 7.3: Crashes by Day of Week... 56 7.4: Time of Day... 58 7.4.1: Fatal Crashes by Time of Day... 58 7.4.2: Injury Crashes by Time of Day... 59 7.4.3: Dooring Crashes... 59 7.4.4: Fatal and Injury Crashes by Hour... 60 7.5: Special Events... 62 Chapter 8: Spatial Distribution... 64 8.1: Overall Spatial Distribution of Crashes... 64
vi 8.2: Chicago Community Areas... 66 8.2.1: Highest and Lowest Number of Bicycle Crashes... 69 8.2.2: Per Capita Crashes: Mapped by Community Areas... 71 8.3: Hotspots... 72 8.4: Major Crash Corridors... 75 8.5: Major Arterial Hotspots... 76 8.6: Dooring Crashes... 76 8.7: Land Uses near Crash Locations... 78 8.7.1: Schools and Universities... 78 8.7.2: Central Business District... 82 8.7.3: Residential Non-Central Business District... 82 Chapter 9: Summary and Limitations of the Study... 85 9.1: Study Summary... 85 9.2: Limitations... 85 Technical Appendix A: Data and Study Area... 87 A.1: Data... 87 A.2: Study Area and Peer Cities... 88 Technical Appendix B: Background--Bicycling Safety Trends and Literature... 91 B.1: Benefits of Bicycling... 91 B.2: Overall Transportation Safety Trends... 92 B.2.1: Trends in Bicycle Safety... 92 B.2.2: Chicago-Area Trends in Bicycling and Bicycle Crashes... 94 B.3: Risk Factors to Bicycling: A Review of the Safety Literature... 94 B.3.1: Crash Causation and Crash Risk... 95 B.3.1.1: Dynamics of Bicycle Crashes... 95 B.3.1.2: Risk Factors Contributing to Crash Involvement... 97 B.3.1.3: Exposure-Based Risk Estimation... 100 B.3.2: Crash Severity and Effects... 100 B.3.2.1: Factors Determining Crash Severity... 101
vii B.3.2.2: Type of Trauma and Extent of Injury... 101 B.3.3: Comparative Studies... 102 B.3.4: Data and Information Systems... 103 B.3.5: Crash Countermeasures and Evaluation... 104 References... 107
viii List of Tables Table 3-1: Bicycle crashes by type of injury and fatalities, City of Chicago, 2005-2010... 8 Table 3-2: Fatal and injury crashes in City of Chicago, 2005-2010... 9 Table 3-3: Fatalities in Illinois and indices by number of bicycle commuters and population 11 Table 3-4: Peer city fatality indices, 2005-2009... 14 Table 3-5: Peer city fatalities by gender, 2005 to 2010... 15 Table 3-6: Bicycling and walking as mode of transportation to work in major cities, 2010... 17 Table 4-1: Gender mix of bicycling... 18 Table 4-2: Chicago bicycling estimates, 2007... 19 Table 4-3: Gender of cyclists injured in bicycle crashes... 19 Table 4-4: Fatalities and injury crashes per 100 million miles of travel... 20 Table 4-5: Bicycling by age, miles and minutes per day, 2007*... 21 Table 4-6: Bicyclist fatalities by age and gender in City of Chicago, 2005-2010... 22 Table 4-7: Bicyclists injured by age and gender in City of Chicago, 2005-2010... 23 Table 5-1: Blood alcohol content of drivers involved in fatal bicycle crashes... 26 Table 5-2: Apparent physical condition of drivers in bicycle injury crashes... 27 Table 5-3: Blood Alcohol Content of bicyclists in fatal crashes, 2005 to 2010... 28 Table 5-4: Known age of driver involved in fatal crash... 29 Table 5-5: Gender of drivers involved in bicycle injury crashes... 29 Table 5-6: Vehicle type involved in bicycle injury crashes... 30 Table 5-7: Vehicle use during crash... 32 Table 5-8: Driver action in fatal crashes, City of Chicago, 2005-2010... 33 Table 5-9: Driver action in bicycle-vehicle injury crashes... 34 Table 5-10: Vehicle maneuver prior to bicycle injury crashes... 35 Table 5-11: All versus dooring bicycle crashes, by injury type... 36 Table 5-12: Bicyclist action in fatal crashes, 2005-2010... 36 Table 5-13: Bicyclist action in injury crashes... 37 Table 5-14: Bicyclist location in fatal crashes, City of Chicago, 2005-2010... 38 Table 5-15: Bicyclist location in bicycle injury crashes... 38
ix Table 5-16: Helmet use, 2005 to 2009... 39 Table 6-1: Weather conditions during bicycle crashes... 40 Table 6-2: Light conditions during bicycle crashes... 41 Table 6-3: Road surface conditions during bicycle crashes, 2005-2010... 42 Table 6-4: Bicycle injury crashes at intersections... 42 Table 6-5: Intersections with the greatest number of injury crashes... 44 Table 6-6: Road defects... 46 Table 6-7: Location-related factors for fatal bicycle crashes, 2005-2010... 46 Table 6-8: Roadway type... 47 Table 6-9: Number of travel lanes... 48 Table 6-10: Fatal and Type A crashes by roadway classification... 49 Table 6-11: All injury crashes by roadway classification... 49 Table 6-12: Traffic control device at fatal crashes... 50 Table 6-13: Traffic control device at injury crashes... 50 Table 6-14: Condition of traffic control device at fatal and injury crashes... 51 Table 6-15: Injury crashes in work zones, 2005-2010... 52 Table 7-1: Bicycle injury crashes by calendar quarter in Chicago, 2005-2010... 53 Table 8-1: Bicycle crashes and miles cycled in six community areas with the most crashes... 67 Table 8-2: Fifteen community areas with the highest number of injury crashes... 69 Table 8-3: 15 community areas with the lowest number of injury crashes... 70 Table 8-4: Dooring crashes compared to all injury crashes by major arterials, 2010... 77 Table A-1: CMAP s Travel Tracker Survey mode share for selected modes... 90 Table B-1: Bicycle crash literature categories... 95 Table B-2: Safety countermeasures and strategies used in cities and states... 106
x List of Figures Figure 3-1: Number of daily bicycle commuters, City of Chicago, 2000 to 2010... 5 Figure 3-2: Number of fatalities by selected transportation modes, 1995-2009... 7 Figure 3-3: Number of bicycle injury crashes per 100,000 population... 8 Figure 3-4: Comparison of pedestrian and bicycle injury crashes, 2005 to 2010... 10 Figure 3-5: Bicyclists as a percent of all daily commuters, peer cities, 2010... 13 Figure 4-1: Ratio of male to female injury rates... 24 Figure 4-2: Annual average injury crash rate per 100,000 residents... 24 Figure 5-1: Hit-and-run bicycle crashes, 2005 to 2010... 28 Figure 5-2: Number of SUVs involved in bicycle crashes... 31 Figure 7-1: Injury crashes by month and injury type, 2005 to 2010 total... 54 Figure 7-2: Fatal bicycle crashes by month, 2005-2010... 55 Figure 7-3: Fatal bicycle crashes by day of week, 2005-2010... 56 Figure 7-4: Injury crashes by type of injury and day of week... 57 Figure 7-5: Fatal bicycle crashes by time of day, 2005-2010... 58 Figure 7-6: Injury bicycle crashes by time of day, 2005-2010... 59 Figure 7-7: Dooring crashes, 2010-2011... 60 Figure 7-8: Fatal and Type A injury crashes by hour, 2005 to 2010... 61 Figure 7-9: Type B and C injury crashes by hour, 2005 to 2010... 62
xi List of Maps Map 6-1: Intersections with at least ten injury crashes... 43 Map 7-1: Bicycle injury crashes on the six Fourth of Julys from 2005 to 2010... 63 Map 8-1: Fatal and serious (Type A) injury crashes in City of Chicago, 2005-2010... 64 Map 8-2: Type B and C injury crashes in City of Chicago, 2005-2010... 65 Map 8-3: Fatalities and Type A injury crashes, 2005-2010... 66 Map 8-4: Fatalities and Type A crashes from 2005-2010 per 2010 population... 71 Map 8-5: Type B and C crashes from 2005-2010 per 2010 population... 72 Map 8-6: All injury crashes hotspots... 73 Map 8-7: Fatal and Type A injury crash hotspots... 74 Map 8-8: Major arterials of injury crashes... 75 Map 8-9: Non-intersection injury crashes... 76 Map 8-10: High school vicinities with injury crashes... 78 Map 8-11: Primary school hotspots... 80 Map 8-12: Primary school hotspots in the far west side... 81 Map 8-13: Downtown Type B and C injury crashes... 82 Map 8-14: North Side Type B and C injury crashes... 83 Map 8-15: Hyde Park / University of Chicago area Type B and C injury crashes... 84
1 Chapter 1: Key Findings CHICAGO BICYCLE CRASH SAFETY TRENDS, 2005-2010 1. Thirty-two cyclists were killed in crashes with motor vehicles from 2005 to 2010. 2. The number of fatal crashes decreased by 28 percent from seven in 2005 to five in 2010. 3. Almost 9,000 bicyclists incurred injury crashes during the six-year period. 4. The number of injury crashes increased from 1,236 in 2005 to 1,566 in 2010. WHERE DID CRASHES OCCUR: SPATIAL AND LOCATIONAL DIMENSIONS 5. Approximately 55 percent of fatal and injury crashes occurred at intersections. 6. A high number of crashes have occurred on or near major diagonal arterial streets including Milwaukee Avenue. 7. Six of the 77 community areas just north and northwest of the Loop accounted for onethird of the injury crashes but more than one-third of the bicycle miles. 8. The highest number of injury crashes was in West Town (just west of the Loop) followed by Near North Side and Logan Square. WHEN DID THE CRASHES OCCUR 9. The largest number of injury crashes occurred from 4:00 pm to 7:00 pm but fatalities were highest from 8:00 pm to midnight. 10. There were five fatalities from 4:00 pm to 7:00 pm but nine fatalities from 8:00 pm to midnight. 11. Approximately 45 percent of the fatal and injury crashes occurred during three summer months. 12. The great majority of crashes occurred during day light hours and in good weather. 13. Sundays accounted for the highest number of fatalities but the fewest number of injury crashes. CHARACTERISTICS OF CYCLISTS: GENDER AND AGE 14. Males were three times more likely to be involved in bicycle crashes than females overall, and in most age groups. 15. The ratio of male to female crashes was lowest in the 20-24 age group (1.98) but increased steadily with age. It was 12 times higher for males in the 75-84 age group. 16. The greatest number of miles cycled were logged by cyclists aged 25-34 but they had much lower crash rates than younger cyclists.
2 EXTENT OF CYCLING AND HOW HAS IT CHANGED 17. Since 2000, the number of bicycle commuters has increased by 150 percent. 18. Nationally 0.6 percent of workers commuted to work by bicycle in 2010. In Chicago, that percent was 1.3 percent (15,000 cyclists daily). 19. Among peer cities, Chicago has more bicycle commuters per capita than New York or Los Angeles, but fewer than Philadelphia and Seattle. COMPARING BICYCLE CRASHES WITH PEDESRIAN CRASHES, 2005-2010, 20. While the number of motor-vehicle crashes with pedestrians declined during the 2005-2010 study period, crashes involving bicycles increased. 21. Hit and run accounted for 25 percent of both injury and fatal bicycle crashes. It was much lower than pedestrian fatal and injury crashes, 41 and 33 percent respectively. CRASH CIRCUMSTANCES HELMET USE, TYPES OF MOTOR VEHICLES AND ALCOHOL 22. Helmets were known to be worn in only one fatal crash. 23. Cyclists are reported to have crossed against the traffic light in 20 percent of the fatal crashes but in only seven percent of the injury crashes. 24. Four in ten fatalities and injury crashes were due to motorists not yielding right of way. 25. Taxis (for-hire vehicles) were involved in one in twelve injury crashes. 26. Cyclists had blood alcohol content (BAC) over the legal limit in 22 percent of the fatal crashes.
3 Chapter 2: Introduction, Report Objectives and Organization of the Report The chapter begins with a brief overview of bicycle safety in Chicago from 2005 to 2010 followed by a description of the primary report objectives. The chapter concludes with a summary of the report organization. 2.1: Introduction Nationwide, bicycle fatality and injury rates have been declining along with most other forms of transportation. In the City of Chicago, motor-vehicle crashes have similarly been on the decline. Yet the recent history of bicycle injury crashes has been mixed with an overall increase in bicycle crashes from 2005 to 2010. This may be attributable to the large increase in cycling. These increases in both bicycling and injury crashes have necessitated the need to develop a series of safety strategies that address bicycle safety. Between 2005 and 2010, a total of 1,021 persons were killed in the City of Chicago in all crashes involving motor vehicles, including drivers, other motor-vehicle occupants, pedestrians and bicyclists. Total transportation fatalities declined 32 percent in the city during this period, with 191 persons killed in 2005 compared to 128 in 2010. The total number of persons injured in all crashes in the city declined from 25,831 in 2005 to 19,865 in 2010 (a decrease of about 23 percent). These decreases in the city reflect national trends. By contrast, during the same period, 32 bicyclists were killed in the City of Chicago. Bicycle fatalities during this period exhibit improvement over these six years, with seven fatalities in each of the first two years of the six-year period and five fatalities in each of the last three years. The lowest number of fatalities was in 2007 with three. The number of injured bicyclists, however, has increased from 1,236 in 2005 to 1,566 in 2010, with a high of 1,782 in 2007. Two additional statistics, however, show slightly more consistency in the modest trend of increasing bicycle injury crashes. First, bicycle crashes, as a percent of all crashes, have increased from less than seven percent to nearly ten percent. Second, the number of bicycle crashes per capita has increased from approximately 47 in 2005 to 62 in 2010. Regardless of the metric, there is evidence that bicycle crashes have increased during the study period. Still, bicycling (as measured by the number bicycle commuters) has grown more rapidly than any measure of the number of crashes. 2.2: Objectives of the Report The purpose of this report is to review trends in bicycle safety in the City of Chicago, and to identify ways to improve bicycle safety. The overall goal of the report is to provide a systematic assessment of the who, what and how of safety risks to bicyclists to support safety countermeasures and long-term bicycle planning activities. The report has two objectives. Objective 1: To analyze bicycle crashes in the City of Chicago. Our overall objective is to make a comprehensive presentation of bicycle crash trends in Chicago over the 2005-2010 period.
4 First, we have presented an overview of the bicycling usage and safety trends in the city and compared these to national trends and to those in peer cities. We have considered fatalities and injuries incurred. We have examined the characteristics of cyclists involved in crashes and that of the vehicles and vehicle operators. Environmental factors (weather and light conditions) and the characteristics of the roadway where crashes have occurred were then examined as well as road surface conditions. We then analyzed seasonal and time-of-day pattern of crashes, including crash patterns relating to special events. We also examined the spatial distribution of crashes by community area, corridors, location-type and identify hotspots where crashes have occurred. We used two types of data for the analysis: (A) safety data on crashes, injuries and trauma from the Illinois Department of Transportation (IDOT), National Highway Traffic Safety Administration (NHTSA) and the American College of Surgeons, and (B) travel trend data to identify patterns in bicycle use from the U.S. Census Bureau, National Household Travel Survey (NHTS) and the Chicago Metropolitan Agency for Planning (CMAP). More information regarding the data used is given in Technical Appendix A. Objective 2: To recommend a collection of strategies to improve bicycle safety that incorporates our summary and the crash data analysis. These are designed to assist in developing future courses of action regarding ways in which bicycle safety can be improved in the City of Chicago. To make the report self-contained as a study of bicycle crashes, we have undertaken a review of the published literature, as well as of policy and planning documents published by the Federal Highway Administration and state and metropolitan planning organizations to understand the types of activities that are being undertaken to improve bicycle safety. 2.3: Organization of the Report The report is organized as follows: Chapters 3 through 8 present the findings on bicycle safety trends. Chapter 9 presents a summary of the study and its limitations. The report has two technical appendices: Appendix A describes the data used and Appendix B presents additional background material and a literature review.
5 Chapter 3: Background and Overview of the Bicycling Environment This chapter begins the analysis of crash data. It examines the growth in Chicago and compares it to national data and information from other cities. 3.1: Trends in Bicycle Use Nationally there has recently been an appreciable increase in bicycling. Close to one percent of all trips reported for all trip purposes (including work, shopping, social trips) were by bike (NHTS, 2009) and the total number of bicycle trips increased from 1.7 billion annual trips in 2001 to four billion reported trips in 2009 (NHTS, 2009).The Census Bureau journey-to-work (commuting) data shows that bicycling to work has increased from 0.4 percent in 2000 to 0.53 percent in 2010 (U.S. Census Bureau, 2010). Bicycling has increased at a higher rate in Chicago than nationally. Using the same census data, the number of daily bicycle commuters in Chicago has risen in this millennium from just under 6,000 to over 15,000 (Figure 3-1). This increase of approximately 9,000 additional bike 4.0% Figure 3-1: Number of daily bicycle commuters, City of Chicago, 2000 to 2010 3.5% 3.0% 2.5% 2.0% 1.5% 1.3 % 1.0% 0.5% 0.0% New York Los Angeles Chicago Philadelphia Seattle Baltimore Milwaukee Source: ACS, 2000 to 2010
6 commuters were achieved during a period in which there was a decline, though modest, in the number of commuters residing in the city. Therefore, the rise in bicycling mode share was proportionately slightly higher, increasing from 0.5 percent to 1.3 percent of commuters, 2000 to 2010. This puts Chicago well above the national level in terms of commuting trips and higher than many other metropolitan areas. 3.2: National Trends in Bicycle Safety While bicycling has grown in popularity, the number of bicycle fatalities nationally exhibits a slight downward trend with a substantial decline during our study period. From 2005 to 2009, the national number of bicycle fatalities declined from 786 to 630 (Figure 3-2). This is a decrease of 20 percent. However, safety gains for all motor-vehicle crash fatalities were considerably higher, with a decline in fatalities among motor-vehicle drivers of 24 percent and among passengers of 31 percent. This difference may be attributable the 13 percent decline in highway passenger miles (http://www.bts.gov/publications/national_transportation_statistics) from 2005 to 2009, a period during which bicycle use has shown the opposite trend, i.e., has grown rapidly. Also important is the decline among pedestrian fatalities, 16 percent, though the decline is not as great as for the modes (driver and motor-vehicle passenger) cited above. It may be noted that the national population increased during the 2005-2009 period by 18 million people, making the decline in fatalities even more impressive. Although there are many important differences with respect to the sociodemographics of users and overall use patterns, perhaps the most direct comparison is with motorcycle fatalities. Both bicycle and motorcycle use has increased during the study period and both typically have just two wheels. During 2005 to 2009, motorcycle fatalities decreased by 2.5 percent but actually increased by 16 percent in three years (2005 2008) before the sharp one-year drop from 2008 to 2009. During the longer period, since 1995, however, there has been a dramatic 100 percent increase in the number of motorcycle fatalities. By contrast, bicyclist fatalities recorded approximately a 25 percent decline between 1995 and 2009.
7 Bicycle, pedestrian and motorcycle fatalities 6,000 5,000 4,000 3,000 2,000 1,000 0 Figure 3-2: Number of fatalities by selected transportation modes, 1995-2009 25,000 20,000 15,000 10,000 5,000 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Driver and motor vehicle passenger fatalities Motorcyclists Pedestrians Bicycle Drivers Passengers Source: FARS, 1995-2009, http://www-fars.nhtsa.dot.gov/main/index.aspx 3.3: Bicycle Safety in Chicago Nationally the number of bicycle fatalities has declined considerably but since the growth of cycling in Chicago has exceeded the national trend it is necessary to examine Chicago crashes with this in mind. Below we examine Chicago bicycle crashes and compare them to other modes. 3.3.1: Chicago Bicycle Safety Trends During the study period, bicycle fatalities in Chicago have declined but due to their small number it is difficult to point definitively to a solid trend. In the first two years of the study period, 2005 and 2006, there were seven fatalities (Table 3-1) in each year
8 versus five fatalities in each of the last three years (2008 and 2010). These numbers indicate a decrease of 28 percent, with the lowest number of three fatalities in 2007. Table 3-1: Bicycle crashes by type of injury and fatalities, City of Chicago, 2005-2010 Type 2005 2006 2007 2008 2009 2010 Total Fatalities 7 7 3 5 5 5 32 A* 127 186 178 159 162 149 961 B** 734 645 895 719 648 851 4492 C*** 375 554 709 628 576 566 3408 Total injuries 1236 1385 1782 1506 1386 1566 8861 * Type A Injuries: any injury other than fatal injury which prevents the injured person from walking, driving, or normally continuing the activities he/she was capable of performing before the injury occurred. Includes severe lacerations, broken limbs, skull or chest injuries, and abdominal injuries. ** Type B Injuries: Any injury, other than fatal or incapacitating injury, which is evident to observers at the scene of the crash. Includes bump on the head, abrasions, bruises, minor lacerations. *** Type C Injuries: Any injury reported or claimed which is neither of the above. Includes momentary unconsciousness, claims of injuries not evident, limping, complaint of pain, nausea, hysteria. Source: IDOT Motor Vehicle Crash Data The data on bicycle crash injuries are less symbolic of a clear trend. Although the numbers have increased overall from 2005 to 2010, injury crashes were fewer in 2010 than the peak year of 2007. Shown on a per capita basis, Figure 3-3 indicates the highest injury-crash level was in 2007, the same year with the lowest number of fatalities. Generally, since 2007, there has been a decrease, though the lowest overall number of injury crashes was in 2005. Number of Injury Crashes per 100,000 population 70 65 60 55 50 45 40 Figure 3-3: Number of bicycle injury crashes per 100,000 population 2005 2006 2007 2008 2009 2010 Year Source: computed from IDOT Motor Vehicle Crash Data and American Community Survey (ACS) population data
9 3.3.2: Chicago Bicycle Crashes Compared with Crashes for Other Modes There has been a relatively steady decline in fatal and injury crashes involving all transportation modes in the city. From 2005 to 2010, both bicycle and motor-vehicle fatal crashes declined by approximately 28 percent (Table 3-2). The use of both modes may have increased however bicycle usage has certainly increased more. Perhaps due to the sharp rise in bicycle use, the number of injury crashes has increased from 2005 to 2010 (27 percent) while injury crashes involving all modes have declined (14 percent). Still, the number of bicycle injury crashes rose dramatically in the first two years only to drop almost the same amount the next two years (to 2009). This was followed by another increase in 2010. Given these data, it is difficult to conclusively argue there is a long-term, ongoing phenomenon, especially since there appears to be no change in the number of injury crashes from 2006 to 2009 (1,385 versus 1,386). Overall, however, the prevailing trend points to an increase in the annual number of bicycle crashes. Fatal Crashes Injury Crashes Table 3-2: Fatal and injury crashes in City of Chicago, 2005-2010 Mode 2005 2006 2007 2008 2009 2010 TOTAL* Bicycle 7 7 3 5 5 5 32 Pedestrian 65 48 49 55 34 30 281 All 179 176 164 156 141 127 943 % Bicycle 4% 4% 2% 3% 4% 4% 3% Bicycle 1236 1385 1782 1506 1386 1566 8861 Pedestrian 3406 3781 3686 3484 3130 2914 20,401 All 18,505 18,516 17,541 15,599 15,645 15,881 101,687 % Bicycle 6.7% 7.5% 10.2% 9.7% 8.9% 9.9% 8.7% *Crashes involving property damage are not included Source: IDOT Motor Vehicle Crash Data Figure 3-4 shows cycling and pedestrian injury crashes over the study period. In contrast to bicycle injury crashes which have increased overall from 2005 to 2010, pedestrian injury crashes declined (from 3,406 in 2005 to 2,914 in 2010). Perhaps this is partly due to the large increase in bicycling in the region. If the use of a bicycle in the work trip is an indication of a more universal use of bicycles, then it is not surprising that, with a surge in use, there is not an obvious decline in injury crashes. At some point, however, we might expect a long-term decrease. Many modes of transportation experience increases in crashes when they first become popular and then decline as they become more universally used. Motor-vehicle fatalities in the U.S. have been declining for nearly forty years (since the early 1970s) despite the growth in population and levels of vehicle use. It is also appropriate to note here that the injury crashes in Table 3-1 and Figure 3-4 do not include crashes with only property damage. Comparing these totals with other studies would make this apparent. There are three reasons for not including crashes that only have property
10 damage. First, like the limited number of property damage cases involving pedestrians, there are relatively few such bicycle crashes associated with only property damage. Second, since injuries are not sustained, they fall into a different class of crashes. Third, property damage data vary substantially during the study period and would complicate assessing an overall crash trend. Figure 3-4: Comparison of pedestrian and bicycle injury crashes, 2005 to 2010 4500 4000 3500 3000 2500 2000 Pedestrian Bicycle 1500 1000 500 0 2005 2006 2007 2008 2009 2010 Source: IDOT Motor Vehicle Crash Data 3.4: Chicago versus Suburban Chicago and the Rest of Illinois Table 3-3 shows that the City of Chicago accounts for approximately 22 percent of the statewide fatalities and just over half of the fatalities in Cook County. There are also fewer fatalities in the five collar counties than in suburban Cook County, which may reflect usage levels. Statewide, the number of fatalities in 2010 was lower than in 2005, but the intermediate data do not show an obvious trend. Yet when the data are combined into two-year periods, the last two years, 2009 and 2010, have a lower total than the first two years and one fewer fatality than the middle two years. The sub-areas shown in Table 3-3 similarly do not show upward or downward trends. Perhaps the rest of Illinois and Chicago show the clearest pattern of decreasing fatalities.
11 Table 3-3: Fatalities in Illinois and indices by number of bicycle commuters and population Year City of Suburban Collar Rest of Total Chicago Cook counties Illinois 2005 7 3 6 15 31 2006 7 7 1 10 25 2007 3 4 1 9 17 2008 5 3 7 12 27 2009 5 3 2 9 19 2010 5 8 4 7 24 Total 32 28 21 62 143 Bike 12,706 4388 4507 8877 30,478 commuters Fatalities 2.5 6.4 4.7 7.0 4.7 /1000 commuters Population 2,824,064 2,432,937 3,099,766 4,486,399 12,843,166 Fatalities /100,000 population 1.1 1.2 0.7 1.4 1.1 Source: 2005-2009 ACS for number of commuters and population, and IDOT Motor Vehicle Crash Data for fatalities The lower part of Table 3-3 also provides a means to further interpret the data by computing two indices. The first divides the number of fatalities by thousands of commuters. As a measure of exposure, this shows that Chicago has the lowest index level followed by the collar counties (DuPage, Lake, McHenry, Will and Kane counties in northeastern Illinois). Since the number of commuters does not necessarily reflect the total number of users, we also index by population size. The more accurate exposure measure is likely to be between the two surrogate exposure measures, commuters and population. The population index shows that the collar counties have the lowest index followed by Chicago. The Chicago level mirrors the statewide figure. Again the rest of the Illinois has the highest index. Suburban Cook has the second highest level as in the previous index. These numbers may be further examined by observing that Chicago has 22 percent of the state s fatalities and 22 percent of the state s population, but 42 percent of the state s bicycle commuters. In this regard, Chicago s data ranks well in comparison with the other geographies in Table 3-3.
12 3.5: Peer City Comparison of Cycling to Work and Crashes Peer cities are those places that most resemble Chicago in size and character. While no two places are alike in all criteria, there are a small number of places that are broadly similar to Chicago. Four criteria were developed for the identification of peer cities: (1) over 500,000 in population; (2) population density of over 5,000 residents per square mile; (3) at least 75 square miles; and (4) at least 17 percent proportion of commuters that use non-auto modes. These criteria are patterned after the New York Pedestrian Safety Study, which uses three of the four criteria [not number (3) --New York City Department of Transportation, 2010]. Additional details on the selection of peer cities are given in Technical Appendix A. Since the scientific community does not have guidelines for the definition of a peer city, we also include in this report the twelve largest cities in the U.S. and some other noteworthy places for comparison. Increasing to these twelve cities permits the inclusion of another midwestern city - Indianapolis - while keeping the analysis to a manageable number. Still, the peer cities represent the most meaningful comparisons of statistics such as mode share. We examine U.S. Census bicycling-to-work data because it is the only large-sample, annual data that show levels of bicycling in cities across the nation. We also compared bicycle safety statistics of Chicago to these peer cities. In comparison to our two most immediate peer cities, Los Angeles and New York, Chicago in 2010 had nearly as many bike commuters (15,096) as Los Angeles (16,101), a much larger city. The population of Los Angeles exceeds that of Chicago by approximately one million people. New York City has nearly three times Chicago s population but has less than twice the number of bicycle commuters (27,917). Figure 3-5 shows that the mode share for bicyclists is noticeably higher in Chicago than in New York and Los Angeles, but lower than in Philadelphia and Seattle. This suggests that increases in mode share since 2000 are important but there is still the potential for higher levels of bicycling in the future. Note also that the geographically closest peer city to Chicago is Milwaukee, which has a mode share approximately half that of Chicago.
13 4.0% 3.5% 3.0% 2.5% Figure 3-5: Bicyclists as a percent of all daily commuters, peer cities, 2010 2.0% 1.5% 1.3 % 1.0% 0.5% 0.0% New York Los Angeles Chicago Philadelphia Seattle Baltimore Milwaukee Source: U.S. Census Bureau, ACS, 2010 data Among the peer cities, Chicago ranks third in the number of fatalities, behind New York (photo on right) and Los Angeles (Table 3-4). Further, Chicago is also third based on an index that is computed by dividing the total number of fatalities over a five-year period (2005-2009) by thousands of bicycle commuters during the same period (we use the number of commuters since there is no other universal data on bicycle use). On the basis of this statistic, Chicago is only marginally higher than Baltimore. Table 3-4 shows a relationship between the fatality index and city size. New York clearly has the highest rate and Milwaukee and Seattle have the lowest rates.
14 Table 3-4: Peer city fatality indices, 2005-2009 (Five years of fatalities divided by the average number of daily bike commuters) City Fatalities Commuters 1 Index 2 New York 97 22,420 4.33 Los Angeles 35 13,764 2.54 Chicago 29 3 12,706 2.28 Philadelphia 16 8921 1.79 Seattle 8 8981 0.89 Baltimore 3 1428 2.10 Milwaukee 1 1803 0.55 1 Average number 2005-2009 2 Total fatalities divided by thousands of commuters 3 FARS reports slightly higher numbers than IDOT Source: 2005-2009 American Community Survey 5-Year Estimates and FARS: Fatal Accident Reporting System, ftp://ftp.nhtsa.dot.gov/fars/ What is most remarkable about the peer cities is the extent to which New York dominates the number of fatalities. It accounts for half of the bicycle fatalities, even though it has just under one third of the bicycle commuters (see also Table 3-6). Also, Chicago s fatality number is lower than that of Los Angeles, even though the two cities have about the same number of bicycle commuters. Moreover, the gender mix is very different. Only four percent of the Los Angeles fatalities are female, versus 15 percent for Chicago and 12 percent among the peer cities. Table 3-5 also depicts the change in the number of fatalities on an annual basis. It is apparent that there are large yearto-year fluctuations; thus an assessment of only annual data would likely not be particularly informative. We therefore examined the first two years (2005 and 2006) versus the last two years (2008 and 2009). The 2010 FARS data were not available at the time of this analysis. All of the cities had lower numbers in the last two years versus the first two years, except Seattle (partly due to small numbers). Chicago s fatalities dropped from 14 to 12, Los Angeles from 15 to 12. Collectively, the number of fatalities in the seven cities dropped by 12 percent. Since bicycling is most prevalent in large cities (other than college towns), the drop in fatalities restates the national improvement in bicycle fatalities. However, we see how the addition of one year of data changes the trend. In
15 2010 there were more male and female fatalities than in 2009 and also a large jump in male fatalities in Los Angeles. The overall increase rose from 28 to 42 fatalities, even with the slight decline in Chicago from six to five (we use IDOT data throughout much of this report that shows no change in Chicago from 2009 to 2010). Table 3-5: Peer city fatalities by gender, 2005 to 2010 2005 2006 2007 2008 2009 2010 Total Percent M F M F M F M F M F M F M F F New York City 16 4 15 2 24 2 20 2 11 1 15 3 101 14 12% Los Angeles 5 0 9 1 8 0 7 0 4 1 12 0 45 2 4% Chicago 6 1 7 0 2 1 5 1 4 2 5 0 29 5 15% Philadelphia 2 0 4 0 4 1 2 1 2 0 2 2 16 4 20% Seattle 0 0 1 1 2 0 1 0 2 1 0 1 6 3 33% Baltimore 1 0 1 0 0 0 1 0 0 0 1 0 4 0 0% Milwaukee 0 0 1 0 0 0 0 0 0 0 1 0 2 0 0% Total 30 5 38 4 40 4 36 4 23 5 36 6 203 28 12% Source: FARS: Fatal Accident Reporting System, ftp://ftp.nhtsa.dot.gov/fars/ 3.6: Comparison with Other Large Cities A more thorough assessment of the relative position of Chicago includes a larger collection of cities and characteristics. Table 3-6 shows that when the basis for comparison is expanded to the twelve largest cities, Chicago s rank jumps to second place, since Seattle drops from the list of peer cities and of the 12 largest cities only Philadelphia has a share higher than Chicago. When we further broaden the scope of comparison, places like Portland, San Francisco, Minneapolis and Washington, D.C. appear with higher bicycle mode shares. As stated earlier, some of these high levels have been achieved with relatively small city land areas. In Table 3-6, we have also included the share for walking to work. Places such as Boston, Washington, D.C. and New York are cities where walking to work is common. While Chicago cannot compare with these places in terms of pedestrian mode share, the 6.5 percent is still more than twice the national share of 2.8 percent. The walk percentage is also given as a possible indicator of the potential to which the bike
16 share may rise in the future. This may be useful if there is an interest in knowing how high the bicycle mode may rise with a concerted effort to promote bicycling. The logic for this inference is that walking acts as a surrogate for the density of residents and urban opportunities - mainly jobs -and could possibly be attained by bicycling. Table 3-6 presents the ratio of biking to walking. If one were to subscribe to this logic - that the walking share is a possible target to which bicycling to work might rise -then the relative magnitudes are important. Accordingly, Chicago has already achieved 20 percent of this target, with 1.29 percent that bicycle to work versus 6.54 percent for those that walk to work (five times as many). Note that in Portland, the ratio is just over one (more cyclists than walkers) and in Sacramento the ratio is 0.86. In Sacramento, however, among male commuters, there is 68 percent more cycling than walking to work. Females disproportionately walk more; therefore the cycling/walking ratio is 0.86. Perhaps it would be too ambitious in most places to even reach a ratio of 0.5, but at a minimum the ratio may provide useful information as to what may be possible in the future. Lastly, Table 3-6 shows the percentage of the bicycle commuters that are male. The highest levels are found in Detroit, Dallas and Phoenix. Conversely, women make up the highest percentage of cyclists in Philadelphia, Boston and Portland.
17 Table 3-6: Bicycling and walking as mode of transportation to work in major cities, 2010 Commuters By bicycle Percent bike Walk Percent walk Source: U. S. Census Bureau, ACS, 2010 Ratio bike to walk Bike percent male US 136,941,010 731,286 0.53% 3,797,048 2.77% 0.19 73.6% Illinois 5,792,659 33,427 0.58% 178,901 3.09% 0.19 71.4% Peer cities New York* 3,615,588 27,917 0.77% 364,273 10.08% 0.08 79.1% Los Angeles* 1,706,116 16,101 0.94% 61,154 3.58% 0.26 78.4% Chicago * 1,168,318 15,096 1.29% 76,372 6.54% 0.20 72.3% Philadelphia* 583,734 10,503 1.80% 48,318 8.28% 0.22 58.4% Seattle 339,160 12,306 3.63% 29,070 8.57% 0.42 70.1% Baltimore 256,622 1,788 0.70% 16,532 6.44% 0.11 81.5% Milwaukee 249,594 1,723 0.69% 11,736 4.70% 0.15 77.8% Twelve largest cities*, not found above in peer cities Houston 961,240 4,393 0.46% 20,641 2.15% 0.21 81.3% Phoenix 620,072 3,576 0.58% 11,025 1.78% 0.32 90.2% San Diego 620,939 6,390 1.03% 18,178 2.93% 0.35 69.1% Dallas 543,348 820 0.15% 9,895 1.82% 0.08 92.3% San Antonio 591,725 1,159 0.20% 13,686 2.31% 0.08 77.0% San Jose 426,136 2,708 0.64% 6,768 1.59% 0.40 83.6% Jacksonville 375,579 843 0.22% 6,700 1.78% 0.13 79.2% Indianapolis 366,017 1,935 0.53% 7,035 1.92% 0.28 83.2% Other noteworthy cities San Francisco 437,814 15,208 3.47% 41,362 9.45% 0.37 67.3% Washington, D.C. 296,717 9,288 3.13% 34,895 11.76% 0.27 67.9% Boston 309,620 4,369 1.41% 49,007 15.83% 0.09 61.2% Portland, OR 286,228 17,035 5.95% 15,078 5.27% 1.13 64.9% Columbus, OH 370,337 2,498 0.67% 11,205 3.03% 0.22 67.1% Sacramento 188,974 4,725 2.50% 5,507 2.91% 0.86 75.5% Detroit 196,706 651 0.33% 4,905 2.49% 0.13 100% Austin, TX 412,291 4,242 1.03% 12,184 2.96% 0.35 77.9% Minneapolis 200,853 6,969 3.47% 13,458 6.70% 0.52 75.5% Miami 164,340 1,550 0.94% 6,166 3.75% 0.25 79.9%
18 Chapter 4: Characteristics of Cyclists Involved in Crashes In this chapter, we examine bicycling patterns in the City of Chicago by gender, age and educational levels. We also examine the details of crash statistics by these sociodemographic groupings. 4.1: Bicycle Use and Safety Trends by Age and Gender Most studies show that the majority of cyclists are male, with the exception that women are more likely make trips to school by bicycle (Garrard, et al 2008; Krizek, et al 2005). Using a variety of sources and circumstances, we estimate that males typically account for two-thirds to three-quarters of the cyclists (Table 4-1). Using the CMAP Travel Tracker survey data, we find that in Chicago, males are particularly predominant in recreational and entertainment trips, accounting for 78 percent of these trips. Females account for roughly one-third of the trips in the CMAP Travel Tracker Survey, and 27 percent of the cyclists in a CDOT downtown count. Table 4-1: Gender mix of bicycling Variable Male Female Total Chicago No. Percent No. Percent Bicycle trips per day CMAP survey 66% 34% CMAP routine shopping trips 59% 41% CMAP recreation and entertainment trips 78% 22% 2005-2009 ACS journey to work 9303 72% 3620 28% CDOT Downtown Bike Count 13/9/2011* 73% 27% 9722 Chicago fatalities 2005 to 2010 27 84% 5 16% 32 Beyond Chicago Fatalities in peer cities 2005-2009 167 88.4% 22 11.6% 189 National fatalities 2008 93 87.0% 623 13.0% 716 National fatalities 2009 81 85.2% 549 14.8% 630 *http://www.chicagobikes.org/pdf/nbpdcount_stats.pdf Source for Chicago data: CMAP Travel Tracker Survey, 2007-8 Source for statewide data: IDOT Crash Data, 2005-2010 Source for National data: Fatality Analysis Reporting System (FARS) The largest gender differences are in fatalities statistics. Nationally, females accounted for approximately one of every seven fatalities, or 13.0 and 14.8 percent in 2008 and 2009 respectively. This is similar to the six-year data for Chicago, where the female portion of crashes was 16 percent. For comparison, only nine percent of bicyclists who died as a result of crashes in New York City from 1996 through 2003 were female (NYCDOT), though more recent data show that this statistic has risen to 12 percent in the last six years. Los Angeles result in this same data category is four percent (Table 3-5).
19 Some differences may be attributable to greater exposure and higher speeds for males (Table 4-2). Females account for just under one-third of the miles traveled (Note: The CMAP Travel Tracker survey provides very useful information for this study; however, the data processed and reported are not intended to be precise, in part due to the small sample size and overall objectives of the CMAP survey). Table 4-2: Chicago bicycling estimates, 2007 Daily miles* Daily minutes* Average minutes/ trip Average miles / trip Speed MPH Female 98,200 800,000 22.1 2.70 7.3 Male 221,600 1,660,000 23.1 3.08 8.0 Total 319,800 2,460,000 Ratio 2.3 2.1 1.05 1.14 1.10 *The minutes and miles are estimates computed by the authors from CMAP survey data and are not intended to be precise computations and do not have a defined margin of error Source: Computed by the authors from CMAP Travel Tracker data, 2007 While males account for approximately twice as much cycling, they incur three times more injuries (Table 4-3). The three-to-one ratio largely holds true throughout the six-year study period. Only data for the year 2008 falls below this ratio. Table 4-3: Gender of cyclists injured in bicycle crashes 2005 2006 2007 2008 2009 2010 Total Percent Percent known Male 777 1046 1347 1093 1064 1163 6490 72.86% 75.38% Female 243 334 429 405 328 381 2120 23.80% 24.62% Unknown 226 17 14 9 4 27 297 3.33% - Total 1246 1397 1790 1507 1396 1571 8907 100% 100% Source: IDOT Motor Vehicle Crash Data To further understand the relationship between male and female cycling, we examined the crash rates per miles cycled. Table 4-4 shows that females living in Chicago cycle 36 million miles annually, and together with males log over 110 million miles annually. Using the standard used in transportation literature on motorized fatalities, we estimate that the rate for females is 2.8 fatalities for every 100 million bicycle miles traveled (BMT). For males, the ratio stands at approximately two times higher at 5.5. The rate for all cyclists is 4.6 fatalities per 100 million BMT. Most of the bicycle mileage data used in these calculations were based on household travel surveys compiled largely in 2007 when there were only a total of three fatalities. Using this as the base, the fatality rates for that year was only 2.6 per millions BMT.
20 Table 4-4: Fatalities and injury crashes per 100 million miles of travel (fatalities and injuries represent the annual average over the six-year study period, miles biked are conservative, approximate estimates) Annual miles Fatalities / 100 million miles Injury crashes / 100 million miles Female 36,000,000 2.8 1000 Male 81,000,000 5.5 1400 Ratio M/F 2.25 2.0 1.4 Source: IDOT Motor Vehicle Crash Data and CMAP Travel Tracker data, 2007 These numbers are higher than for national motor-vehicle fatalities, that have decreased in recent years to 1.14 per 100 million miles traveled. In making this comparison two points need to be acknowledged. First, the bike miles are estimated from CMAP household survey data that includes data on bicycling but was not weighted strictly with bicycling in mind. Second, bicycles and motor-vehicles travel on very different roadways, especially in a City of Chicago versus national comparison. Many of the motor-vehicle miles are logged on interstate highways, impractical for most city trips. Still, the statistics provide a crude comparison of the relative safety of two rather different modes of travel. The female per mile fatality rate in Chicago is approximately the rate for motor vehicles about 35 years ago, regardless of gender. The male rate of 6.7 fatalities per million miles traveled was true for highway fatalities about 60 years ago. In the 1930s, the rate of motor-vehicle fatalities per 100 million miles of travel was over ten. Regarding demographics, bicycling seems to increase in popularity up to age 34 and then decreases. The largest number of trips is made by cyclists 25 to 34 years of age, but the data in Table 4.5 need to be evaluated with the caveat that they are based on a relatively small sample. In particular since the CMAP Travel Tracker data in Table 4-5 are divided into eight age categories, some of the age groups may reflect data, in particular, that are based on a small sample size. Nevertheless, there is evidence that cycling miles and minutes also increase with age until age 34, at which point it begins to decline. The 55-64 age group fits the pattern for number of trips and distance, but their averages in the last two columns do not fit any obvious pattern. Without this 55-64 age group it appears the average trip distance (miles and minutes) increases with age (last two columns). Since gender and age are both related to bicycle use, it would be informative to examine both gender and age together. We begin by examining fatality numbers and then injury data rates.
21 Table 4-5: Bicycling by age, miles and minutes per day, 2007 (Eight age categories yield a table with uneven sample sizes so that the data in this table should not be interpreted precisely) Age Trips Distance Minutes Average distance Average minutes <5 <1 1 10 1.3 13.9 5-14 8 17 158 2.1 20.2 15-24 18 40 371 2.1 20.2 25-34 48 109 1096 2.3 23.0 35-44 14 40 356 2.8 25.0 45-54 12 33 314 2.8 26.8 55-64 6 10 96 1.6 16.4 65+ 1 4 34 2.8 27.3 Total 107 252 2434 2.3 22.6 Source: Estimated from CMAP Travel Tracker2007 The greatest difference in the fatality rates between males and females is for cyclists older than 34 (Table 4-6). For these older cyclists, there are no female fatalities but 15 male fatalities. A similar pattern was found in New York City with much higher fatality numbers, where 45-54 year-old men had the highest death rate at 8.3 per million residents (NYCDOT). In Chicago, there is also a noticeable difference in the 10-14 year old age category. Conversely, the fatality rates are essentially the same for the two age categories between 15 and 24 years in age as well as the category 5-9 years. By combining the 5-9 and 10-14 age categories into one age category - 5-14 - we find that both Chicago and New York had five times more male fatalities than female. number) and 65-74 year age groups. Furthermore, Table 4-6 clearly suggests that male cyclists over 24 years of age are in a special category. For these cyclists, the ratio of male to female fatalities is 19:1. There may be more male cyclists in this age range, but it is highly unlikely that it is the same ratio, 19:1. The injury ratio for the age over 24 group is closer to 3:1 (Table 4-7). Like the number of fatalities, the number of injuries increases with age and again peaks at the 25-34 age group. But unlike fatalities, injuries decline consistently with increasing age (Table 4-7). Fatalities seem to peak at numerous age categories including 45-54 (the highest
22 Table 4-6: Bicyclist fatalities by age and gender in City of Chicago, 2005-2010 Age(years) Male Female Total Total killed Population Fatality rate* Total Killed Population Fatality rate Total killed Population Fatality rate <5 0 107,904 0.0 0 103,359 0.0 0 211,263 0.0 5-9 1 88,712 1.9 1 88,685 1.9 2 177,397 1.9 10-14 4 92,581 7.2 0 88,160 0.0 4 180,741 3.7 15-19 1 95,562 1.7 1 93,533 1.8 2 189,095 1.8 20-24 2 109,645 3.0 2 112,518 3.0 4 222,163 3.0 25-34 4 265,102 2.5 1 267,347 0.6 5 532,449 1.6 35-44 3 204,779 2.4 0 198,982 0.0 3 403,761 1.2 45-54 6 175,753 5.7 0 182,307 0.0 6 358,060 2.8 55-64 2 119,382 2.8 0 138,883 0.0 2 258,265 1.3 65-74 3 66,998 7.5 0 87,541 0.0 3 154,539 3.2 75-84 1 38,504 4.3 0 59,902 0.0 1 98,406 1.7 85+ 0 11,046 0.0 0 26,879 0.0 0 37,925 0.0 Total 27 1,375,968 3.3 5 1,448,096 0.6 32 2,824,064 1.9 *2005-2010 average annual fatalities divided by millions of residents in the age group Source: Population data: 2005-2009 American Community Survey 5-Year Estimates Fatality data: IDOT Motor Vehicle Crash Data With more injury data than fatality data, the relationship between age and injury crashes is more evident. For both males and females, the rate per population increases with age until the 20-24 age group. With increasing age beyond the 20-24 group, both rates decline. Moreover, these comments apply to not only the rate but also the number of injury crashes in each age category.
23 Table 4-7: Bicyclists injured by age and gender in City of Chicago, 2005-2010 (Gender is not recorded for 286 injury crashes; therefore these data are not included here) Age Male Female Total Injured Avg. annual injured Population* Injury rate* Injured Avg. annual injured Population Injury rate* Injured Avg. annual injured Population <5 18 3.0 107,904 28 6 1.0 103,359 10 24 4.0 211,263 19 5-9 243 40.5 88,712 457 83 13.8 88,685 156 326 54.3 177,397 306 10-14 668 111.3 92,581 1203 179 29.8 88,160 338 847 141.2 180,741 781 15-19 664 110.7 95,562 1158 209 34.8 93,533 372 873 145.5 189,095 769 20-24 971 161.8 109,645 1476 503 83.8 112,518 745 1474 245.7 222,163 1106 25-34 1365 227.5 265,102 858 571 95.2 267,347 356 1936 322.7 532,449 606 35-44 826 137.7 204,779 672 231 38.5 198,982 193 1057 176.2 403,761 436 45-54 791 131.8 175,753 750 166 27.7 182,307 152 957 159.5 358,060 445 55-64 338 56.3 119,382 472 48 8.0 138,883 58 386 64.3 258,265 249 65-74 118 19.7 66,998 294 17 2.8 87,541 32 135 22.5 154,539 146 75-84 48 8.0 38,504 208 6 1.0 59,902 17 54 9.0 98,406 91 85+ 12 2.0 11,046 181 0 0.0 26,879 0 12 2.0 37,925 53 Unknown 425 70.8 - - 100 16.7 - - 525 87.5 Total 6487 1081.2 1,375,968 786 2119 353.2 1,448,096 244 8606 1434.3 2,824,064 508 *2005-2010 average annual injured divided by millions of residents in the age group Source: Population data: 2005-2009 American Community Survey 5-Year Estimates Fatality data: IDOT Motor Vehicle Crash Data - Injury rate* - More importantly, there is a very dramatic decrease in injury crashes from the 20-24 group to the 25-34 group. There is a 45 percent drop in the injury rate for all cyclists and a 52 percent drop for females. This is compelling evidence that countermeasures to reducing injury crashes need to focus on the young, targeting mainly those over ten years of age and increasing the focus as age increases until at least age 24. Still, all cyclists would benefit from safety training and refreshers, regardless of age. Finally, the gender difference with relation to injury crashes is, again, rather striking. Females account for 24 percent of injuries from crashes but only 16 percent of the fatalities. There are more than three times as many injury crashes among males than females. The highest ratios are for cyclists over 55 years of age (Figure 4-1). There are also above average ratios in the 10 19 age group. The major exception to this trend is in the high risk category, 20-24. In this age group the ratio is just under 2.0. This suggests that while female cyclists have much lower injury crash rates, the 20-24 age group may merit special attention in improving bicycle safety, for both males and females. Figure 4-2 illustrates the relationship between male and female cyclist injury crash rates by age shown above in tabular form. There is generally a consistent pattern with males experiencing higher rates and numbers for each age group. There are, however, two age categories that
24 seem out of step: 10-14 and 45-54 years. In these two age groups, males have higher rates than the pattern implied by the shape of the line for female cyclists, and do not conform to steadily increasing and decreasing rates found for females. Lastly, both lines peak at 20-24 years of age. 14 Figure 4-1: Ratio of male to female injury rates (Rate is injury per population in the age cohort, there is no data point for 85+ since there were no injuries for females) 12 10 8 6 4 2 0 Age <5 5-9 10-14 15-19 20-24 25-34 35-44 45-54 55-64 65-74 75-84 85+ Source: Prepared by the authors from IDOT Motor Vehicle Crash Data 160 Figure 4-2: Annual average injury crash rate per 100,000 residents 140 120 100 80 Male Female 60 40 20 0 <5 5-9 10-14 15-19 20-24 25-34 35-44 45-54 55-64 65-74 75-84 85+ Source: Prepared by the authors from IDOT Motor Vehicle Crash Data
25 4.2: Education Level of Cyclists Education levels attained may also provide us with information regarding how we might develop crash countermeasures. The CMAP Travel Tracker data suggest that there is an apparent relationship between educational attainment and cycling activity, but the sample sizes do not permit definitive analysis. The positive association between (1) level of education attained and (2) the amount of cycling is consistent with the information in Chapter 8 that includes maps depicting the high number of crashes on the North Side of Chicago between the Kennedy Expressway and Lake Michigan. This is an area replete with young professionals.
26 Chapter 5: Vehicles and Operators Involved in Bicycle Crashes In this chapter we examine the condition of vehicles, drivers and bicyclists at the time of the crash as well as hit-and-run statistics. 5.1: Alcohol and Bicycle Crashes High BAC of drivers involved in traffic fatalities has been a long-standing problem. Nationally, the percentage of motor-vehicle drivers exceeding the permitted BAC level of 0.08 has remained steady for over a decade (http://www.fars.nhtsa.dot.gov/trends/trendsalcohol.aspx) at approximately 32 percent and accounted for over 10,000 fatalities in 2009. 5.1.1: Blood Alcohol Content of Motorists Of the 32 bicycle fatalities in our study period, only six motorists received a field sobriety test, and one refused to be tested. Of these six, four had no alcohol in their system and two had positive results, but within the legal limit (Table 5-1, using FARS as a data source). These two positive levels were 0.1 and 0.7, the latter being close to the legal limit. Table 5-1: Blood alcohol content of drivers involved in fatal bicycle crashes BAC level for drivers Frequency (g/dl) BAC = 0 4 0.001< BAC <0.08 2 BAC > 0.08 0 Test refused 1 Test not offered 22 Total 29 Source: FARS
27 Regarding injuries, the IDOT data report nearly 9,000 crash injuries, but less than one percent were classified as having a driver impaired by alcohol or drugs, medicated or had been drinking (Table 5-2). Over 99 percent of motorists are identified as appearing normal, i.e., the contributing cause was not due to drug-induced impairment. Table 5-2: Apparent physical condition of drivers in bicycle injury crashes Condition 2005 2006 2007 2008 2009 2010 Total Percent Percent known Normal 892 983 1283 1063 981 1101 6303 70.73% 99.24% Impaired 5 4 5 4 4 6 28 0.31% 0.44% alcohol Impaired drugs 0 0 2 2 1 0 5 0.06% 0.08% Illness 0 0 2 0 0 0 2 0.02% 0.03% Asleep/fainted 0 0 0 0 0 0 0 0.00% 0.00% Medicated 0 0 1 0 0 0 1 0.01% 0.02% Had been 1 2 2 0 2 1 8 0.09% 0.13% drinking Fatigued 0 2 0 0 0 2 4 0.04% 0.06% Other/unknown 347 406 499 444 403 461 2560 28.73% - Total 1245 1397 1794 1513 1391 1571 8911 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data 5.1.2: Blood Alcohol Content of Cyclists Of the 27 fatalities for which BAC is available (seven were not tested for alcohol), eight bicyclists showed measureable alcohol in their system (Table 5-3). Four of these had levels over 0.08, which is the legal limit for motor-vehicle drivers in Illinois, or almost 15 percent of those who were tested. Two had levels of 0.06, just below the 0.8 level. Using 0.08 as a benchmark, the DUI percentage for bicyclists is 14.8 percent. This compares to 32 percent for motor-vehicle fatalities in the U.S. and 35 percent in Illinois in 2009 (http://www.census.gov/compendia/statab/cats/transportation/motor_vehicle_accidents_and _fatalities.html). Using a lower level of 0.06, the statistic rises to 22 percent for bicyclists. The percentage for bicyclists compared to motor-vehicle operators may be lower in part due to the lack of age limit for bicycle use, unlike motor-vehicle drivers who need to be at least 15 years of age. In Chicago, six cyclists under the legal driving age were killed in bicycle crashes.
28 Table 5-3: Blood Alcohol Content of bicyclists in fatal crashes, 2005 to 2010 BAC test result (g/dl) Frequency 0.00 19 0.01 1 0.02 1 0.06 2 0.11 1 0.14 1 0.18 1 0.25 1 Total 27 14.8 percent DUI among total tested bicyclists Source: FARS 5.1.3: Hit-and-Run Crashes Hit-and-run crashes continue to be a vexing problem for bicycle crashes (Figure 5-1). They account for approximately a quarter of all fatalities and bicycle injury crashes. While these proportions may seem high, they are lower than in the case of pedestrian crashes. From 2005 to 2009, 33 percent of the pedestrian injury crashes and 41 percent of the pedestrian fatalities were hit and run. Figure 5-1: Hit-and-run bicycle crashes, 2005 to 2010 Bicycle Fatal Crashes Involved a hitand-run: 25.0% Bicycle Injury Crashes Involved a hitand-run: 24.9% Source: IDOT Motor Vehicle Crash Data
29 5.2: Age and Gender of Motorists 5.2.1: Age of Motorists Over 60 percent of the drivers involved in fatal crashes were between 25 and 44 years of age (Table 5-4). Only one in four was over 44 years of age. Males accounted for approximately two thirds of the fatal crashes, and only in the 25 to 34 age group did female drivers outnumber male drivers. Table 5-4: Known age of driver involved in fatal crash Age Male Female Total Percent 15-24 3 0 3 11.1% 25-34 3 4 7 25.9% 35-44 9 1 10 37.0% 45-54 3 0 3 11.1% 55-64 1 3 4 14.8% 65+ 0 0 0 0.0% 19 8 27 Source: FARS 5.2.2: Gender of Motorists Involved in Bicycle Crashes It is also informative to examine the gender mix of motor-vehicle drivers involved in bicycle crashes. Table 5-5 shows that males comprise the majority of drivers involved in bicycle crashes. They account for almost two-thirds of the drivers involved in injury bicycle crashes. This ratio seems to be constant over the six-year study period. Table 5-5: Gender of drivers involved in bicycle injury crashes 2005 2006 2007 2008 2009 2010 Total Percent Percent known Male 635 703 880 752 670 801 4441 49.84% 63.91% Female 331 399 545 414 394 425 2508 28.14% 36.09% Unknown 279 295 369 347 327 345 1962 22.02% - Total 1245 1397 1794 1513 1391 1571 8911 100% 100% Source: IDOT Motor Vehicle Crash Data
30 5.3: Vehicle Type and Use Typical household vehicles, including passenger cars, SUVs and vans, account for approximately 90 percent of bicycle injury crashes (Table 5-6). Buses account for another two percent. Large vehicles such as buses and trucks may inflict serious damage, but together account for less than five percent of the injuries. We pursue this line of examination because the literature is replete with similar studies, such as the association between bicycle crashes, buses and taxis and their use (Pai, 2010). Table 5-6: Vehicle type involved in bicycle injury crashes Vehicle type involved in 2005 2006 2007 2008 2009 2010 Total Percent Percent bicycle injury crashes known Passenger car 880 921 1167 980 937 1109 5994 71.45% 74.16% Sport utility vehicle (SUV) 87 91 122 120 130 165 715 8.52% 8.85% Van/mini-van 93 100 103 82 83 106 567 6.76% 7.02% Pickup truck 30 41 40 45 38 54 248 2.96% 3.07% Bus 25 18 26 20 20 38 147 1.75% 1.82% Truck single unit 16 14 26 18 14 9 97 1.16% 1.20% Tractor w/semi-trailer 4 3 3 4 5 8 27 0.32% 0.33% Motorcycle (over 150 cc) 2 2 6 1 3 6 20 0.24% 0.25% Motor-driven cycle 0 0 1 3 3 1 8 0.10% 0.10% Tractor w/o semi-trailer 3 1 1 0 1 1 7 0.08% 0.09% Other vehicle with trailer 0 0 1 2 0 2 5 0.06% 0.06% All-terrain vehicle (ATV) 0 1 0 0 0 2 3 0.04% 0.04% Other 39 40 51 40 35 39 244 2.91% 3.02% Unknown/NA 40 51 64 59 56 37 307 3.66% - Total 1219 1283 1611 1374 1325 1577 8389 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data
31 The total number of injury crashes has fluctuated from year to year, but a few of the vehicle types show evidence of a trend over the study period. During the last four-year period, the number of single-unit trucks involved in bicycle crashes has steadily declined. They decreased from 26 in 2007 to only nine in 2010. Increases have been registered by pick-up trucks. Though the increases are not steady, the rise in their involvement has been from 30 in 2005 to 54 in 2010. The most noticeable increase, however, has been with SUVs. Figure 5-2 shows the rise from 87 in 2005 to 165 in 2010. Not only has there been an increase every year (modest in some years) but there has been almost a doubling in the number during the study period. Figure 5-2: Number of SUVs involved in bicycle crashes 180 160 140 120 100 80 60 2005 2006 2007 2008 2009 2010 Source: IDOT Motor Vehicle Crash Data
32 Table 5-7 examines the role of motor vehicles more closely by noting the type of use during the injury crash. As anticipated from the previous table, personal use is the most common vehicle use. This is followed by taxis (together with other for-hire vehicles). This group accounts for one in twelve bicycle crashes with injuries. CTA buses, commercial vehicles and police all account for more than one percent of the crashes. Many of the individual uses shown in Table 5-7 trend with the total number of bicycle injury crashes, making it difficult to identify their individual trends. Police vehicles, however, show a steady decline since 2007. The numbers have declined from 20 in 2007 to 12 in 2010, two years that had a high number of injury crashes, 1611 and 1577 respectively, showing little overall decrease in crashes. Table 5-7: Vehicle use during crash Type of use 2005 2006 2007 2008 2009 2010 Total Percent Percent known Not in use 50 38 54 51 53 50 296 3.53% 4.21% Personal 810 846 1084 885 855 1024 5504 65.61% 78.36% Taxi/for hire 80 91 110 114 90 107 592 7.06% 8.43% City bus 19 15 23 11 13 31 112 1.34% 1.59% Commercial single unit 7 14 20 11 16 15 104 1.24% 1.48% Police 12 13 20 17 14 12 88 1.05% 1.25% Construction/maintenance 8 4 6 9 1 9 37 0.44% 0.53% Other transit 4 3 4 7 6 9 33 0.39% 0.47% Mass transit 8 6 7 8 0 3 32 0.38% 0.46% State owned 2 2 0 2 3 3 12 0.14% 0.17% School bus 1 0 2 1 1 3 8 0.10% 0.11% Tow truck 1 2 1 0 2 1 7 0.08% 0.10% Camper/RV 1 3 0 0 0 1 5 0.06% 0.07% Fire 1 0 2 0 1 0 4 0.05% 0.06% Driver education 0 1 2 0 0 0 3 0.04% 0.04% Ambulance 1 0 2 0 0 0 3 0.04% 0.04% House trailer 0 1 0 0 1 0 2 0.02% 0.03% Other 27 33 39 35 33 36 203 2.42% 2.89% Unknown/NA 187 211 235 223 236 273 1365 16.27% - Total 1219 1283 1611 1374 1325 1577 8389 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data
33 5.4: Driver and Vehicle Maneuvers In 24 of the 32 fatalities, the action taken by the motor-vehicle driver is known. In half of these cases, the driver is reported to have contributed to the fatality by either failing to yield, driving too fast or engaging in an improper lane change (Table 5-8). In one-third of the cases, there was no identified action and in another four cases the action was classified as other. Table 5-8: Driver action in fatal crashes, City of Chicago, 2005-2010 Driver action Frequency Percent known None 8 33% Failed to yield 10 42% Too fast for conditions 1 4% Improper passing 1 4% Other 4 17% Unknown 8 - Total 32 100% Source: IDOT Motor Vehicle Crash Data Failure to yield right-of-way was also a major factor in approximately 40 percent of the injury crashes (Table 5-9), similar to the trend shown for fatal crashes shown in the previous table. Improper actions associated with lane change, backing, passing, parking and turning are also contributors, but collectively account for approximately only five percent of the injury crashes. In just over a third of the crashes, the driver was not involved in a maneuver listed in Table 5-9. In essence, the driver maneuvers in both fatal and injury crashes are rather similar. The difference may be attributed to the much larger data base of injury crashes that permits more detail regarding other maneuvers.
34 Table 5-9: Driver action in bicycle-vehicle injury crashes Driver action 2005 2006 2007 2008 2009 2010 Total Percent Percent known None 360 390 477 368 358 431 2384 26.75% 35.29% Failed to yield 357 385 551 452 463 525 2733 30.67% 40.46% Disregarded control 21 38 34 26 25 14 158 1.77% 2.34% devices Too fast for conditions 9 14 19 16 13 11 82 0.92% 1.21% Improper turn 15 23 24 35 27 33 157 1.76% 2.32% Wrong way/side 3 8 8 4 2 6 31 0.35% 0.46% Followed too closely 12 11 15 10 16 13 77 0.86% 1.14% Improper lane change 12 10 15 16 15 14 82 0.92% 1.21% Improper backing 13 16 15 10 9 8 71 0.80% 1.05% Improper passing 7 10 10 10 6 9 52 0.58% 0.77% Improper parking 3 2 2 2 0 4 13 0.15% 0.19% License restrictions 1 1 0 0 0 0 2 0.02% 0.03% Stopped school bus 0 2 2 1 1 2 8 0.09% 0.12% Emergency vehicle on 0 1 1 1 4 0 7 0.08% 0.10% call Evading police vehicle 0 0 2 0 0 0 2 0.02% 0.03% Other 136 155 165 165 137 138 896 10.05% 13.26% Unknown 296 331 454 397 315 363 2156 24.19% - Total 1245 1397 1794 1513 1391 1571 8911 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data The motion or direction of the vehicle is given in Table 5-10. The previous table (Table 5-9) described the action of the driver. According to available data, the motor vehicle was moving straight ahead in nearly all cases. Both turning vehicles and entering traffic from parking each accounted for less than one percent of driver actions.
35 Table 5-10: Vehicle maneuver prior to bicycle injury crashes Vehicle maneuver 2005 2006 2007 2008 2009 2010 Total Percent Percent known Straight ahead 735 803 1014 825 791 918 5086 56.81% 98.81% Passing/overtaking 1 1 2 0 0 2 6 0.07% 0.12% Turning left 1 1 0 0 0 1 3 0.03% 0.06% Turning right 1 0 0 1 0 0 2 0.02% 0.04% Turning on red 0 0 0 0 1 0 1 0.01% 0.02% U-turn 1 0 0 0 1 0 2 0.02% 0.04% Starting in traffic 1 0 0 0 0 0 1 0.01% 0.02% Slow/stop left turn 0 0 1 1 0 0 2 0.02% 0.04% Slow/stop right turn 6 5 2 2 0 1 16 0.18% 0.31% Slow/stop 0 0 0 1 0 0 1 0.01% 0.02% load/unload Slow/stop in traffic 0 0 1 0 0 0 1 0.01% 0.02% Driving wrong way 0 1 0 0 0 0 1 0.01% 0.02% Changing lanes 0 0 1 0 0 0 1 0.01% 0.02% Enter traffic from 2 4 10 2 3 3 24 0.27% 0.47% parking Unknown/NA 501 587 774 689 602 652 3805 42.50% - Total 1249 1402 1805 1521 1398 1577 8952 100% 100% Source: IDOT Motor Vehicle Crash Data 5.5: Dooring When a bicyclist runs into a motor-vehicle door that is opened unexpectedly, it is known as a case of dooring. We use the IDOT data on dooring starting with 2010; therefore the summaries reported below are for a relatively short period of time. It shows, however, that dooring is associated with disproportionately more Type B injuries (Table5-11). Adding Types A and B together shows the high degree of severe injuries common with dooring (Type C injuries are the least serious). (The photographs are through a rear view mirror).
36 Table 5-11: All versus dooring bicycle crashes, by injury type Injury type All non-dooring crashes Dooring crashes Type A 11% 8% Type B 51% 61% Type C 38% 31% Source: IDOT Motor Vehicle Crash Data 2005-2010 and IDOT Dooring Data 2010-2011 5.6: Bicyclist Activity In this section we explore the actions of bicyclists and where the crash occurred. It concludes by examining the propensity to wear a helmet. 5.6.1: Bicyclist Action We first consider fatal crashes followed by injury crashes. Over 45 percent of the 32 fatalities occurred while bicyclists were moving in traffic. In over one-third (11) of the fatalities the cyclist was moving with the flow of traffic (Table 5-12) and in only two cases was the cyclist moving against the flow. Signalized intersections represent a significant problem area. At signalized intersections where fatalities occurred, cyclists crossed against the signal in six of the cases, or 20 percent of the time. Lastly, left turns were the contributing factor in three of 32 fatalities. Table 5-12: Bicyclist action in fatal crashes, 2005-2010 Bicyclist action Fatalities Percent Percent known Turning left 3 9.4% 10.3% Enter from drive/alley 1 3.1% 3.5% Crossing with signal 2 6.3% 6.9% Crossing against signal 6 18.8% 20.7% Walking / Riding with traffic 11 34.4% 37.9% Walking / Riding against 2 6.3% 6.9% traffic Playing in roadway 1 3.1% 3.5% Other action 3 9.4% 10.3% Unknown/NA 3 9.4% - Total 32 100% 100% Source: IDOT Motor Vehicle Crash Data
37 Like fatal crashes, a similarly high percentage of injury crashes (50 percent) also occurred while the cyclist was moving in traffic (Table 5-13 percent known). Again, a similarly high proportion of these in traffic injuries occurred when moving against traffic, approximately 20 percent. The lack of exposure data make it impossible to know if this is disproportionate to actual traffic. Also, crossing at signalized intersections is the second highest contributor to bicycle crashes, accounting for 20 percent of the crashes. In slightly more than one-third of these crashes, the cyclist was crossing against the signal. Table 5-13: Bicyclist action in injury crashes Bicyclist action Injury type Total Percent Percent A B C known Turning left 25 107 86 218 2.45% 2.94% Turning right 15 46 40 101 1.13% 1.36% Enter from drive/alley 33 209 147 389 4.37% 5.25% No action 26 119 112 257 2.89% 3.47% Crossing with signal 91 505 372 968 10.87% 13.05% Crossing against signal 69 275 185 529 5.94% 7.13% Entering/Leaving/Crossing school bus 0 0 1 1 0.01% 0.01% (within 50ft) Entering/Leaving/Crossing parked vehicle 1 13 9 23 0.26% 0.31% Entering/Leaving/Crossing not at intersection 13 52 38 103 1.16% 1.39% Walking/Riding with traffic 288 1624 1084 2996 33.64% 40.40% Walking/Riding against traffic 94 374 296 764 8.58% 10.30% Walking/Riding to/from disabled vehicle 0 6 2 8 0.09% 0.11% Waiting for school bus 0 2 3 5 0.06% 0.07% Playing/working on vehicle 0 1 1 2 0.02% 0.03% Playing in roadway 7 25 29 61 0.68% 0.82% Standing in roadway 0 5 10 15 0.17% 0.20% Working in roadway 0 1 4 5 0.06% 0.07% Other action 87 483 382 952 10.69% 12.84% Intoxicated 2 10 8 20 0.22% 0.27% Unknown/NA 176 658 658 1492 16.75% - Total 927 4515 3465 8907 100% 100% Source: IDOT Motor Vehicle Crash Data
38 5.6.2: Bicyclist Location Over two-thirds of fatalities occurred in roadways (Table 5-14). Next in frequency are crashes that occurred in a location where a crosswalk was not available. Table 5-14: Bicyclist location in fatal crashes, City of Chicago, 2005-2010 Bicyclist location Number Percentage In roadway 22 68.8% In crosswalk 3 9.4% Not in available crosswalk 1 3.1% Crosswalk not available 4 12.5% Driveway access 1 3.1% Not in roadway 1 3.1% Total 32 100% Source: IDOT Motor Vehicle Crash Data Injuries were also most prevalent in roadways, but the second most common location was in crosswalks (Table 5-15). Studies have shown that marked crosswalks are perceived by individuals as safe zones, and they may not be as attentive to motorists who are not fully engaged in driving. Table 5-15: Bicyclist location in bicycle injury crashes Bicyclist location Injury type Total Percent Percent A B C known In roadway 604 2877 2052 5533 62.12% 71.59% In crosswalk 128 724 539 1391 15.62% 18.00% Not in available crosswalk 17 55 57 129 1.45% 1.67% Crosswalk not available 10 27 10 47 0.53% 0.61% Driveway access 10 78 93 181 2.03% 2.34% Not in roadway 22 127 103 252 2.83% 3.26% Bikeway 15 110 71 196 2.20% 2.54% Unknown/NA 121 517 540 1178 13.23% - Total 927 4515 3465 8907 100% 100% Source: IDOT Motor Vehicle Crash Data
39 5.6.3: Bicyclist Helmet Use One of the proven means of minimizing serious injuries from bicycle crashes is helmet use. Among the 29 fatalities recorded between 2005 and 2009, only one cyclist is known to have used a helmet (Table 5-16). Importantly, the reporting for the remaining 28 crashes was not conclusive as to whether a helmet was worn or not (recorded on crash reports as none used/not applicable ). For this reason, conclusions regarding the true rate of helmet use in these crashes, or the impact on crash severity cannot be drawn from these data. Table 5-16: Helmet use, 2005 to 2009 Helmet use Number Percent known None Used/Not Applicable 26 97% Used 1 4% Unknown 2 - Total 29 100% Source: FARS ftp://ftp.nhtsa.dot.gov/fars/ However, national trauma data indicate that the percentage of incidents in which helmets are used has ranged from 22 to 24 percent (based on approximately 50,000 trauma incidents between 2007 and 2010).
40 Chapter 6: Environmental Factors and Road Conditions In this chapter we examine the relationship of bicycle crashes to environmental factors such as weather and light conditions. This is followed by a discussion of road conditions. 6.1: Environmental Factors during Crashes Previous research suggests that bicycle/motor-vehicle crashes are associated with poorly-lit streets, streets without medians and high speed limits. We explore some of these points in this section. 6.1.1: Weather-Related Factors Perhaps surprisingly, inclement weather does not seem to be a major contributor to bicycle crashes. This may be due to the fact that cycling levels are low during inclement weather; however, we have no exposure data (miles traveled in inclement weather) to assess how weather may disproportionately contribute to crashes. The great majority of injury and fatal crashes occurred in clear weather (Table 6-1). Rain was present in less than ten percent of crashes, suggesting that bicycling predominantly occurs during good weather. Table 6-1: Weather conditions during bicycle crashes Weather Fatal Injury crash type Injury Percent Percent crashes A B C total known Clear 29 807 3932 2939 7678 86.65% 88.70% Rain 2 93 320 252 665 7.50% 7.68% Snow 11 22 23 56 0.63% 0.65% Fog/smoke/haze 20 91 69 180 2.03% 2.08% Sleet/hail 4 8 12 24 0.27% 0.28% Severe cross wind 1 3 3 7 0.08% 0.08% Other 1 6 30 10 46 0.52% 0.53% Unknown 19 86 100 205 2.31% - Total 32 961 4492 3408 8861 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data
41 6.1.2: Light Conditions In addition to clear weather, the majority of bicycle crashes occurred during daylight hours, in nearly three of four crashes (Table 6-2). Most of the remaining crashes occurred during hours of darkness, but in locations where the roadway is lighted. This is the case in 18 percent of injury crashes and more than onethird of fatal crashes. In Chapter 7, the disproportionate number of fatal crashes in the evening hours is explored further. Table 6-2: Light conditions during bicycle crashes Light Conditions Injury crashes Fatal Injury crash type Total Injury Percent crashes A B C percent known Daylight 635 3269 2479 6383 72.03% 73.02% 17 Dawn 18 54 45 117 1.32% 1.34% Dusk 35 168 126 329 3.71% 3.76% 2 Darkness 53 154 137 344 3.88% 3.94% 2 Darkness, lighted road 209 794 566 1569 17.71% 17.95% 11 Unknown 11 53 55 119 1.34% - 0 Total 961 4492 3408 8861 100.00% 100.00% 32 Source: IDOT Motor Vehicle Crash Data
42 6.1.3: Weather-Related Road Surface Expectedly, the data on road surfaces can be inferred from the findings relating to the weather data (Table 6-3). Nearly 90 percent of crashes occurred when the road surface was dry and ten percent when it was wet. Again, as expected, snow or slush was rarely the contributing factor. Table 6-3: Road surface conditions during bicycle crashes, 2005-2010 Road surface Fatal Injury crash type Injury Injury Percent A B C total percent known Dry 29 802 3875 2878 7555 85.26% 89.16% Wet 2 114 415 322 851 9.60% 10.04% Snow or slush 5 18 22 45 0.51% 0.53% Ice 1 2 6 9 0.10% 0.11% Sand, mud, 1 3 2 6 0.07% 0.07% dirt Other 1 7 0 8 0.09% 0.09% Unknown 1 37 172 178 387 4.37% - Total 32 961 4492 3408 8861 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data 6.2: Roadway Environment 6.2.1: Relation to Intersections Intersections represent a greater hazard to bicyclists compared to other roadway sections. Table 6-4 shows that over half of both fatal and injury crashes occurred at intersections. The data reflect the nature of the Chicago street system, with many diagonal streets creating complex intersections. Table 6-4: Bicycle injury crashes at intersections Intersection Fatal crashes Injury crash type Total Percent related Number Percent A B C Yes 18 56.3% 541 2436 1836 4813 54.3% No 14 43.8% 420 2056 1572 4048 45.7% Total 32 100% 961 4492 3408 8861 100% Source: IDOT Motor Vehicle Crash Data
43 Intersections represent a potential hazard for all traffic, and particularly to bicycles and pedestrians. Map 6-1 shows intersections that recorded at least ten crashes. The vast majority of these intersections are located northwest of downtown Chicago. Only one intersection is located south of the Loop and outside the greater downtown area, at Archer and Western. Map 6-1: Intersections with at least ten injury crashes Source: IDOT Motor Vehicle Crash Data The size of an intersection (geographic scope) is not fixed and varies by intersection design and complexity. We, however, use the same size definition for all intersections. Specifically, if a crash occurs within 125 feet of the center point of the intersection, it is counted as an intersection crash. Table 6-5 shows that none of the 12 intersections with the highest number of crashes are located in the immediate downtown area. The intersection at Chicago and Halsted is the site closest to the Loop. The two intersections with the highest numbers of crashes are large, complex intersections where three major arterial roadways converge. The highest number of intersection crashes occurs at the intersection of Damen, Fullerton and Elston Avenues, followed closely by the intersection of Chicago, Milwaukee and Ogden Avenues. Many of the high-crash intersections are associated with diagonal streets, including Milwaukee, Clybourn and Lincoln. The largest number of crashes at a non-diagonal intersection is at Chicago and Halsted.
44 Crash count Table 6-5: Intersections with the greatest number of injury crashes N/S street E/W street Diagonal street / Avenue Community area 20 Damen Ave Fullerton Ave Elston Ave Logan Square 19 n/a Chicago Ave Milwaukee Ave& Ogden West Town Ave 17 Halsted St Chicago Ave n/a West Town 16 Lake Shore Dr Montrose Ave n/a Uptown 16 California Ave n/a Milwaukee Ave Logan Square 15 Halsted St Fullerton Ave Lincoln Ave Lincoln Park 15 Damen Ave North Ave Milwaukee Ave West Town 14 Damen Ave Diversey Ave Clybourn Ave North Center/Lincoln Park 14 n/a Fullerton Ave Milwaukee Ave Logan Square 14 Ashland Ave Cortland St n/a Logan Square 14 Halsted St Armitage Ave n/a Lincoln Park 14 Damen Ave n/a Wicker Park Ave West Town 14 Clark St n/a Ridge Ave Edgewater Source: IDOT Motor Vehicle Crash Data Map 6-2 shows the locations of the high-crash intersections more clearly. It is evident that the diagonal arterials account for the vast majority of crashes. This includes Milwaukee, Elston, Clybourn, Lincoln and Clark. These are all important arterials radiating from downtown Chicago. Halsted is the major exception, followed by North Avenue. Among the intersections closest to downtown are at (1) Halsted and Madison and (2) Roosevelt and State. They are not, however, in the list of the 12 highest crash intersections.
45 Map 6-2 Intersections with large number of injury crashes Source: IDOT Motor Vehicle Crash Data
46 6.2.2: Road Defects The road circumstances seem not to be an important element in bicycle crashes. No defects were reported in 97 percent of the crashes (Table 6-6). Some of the defects listed in Table 6-6 are inevitable over the short term before maintenance crews can act, such as debris on the road. Table 6-6: Road defects Road defects Fatal Injury crash type Injury A B C total Injury percent Percent known No defects 30 864 4000 2770 7634 86.15% 97.08% Construction zone 0 10 46 28 84 0.95% 1.07% Maintenance zone 0 0 2 2 4 0.05% 0.05% Utility work zone 0 0 0 3 3 0.03% 0.04% Work zone unknown 0 2 3 2 7 0.08% 0.09% Shoulders 0 0 0 1 1 0.01% 0.01% Rut, holes 1 2 11 9 22 0.25% 0.28% Worn surface 0 3 11 10 24 0.27% 0.31% Debris on roadway 0 6 19 38 63 0.71% 0.80% Other 0 3 13 6 22 0.25% 0.28% Unknown 1 71 387 539 997 11.25% - Total 32 961 4492 3408 8861 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data 6.2.3: Roadway Type and Number of Lanes Most fatal bicycle crashes occur on two-way streets. Over 80 percent are on such streets (Table 6-7). One-way streets account for one in eight fatalities. The lack of exposure data prevents us from assessing the degree to which one-way streets are safer. Table 6-7: Location-related factors for fatal bicycle crashes, 2005-2010 Roadway Type Fatalities Percent Two way, Not divided 10 31.3% Two way, Divided, no median barrier 11 34.4% Two way, Divided w/median barrier 5 15.6% One-way or ramp 4 12.5% Alley or driveway 1 3.1% Other 1 3.1% Total 32 100.00% Source: IDOT Motor Vehicle Crash Data Injury crashes tend to occur on the same roadway types as fatalities (Table 6-8). The largest difference appears to be on two-way divided streets with a median. Only 6.2 percent of the
47 injury crashes occur on these roadways in contrast to 15.6 percent of fatal crashes, more than twice as many. Table 6-8: Roadway type Roadway type Injury crash type Total Percent Percent A B C known Two-way, Not Divided 282 1352 1028 2662 30.04% 32.44% Two-way, Divided, no median 369 1713 1093 3175 35.83% 38.70% barrier Two-way, Divided w/median 63 269 180 512 5.78% 6.24% barrier Two-way, Center turn lane 5 19 17 41 0.46% 0.50% One-way or ramp 105 526 357 988 11.15% 12.04% Alley or driveway 41 230 182 453 5.11% 5.52% Parking lot 5 19 23 47 0.53% 0.57% Other 37 167 123 327 3.69% 3.99% Unknown 54 197 405 656 7.40% - Total 961 4492 3408 8861 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data The data on number of lanes is not easily interpreted, since over half of the fatal crashes fall into the not applicable category (51.7 percent). What stands out, however, as seen from Table 6-9, is that approximately half of the nonintersection fatal crashes occurred on four-lane facilities (24.1 percent of all crashes). Non-fatal injury crashes are much more likely to occur on four-lane streets. Four-lane facilities frequently have higher speed limits; therefore, speed may be a contributing factor to the high proportion of fatal crashes.
48 Total no. of travel lanes(both directions) NA-Used for intersection Fatal Table 6-9: Number of travel lanes Percent Injury crash type Total Percent Percent known A B C known 15 51.7% 183 781 636 1600 18.06% 19.85% 1 4 13.8% 121 675 558 1354 15.28% 16.80% 2 3 10.3% 328 1632 1187 3147 35.52% 39.04% 3 30 102 73 205 2.31% 2.54% 4 7 24.1% 189 775 570 1534 17.31% 19.03% 5 5 27 16 48 0.54% 0.60% 6 18 83 30 131 1.48% 1.63% 7 0 8 2 10 0.11% 0.12% 8 1 7 4 12 0.14% 0.15% 9 3 8 5 16 0.18% 0.20% 10+ 3 0 1 4 0.05% 0.05% Unknown 3 80 394 326 800 9.03% - Total 32 100% 961 4492 3408 8861 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data 6.2.4: Roadway Classification In most urban areas, local streets account for over two-thirds of roadway miles and serve primarily residential areas. Collectors account for generally less than ten percent of road miles and typically connect residential areas with higherorder roadways and commercial areas. Arterials usually account for one quarter of roadway miles but considerably more traffic. In Chicago, collectors and minor arterials account for the great majority of fatal and Type A bicycle crashes (Table 6-10). Principal arterial and local roads show roughly equivalent numbers of these crashes. Trends from 2005 to 2010 are not immediately apparent, but from 2006 to 2010 the number of fatal and Type A
49 crashes show signs of decreasing. The same holds for principal arterials, down from 40 to 23, and for collectors, decreasing from 77 to 53. Fatal and Type A crashes on minor arterials have held steady over the last four years. Table 6-10: Fatal and Type A crashes by roadway classification Roadway class 2005 2006 2007 2008 2009 2010 Total Percent Principal Arterial 22 40 37 25 32 23 179 18.0% Minor Arterial 32 39 45 45 43 46 250 25.2% Collector 50 77 61 68 50 53 359 36.2% Local road or Street 28 35 32 26 34 28 183 18.4% Interstate 2 1 0 0 3 2 8 0.8% Unknown 0 1 3 3 5 2 14 1.4% Total 134 193 178 167 167 154 993 100.0% Source: IDOT Motor Vehicle Crash Data Considering all crashes (Table 6-11), minor arterials and collectors again dominate, but local streets have a higher percentage of the crashes than principal arterials. Since local streets tend to have lower speeds, the crashes apparently are proportionately less serious. Subtracting Table 6-10 data from Table 6-11 yields Type B and Type C injuries. These data show that local streets have 46 percent more Type B and C injury crashes than principal arterials, whereas fatal and Type A crashes were very similar, 183 versus 179. Table 6-11: All injury crashes by roadway classification Roadway class 2005 2006 2007 2008 2009 2010 Total Percent Principal Arterial 201 197 264 226 227 259 1374 15.5% Minor Arterial 293 346 447 377 357 416 2236 25.2% Collector 403 501 654 546 485 570 3159 35.7% Local road or Street 323 318 386 328 282 293 1930 21.8% Interstate 4 5 9 12 14 9 53 0.6% Unknown 12 18 22 17 21 19 109 1.2% Total 1236 1385 1782 1506 1386 1566 8861 100.0% Source: IDOT Motor Vehicle Crash Data 6.2.5: Traffic Signal Control As seen in Table 6-12, 50 percent of fatal bicycle crashes occurred at locations with no traffic signals. About 34 percent of the crashes occurred at signalized locations and another nine percent occurred at stop signs. Given the small number of fatal crashes versus the injury crashes, there are no noteworthy differences between Tables 6-12 and 6-13, i.e., the presence of traffic control devices seemed to be similar in fatal and injury crashes. A possible exception is lane-use markings that accounted for six percent of the fatal crashes but less than one percent injury crashes. Still, this describes only two fatalities, which may have been an aberration.
50 Table 6-12: Traffic control device at fatal crashes Traffic control device Frequency Percent No controls 16 50.0% Traffic signal 11 34.4% Stop sign/flasher 3 9.4% Lane-use marking 2 6.3% Total 32 100.0% Source: IDOT Motor Vehicle Crash Data Table 6-13: Traffic control device at injury crashes Traffic control device Injury crash type Total Percent Percent known A B C No controls 468 2207 1665 4340 48.98% 50.37% Traffic signal 303 1316 956 2575 29.06% 29.89% Stop sign/flasher 148 760 596 1504 16.97% 17.46% Yield 3 16 14 33 0.37% 0.38% Police/flagman 2 4 0 6 0.07% 0.07% Railroad crossing gate 0 3 0 3 0.03% 0.03% Other Railroad crossing 0 0 1 1 0.01% 0.01% School zone 0 1 0 1 0.01% 0.01% No passing 3 18 13 34 0.38% 0.39% Other regulatory sign 2 4 4 10 0.11% 0.12% Other warning sign 1 0 0 1 0.01% 0.01% Lane-use marking 4 33 17 54 0.61% 0.63% Other 5 23 25 53 0.60% 0.62% Delineators (added in 0 1 0 1 0.01% 0.01% 2008) Unknown 22 106 117 245 2.76% - Total 961 4492 3408 8861 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data The following table (Table 6-14) shows the condition of the traffic control device during the crash. In both fatal and injury crashes there were no control devices in half of the crashes. In places where there was a control device, it was improperly functioning in both fatal and injury crashes in about three percent of the cases. While this is a low percentage, it clearly needs to be addressed.
51 Table 6-14: Condition of traffic control device at fatal and injury crashes Fatal crash Traffic Control Device Condition Injury Crash Type Total Percent Percent Known A B C No controls 15 480 2265 1706 4451 50.23% 52.59% Not functioning 0 10 43 36 89 1.00% 1.05% Functioning improperly 1 32 158 106 296 3.34% 3.50% Functioning properly 15 391 1769 1337 3497 39.47% 41.32% Worn reflective 0 0 4 2 6 0.07% 0.07% material Missing 0 0 1 1 2 0.02% 0.02% Other 0 13 60 49 122 1.38% 1.44% Unknown 1 35 192 171 398 4.49% - Total 32 961 4492 3408 8861 100.00% 100.00% Source: IDOT Motor Vehicle Crash Data
52 6.3: Work Zones None of the 32 fatalities occurred in a work zone. Only one percent of injury crashes occurred in work zones with the majority incurring Type B injuries (Table 6-15). There were also 12 serious injury crashes (Type A) in work zones. It is likely that cyclists are especially careful as they proceed into road conditions which are uncertain, commonly the case in work-zone areas. Table 6-15: Injury crashes in work zones, 2005-2010 Work zones Injury crash type related A B C Total Percent Yes 12 51 35 98 1.1% No 949 4441 3373 8763 98.9% Total 961 4492 3408 8861 100.0% Source: IDOT Motor Vehicle Crash Data
53 Chapter 7: Temporal Distributions of Crashes Traffic safety patterns have seasonal and temporal variations, reflecting both levels of use as well as inherent risks that affect crash causation and severity. In this chapter, we consider such variations, in terms of quarter of the year, month, day of week and hour of day. 7.1: Crashes by Quarter Table 7-1 shows that for the years considered, fatalities and injuries are highest during the third quarter. Since weather (temperature) tends to be a factor in biking, it is expected that this is the period with the highest level of biking activity. Accordingly, the winter months of January to March have the lowest levels of injuries. The fourth quarter has the second lowest followed by the second quarter. The number of fatalities tends to follow this pattern with the slight exception that the fourth quarter has one fewer fatality during the study period than the first quarter, yet the first quarter has the fewest injury crashes. Table 7-1: Bicycle injury crashes by calendar quarter in Chicago, 2005-2010 Quarter Crashes 2005 2006 2007 2008 2009 2010 Average 1 Injury 67 117 134 100 119 117 109.0 Jan-Mar Fatal 0 2 1 1 0 1 0.8 2 Injury 345 442 524 491 390 485 446.2 Apr-Jun Fatal 2 2 0 3 3 1 1.8 3 Injury 614 598 803 710 626 684 672.5 Jul-Sept Fatal 3 3 2 0 1 3 2.0 4 Injury 210 228 321 205 251 280 249.2 Oct-Dec Fatal 2 0 0 1 1 0 0.7 Total Injury 1236 1385 1782 1506 1386 1566 Fatal 7 7 3 5 5 5 Source: IDOT Motor Vehicle Crash Data
54 7.2: Bicycle Crashes by Month The seasonal pattern of injury crashes is quite predictable. Crashes are low in December through February, start building in March and increase almost linearly until July. Injury crashes begin to decline rapidly from August until December. Type A injury crashes are the exception. Rather than peaking in July as Types B and C injury crashes, they peak in August. But the three injury types are very similar in that the months of December, January and February have few crashes. This implies that winter months have fewer cyclists. The annual pattern suggests that a natural division of the year into four quarters would include these three winter months -- December, January and February - into one quarter, and the three summer months of June, July and August into another quarter. The remaining three months before and after the winter months would constitute the other two quarters. With this alignment of quarters, the autumn quarter of September, October and November has higher injury numbers than the spring quarter of March, April and May. This suggests that the inertia of cycling activity continues from the summer into the colder fall months, but is slow to start in the spring months. 800 700 600 500 400 300 200 100 0 Figure 7-1: Injury crashes by month and injury type, 2005 to 2010 total 20.6% 19.7% Type A 17.6% 17.1% Type B Type C 15.0% 15.6% 15.0% 12.9% 14.0% 11.2% 10.0% 9.3% 7.6% 6.1% 6.6% 4.9% 4.9% 21.2% 15.9% 18.0% 4.0% 11.9% 14.8% 2.5% 11.0% 1.9% 1.8% 8.4% 1.9% 5.9% 1.8% 1.5% 2.5% 2.6% 3.2% 3.1% Source: IDOT Motor Vehicle Crash Data
55 While the monthly injury data reveals a pattern of increasing and then decreasing numbers, the low overall fatality figures cause the pattern to be less discernible (Figure7-2). Again, perhaps due to the small number of winter bicyclists suggested in the previous table, it is not surprising that November and December have the fewest number of fatalities. Still, November is not among the four lowest injury-crash months. Likewise, the four fatalities in the first two months of the year, January and February, versus none in the last two months of the year, may have more to do with the small number of fatalities than with weather-related factors. 7 6 Figure 7-2: Fatal bicycle crashes by month, 2005-2010 18.8% 18.8% 5 4 12.5% 12.5% 12.5% 3 2 1 0 6.3% 6.3% 3.1% 3.1% 6.3% 0 0 Source: IDOT Motor Vehicle Crash Data
56 7.3: Crashes by Day of Week Regarding when during the week bicycle crashes occur, fatality patterns are somewhat different than injury crash patterns. The main difference is on Sunday. Sundays are likely to be characterized by recreational cycling, and perhaps correspondingly they account for over one-fifth of the fatalities. Indeed, it is the day with the largest number of fatalities (Figure 7-3). The low number of injury crashes on Sundays further suggests that the fatality rates are unusually high. Closer scrutiny shows that four of the seven Sunday fatalities occurred well after 8:00 pm or before 2:00 am, implying that darkness was a factor. Remarkably, Saturdays have less than half the number of fatalities than recorded on Sundays. Sundays deserve further consideration in establishing counter measures. Saturdays are tied with Mondays and Thursdays for the lowest number of fatalities. The injury data below suggest that these latter two days have the lowest weekday traffic levels. In this regard, Mondays and Thursday, are in sync with the low number of fatalities and injury crashes (though Type C injuries are relatively high on Thursdays). 8 7 6 5 4 3 2 1 0 Figure 7-3: Fatal bicycle crashes by day of week, 2005-2010 21.9% 18.8% 15.6% 15.6% 9.4% 9.4% 9.4% Source: IDOT Motor Vehicle Crash Data Injury crashes show more uniformity than fatalities over the days of the week (Figure 7-4). Wednesdays and Fridays have the largest number of Type B injuries. Fridays show the highest number of Type A and Type C injury crashes. Collectively, Fridays have the highest total number
57 of injury crashes. Conversely, Sundays have the lowest injuries of each of the three injury types, underscoring the anomalous fatality numbers of Sundays (though the small overall number of fatalities may be a factor). Mondays have the lowest weekday injury crashes for each of the three injury types. Based on Type B injuries, bicycle traffic appears to build during the first three days of the week (Monday to Wednesday). 800 700 600 500 400 300 Figure 7-4: Injury crashes by type of injury and day of week (the percentages sum to 100 over the seven-day week for each injury type) 16.9% 17.4% 18.7% 18.4% 19.9% 18.0% 18.7% 19.5% 19.1% 19.5% 17.1% 14.5% Type A Type B Type C 12.5% 13.0% 200 100 16.4% 17.9% 18.6% 17.5% 19.1% 17.6% 11.3% 0 Monday Tuesday Wednesday Thursday Friday Saturday Sunday Source: IDOT Motor Vehicle Crash Data
58 7.4: Time of Day The time of day is divided into five periods with the last one consisting of four hours. Hourly data are also presented to provide some detail. 7.4.1: Fatal Crashes by Time of Day While the number of injuries peaks earlier in the day, the number of fatalities continues to rise into evening hours. The highest total is in the 8:00 pm to midnight time interval, even though it is the only four hours in length. The other time periods in Figure 7-5 are also divided into four hour increments. Due to the likelihood that there is less cycling late in the evening, the high number of fatalities warrants further study. 10 9 8 7 6 5 4 3 2 1 0 Figure 7-5: Fatal bicycle crashes by time of day, 2005-2010 28.1% 25.0% 21.9% 15.6% 9.4% midnight-5am 5am-10am 10am-3pm 3pm-8pm 8pm-midnight Source: IDOT Motor Vehicle Crash Data
59 7.4.2: Injury Crashes by Time of Day Crashes with injuries (Figure 7-6) tend to reflect that same pattern as fatal crashes with the important exception that the 8:00 pm 12:00 am time period does not have the largest number of injury crashes, although it represents the largest share of fatality crashes. For each of the three injury types, the late night time slot has the third lowest number of injuries among the five time categories. In essence, it falls into the median range. This temporal distribution of crashes is very similar to the pattern in New York City, where more crashes occur between 3:00 pm and 8:00 pm than any other period (NYCDOT). 2000 1800 1600 1400 1200 Figure 7-6: Injury bicycle crashes by time of day, 2005-2010 51.9% 54.2% 31.5% Type A Type B Type C 1000 800 600 400 200 0 30.1% 16.7% 17.6% 14.9% 16.5% 42.6% 5.6% 29.8% 4.2% 17.4% 22.5% 6.0% midnight-5am 5am-10am 10am-3pm 3pm-8pm 8pm-midnight Source: IDOT Motor Vehicle Crash Data 7.4.3: Dooring Crashes In large part, dooring incidents reflect the same time of day pattern as for all other injury crashes (Figures 7-6 and 7-7). They grow during the course of the day and peak in the 3:00-8:00 pm time slot. This time period accounts for nearly half of all dooring crashes. This percentage, 45.7 percent, is lower than for Type B and C crashes but higher than Type A crashes in Figure 7-6. But in all four cases on Figure 7-6 and 7-7, the last time period, 8:00 pm to midnight, has the third highest proportion of crashes.
60 50.0% 45.0% Figure 7-7: Dooring crashes, 2010-2011 145 45.7% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 9 2.8% 39 12.3% 77 24.3% 47 14.8% midnight-5am 5am-10am 10am-3pm 3pm-8pm 8pm-midnight Source: IDOT Dooring Data 2010-2011 7.4.4: Fatal and Injury Crashes by Hour Given the late-night differences cited above, more examination is merited. We therefore present hourly data, initially for fatal and Type A data together, since graphing only 32 fatal crashes over 24 hours would not be very enlightening. Note that Figure 7-5 above depicts only fatal crashes; therefore it looks different than Figure 7-8. Figure 7-8 shows that serious injury and fatal crashes seem to grow steadily from 4:00 am and peak at 5:00-6:00 pm (17 on Figure 7-8). The sharpest rise runs from very early morning until 8:00 am, while the sharpest decline occurs after 9:00 pm. The overall highest levels are from 2:00 pm until 9:00 pm.
61 100 Figure 7-8: Fatal and Type A injury crashes by hour, 2005 to 2010 Fatal and Type A Injury Crashes 90 80 70 60 50 40 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of Day Source: IDOT Motor Vehicle Crash Data An examination of only fatal crashes reveals that nine of the fatalities occurred from 8:00 pm to midnight, with two occurring each hour with one exception (three fatalities from 11:00 pm to midnight). The busy four-hour cycling period from 3:00 pm to 7:00 pm shows eight fatalities. Darkness may be a factor in the large number of fatal crashes after 8:00 pm. The graph showing Type B and C injury crashes (Figure 7-9) resembles in large part the previous graph (Figure 7-8). The most distinctive difference is the mini-peak at 8:00 am on Figure 7-9. Overall it is evident that the afternoon period has the highest numbers of crashes of all types.
62 It is also again evident that evening hours have a disproportionate number of fatal and Type A injury crashes. On Figure 7-8 the next three hours after the 5:00 6:00 pm peak all had very high numbers, not so on Figure 7-9 (for less serious crashes). Type B and C Injury Crashes 900 800 700 600 500 400 300 200 100 0 Figure 7-9: Type B and C injury crashes by hour, 2005 to 2010 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of Day Source: IDOT Motor Vehicle Crash Data 7.5: Special Events Crashes during major holidays provides us with a unique opportunity to examine the spatial pattern of crashes when commercial and work-trip related cycling is a much smaller part of bicycle travel. We therefore selected July 4 th as a day in which much of the cycling is largely for recreational and personal business purposes. Map 7-1 shows the pattern for the six Fourth of July holidays from 2005 to 2010. On those days there were a total of 24 injury crashes. There were no fatal crashes on these six days. It shows a pattern that is very different from other maps in this report. First, the crashes seem to be scattered throughout the city. Second, the largest cluster is in the general vicinity of 71 st Street and Ashland. There are eight crashes here, counting the serious crash near 63 rd and California (red dot). This vicinity accounts for one-third of the crashes. Third, there are very few crashes east of the Kennedy Expressway. A large number of crashes occurred in this area over the six-year period (see Map 8-1). Fourth, there are no crashes in the Loop and only two in the Central Area, one to the north near Division and one to the south on Roosevelt. What is similar to other maps in this report, however, is the Milwaukee Avenue corridor. There are four crashes on Milwaukee with one serious crash plus three others in close proximity. Milwaukee Avenue may then be one of the few all-purpose arterials that services both daily and special-occasion traffic. No other arterial stands out on Map 7-1.
63 Map 7-1: Bicycle injury crashes on the six Fourth of Julys from 2005 to 2010 Source: IDOT Motor Vehicle Crash Data
64 Chapter 8: Spatial Distribution This chapter presents a series of maps depicting the spatial pattern of fatal and injury crashes. 8.1: Overall Spatial Distribution of Crashes During the six-year study period there were 32 fatalities and 961 serious injury (Type A) crashes. As is evident on Map 8-1, most of these serious crashes were just north and northwest of the downtown area. The number of crashes seems to continue north near Lake Michigan all the way to the northern boundary with Evanston at Howard Street. This finding is consistent with previous research on other cities that has found that crash frequency, severity and circumstances differed systematically in different parts of these cities (Loo, 2010). Map 8-1: Fatal and serious (Type A) injury crashes in City of Chicago, 2005-2010 Source: IDOT Motor Vehicle Crash Data
65 There are scattered clusters on the West Side and few on the South Side. The Hyde Park/University of Chicago neighborhood and the area along Archer Avenue appear to have a small cluster of crashes. As a whole, the South Side has relatively fewer serious crashes, though some of the void areas are industrial and generate few bicycle trips. All other injury crashes are shown in Map 8.2, including Type B non-incapacitating injuries and Type C possible injuries. The heavy concentrations are more difficult to see here versus the previous map. Map 8-2 better shows, however, the locations of land uses where bicycling is uncommon, such as O Hare Airport, the far south Lake Calumet area and, to a lesser extent, the southwest industrial corridor and Midway Airport. It again displays a high concentration of crashes in the downtown area. Map 8-2: Type B and C injury crashes in City of Chicago, 2005-2010 Source: IDOT Motor Vehicle Crash Data
66 8.2: Chicago Community Areas Map 8-3 shows the pattern of fatal and Type A crashes by community area (see Map 8-4 for community-area names). As above, the distribution of fatal and Type A crashes shows a large number of crashes in the community areas north of the downtown area. Map 8-3: Fatalities and Type A injury crashes, 2005-2010 Source: IDOT Motor Vehicle Crash Data
67 In interpreting this map it is important to note that populations have shifted since these community-area boundaries were drawn. There are four community areas on the North Side each with populations over 80,000, and six on the South Side each with fewer than 10,000 residents. There has also been a population shift in the downtown area during the study period. From 2000 to 2010, the Loop and the three adjacent community areas had a combined population increase of approximately 41,000 residents at a time that the rest of the city experienced a drop of 240,000 residents. Considering the underlying population data, Map 8.3 expectedly shows that there are six near North Side community areas that have 34 percent of the fatal and serious crashes (Table 8.1). These six community areas account for 333 of these crashes, or more than one-third of the city total of 961. But it is estimated that the six community areas also account for 40 percent of the bicycle miles traveled by Chicago residents (CMAP, Travel Tracker Survey). Table 8-1: Bicycle crashes and miles cycled in six community areas with the most crashes (by crash location; miles by place of residence) Bicycle crashes 000s Community area (CCA) Fatal + miles/ Total Type A day Total of six highest CCAs 333 3206 129 City total 976 8740 320 Six CCAs as percent of city total 34% 37% 40% Source: 2007 CMAP Travel Tracker data and IDOT Motor Vehicle Crash Data. The miles traveled are calculated estimates by the authors using publically available files.
68 The Loop, with 35 crashes, could be added as a seventh community area to the six in the previous paragraph, but its main reason for exclusion is its small population base and high bicycle traffic volumes. Still, the highest category needs to be defined at some value (over 45 on Map 8-3) and there may always be community areas that could be on the other side of the dividing line (category definition). This needs to be considered in interpreting the data on Map 8.3, where the highest category is over 35, as well as other maps. Specifically, some areas have small numbers of crashes either because they are small in area or less visually obvious; they have few residents or few traffic generators. Moreover, some areas experience heavy through traffic that is not attributable to the land use or density within the area. Map 8.3 also shows a concentric pattern on the north side. As the distance from the downtown increases, the number of serious crashes declines. This concentric pattern extends farther north along the lake and northwest in contrast to the west.
69 8.2.1: Highest and Lowest Number of Bicycle Crashes The community areas with the highest number of crashes are displayed in Table 8-2. The data in the fatal and Type A columns of Table 8-2 are shown on Map 8-3 and the percent of all bicycle crashes in the community area that fall into these two categories is given in the last column of Table 8-2. Note that most of the percentages are close to ten percent (see Map 8-4 for community area names). This ten-percent level is relatively low in contrast to community areas with lower levels of cycling. Table 8-2: Fifteen community areas with the highest number of injury crashes No. Community area Fatal A B C Total Percent fatal + A 24 West Town 2 72 387 300 761 9.72% 8 Near North Side 2 51 287 217 557 9.52% 22 Logan Square 4 50 277 215 546 9.89% 7 Lincoln Park 0 59 241 182 482 12.24% 6 Lake View 0 47 229 193 469 10.02% 28 Near West Side 0 46 197 148 391 11.76% 32 Loop 0 35 190 106 331 10.57% 3 Uptown 0 29 127 108 264 10.98% 25 Austin 2 18 116 91 227 8.81% 23 Humboldt Park 0 31 95 67 193 16.06% 19 Belmont Cragin 0 19 85 88 192 9.90% 77 Edgewater 0 20 85 83 188 10.64% 15 Portage Park 0 18 92 68 178 10.11% 21 Avondale 1 16 83 76 176 9.66% 1 Rogers Park 0 17 85 73 175 9.71% Source: IDOT Motor Vehicle Crash Data
70 Table 8-3 shows the number of crashes for the 15 community areas with the lowest number of crashes. Collectively they have 237 crashes. This total is considerably less than the highest values for individual community areas at the top of Table 8-2. Each of the top three community areas in Table 8-2 has over 500 bicycle crashes. Table 8-3: 15 community areas with the lowest number of injury crashes No. Community area Fatal A B C Total Percent fatal + A 52 East Side 0 0 20 5 25 0.00% 62 West Elsdon 1 2 12 10 25 12.00% 9 Edison Park 0 3 13 7 23 13.04% 18 Montclare 0 4 10 9 23 17.39% 45 Avalon Park 0 4 11 8 23 17.39% 48 Calumet Heights 0 2 11 8 21 9.52% 74 Mount Greenwood 1 3 9 7 20 20.00% 51 South Deering 0 3 10 4 17 17.65% 37 Fuller Park 0 0 8 5 13 0.00% 76 O Hare 0 2 4 7 13 15.38% 55 Hegewisch 0 3 4 4 11 27.27% 36 Oakland 0 1 1 5 7 14.29% 50 Pullman 0 1 2 4 7 14.29% 47 Burnside 0 1 3 2 6 16.67% 54 Riverdale 0 0 2 1 3 0.00% Source: IDOT Motor Vehicle Crash Data The percentage of the crashes that are either fatal or have serious injuries (Type A), however, is considerably higher in Table 8-3 than Table 8-2. Three community areas (Table 8-3) have no crashes that are either fatal or Type A; therefore the percentage is zero. Still, many of the percentages hover around 15 percent, higher than on Table 8.2. Some of the high percentages may be attributable to small values, but collectively they show that in these areas, where there are few crashes, high proportions tend to be either serious or fatal.
71 8.2.2: Per Capita Crashes: Mapped by Community Areas Given the discussion on variable population levels in each community area, from less than 3,000 to almost 100,000, it would be useful to examine maps that show per population patterns. Map 8-4 shows the cumulative number of fatalities and Type A bicycle crashes from 2005 to 2010 divided by the 2010 population per community area. In this regard it is not an annual figure but rather more akin to an index that shows the pattern of highest per capita crashes. Map 8-4: Fatalities and Type A crashes from 2005-2010 per 2010 population Source: IDOT Motor Vehicle Crash Data The pattern on Map 8-4 resembles Map 8-3 with several noteworthy differences, at least one in presentation. Specifically, in Map 8-4 the data are divided into five equal quintiles; each category has approximately 15 community areas. On Map 8-4 the categories are chosen by the mapping system searching for natural breaks in the data. Map 8-4 shows that many of the areas that have large numbers of bicycle crashes (Maps 8-1 and 8-2) also have high per capita numbers. West Englewood on the South Side is an area with
72 high crashes per capita. Also, Jefferson Park stands out on the far Northwest Side. It is a transportation hub with numerous Chicago Transit Authority (CTA) and Pace bus lines converging on a CTA station, with a well-used protected bicycle rack (see photo on page 70). The map of all Type B and C crashes (Map 8-5) shows two distinctions from Map 8-4 of the fatal and Type A crashes. First, the highest category reaches from the Loop all the way to the northern city limits. Second, in the southern part of the city there are proportionately few Type B and C injury crashes. Map 8-5: Type B and C crashes from 2005-2010 per 2010 population 8.3: Hotspots Source: IDOT Motor Vehicle Crash Data There are several concentrations of bicycle crashes through the city. These are places where there are unusually high numbers of crashes that warrant further examination to ascertain the
73 proper counter measures to reduce crashes. Map 8-6 identifies the locations of many of these clusters and they are called hotspots. The hotspots are calculated by first mapping the locations of the crashes. These places are shaded in light blue against the dark blue background. At each crash site the system searches a radius of one-half mile, and as it identifies additional crashes, the color changes initially to light Map 8-6: All injury crashes hotspots Source: IDOT Motor Vehicle Crash Data green, yellow, orange and finally to red, signalizing an increasing number of crashes within the search radius. On Map 8-6 we use a search of all injury crashes within a radius of one-half mile, but in the Chicago Loop, where there is a high concentration of crashes, we use a radius of oneeighth mile.
74 Map 8-6 shows graphically what we have seen on previous tables and maps. The downtown area stands out as do several diagonal corridors. Milwaukee Avenue is most prominent, but parts of Elston, Clybourn, Clark and Lincoln are also hotspots. Map 8-7 is similar to Map 8-6 but displays only the hotspots for fatal and Type A injury crashes. Again the downtown area and Milwaukee Avenue stand out as does Lincoln Avenue, especially where it begins to converge with Clark Street. Map 8-7: Fatal and Type A injury crash hotspots Source: IDOT Motor Vehicle Crash Data
75 8.4: Major Crash Corridors In addition to the map of intersections with high numbers of crashes and hotspot areas, dot maps of individual crashes are informative. Map 8-8 shows that there are almost a continuous series of sites that have experienced bicycle crashes. These are shown on a map that ranges from Grand on the south to Belmont on the north and west to east from approximately Kedzie to Halsted. Damen (north-south street in the middle of the map) and Milwaukee Avenues have particularly high concentrations of crashes. Similarly, Halsted has a large number of crashes, but the density is lower. Map 8-8: Major arterials of injury crashes (Type B injuries are large blue dots and Type C injuries are small red dots) Source: IDOT Motor Vehicle Crash Data
76 8.5: Major Arterial Hotspots Other than the concentrations of crashes at intersections (55 percent), there may be street segments that have high numbers of crashes. Map 8-9 depicts those street segments that have the greatest concentrations of crashes not associated with intersections. Milwaukee and Clark appear to have the highest linear densities of crashes though the highest overall area is in downtown Chicago and the immediate area to the north. Map 8-9: Non-intersection injury crashes Source: IDOT Motor Vehicle Crash Data 8.6: Dooring Crashes Some arterials are more prone to dooring crashes than others. Since we do not have exposure data, we use the number of injury crashes as a surrogate. On this scale, arterials such as Milwaukee, Clark and Halsted have the highest levels of bicycle activity. Milwaukee and Clark
77 also have the highest number of dooring crashes, but Lincoln has almost twice as many dooring crashes as Halsted, even though it has less than half as many overall bicycle crashes. We therefore present a ratio of dooring to all bicycle crashes. In this ratio, Lincoln has the highest value, 6.0, followed by Clark and Milwaukee. Other arterials with high values are Armitage, Damen and Belmont. Conversely, lowest ratios among the ten arterials with the highest number of bicycle crashes are found on Fullerton, Chicago, Western and Ashland. These are relatively wide arterials. More study, however, is suggested to identify some of the underlying reasons for these differences, such as parking rates and densities. Table 8-4: Dooring crashes compared to all injury crashes by major arterials, 2010 There exists double counting since intersection injury crashes are attributed to more than one arterial. Arterial Fatalities Injury Dooring Ratio* crashes Milwaukee 0 834 33 4.0 Halsted 0 715 11 1.5 Clark 4 658 28 4.3 Western 4 590 5 0.8 Ashland 2 520 4 0.8 Damen 4 509 19 3.7 North 0 494 7 1.4 Fullerton 0 454 1 0.2 Chicago 5 428 2 0.5 Division 2 367 7 1.9 Diversey 1 350 4 1.1 Belmont 0 332 12 3.6 Lincoln 2 331 20 6.0 California 0 319 5 1.6 Kedzie 4 302 6 2.0 State 0 294 6 2.0 Grand 0 277 2 0.7 Elston 3 255 0 0.0 Lake Shore 0 255 0 0.0 Irving Park 2 241 4 1.7 Armitage 4 238 9 3.8 Cicero 4 225 0 0.0 *(Dooring x 100/Injury crashes) Source: IDOT Dooring Data 2010-2011 and IDOT Motor Vehicle Crash Data, 2005-2010
78 8.7: Land Uses near Crash Locations In this section, we explore the association between bicycle crashes and land uses. 8.7.1: Schools and Universities In assessing whether there may be an unusual problem in the vicinity of schools, we searched for crashes in close proximity to schools during the beginning and the end of the school day. We have selected the period 7:00 am to 9:00 am for the beginning of school and 1:00 pm to 4:00 pm as the period associated with dismissal. We have also selected a quarter-mile radius around the school as the primary target area. For high schools, we have included bicycle crashes that fit the above criteria for riders aged 15 to 18. The result is Map 8-10. It shows the locations of these crashes and lists the three high schools with three such crashes and another 12 with two crashes each. The high schools that have three crashes are scattered across the city with one each on the north, west and south sides. In general, the distribution of crashes on Map 8-10 shows more crashes on the northern periphery of the city than previous maps. Map 8-10: High school vicinities with injury crashes Source: IDOT Motor Vehicle Crash Data
79 For primary schools, we use the age group 5 to 14. There are far more primary schools than high schools in Chicago and, more potential areas for analysis. Indeed, Map 8-11 shows a scattering of hotspots throughout the city. Of all the maps presented in this report, this seems to display the greatest balance across the study area. There are few overwhelming pockets though we will focus in the next paragraph on a cluster on the far west side. This balance implies two things. First, the problem is uniform across the city, not just primarily in one region. Second, it suggests that young residents throughout the city are becoming bicycle riders, which could translate to more bicycle traffic throughout the city in the future.
80 Map 8-11: Primary school hotspots Source: IDOT Motor Vehicle Crash Data
81 A closer examination of the far West Side shows that the area between Madison and Ogden at Central Park is a problem area (Map 8-12). Perhaps expectedly, since the bicyclists in this case are young, the crashes are predominantly on neighborhood streets. There are two diagonal arterials on this map, Grand and Ogden, and neither seems to represent problem arterials. Map 8-12: Primary school hotspots in the far west side Source: IDOT Motor Vehicle Crash Data
82 8.7.2: Central Business District The Chicago downtown clearly has a large number of bicycle crashes. Many of these are on north-south streets from Michigan Avenue to Wells including State, Dearborn, Clark and LaSalle (Map 8-13). Outside the Loop, Halsted also has a large number of crashes. Madison is one of the few east-west arterials with a large number of crashes. Just north of the Loop, Grand Avenue and Kinzie Street also are evident on Map 8-13. Beyond this immediate downtown area Chicago and Milwaukee Avenues appear prominently. Map 8-13: Downtown Type B and C injury crashes Source: IDOT Motor Vehicle Crash Data 8.7.3: Residential Non-Central Business District Community areas from Rogers Park to the Near North Side, including Edgewater, Uptown, Lake View and Lincoln Park, have a considerably high level of bicycling and bicycle crashes (Map 8-14). Clark Street is a major north-south arterial that tends to parallel the lake and has an unusually high concentration of crashes especially near the south end.
83 Map 8-14: North Side Type B and C injury crashes Source: IDOT Motor Vehicle Crash Data
84 The University of Chicago / Hyde Park area is one of the few areas on the southern half of the city that has a substantial number of crashes. Map 8-15 shows the Type B and C injury crashes. Many are on 55 th and 57 th Street in and near the university. There are also a large number of crashes on Dorchester, a north-south arterial east of Woodlawn (street name not shown on Map 8-15). Map 8-15: Hyde Park / University of Chicago area Type B and C injury crashes Source: IDOT Motor Vehicle Crash Data
85 Chapter 9: Summary and Limitations of the Study This section reviews the major findings of this study. It also discusses the principal limitations. 9.1: Study Summary The findings in this report are important since cycling is growing in popularity nationwide, and Chicago remains a leading cycling community among the largest cities. Chicago has a higher mode share of bicycle commuters than Los Angeles or New York City. It also has a lower fatality rate than these two cities. Nevertheless, there is room for further growth in bicycle use and bicycle safety. Among seven peer cities, both Seattle and Philadelphia have a higher bicycle modes share among commuters. There are also more bicycle commuters in San Francisco and Portland. Also, there are five times as many commuters walking to work versus bicycle use in Chicago. In many other places the ratio is much smaller, and in Portland, Oregon there are more commuters using bicycles than walking to work. Walking to work is an indication of the proximity to places of work and a surrogate for the feasibility of bicycling to work. The main finding in this report is that while the number of bicycle crashes in Chicago has risen, the level of bicycling has increased at a greater pace. In this millennium, the number of bicycle commuters has more than doubled, while during the six-year study period the number of bicycle fatalities has increased, but declined since 2007, the peak year of bicycle crashes. Given the sharp rise in cycling, the fact that the number of injury crashes has increased but only modestly is also a positive development. Still, any increase in crashes requires attention. Another positive sign is that alcohol is much less of a problem associated with bicycle crashes than with motor-vehicle crashes. At the same time, there are a few areas of concern. Intersections account for over half of bicycle crashes. Roadway design features hold promise in mitigating this problem. The evening hours are associated with a disproportionate number of fatalities. Better visibility and better awareness among drivers would help this situation. A lack of helmet use continues to be a factor in the severity of bicycle crashes. While only one of the cyclists involved in fatal crashes are known to have worn a helmet, crash reports record are numerous unknowns, making analysis of this data difficult. However, national trauma data show that only about a quarter of their bicycle-crash patients wore a helmet. 9.2: Limitations The main limitation of the study is the unavailability of data. IDOT s crash data are very useful and extensive source of information that we have used throughout this study. As complete as it is, it only includes data on bicycle crashes with motor vehicles. They does not include information on crashes with other bicyclists or pedestrians (i.e. on bike paths) nor on crashes with stationary objects. This has led us to examine the National Trauma Data Bank (NTDB). While the trauma data are useful, the dataset includes information only from participating hospitals and without location identifiers for those hospitals. Hence, we were only able to do a
86 national scan of trauma registries without being able to infer trends for the City of Chicago specifically. Also, crash data are most useful when there is a comparable data set that includes exposure information. The Chicago Metropolitan Agency for Planning collected household travel information in 2007 that includes over 550 bicycle trips in the City of Chicago. These data have been remarkably useful, even though they were collected for regional travel demand modeling purposes and had numerous objectives, not just to collect information on bicycling levels. Ideally, a larger data source would be available in the future.
87 Technical Appendix A: Data and Study Area A.1: Data The analysis presented in this report used two types of data: (A) safety data and (B) travel trend and exposure data. Safety Data: We analyzed four sources of secondary sources of information on safety: Illinois Department of Transportation (IDOT) Motor Vehicle Crash Data, 2005 to 2010; Illinois Department of Transportation (IDOT) dooring data, 2010-2011; National Highway Traffic Safety Administration s (NHTSA) Fatality Analysis Reporting System (FARS), 2005 to 2009; American College of Surgeons National Trauma Data Bank (NTDB), 2005-2010). The primary data source used in this study is the IDOT motor-vehicle crash file. These data include bicycle crashes that occur on roadways shared with motor vehicles. Therefore, crashes that occur on bicycle trails or single-bicycle crashes that occur from falls or hitting obstructions are not included in the analysis based on this data. The dataset is composed of police reports. In examining the six-years study period, we have focused on fatal crashes involving bicyclists and crashes involving three levels of injury crashes (Types A,B and C). We have not examined bicycle crashes leading to property damage. We have analyzed fatal and injury crashes involving cyclists by the spatial distribution and characteristics of communities and neighborhoods, location of crashes, time of day and year, weather conditions and trends over time. Results have been disaggregated by sociodemographics such as age and gender and by crash characteristics (type of crash, level of injury). We have compared trends in the City of Chicago to those in a number of other areas. These include Cook County, the state of Illinois, peer cities which most resemble Chicago in size and character and the U.S. as a whole. For 2010 and 2011, we were able to analyze dooring information compiled by IDOT. In order to contextualize bicycle fatalities in the City of Chicago to national trends and also to obtain greater details on fatalities that occurred in the city that are not available from the IDOT data, we used the FARS data. As noted above, the safety data do not include crashes on bike paths, single-bicycle crashes with stationary objects or pedestrian-bicycle crashes. In order to obtain information on injuries resulting from these types of safety events and also to understand helmet wearing behavior to a greater extent, we analyzed trauma data from the American College of Surgeons National Trauma Data Bank. The NTDB contains trauma registry data from participating trauma centers on an annual basis, and it is important to note that the data are not of all persons admitted to hospitals. At the time of writing this report, we were not able to obtain the location identifiers of the hospitals in the dataset. Hence, we have analyzed the trauma data at the national level only.
88 Travel Trend and Exposure Data. We have also developed a limited amount of exposure data for the purpose of estimating relative risk. The following sources used to estimate exposures are: U.S. Census Bureau, Decennial Census (Census 2010), U.S. Census Bureau, American Community Survey (ACS 2005 to 2010), National Household Travel Survey (NHTS 2009) and Chicago Metropolitan Agency for Planning s (CMAP), Travel Tracker Survey (TTS 2007). The Census Bureau data provides population figures and also commuting mode shares. The ACS provides, in addition to population, annual data on bicycle use in the journey to work, thereby giving us the ability to track trends. We have also extensively used the Travel Tracker Survey conducted by the Chicago Metropolitan Agency for Planning, the Metropolitan Planning Organization for northeastern Illinois. This survey includes data from approximately 10,500 households in northeastern Illinois. It provides daily travel diaries and information on approximately 550 bicycle trips made in Chicago, regardless of trip purpose. Collected from January of 2007 to March of 2008, the dataset is frequently known as the Travel Tracker Survey, and includes information on trip origins and destinations and well as on the characteristics of the traveler. A.2: Study Area and Peer Cities The analysis presented in this report pertains only to the City of Chicago. While in some studies the term city is ambiguous and sometimes includes suburban areas, the focus of this study is entirely on the area within the Chicago city limits. In this context, it is important to note that there is no national standard on the scope of city limits. Places like Boston, San Francisco and Miami are less than one quarter of the size of Chicago (in square miles), while places like Oklahoma City, Houston, Phoenix, Jacksonville and Los Angeles are more than twice the area of Chicago. In Florida, Jacksonville is more than 20 times larger than Miami in land area, but the Miami metropolitan population is more than four times greater. These distinctions need to be considered in making comparisons with other cities and selecting peer cities. Our selection of peer cities includes the following criteria (1) population greater 500,000; (2) density greater than 5,000 residents per square mile; (3) at least 17 percent of the commuters use public transit, bicycle or walk to work; and (4) the area of the city is at least 75 square miles. For peer cities, therefore, we include the city size (square miles) in the list of criteria, since several cities are particularly small and encompass only the high density core. San Francisco, Boston and Miami are all less than 50 square miles. Since Chicago is 227
89 square miles, a comparison with Boston or Miami would not be particularly meaningful. A more appropriate comparison with these cities would be with the core area of Chicago, but the definition of such an area is likely to be arbitrary. The area size criterion is applied because small cities tend to be of high density, therefore having disproportionately higher bicycle mode share rates in their core areas. As the area increases, the number of cyclists increases but the share declines. For example, Washington, D.C. has a bicycle commuting mode share of 3.1 percent, considerably higher than the 1.3 percent in Chicago. But D.C. s metropolitan share is only 0.54 percent, actually lower than the Chicago metropolitan figure of 0.61 percent. Also, both Boston and San Francisco have higher population densities than Chicago due to their small areas, though the inner City of Chicago certainly has much higher density than the entire city as a whole. No definition of peer cities meets all situations. San Francisco is not one of our peer cities, nor is it among the twelve largest U.S. cities, but it has approximately the same number of bicycle commuters as Chicago. The same is true for Portland, Oregon. Portland is a unique place in many regards. It is the only city among the 50 largest that has more bicycle commuters than those that walk to work. Hence, there are frequently limitations to comparisons with other large cities. The point that bicycling varies by region in the metropolitan area can be seen using CMAP s Travel Tracker data (Table A-1). The City