TRAFFIC CRASH FACTS FOR CHAMPAIGN-URBANA SELECTED CRASH INTERSECTION LOCATIONS (SCIL)

Transcription

1 TRAFFIC CRASH FACTS FOR CHAMPAIGN-URBANA SELECTED CRASH INTERSECTION LOCATIONS (SCIL) February 2014 Prepared by: Champaign Urbana Urbanized Area Transportation Study (CUUATS)

2 TABLE OF CONTENTS LIST OF TABLES... III LIST OF FIGURES... IV EXECUTIVE SUMMARY... V 1.0 INTRODUCTION CRASH DATA DATA SOURCE DATA LIMITATIONS AND ERRORS DATA REDUCTION VEHICLE CRASH ANALYSIS METHODOLOGY INTERSECTION TYPE CLASSIFICATION VOLUME BASED CLASSIFICATION AVERAGE CRASH FREQUENCY AVERAGE CRASH RATE CRASH SEVERITY METHOD Crash Severity Equivalent Property-Damage-Only (EPDO) Method CRITICAL INTERSECTION PRIORITIZATION TRAFFIC CRASH TRENDS AND FACTS FOR CHAMPAIGN-URBANA ILLINOIS STATEWIDE RATES AND CUUATS LONG RANGE TRANSPORTATION PLAN GOALS Total Crashes per 100 MVMT Fatalities per 100 MVMT Severe Crashes per 100 MVMT CORRIDOR CRASH ANALYSES ADDITIONAL TRAFFIC CRASH FACTS FOR Day of the Week Analysis Time of the Day Analysis Collision Types Weather Conditions Roadway Surface Condition Roadway Lighting Condition Driver Condition Driver Age and Gender Traffic Control PEDESTRIAN AND BICYCLE CRASHES PEDESTRIAN CRASHES Pedestrian Age and Gender Pedestrian Pre-crash Behavior BICYCLE CRASHES Bicyclist Age and Gender Bicyclist Pre-Crash Behavior PEDESTRIAN & BICYCLE CRASHES IN THE UNIVERSITY DISTRICT SELECTED CRITICAL INTERSECTION LOCATIONS ( ) INTERSECTION LOCATIONS WITH FATAL CRASHES CRASH FREQUENCY REGRESSION ANALYSIS CONCLUSIONS REFERENCES ii

3 LIST OF TABLES TABLE 1: VOLUME BASED CLASSIFICATION...5 TABLE 2: CRASH FREQUENCY CRITERIA...6 TABLE 3: CRASH RATE CRITERIA...6 TABLE 4: EQUIVALENT PROPERY DAMAGE ONLY (EPDO) CRITERIA...8 TABLE 5: IDENTIFYING PRIORITY LEVELS...9 TABLE 6: CRASH DATA SUMMARY ( ) TABLE 7: VMT AND TOTAL CRASH RATES IN CHAMPAIGN-URBANA TABLE 8: SEVERITY CRASHES PER 100 MVMT FOR CHAMPAIGN-URBANA TABLE 9: CRASHES ALONG MAJOR CORRIDORS IN CHAMPAIGN-URBANA TABLE 10: CRASHES ALONG MAJOR CORRIDORS BY YEAR TABLE 11: CRASHES ALONG MAJOR CORRIDORS IN CHAMPAIGN-URBANA TABLE 12: SEVERE AND FATAL CRASHES ALONG MAJOR CORRIDORS BY YEAR TABLE 13: VEHICLE COLLISION TYPES TABLE 14: TRAFFIC CRASHES IN DIFFERENT WEATHER CONDITIONS TABLE 15: CRASHES BY ROADWAY LIGHTING CONDITION TABLE 17: DRIVERS INVOLVED IN TRAFFIC CRASHES IN CHAMPAIGN-URBANA TABLE 18: CRASHES BASED ON TRAFFIC CONTROL TABLE 19: PEDESTRIAN CRASHES IN CHAMPAIGN-URBANA TABLE 20: PEDESTRIANS BEHAVIOR LEADING TO CRASHES TABLE 21: CRASHES IN CHAMPAIGN-URBANA TABLE 22: BICYCLIST BEHAVIOR LEADING TO CRASHES TABLE 23: PEDESTRIAN AND BICYCLE CRASHES IN THE UNIVERSITY DISTRICT TABLE 24: CRITICAL SIGNALIZED INTERSECTIONS TABLE 25: CRITICAL UNSIGNALIZED INTERSECTIONS iii

4 LIST OF FIGURES FIGURE 1: PROCESS INVOLVED IN IDENTIFYING CRASH TRENDS IN CHAMPAIGN- URBANA...2 FIGURE 2: TOTAL CRASHES IN CHAMPAIGN-URBANA FIGURE 3: TOTAL CRASH INJURIES IN CHAMPAIGN-URBANA FIGURE 4: TOTAL CRASHES PER 100 MILLION VMT FIGURE 5: TRAFFIC FATALITIES IN CHAMPAIGN-URBANA FIGURE 6: TRAFFIC FATALITIES PER 100 MILLION VMT FIGURE 7: FATAL CRASH LOCATIONS IN CHAMPAIGN-URBANA FIGURE 8: SEVERE INJURY PER 100 MVMT IN CHAMPAIGN-URBANA FIGURE 9: TYPE A INJURY CRASH LOCATIONS IN CHAMPAIGN-URBANA FIGURE 10: UNIVERSITY AVENUE CRASH STATISTICS FIGURE 11: PROSPECT AVENUE CRASH STATISTICS FIGURE 12: FATAL AND SEVERE CRASHES ALONG MAJOR CORRIDORS IN CHAMPAIGN- URBANA FIGURE 13: PEDESTRIAN AND BICYCLE CRASHES ALONG MAJOR CORRIDORS IN CHAMPAIGN-URBANA FIGURE 14: CRASHES BY DAY OF THE WEEK FIGURE 15: TIME OF THE DAY ANALYSIS FIGURE 16: PERCENT DISTRIBUTION OF CRASHES BY COLLISION TYPE FIGURE 17: PERCENT DISTRIBUTION OF CRASHES BY WEATHER CONDITION FIGURE 18: CRASHES BY ROAD SURFACE CONDITION FIGURE 19: PERCENT DISTRIBUTION OF CRASHES BY ROADWAY LIGHTING CONDITIONS FIGURE 20: PERCENT DISTRIBUTION OF IMPAIRED/DISTRACTED DRIVING CRASHES FIGURE 21: AGE DISTRIBUTION OF MOTOR VEHICLE DRIVERS INVOLVED IN TRAFFIC CRASHES FIGURE 22: TOTAL AND SEVERE INJURIES FOR DIFFERENT AGE GROUPS FIGURE 23: CRASH FREQUENCY COMPARISON BETWEEN MALE AND FEMALE DRIVERS 36 FIGURE 24: PERCENT DISTRIBUTION OF CRASHES BASED ON TRAFFIC CONTROL FIGURE 25: AGE DISTRIBUTION OF PEDESTRIANS INVOLVED IN CRASHES FIGURE 26: PEDESTRIAN AGE AND GENDER DISTRIBUTION FIGURE 27: PERCENT DISTRIBUTION OF PRE-CRASH ACTION FOR INJURED PEDESTRIANS FIGURE 28: PEDESTRIAN CRASH LOCATIONS IN CHAMPAIGN-URBANA FIGURE 29: AGE DISTRIBUTION OF BICYCLIST INVOLVED IN CRASHES FIGURE 30: BICYCLIST AGE AND GENDER DISTRIBUTION FIGURE 31: PERCENT DISTRIBUTION OF PRE-CRASH ACTIONS FOR INJURED BICYCLISTS FIGURE 32: BICYCLE CRASH LOCATIONS IN CHAMPAIGN-URBANA FIGURE 33: BICYCLE AND PEDESTRIAN CRASH LOCATIONS IN THE UNIVERSITY DISTRICT FIGURE 34: BICYCLE AND PEDESTRIAN CRASH SEVERITY IN THE UNIVERSITY DISTRICT.. 49 FIGURE 35: SELECTED CRITICAL INTERSECTION LOCATIONS FIGURE 36: FATALITY INTERSECTION LOCATIONS AND SELECTED CRITICAL INTERSECTION LOCATIONS FIGURE 37: CRASH FREQUENCY WITH INTERSECTION VOLUME AND CONTROL TYPE. 55 iv

5 EXECUTIVE SUMMARY This report summarizes the traffic crash facts in the Champaign-Urbana area based on traffic crash data. According to the Unified Technical Work Program (UTWP) of the Champaign Urbana Urbanized Area Transportation Study (CUUATS), a study must be completed every two years in order to identify critical crash intersection locations in the Champaign-Urbana area. Identification of these intersection locations is a vital component of traffic safety improvements. In this report, the intersections in Champaign-Urbana were analyzed and the Selected Crash Intersection Locations (SCIL) were identified utilizing indexes such as crash frequency, crash rate, and crash severity. Additional data analyses were performed to illustrate the trend in crashes over the 5-year period (2007 to 2011) based on the time & day of the crash, crash conditions, and driver characteristics. Crashes along the major east-west and north-south corridors in Champaign-Urbana were highlighted. A regression model for crash frequency corresponding to intersection volume and intersection control type was also developed. Important findings for the Champaign-Urbana study area during the 2007 to 2011 study period include: Vehicle miles traveled (VMT) in Champaign-Urbana remained relatively constant between 2007 and Crashes per 100 million vehicles miles traveled (MVMT) decreased from 358 in 2007 to 235 in Furthermore, the crash rates per year within the study area remained consistently lower than statewide crash rates (per 100 MVMT). The total fatalities in Champaign-Urbana fluctuated between four and seven from 2007 to There were no fatal crashes in the City of Champaign in Fatality rates for the urbanized area remained lower than statewide fatality rates and target rates proposed by IDOT s Highway Safety Performance Plan (HSPP), for each study year. Total injuries steadily decreased from 2007 to 2011, with the exception of Overall, traffic crash injuries decreased by 20% from 2007 to The severe injury crashes (Type A) increased from 134 in 2008 to 178 in 2009 and then decreased to 135 in About 21% of crashes each year were severe injury crashes. Severe injury rates (per 100 million VMT) for Champaign-Urbana in 2009, 2010, and 2011 failed to meet HSPP targets. Furthermore, severe injury rates were consistently above corresponding statewide severe injury rates for the five year period. v

6 Two East/West corridors (University Ave. and Springfield Ave.) and two north/south corridors (Prospect Ave. and Neil St.) experienced around 1,000 crashes over the five year analysis period. University Ave. had 1,110 crashes and Prospect Ave. had 1,092 traffic crashes. The traffic crashes along the major urban corridors decreased over the five year period with a marginal increase in Fourteen (14) of the 30 fatal crashes were along the urban corridors. Additional data analysis indicated the following trend in crashes over the analysis period: o Friday was the most hazardous day of the week, while Sunday had the fewest crashes. o The majority of crashes occurred between 12:00 PM and 6:00 PM, while 3:00 AM to 6:00 AM had the fewest crashes. o Rear-end, turning, and angle crashes were the predominant crash types in Champaign-Urbana. o The majority of crashes were under clear weather conditions (79%), during daylight (70%) and on dry roadway pavement surfaces (69%). o The majority of impaired/distracted driving crashes were alcohol related (64%). Drivers between the ages of 15 and 29 were involved in 45% of the total crashes reported in Champaign-Urbana. The highest number of severe injuries and fatalities were reported in crashes involving drivers between the ages of 20 and 24. About 42% of all crashes in the urbanized area occurred at intersections controlled by a traffic signal or stop sign/flasher. There were 215 pedestrian crashes reported in Champaign-Urbana over the past five years, resulting in 10 fatalities and 208 injuries (33% were type A injuries). There were 270 bicyclist crashes reported in Champaign-Urbana, resulting in 2 fatalities and 252 injuries (19% were type A injuries). Almost all pedestrian and bicycle crashes resulted in an injury. Pedestrian and bicyclist deaths accounted for about 32% of the total fatalities in Champaign- Urbana over the five year period. A significant number of pedestrians (52.5%) and bicyclists (53.5%) involved in traffic crashes were under 25 years of age. vi

7 Thirteen signalized and twenty-one unsignalized intersections were identified as critical intersections. Different priority levels were assigned to the critical intersections based on crash frequency, crash rate, and crash severity values. vii

8 1.0 INTRODUCTION Traffic crashes are one of the leading causes of death in the United States. In 2011, 32,367 lives were lost in traffic crashes in the U.S. 1 In the same year, Illinois recorded 918 fatalities and 84,172 injuries, 11,942 of which were type A injuries 2. Roadway intersections are found to be the predominant locations of traffic crash occurrences in urban areas. At the national level, more than 50 percent of all traffic crashes in urban areas and more than 30 percent of crashes in rural areas are intersection related 3. The Federal Highway Administration (FHWA) notes that 21 percent of the total fatalities nationwide over the last several years were intersection-related, and approximately 50 percent of serious injury crashes in the U.S. were intersection related 4. In total, 26.3 percent of fatal crashes in Illinois in 2011 occurred at intersections 2. The FHWA has identified intersection safety as one of four high-risk areas. The American Association of State Highway and Transportation Officials (AASHTO) added improving the design and operation of highway intersections as a key emphasis area in its 2005 Strategic Highway Safety Plan (SHSP). According to the Unified Technical Work Program (UTWP) of the Champaign-Urbana Urbanized Area Transportation Study (CUUATS), a study must be completed every two years to identify critical crash intersection locations in the Champaign-Urbana area. This report summarizes the Selected Crash Intersection Locations (SCIL) for 2007 to Figure 1 illustrates the different steps developed in this study. Local Accident Reference System (LARS) data was obtained from the Illinois Department of Transportation (IDOT). CUUATS staff decoded the data and identified the Selected Crash Intersection Locations (SCIL) following CUUATS crash data analysis methodology. The report presents the summary of crashes in the Champaign-Urbana area and compares it against the various goals established by IDOT to reduce fatalities and injuries on Illinois roadways. The report also identifies the measures of effectiveness (performance measures) addressed in the CUUATS 2035 Long Range Transportation Plan (LRTP) regarding transportation safety. The measures of effectiveness include the total crashes per 100 Million Vehicle Miles Travelled (MVMT), total fatalities per 100 MVMT, total severe injuries per 100 MVMT, total pedestrian-related crashes, and total bicycle-related crashes. This report quantifies and helps achieve the LRTP goals by identifying the crash trends in the region. The LRTP transportation safety goals include reducing the total number of crashes in Champaign-Urbana by 5 percent between 2009 and 2014; reducing the total number of fatalities and severe injuries by 25 percent between 2009 and 2014; and reducing the total number of crashes involving bicyclists and pedestrians by 15 percent by

9 Figure 1: Process Involved in Identifying Crash Trends in Champaign-Urbana This report includes all crashes in the region (including intersection and nonintersection crashes). Pedestrian and bicycle crashes in the Champaign-Urbana area are also analyzed separately. Based on the frequency summary of intersection crashes, a nonlinear regression model is given for crash estimation use. 2

10 2.0 CRASH DATA Crash data is observed and collected in the field. Reported crashes are recorded in the field by law enforcement agencies. Any error in observation affects the quality of the data. Furthermore, there is a chance of making errors while coding the data. Crash data has to be carefully studied in order to identify the errors. The errors then must be corrected and any flawed data is excluded from further analysis. As a result of this process, quality, high level information from the crash data helps engineers better understand crash patterns and identify the cause(s) of crashes. This understanding is very important in order to come up with recommendations for improving intersection safety. It should be noted that the law regarding the reporting threshold for property damage crashes has changed effective January 1, If all of the drivers involved in the crash are insured, the amount of damage to the property that must be reported increased from $500 to $1,500. The threshold remains at $500 if any of the drivers involved in the crash are not insured Data Source The crash database is compiled every year from the local and state police crash records submitted to the Illinois Department of Transportation (IDOT) Division of Traffic Safety. The crash data files contain information required to identify and analyze the crash records including crash ID, street codes, day, time, number of persons injured or killed, road feature, road surface condition, light condition, type of collision, crash severity information, direction, maneuvers and type of vehicle for up to four vehicles involved in a crash. Until 2004, IDOT used Local Accident Reference System (LARS) to identify the location of the crash records. The LARS data is coded numerically based on standard coding rules and is distributed in an electronic database. In 2005, IDOT started transitioning from LARS codes to a Geographic Information System (GIS) X/Y coordinate system to identify the crash locations. During the transition period from 2005 to 2010, IDOT provided both LARS codes and x/y coordinates for each crash record. Starting in 2011, IDOT discontinued LARS codes, and now only provides the GIS shapefiles with all of the essential crash information. In addition to IDOT raw crash data, the yearly city summary crash report published for the City of Champaign and City of Urbana by IDOT was also used to summarize and analyze the crash trends in the Champaign-Urbana urbanized area. 3

11 2.2 Data Limitations and Errors The data used in this study had the following limitations: Data does not include unreported crashes. Minor property damage (below the qualifying value), lack of willingness to report, crashes on private properties, etc., are not included in the reported crashes. Some crash records were incomplete. A few crash records in the LARS list contained coding errors. 2.3 Data Reduction For the crash data prior to 2010, the numerically coded LARS data was decoded and then checked for any errors (e.g., wrong coding). Erroneous and/or incomplete data was removed. The LARS data includes crashes from all kinds of road features (including mid-block, intersections, freeways, etc.). The intersection crashes were extracted from this mixture of data. The data was then grouped into different coverage areas (Champaign and Urbana) so that individual municipalities could track what has occurred in each jurisdiction. The crashes were grouped again by intersection. The number of crashes at each intersection was obtained from this grouped data and analyzed to identify critical intersections. For the 2011 crash data, GIS shapefiles were used to identify the intersection crashes, since the LARS codes were no longer provided. The intersection crashes were obtained by creating a buffer around each intersection in GIS and separating the crashes with the buffer area of the intersection. Each crash record contains information on whether or not it was an intersection crash, which was used to further filter the results. 4

12 3.0 VEHICLE CRASH ANALYSIS METHODOLOGY This study utilized specific methods (crash frequency, crash rate, and crash severity) to identify critical intersections. The final list of SCILs was produced using an enhanced combination of the above methods approved by the CUUATS member agencies. The criteria used for identifying Critical Intersection Locations include the following: Intersection Type Classification Volume-Based Classification Average Crash Frequency Average Crash Rate Average Crash Severity 3.1 Intersection Type Classification All crash intersections should be classified into two groups (signalized and unsignalized) based on existing traffic control types. 3.2 Volume Based Classification Each intersection should be classified based on its total entering volume per day. Roadway ADT information is generally obtained from IDOT traffic maps and an inhouse traffic count database. Table 1 shows intersection classifications based on daily entering traffic. Table 1: Volume Based Classification Intersection Class Daily Entering Traffic A 20,000 B 10,000-19,999 C 5,000-9,999 D 2,000-4,999 E 1, Average Crash Frequency For this criterion, the average crash frequency and its standard deviation for each volume class of signalized and unsignalized intersections are calculated. Crash data over a period of five years or more are analyzed for determining the average crash frequency of each intersection. An intersection s crash frequency should be termed as very high, high, or above mean as per the criteria shown in Table 2. 5

13 Intersection Table 2: Crash Frequency Criteria Crash Frequency Very High High Above Mean Signalized F savi + 2S sfi F savi + S sfi > F savi Unsignalized F uavi + 2S ufi F uavi + S ufi > F uavi Where; F savi = Average Crash Frequency of all Signalized Intersections in Volume Class i. S sfi = Standard Deviation of Crash Frequencies of all Signalized Intersections in Volume Class i. F uavi = Average Crash Frequency of all Unsignalized Intersections in Volume Class i. S ufi = Standard Deviation of Crash Frequencies of all Unsignalized Intersections in Volume Class i. 3.4 Average Crash Rate The average crash rate method is applied to each volume class of signalized and unsignalized intersections. Crash data over a period of five years or more are analyzed to determine the average crash rate of each intersection. The standard deviation of average crash rates for each volume class of signalized and unsignalized intersections is also calculated. An intersection s crash rate should be termed as very high, high, or above mean as per the criteria shown in Table 3. Intersection Table 3: Crash Rate Criteria Crash Rate Very High High Above Mean Signalized R savi + 2S sri R savi + S sri > R savi Unsignalized R uavi + 2S uri R uavi + S uri > R uavi Where; R savi = Average Crash Rate of all Signalized Intersections in Volume Class i. S sri = Standard Deviation of Crash Rates of all Signalized Intersections in Volume Class i. R uavi = Average Crash Rate of all Unsignalized Intersections in Volume Class i. S uri =Standard Deviation of Crash Rates of all Unsignalized Intersections in Volume Class i. 6

14 3.5 Crash Severity Method In this method, crashes or injuries judged as more severe are given greater relative importance/weight than those judged as less severe Crash Severity The National Safety Council (NSC) and the American National Standards Institute (ANSI) provided the following standard definitions of severity of crashes and injuries 7 : Fatal: One or more deaths (commonly signified by K). A-level Injury: Incapacitating injury preventing victim from functioning normally (e.g., paralysis, broken/distorted limbs, etc.). B-level Injury: Non-incapacitating but visible injury (e.g., abrasions, bruising, swelling, limping, etc.). C-level Injury: Probable but not visible injury (e.g., stiff neck, muscle pain). PDO: Property-damage only. IDOT also defines the severity of crashes and injuries in a similar way. IDOT s definitions of crash severities and injuries are the following: Fatal Crash: A motor vehicle crash (single or multiple) that results in the death of one or more persons. Injury Crash: Any motor vehicle crash that results in one or more non-fatal injuries. A-Injury (Incapacitating Injury): Any injury, other than a 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. Type A crashes includes severe lacerations, broken limbs, skull or chest injuries, and abdominal injuries. B-Injury (Non-incapacitating Injury): Any injury other than a fatal or incapacitating injury, which is evident to observers at the scene of the crash. Includes lump on head, abrasions, bruises, minor lacerations. C-Injury (Possible Injury): Any injury reported or claimed which is not either of the above injuries. It includes momentary unconsciousness, claims of injuries not evident, limping, complaint of pain, nausea, and hysteria. 7

15 PDO: Property-damage only crash (property damage in excess of $1,500 for crashes involving insured drivers and $500 for crashes involving uninsured driver(s) Equivalent Property-Damage-Only (EPDO) Method In this method, weights of fatal and injury crashes are determined against a baseline of property damage only crashes. Each of the injury levels (as described above) is given a specific weight that is compared against property-damage-only crashes, which is given a weight of 1. IDOT s EPDO calculation formula 8 is as follows: (25)*(# of FA) + 10*(# of AA) + (# of BA) + (# of CA) + (# of PDO) (3-1) Total Crashes Where, FA=Fatal crashes AA=Crash where the most severe injury is an A injury BA=Crash where the most severe injury is a B injury CA=Crash where the most severe injury is a C injury PDO=Property Damage Only Equation 3-1 is used to calculate EPDO values of crash intersections of each volume class for signalized and unsignalized intersections. Crash data over a period of five years or more should be analyzed for determining the EPDO of each intersection. An intersection s EPDO should be termed as high severity or moderate severity as per the criteria shown in Table 4. Table 4: Equivalent Propery Damage Only (EPDO) Criteria Crash Severity Intersection High Severity Moderate Severity Signalized EPDO savi + S sei EPDO savi Unsignalized EPDO uavi + S uei EPDO uavi Where; EPDO savi = Average EPDO of all Signalized Intersections in Volume Class i. S sei = Standard Deviation of EPDO values of all Signalized Intersections in Volume Class i. 8

16 EPDO uavi = Average EPDO of all Unsignalized Intersections in Volume Class i. S uei = Standard Deviation of EPDO values of all Unsignalized Intersections in Volume Class i. IDOT used the EPDO calculation formula to identify the top five percent of critical highway locations in Illinois for the 2006 FHWA Highway Safety Improvement Plan (HSIP) Five Percent Report. 9 The crash weighting scheme used by IDOT to calculate the equivalent crash severity for the HSIP-Five Percent Report was changed in As per the FHWA requirement for the HSIP report, the analysis focuses only on the most severe crashes. The recommended weighting structure is 25 times a fatal crash, 10 times a type A injury crash, and equivalent to a type B injury crash. The IDOT EPDO calculation formula is used to identify the critical locations in Champaign- Urbana which includes the C-injury and PDO crashes in the analysis. 3.6 Critical Intersection Prioritization All the SCIL intersections identified through the above procedures are prioritized according to their crash frequency, crash rate, and crash severity levels. Table 5 shows priority levels of SCIL intersections. Table 5: Identifying Priority Levels Priority Level Crash Frequency Crash Rate Crash Severity Priority 1 Very High Very High High Severity Priority 2 High Very High High Severity Priority 3 High High Moderate Severity Priority 4 Above Mean Above Mean N/A 9

17 4.0 TRAFFIC CRASH TRENDS AND FACTS FOR CHAMPAIGN-URBANA Table 6 shows the crash data summary which includes all the crashes that occurred in the Champaign-Urbana area from 1992 to Table 6: Crash Data Summary ( ) Year Total Crashes Persons Injured Average Fatalities Injuries Champaign Urbana Total Champaign Urbana Total per Champaign Urbana Total Crash , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,345 1,009 3, , , , , , , , , , , Total 59,700 23,072 82,772 20,834 7,358 28, Average/yr 2, , , Figure 2 shows a decreasing trend for total crashes and persons injured from 2007 to There were 2,030 reported crashes in the Cities of Champaign and Urbana in This reflects a 6.9 percent decrease in the total number of crashes from the previous year and a 33.5 percent decreasing change from The City of Champaign had more crashes and injuries than Urbana. There is no significant trend for crash fatalities. Figure 2 and Figure 3 show the trend of the total crashes and total injury crashes in the Champaign-Urbana area from 2007 to 2011 respectively. 10

18 Figure 2: Total Crashes in Champaign-Urbana Figure 3: Total Crash Injuries in Champaign-Urbana Figure 3 shows that the total number of injuries per year in the City of Champaign has been decreasing from 2007 to 2011; the number of injuries in the City of Urbana continued to decrease with the exception of

19 4.1 Illinois Statewide Rates and CUUATS Long Range Transportation Plan Goals The IDOT Division of Traffic Safety produces the annual Highway Safety Performance Plan (HSPP) to promote practices and strategies to reduce fatal and injury crashes in the state. The document analyses the statewide crash data and identifies safety goals and suitable performance measures for the future years. The IDOT Division of Traffic Safety also publishes an annual Illinois Crash Facts and Statistics Report, which identifies the key statewide crash information. The CUUATS Long Range Transpiration Plan (LRTP) for the Champaign-Urbana urbanized area identifies traffic safety as an important long term goal to achieve the LRTP s mission of providing a safe, efficient, and economical transportation system. The plan establishes regional safety objectives and the strategies to achieve the objectives over a 5 year period. The report also developed Measures of Effectiveness (MOEs) to track and measure the progress towards the desired outcomes. In this section of the report, the Champaign-Urbana crash data from is compared against the Illinois statewide rates, as well as the regional LRTP goals Total Crashes per 100 MVMT The number of crashes in relation to the vehicle miles travelled is an important index to analyze traffic safety in the urbanized area. The VMT data for the Champaign- Urbana urbanized area was obtained from Illinois Travel Statistics 7 published annually by IDOT. Illinois Statewide Crash Rates The crash per 100M VMT for Illinois was calculated using the VMT and total crash information provided in the Illinois Crash Facts and Statistics Report. Table 7 and Figure 4 compare the crashes per 100 MVMT for the Champaign-Urbana urbanized area against the statewide crash rates. The crashes per 100 MVMT in Champaign-Urbana has been steadily declining since 2007, while the VMT has remained relatively constant. The total crash rate decreased from in 2007 to in 2011, reflecting a % change. The large drop in crash rates between 2008 and 2009 can be attributed to the change in the crash reporting criteria in The crash rate for the urbanized area is consistently lower than the Illinois crash rate for the five-year period. 12

20 Table 7: VMT and Total Crash Rates in Champaign-Urbana Crashes per 100M VMT Daily VMT Yearly VMT Year (Thousands) (100 Millions) Champaign- Illinois Urbana , , , , , Figure 4: Total Crashes per 100 Million VMT CUUATS LRTP Goals The LTRP 2035 set an objective of reducing the total number of crashes in Champaign-Urbana by 5% by The total number of crashes reduced 10.3% from 2,264 in 2009 to 2,030 in 2011 and crash rates declined 8.3% in the same period. The VMT in the region decreased 2.2% between 2009 and The MOE (crashes per 100 MVMT) for this objective gets a positive rating due to the 8.3% reduction in total crashes per 100 MVMT since This is 3.3% higher than the target of 5% decrease in crashes in the urbanized area by

21 4.1.2 Fatalities per 100 MVMT Figure 5 shows the traffic crash fatalities in Champaign and Urbana from 2007 to The graph shows that total fatalities reached their lowest point in In the same year, no fatalities were reported in the City of Champaign. Though the number of fatalities increased for 2010 and 2011, the number of fatalities was lower than the years 2007 and Illinois Statewide Crash Rates and Goals Figure 5: Traffic Fatalities in Champaign-Urbana The Illinois Highway Safety Performance Plan (HSPP) for FY proposed a goal of reducing the statewide fatality rate (per 100 million VMT) from 0.86 in 2009 to 0.84 by 2010, and 0.76 by Illinois failed to meet the set goals with the fatality rate of 0.88 in 2011 and 0.89 in The HSPP for FY set a new goal to reduce the statewide traffic fatality rate per 100 million VMT to 0.64 by December 31, Figure 6 shows fatalities per 100 million VMT from 2007 to 2011 in Champaign- Urbana and the corresponding values for the state of Illinois. The graph also presents the statewide goal for fatalities per million VMT proposed by IDOT s Division of Traffic Safety in the HSPP FY The fatalities per 100 million VMT in Champaign-Urbana were well below the statewide rates and the proposed goals. 14

22 Figure 6: Traffic Fatalities per 100 Million VMT CUUATS LRTP Goals The 2035 LRTP objective is to reduce the number of fatalities in Champaign-Urbana by 25% between 2009 and The MOE for this objective is total fatalities per 100 MVMT. The number of fatalities in the urbanized area dropped from 8 in 2008 to 4 in 2009 but increased back to 6 in This MOE gets a negative rating for the year 2011 because the fatalities per 100 MVMT increased from 0.45 in 2009 to 0.69 in Figure 7 shows fatal crash locations in Champaign-Urbana from 2007 to

23 Figure 7: Fatal Crash Locations in Champaign-Urbana 16

24 4.1.3 Severe Crashes per 100 MVMT IDOT s Division of Traffic Safety categorizes injury severity levels as: A (most severe), B (moderate severity) and C (least severe). More specific details about injury levels can be referred to in section After fatal crashes, the A injury crashes are of major concern. Table 8 shows the severe injury crashes in the Champaign-Urbana urbanized area and severe crash rate per 100 MVMT. The severity injuries in the urbanized area peaked at 178 in 2009 and then decreased to 135 in Year Table 8: Severity Crashes per 100 MVMT for Champaign-Urbana Severe Crashes Total Severe Injuries Champaign Urbana Total Severe Injuries per 100 MVMT Illinois Statewide Crash Rates and Goals The severe A injuries and the annual VMT was obtained from the Illinois Crash Fact and Summary Report. Figure 8 compares the severe injury rates (per 100 MVMT) for the urbanized area against the statewide severe injury rates. The severe injury crashes rates in Champaign-Urbana are consistently higher than the statewide rates, especially for the year The severe crashes per 100 MVMT increased 19.7% from 2007 to 2009 and declined 22% by Champaign Urbana Illinois Figure 8: Severe Injury per 100 MVMT in Champaign-Urbana 17

25 CUUATS LRTP Goals The 2035 LRTP objective is to reduce the severe injuries in Champaign-Urbana by 25% between 2009 and The MOE for this objective is severe injuries per 100 MVMT. The severe injuries per 100 MVMT decreased 22% from in 2009 to in This MOE receives a neutral rating, since the severe A injures per VMT decreased 22% in 2011, which is close to the LTRP target of a 25% reduction in severe injuries by Figure 9 shows the Type A injury (severe) crash locations in Champaign- Urbana from 2007 to

26 Figure 9: Type A Injury Crash Locations in Champaign-Urbana 19

27 4.2 Corridor Crash Analyses Identifying and analyzing crashes along the key corridors in the region is important to help improve corridor safety and promote efficient traffic flow. The traffic crashes along the five major east-west and six north-south corridors in Champaign-Urbana are summarized in this section. Table 9 provides the total number of crashes, injuries, fatalities, and the number of different injury level crashes along the major corridors. Table 10 shows the number of crashes along the major corridor by year. Table 9: Crashes along Major Corridors in Champaign-Urbana Roadway Crashes Fatalities Total Injuries A-Injury Crashes B-Injury Crashes C-Injury Crashes East-West Corridor Bradley Ave University Ave 1, Springfield Ave Kirby Ave/Florida Ave Windsor Rd North-South Corridor Mattis Ave Prospect Ave 1, Neil St Lincoln Ave Cunningham Ave/Vine St High Cross Rd/IL Total 8, , Table 10: Crashes along Major Corridors by Year Roadway Total East-West Corridor Bradley Ave University Ave ,110 Springfield Ave Kirby Ave/Florida Ave Windsor Rd North-South Corridor Mattis Ave Prospect Ave ,094 Neil St Lincoln Ave Cunningham Ave/Vine St High Cross Rd/IL Total 1,903 1,853 1,482 1,368 1,402 8,008 20

28 University Avenue and Prospect Avenue had the highest number of crashes (both were over 1,000 crashes over five years) and fatalities among the major corridors in the Champaign-Urbana area. Springfield Avenue and Neil Street also have close to 100 crashes over the five-year period. There were three fatalities along University Avenue from 2007 to Figures 10 and 11 show the show the crash statistics along University Avenue and Prospect Avenue respectively. 21

29 Figure 10: University Avenue Crash Statistics 22

30 Figure 11: Prospect Avenue Crash Statistics Table 11 shows the corridor crashes occurring mid-block and at intersections. The analysis shows that the majority of corridor crashes (>70%) occur at intersections. 23

31 Table 11: Crashes along Major Corridors in Champaign-Urbana Roadway Midblock Crashes Intersection Crashes % Intx. Crashes East-West Corridor Bradley Ave % University Ave % Springfield Ave % Kirby Ave/Florida Ave % Windsor Rd % North-South Corridor Mattis Ave % Prospect Ave % Neil St % Lincoln Ave % Cunningham Ave/Vine St % High Cross Rd/IL % Total 2,252 5,756 72% Table 12 shows the change in the number of severe crashes and fatal crashes in the corridors over the five year study period. The number of severe crashes increased considerably along Springfield Avenue and Lincoln Avenue. Table 12: Severe and Fatal Crashes along Major Corridors by Year Roadway Total East-West Corridor Bradley Ave University Ave Springfield Ave Kirby Ave/Florida Ave Windsor Rd North-South Corridor Mattis Ave Prospect Ave Neil St Lincoln Ave Cunningham Ave/Vine St High Cross Rd/IL Total

32 Figure 12 shows the fatal crash locations and severe crash locations along the corridors. Figure 13 presents the bicycle and pedestrian crash locations along the study corridors. 25

33 Figure 12: Fatal and Severe Crashes along Major Corridors in Champaign-Urbana 26

34 Figure 13: Pedestrian and Bicycle Crashes along Major Corridors in Champaign-Urbana 27

35 4.3 Additional Traffic Crash Facts for Additional analyses have been performed to highlight some important facts associated with crash data in the City of Champaign and City of Urbana from 2007 to 2011, including crash time, collision types, weather, lighting, intersection control types and driver condition Day of the Week Analysis Figure 14 shows the number of crashes that occurred by day of the week. Analysis shows the Friday was the most hazardous day of the week while Sunday has the fewest crashes. Figure 14: Crashes by Day of the Week 28

36 4.3.2 Time of the Day Analysis Crashes during different hours of the day are shown in Figure 15. As can be seen in Figure 16, the majority of the crashes occurred between 12:00 PM and 6:00 PM. The period from 3:00 AM to 6:00 AM has the fewest crashes. Figure 15: Time of the Day Analysis 29

37 4.3.3 Collision Types Table 13 shows a summary of different collision types from 2007 to Figure 16 presents the percent distribution of crashes by collision type. The crash analysis shows that the majorities of the crashes at intersections are rear-end, turning, and/or angle crashes. These three crash types account for 60 percent of all crashes in the region. Table 13: Vehicle Collision Types Crash Type Year Total % of Total Crashes Angle , % Animal % Fixed Object , % Head on % Other non-collision % Other object % Overturned % Parked motor vehicle , % Pedal-cyclist % Pedestrian % Rear End , % Sideswipe opp. direction % Sideswipe same direction % Train % Turning , % Figure 16: Percent Distribution of Crashes by Collision Type 30

38 4.3.4 Weather Conditions Figure 17 shows a summary of crashes during different weather conditions. The majority of the crashes occurred in clear weather conditions. The percentages of crashes in different adverse weather conditions were similar every year from 2007 through Figure 17: Percent Distribution of Crashes by Weather Condition Table 14 provides the number of crashes during different weather conditions in Champaign-Urbana from 2007 through Weather Conditions Table 14: Traffic Crashes in Different Weather Conditions Year Total % of Total Crashes Clear 2,465 2,188 1,762 1,763 1,633 9, % Fog/Smoke/Haze % Rain % Snow/Sleet/Hail % Other/Unknown % 31

39 4.3.5 Roadway Surface Condition Figure 18 shows crashes occurring on different roadway surface conditions. About 69 percent of crashes over the five year period were on dry pavement conditions. Around 28 percent of the crashes occurred on snowy, icy, or wet pavement. The main contributing factors for wet pavement crashes are slippery pavement (reduced friction), water ponding on the roadway, and inadequate retro-reflectivity of pavement markings. The number of crashes for each roadway surface condition category shows a decreasing trend from 2007 to Figure 18: Crashes by Road Surface Condition 32

40 4.3.6 Roadway Lighting Condition The relation of intersection crashes to the roadway lighting conditions was analyzed. Table 15 and Figure 19 show that the majority of the crashes occurred during the daytime. About 70 percent of the crashes occurred during daylight, while 28.2 percent occurred during poor lighting conditions (dawn, dusk, darkness, & darkness with a lighted road). Table 15: Crashes by Roadway Lighting Condition Roadway Lighting Condition Year Total % of Total Crashes Daylight 2,152 2,063 1,572 1,544 1,358 8, % Dawn % Dusk % Darkness , % Darkness, Lighted Road , % Unknown % Figure 19: Percent Distribution of Crashes by Roadway Lighting Conditions 33

41 4.3.7 Driver Condition Table 16 summarizes the driver condition information for crashes in Champaign- Urbana. The driver condition is likely to contribute to crashes in Champaign-Urbana, and increase the severity of the crashes. The majority of the injuries (88.3 percent) occurred during normal driving conditions. For other conditions, alcohol was the main factor leading to crashes. Figure 20 shows that the majority of impaired/distracted driving crashes (64 percent) were alcohol related. Table 16: Driver Condition Information Driver Condition Year Total Crashes Total Injured % of Total Injured Alcohol Impaired % Asleep/Fainted % Drug Impaired % Fatigued % Has Been Drinking % Illness % Normal 4,954 4,702 3,639 3,487 3,329 20,111 1, % Other/Unknown , % Figure 20: Percent Distribution of Impaired/Distracted Driving Crashes 34

42 4.3.8 Driver Age and Gender Figure 21 shows the age distribution of motor vehicle drivers involved in crashes. The graph shows that drivers between the ages of 20 and 24 were the most vulnerable to being involved in a traffic crash (21 percent), followed by drivers between the ages of 15 and 19 (12 percent). Drivers between the ages of 15 and 29 were involved in 45 percent of the total crashes reported in Champaign-Urbana. Figure 22 shows the total injuries and severe type A injuries for each age group. Figure 21: Age Distribution of Motor Vehicle Drivers involved in Traffic Crashes Figure 22: Total and Severe Injuries for Different Age Groups 35

43 Table 17 categorizes the driver involved in crashes by gender and age in the study region. The drivers between 10 to 29 years of age account for 55 percent of all the drivers involved in crashes. Figure 23 compares the number of male and female crash-related drivers of different age groups from 2007 to As shown in Figure 22, female drivers were involved in a smaller number of crashes in all age groups. Table 17: Drivers Involved in Traffic Crashes in Champaign-Urbana Age Driver Male Female Total ,321 1, ,270 2, ,343 1, and above Figure 23: Crash Frequency Comparison between Male and Female Drivers 36

44 4.3.9 Traffic Control Table 18 and Figure 24 present the percent distribution of crashes based on traffic control type. About 42 percent of the crashes were at intersections controlled by a traffic signal or stop sign/flasher, out of which 27.1% of the crashes occurred at signalized intersections. Table 18: Crashes Based on Traffic Control Traffic Control Total Crashes % of Total Crashes Lane Use Marking , % No Control 1,441 1,330 1,057 1, , % Other Warning Sign % Stop Sign/Flasher , % Traffic Signal , % Other % Unknown % Figure 24: Percent Distribution of Crashes based on Traffic Control 37

45 5.0 Pedestrian AND BICYCLE CRASHES Pedestrians and bicyclists are the most vulnerable road users; collisions with motor vehicles often result in serious injury or death. In 2011, 4,432 pedestrians and 677 bicyclists were killed in the United States 1. In 2010, 618 bicyclists were killed and an additional 52,000 were injured in motor vehicle traffic crashes 13. National statistics show that 30.7 percent of the bicycle fatalities and 19.4 percent of pedestrian deaths in 2011 occurred at intersections 1. The following sections analyze pedestrian and bicycle crashes in Champaign-Urbana over the five year study period. All the bicycle and pedestrian crashes reported between 2007 and 2011 involved at least one motor vehicle. 5.1 Pedestrian Crashes Table 19 shows the pedestrian crash information for the City of Champaign and City of Urbana between 2007 and Four of the ten pedestrian fatalities occurred at intersections: Eureka Street/State Street in 2007, Florida Avenue/Carle Avenue in 2007, University Avenue/Orchard Street in 2009 and University Avenue/McCullough Street in Year Table 19: Pedestrian Crashes in Champaign-Urbana Total Crashes City of Champaign Total Injuries Killed Injury Severity A B C Year Total City of Urbana Total Injuries Killed Injury Severity A B C The crash analysis shows that almost all of the pedestrian crashes resulted in injuries. Ten pedestrian fatalities accounted for 33.3 percent of the total fatalities in Champaign-Urbana over the five year period. Also, a significant number of pedestrians were severely injured (33.2 percent of injured pedestrians had a type A injury). 38

46 5.1.1 Pedestrian Age and Gender Figure 25 shows the age distribution of pedestrians involved in traffic crashes between the years 2007 to As can be seen in the Figure 25, a significant number of pedestrians (52.5 percent) involved in traffic crashes were under age 25. Figure 25: Age Distribution of Pedestrians Involved in Crashes Figure 26 shows age distribution by gender of pedestrians involved in traffic crashes from 2007 to Figure 26: Pedestrian Age and Gender Distribution 39

47 5.1.2 Pedestrian Pre-crash Behavior An important factor leading to pedestrian crashes is pedestrian behavior before crashes. This section summarizes the pre-crash behavior of pedestrian crashes. Table 20 summarizes all the behavior within 11 categories. The analysis shows that crossing with signal resulted in more crashes than crossing against signal. Table 20: Pedestrians Behavior leading to Crashes Action Total Crashes Total Injuries % of Total Injuries Crossing against Signal % Crossing with Signal % Crossing - not intersection % Entering/leaving parked vehicle % Enter from Drive/Alley % Entering/leaving/crossing % Playing/Standing/Working in Roadway % Walking/Riding against Traffic % Walking/Riding with Traffic % Turning Left % Unknown % As seen in the table, most pedestrian injuries occurred when pedestrians were crossing against the signal, crossing with the signal, or playing/standing/working in the roadway. These behaviors accounted for percent of all known pre-crash behavior. Figure 27 illustrates the percentage distribution of pedestrian injuries based on their pre-crash behavior (unknown behavior is excluded). 40

48 Figure 27: Percent Distribution of Pre-Crash Action for Injured Pedestrians Pedestrian crash locations in Champaign-Urbana from 2007 to 2011 are shown in Figure 28. The pedestrian crash frequency shows the number of crashes at a particular location. 41

49 Figure 28: Pedestrian Crash Locations in Champaign-Urbana 42

50 5.2 Bicycle Crashes Table 21 shows the bicycle crash information in Champaign-Urbana between 2007 and In 2009, there was a bicycle fatality on Green Street in Urbana. In 2011, another bicycle fatality happened at the intersection of Market Street & Anthony Drive. Almost all of the bicycle crashes resulted in injuries. Year Table 21: Crashes in Champaign-Urbana City of Champaign Total Crashes Total Injuries Killed Injury Severity A B C Year Total Crashes City of Urbana Total Injuries Killed Injury Severity A B C Bicyclist Age and Gender Figure 29 shows the age distribution by year of bicyclists involved in traffic crashes in 2007, 2008, 2010 and The age and gender distribution for bicyclists was not available for As can be seen in Figure 30, a majority of bicyclists (53.5 percent) involved in traffic crashes were under the age of

51 Figure 29: Age Distribution of Bicyclist Involved in Crashes Figure 30 shows the crash frequency for different age group by gender for bicyclists involved in traffic crashes in 2007, 2008, 2010 and The analysis shows that more male bicyclists were involved in crashes than female bicyclist during the study period. Figure 30: Bicyclist Age and Gender Distribution 44

52 5.2.2 Bicyclist Pre-Crash Behavior Table 22 shows the percent distribution of pre-crash actions for injured bicyclists. Most of the bicyclist injuries occurred when bicyclists were crossing the signal or when walking/riding with traffic. The pre-crash behavior is known for about 68 percent of all the bicyclist crash records. Figure 31 illustrates the percent distribution of bicyclist injuries based on their pre-crash behavior. Unknown behavior is excluded. Bicycle crash locations in Champaign-Urbana from 2007 to 2011 are shown in Figure 32. Table 22: Bicyclist Behavior leading to Crashes Action Total Crashes Total Injuries % of Total Injuries Crossing against signal % Crossing with Signal % Crossing not intersection % Entering/leaving parked vehicle % Enter from Drive/Alley % Entering/leaving/crossing % Playing/Standing/Working in Roadway % Walking/Riding against Traffic % Walking/Riding with Traffic % Turning Left % Unknown % Figure 31: Percent Distribution of Pre-Crash Actions for Injured Bicyclists 45

53 Figure 32: Bicycle Crash Locations in Champaign-Urbana 46

54 5.3 Pedestrian & Bicycle Crashes in the University District The University of Illinois District in Urbana-Champaign accommodates thousands of pedestrians and bicyclists each year during regular academic sessions. Table 23 shows the number of crashes, injuries and fatalities for pedestrians and bicyclists within the University District from 2007 to Figure 33 shows the locations of bicycle and pedestrian crashes in the University District. Figure 34 shows the severity level of the bicycle and pedestrian crashes in the University District. Table 23: Pedestrian and Bicycle Crashes in the University District Pedestrian Crashes Year Total Crashes Total Injuries Killed Injury Severity A B C Bicycle Crashes Year Total Crashes Total Injuries Killed Injury Severity A B C

55 Figure 33: Bicycle and Pedestrian Crash Locations in the University District 48

56 Figure 34: Bicycle and Pedestrian Crash Severity in the University District 49

57 6.0 SELECTED CRITICAL INTERSECTION LOCATIONS ( ) Based on the critical intersection selection and prioritization procedures (described in Chapter 3), the following signalized intersections were identified as critical in Champaign-Urbana. Different priority levels were assigned to the critical intersections based on the frequency, rate, and severity of crash values. Table 24 lists the thirteen critical unsignalized intersections identified in Champaign-Urbana. There were no signalized intersections in the Priority 1 or Priority 2 lists. Thirteen signalized intersections were identified as critical in Champaign-Urbana. Table 24: Critical Signalized Intersections Priority 1 Intersection Volume Class Average Crash Frequency Average Crash Rate Average EPDO Index None Priority 2 Intersection Volume Class Average Crash Frequency Average Crash Rate Average EPDO Index None Priority 3 Intersection Volume Class Average Crash Frequency Average Crash Rate Average EPDO Index First St & Green St A Mattis Ave & Bradley Ave A Mattis Ave & Springfield Ave A Neil St & Green St A Neil St & Kirby Ave A Prospect Ave & Bloomington Rd A State St & Kirby Ave A Cunningham Ave/Vine St & University Ave A Neil St & Columbia Ave B Randolph St & Church St B Guardian Dr & University Ave B Moreland Blvd & Town Center Blvd C High Cross Rd & Tatman Ct C Table 25 lists the twenty one critical unsignalized intersections identified in Champaign-Urbana. Figure 35 shows the selected critical intersection locations for 2007 through

58 Intersection Table 25: Critical Unsignalized Intersections Priority 1 Volume Class Average Crash Frequency Average Crash Rate Average EPDO Index Third St & Springfield Ave B Fifth St & Green St B Smith Rd & Main St C Intersection Priority 2 Volume Class Average Crash Frequency Average Crash Rate Average EPDO Index State St & Clark St B Walnut St & Columbia Ave B Maple St & University Ave B Fifth St & White St D Coler Ave & Church St D Intersection Priority 3 Volume Class Average Crash Frequency Average Crash Rate Average EPDO Index Mattis Ave & Glenn Park Dr A McKinley Ave & Bradley Ave A Prospect Ave & John St A Vine St & Water St A Neil St & Washington St B Vine St & Pennsylvania Ave B Race St & Florida Ave B Center Dr & Marketview Dr C Elm St & University Ave C McKinley Ave & Church St C State St & Columbia Ave C Broadway Ave & Country Club Rd C Broadway Ave & Illinois St D

59 Figure 35: Selected Critical Intersection Locations 52

60 6.1 Intersection Locations with Fatal Crashes Intersections with fatal crashes are not all included within the critical selected intersections. However, the potential hazards of such intersections should be noted nonetheless. A total of 30 fatalities were recorded in Champaign-Urbana from 2007 to Of the 30 fatalities, eight occurred at intersections. Fatalities were recorded at the following intersections over the five year period: Champaign State Street & Eureka Street Prospect Avenue & Healey Street Elm Street & Springfield Avenue Market Street & Anthony Drive Urbana Carle Avenue and Florida Avenue University Avenue and Orchard Street Cunningham Avenue/US 45 & I-74 University Avenue & McCullough Street Figure 36 shows intersection fatality locations in Champaign-Urbana over the five year analysis period. Most of the fatal crashes occurred along corridors. There were no fatal intersection crashes in the University District over the study period. 53

61 Figure 36: Fatality Intersection Locations and Selected Critical Intersection Locations 54