Reducing Urban Arterial Intersection Crashes through Crash Typing Analysis:

Reducing Urban Arterial Intersection Crashes through Crash Typing Analysis: A Case Study To target engineering resources for crash prevention, it is important to identify locations where particular types of occur frequently and where countermeasures likely to reduce them can be implemented. intersections with excessive numbers of of a particular type can be good candidates for relatively simple engineering countermeasures. By Richard A. Retting, Charles M. Farmer, Ph.D., Susan A. Ferguson, Ph.D. and Helen B. Weinstein INTRODUCTION Arterials are the main thoroughfares on which urban motorists do most of their driving. Urban arterials carry heavy traffic volumes in many cases, more than was anticipated when the roads were designed. In the United States, more than 2 billion motor vehicle miles are traveled daily on about 151,000 miles of urban arterials. 1 More than 8,000 fatal and 1 million non-fatal injury occur each year on these roads. 2,3 Many of the tend to cluster at specific intersections and often occur in patterns that can be mitigated through traffic engineering countermeasures. Because of the limited resources available for roadway safety improvements, it is important to identify and target for improvement those locations where engineering changes will have the biggest effect on. It is not a new idea to evaluate and remediate high-crash locations. Numerous procedures exist for identifying and prioritizing crash-prone locations. Many transportation agencies identify problem intersections based on crash frequency. 4,5 A drawback to this approach is that some locations with high numbers of simply have high traffic volumes. Such locations do not necessarily exhibit specific crash patterns amenable to engineering interventions. Some transportation agencies avoid this drawback by prioritizing locations based on rates of per traffic volume rather than crash frequencies. This approach requires traffic counts that are accurate, reliable and timely. However, traffic counts are incomplete for many locations, especially in jurisdictions where periodic traffic count programs estimate vehicle exposure only on major roads and do not count vehicles on cross streets. In addition, this method may identify intersections and crash patterns that are not amenable to engineering improvements. Retting, Weinstein, Williams and Preusser proposed a relatively simple method for identifying crash-prone locations that takes into account the limitations of traditional approaches. 6 The method entails analyses of collision patterns along arterials and identification of clusters of with common vehicle movements prior to the crash. The premise is that associated with specific traffic movements are more amenable to traffic engineering countermeasures than locations that simply have high numbers of. This method of crash typing analysis was applied to crash data for three urban arterials in the Washington, DC, USA, metropolitan area. For one of the arterials, Leesburg Pike in northern Virginia, traffic engineering countermeasures were applied based on the analyses and crash effects were examined. This feature describes the results of this process as a case study for identifying and reducing crash problems on urban arterials. Methods Retting, Weinstein, Williams and Preusser analyzed police-reported for the 3-year period from 1995 to 1997 for a 6-mile section of Leesburg Pike in Fairfax County, VA. Leesburg Pike, also known as Route 7, is a divided road that varies in width from four to six lanes plus additional turn lanes at many intersections. 7 Annual average daily traffic volumes from 32,000 to 64,000 vehicles per day were estimated in 1995 on the Leesburg Pike study corridor. 8 To identify clusters of with common vehicle movements prior to the crash, crash data were analyzed for 14 major intersections along Leesburg Pike. 18 ITE Journal / December 2006

Table 1. Number and percentage of of each type (including all intersection approaches), 1995 1997. Leesburg Pike intersection Left-turn oncoming Ran traffic control Stopped/ stopping Lane change All others Total Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent Westpark Drive 6 6 23 23 55 54 10 10 8 8 102 100 George Marshall Drive 1 2 7 12 44 76 2 3 4 7 58 100 Lewinsville Road 11 17 4 6 34 54 3 5 11 17 63 100 Pimmit Drive 3 8 3 8 22 61 4 11 4 11 36 100 Ring Road 28 54 4 8 9 17 4 8 7 13 52 100 Chain Bridge Road 4 10 6 14 20 48 7 17 5 12 42 100 Margarity Road 0 0 4 10 32 78 2 5 3 7 41 100 Old Gallows Road 2 6 5 14 15 42 11 31 3 8 36 100 Towlston Road 2 9 2 9 16 70 0 0 3 13 23 100 Spring Hill Road 3 11 7 25 13 46 3 11 2 7 28 100 Watson Street 2 8 8 32 7 28 2 8 6 24 25 100 Aline Avenue 7 24 0 0 8 28 1 3 13 45 29 100 Towers Crescent Drive 0 0 3 11 9 33 8 30 7 26 27 100 Patterson Road 1 4 3 11 17 63 2 7 4 15 27 100 Total 70 12 79 13 301 51 59 10 80 14 589 100 Annual average 23 26 100 20 27 196 Treated location The percentage of of each major type was computed for each intersection and then compared with percentages for all 14 intersections combined. Table 1 shows the number and percentage of of each type for intersections along the study corridor. Clusters of were identified at locations where specific types of were overrepresented, compared with data for all 14 intersections combined. The major crash types are: Left-turn oncoming: During a left turn in front of oncoming traffic, a vehicle is struck by or strikes a vehicle that is coming from the opposite direction and has the right of way. Ran traffic control: A vehicle required to stop, remain stopped, or yield does not do so and collides with some other vehicle. Stopped/stopping: A vehicle stopped, stopping, or just starting up in a travel lane is struck from the rear. Lane change: A vehicle in a travel lane swerves or moves into another same-direction lane that already is occupied. Figure 1. Example crash ring diagram. For selected intersections with large numbers of of a particular type, collision diagrams were prepared to help identify the pre-crash movements and travel directions of the crash-involved vehicles. These diagrams were based on information in police crash reports. Six locations were selected for study: the intersections of Leesburg Pike with Lewinsville Road; Magarity Road; George Marshall Drive; Patterson Road; Ring Road; and Westpark Drive. Figures 1 and 2 provide examples of crash diagrams for these locations. Crash analyses were followed by field inspections and then by development of countermeasure recommendations. Findings from the crash analyses and field in- ITE Journal / December 2006 19

spections were presented to the Virginia Department of Transportation, which is responsible for roadway safety improvements on Leesburg Pike and implemented engineering changes at the six intersections between 1999 and 2001. These improvements targeted specific approaches and vehicle movements that were identified through the crash typing analyses and field inspections. Figure 2. Example crash ring diagram. Figure 3. Special pavement markings were installed to warn drivers of potential conflicts with right-turning vehicles. Study Sites Altogether, seven strategies were identified. At two intersections with distinct patterns of left-turn oncoming (Lewinsville Road and Ring Road), protected turn signals were installed to permit drivers to turn left only when a green arrow is displayed and oncoming traffic is stopped. Prior to this, left turns were governed by protected/permitted leftturn phasing, which involves an initial green arrow in conjunction with a red signal for opposing traffic. After termination of the green arrow, drivers still were permitted to turn left after yielding to oncoming traffic. Two intersections had patterns of stopped/stopping involving lead vehicles that were slowing to turning right (George Marshall Drive and Magarity Road). These locations did not have separate lanes for right turns. Special pavement markings were installed to warn drivers of potential conflicts with rightturning vehicles (see Figure 3). Two locations had patterns of stopped/ stopping associated with buses stopping to pick up or discharge passengers in a lane of moving traffic (Patterson Road and Magarity Road). At Patterson Road, the bus stop was eliminated and consolidated with a nearby bus stop situated in a separate right-turn lane. At Magarity Road, there is a shoulder area adjacent to the right lane. Buses would stop partially on the shoulder and partially in the right lane, thus obstructing traffic in the right lane. To facilitate buses stopping on the shoulder outside of the moving traffic lanes, the shoulder was widened by about 2 feet by restriping the width of the adjacent traffic lanes from 12 feet to 11 feet. At Westpark Drive, a large number of stopped/stopping were occurring in a free-right-turn lane, where side street 20 ITE Journal / December 2006

Table 2. Specifically targeted on Leesburg Pike. Before After Westpark Drive 36 Patterson Road 16 Lewinsville Road 29 Ring Road 60 Magarity Road 13 Marshall Drive 25 Period 05/1999 07/1999 04/2001 11/2001 12/1997 12/1997 years Annual number of Changes made 4.4 8.2 06/1999 4 4.6 3.5 09/1999 6 6.3 4.6 05/2001 0 6.9 8.7 12/2001 0 3.0 4.3 3.0 8.3 2000 2001 2000 2001 5 6 Period 07/1999 10/1999 06/2001 01/2002 01/2002 01/2002 years Annual number of 4.50 0.9 4.25 1.4 2.60 0.0 2.00 0.0 2.00 2.5 2.00 3.0 traffic merges onto Leesburg Pike using a yield-controlled merge lane. The merge lane on Leesburg Pike was extended to improve the merge between traffic entering from the side street and vehicles already traveling on Leesburg Pike. Crash Analyses To gauge the effectiveness of implementing traffic engineering improvements, two sets of analyses were conducted. The first analysis examined occurring at the specific approaches and involving vehicle movements targeted by the traffic engineering changes at each of the six targeted intersections. That is, the analysis examined effects on that were specifically targeted by engineering improvements. In this analysis, the average annual number of such was computed for each treatment site for periods before and after the changes. The length of the before and after periods for each site varied because changes were implemented at different times at different sites. For two of the locations, Magarity Road and Marshall Drive, the precise dates of the changes were uncertain, so before data were restricted to 1995 1997 (the initial analysis period) and after data were restricted to 2002 2003 (when changes were known to be in effect). Overall, the length of the before periods ranged from 3 to 6.9 years and the length of the after periods ranged from 2 to 4.5 years. The second analysis examined at all four approaches at all 14 intersections in the study corridor to compare changes in at the treated sites with general crash trends at other (untreated) intersections in the study corridor. Logistic regression was used to examine changes in the distribution of two types of at the 14 intersections from a common before period (1995 1997) to a common after period (2002 2003). One logistic regression modeled the odds of a crash being left-turn oncoming as a function of time period; whether the intersection was treated with engineering measures designed to reduce such ; and the interaction of these two effects. The interaction term represents the relative change over time at the two treated sites combined (Lewinsville Road and Ring Road) compared with the other 12 sites (the effect of the treatment). The second regression modeled the odds of a crash being stopped/stopping as a function of time period; whether the intersection was treated with engineering measures designed to reduce such ; and the interaction of these two effects. The treated sites for stopped/stopping were the intersections at Westpark Drive, Marshall Drive, Magarity Road and Patterson Road. Results Table 2 provides a summary of before and after data for targeted at the six study sites. Targeted included those occurring on specific intersection approaches and involving vehicle movements specifically addressed by traffic engineering changes. The average annual number of targeted declined substantially at all treated locations. For example, the number of stopped/ stopping in the free-right-turn lane of Westpark Drive declined from 8.2 to 0.9 per year after the lane was extended. (In accordance with the crash typology discussed earlier, left-turn oncoming were classified as ran traffic control if one or both vehicles involved had entered the intersection on red. This occurred in both the before and the after periods. Had such been counted as left-turn oncoming at Ring Road and Lewinsville Road, where countermeasures were directed at such, the results would have been largely unaltered. There were five such in the before period, four at Ring Road and one at Lewinsville Road, and one in the after period at Ring Road.) Distributions of crash types across the entire Leesburg Pike study corridor were analyzed for a common period before (1995 1997) and after (2002 2003) the changes had been implemented at all six treatment sites. Table 3 provides crash data for 2002 2003. The average annual number of all police-reported intersection in the study corridor increased 15 percent, from 196 per year during 1995 1997 (589 in 3 years) to 225 per year during 2002 2003 (450 in 2 years). Traffic volume on Leesburg Pike increased by an estimated 38 percent be- ITE Journal / December 2006 21

Table 3. Number and percentage of of each type (including all intersection approaches), 2002 2003. Leesburg Pike Intersection Left-turn oncoming Ran traffic control Stopped/ stopping Lane change All others Total Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent Westpark Drive 2 2 27 33 35 42 6 7 13 16 83 100 George Marshall Drive 0 0 2 7 19 63 2 7 7 23 30 100 Lewinsville Road 2 4 1 2 41 77 3 6 6 11 53 100 Pimmit Drive 5 17 1 3 20 67 2 7 2 7 30 100 Ring Road 0 0 4 21 8 42 3 16 4 21 19 100 Chain Bridge Road 3 5 5 8 36 58 12 19 6 10 62 100 Margarity Road 0 0 2 6 21 68 4 13 4 13 31 100 Old Gallows Road 0 0 2 11 8 42 6 32 3 16 19 100 Towlston Road 0 0 2 13 12 80 0 0 1 7 15 100 Spring Hill Road 6 11 14 26 16 30 10 19 7 13 53 100 Watson Street 1 7 4 27 7 47 2 13 1 7 15 100 Aline Avenue 1 7 2 14 8 57 1 7 2 14 14 100 Towers Crescent Drive 0 0 0 0 4 40 3 30 3 30 10 100 Patterson Road 2 13 4 25 8 50 0 0 2 13 16 100 Total 22 5 70 16 243 54 54 12 61 14 450 100 Annual Average 11 35 122 27 30 225 Treated location tween 1995 and 2003. 9 Highlighted cells indicate intersections and crash types that received traffic engineering interventions. In the before period, each of the treated intersections had a higher percentage of of a certain type than was observed for the overall group of intersections. For example, 54 percent of the at Ring Road and 17 percent of those at Lewinsville Road were left-turn oncoming, but such accounted for only 12 percent overall. The 55 stopped/stopping at the intersection of Leesburg Pike and Westpark Drive were only slightly more than expected. However, the fact that so many of these occurred in the merge lane warranted the intersection for treatment. For each treated intersection, the highlighted percentages are lower in the after period than in the before period. For example, at Lewinsville Road, left-turn oncoming accounted for 17 percent of all before treatment versus 4 percent after treatment. For the other intersections and other crash types not targeted for traffic engineering changes, there was no consistent pattern of change in the percentages of crash types. The first regression analysis, which modeled the odds of a crash being leftturn oncoming, estimated that the odds of a crash being left-turn oncoming were reduced by 93 percent at the two treatment sites (95-percent confidence interval of 66 98 percent). The second regression, which modeled the odds of a crash being stopped/stopping, estimated that the odds of a crash being stopped/stopping were reduced by 65 percent at the four treatment sites (95-percent confidence interval of 42 79 percent). Finally, the declines in targeted crash types were not offset by increases in other. The total number of per year at the six treated intersections increased by 2 percent, from 114 per year during 1995 1997 to 116 per year during 2002 2003. This compares with a 33-percent increase in the annual number of at the eight untreated intersections (from 82 to 109 per year) during the same time periods. Discussion This case study provides evidence that intersections with excessive numbers of of a particular type can be good candidates for relatively simple engineering countermeasures that reduce. Analyses that examined the treated intersections as a whole, as well as analyses that focused on types of and approaches specifically targeted by engineering improvements, indicate the treatments were effective in reducing both the overrepresentation of certain types of and the annual numbers of specifically-targeted. Although many state and local governments have procedures to identify locations with high numbers of policereported, consideration should be given to supplementing such efforts with the approach described in this feature. The approach may be especially useful for identifying intersections on urban arterials that would benefit from remediation. Although the average annual number of targeted declined sharply at all treated locations, this study was not designed to quantify the crash effects associated with specific traffic engineering measures, such as installing protected left-turn signals. This would require a more complex 22 ITE Journal / December 2006

experimental design including carefully chosen control sites, traffic volume estimates for the before and after periods and relatively large numbers of study sites. This case study employed low-cost traffic engineering countermeasures that can be implemented relatively quickly. Although some crash problems can be mitigated by such treatments, others require more extensive infrastructure modifications, such as construction of additional traffic lanes. Regardless of the relative ease or difficulty of implementing the countermeasures required to mitigate crash problems, communities can benefit from using the simple method described in this feature to identify locations with excessive numbers of of a particular type. Acknowledgment This work was supported by the Insurance Institute for Highway Safety. n References 1. Federal Highway Administration (FHWA). Highway Statistics, 2003; Section V: Roadway Extent, Characteristics, and Performance. Washington, DC, USA: U.S. Department of Transportation (U.S. DOT), 2005. Accessible via www.fhwa.dot.gov/policy/ohim/hs03/re.htm. 2. Ibid. 3. FHWA. Highway Statistics Summary to 1995; Section V: Roadway Extent, Characteristics, and Performance. Washington, DC: U.S. DOT, 2005. Accessible via www.fhwa.dot. gov/ohim/summary95/section5.html. 4. Bretherton, W.M. Statistical Approach to the Analysis of Intersection Safety, Chapter 25. The Traffic Safety Toolbox. Washington, DC: Institute of Transportation Engineers, 1999. 5. Brown, R.J. The Identification and Improvement of Accident Black Spots. Technical Manual K21. Pretoria, South Africa: National Institute of Road Research, 1972. 6. Retting, R.A., H.B. Weinstein, A.F. Williams and D.F. Preusser. A Simple Method for Identifying and Correcting Crash Problems on Urban Arterial Streets. Accident Analysis and Prevention, Vol. 33, No. 6 (2001): 723 734. 7. Ibid. 8. Virginia Department of Transportation. Average Daily Traffic Volumes on Interstate, Arterial, and Primary Routes, 1995. Richmond, VA, USA, 2005. Accessible via www.virginiadot. org/ comtravel/resources/aadt_1995.pdf. 9. Ibid. Richard A. Retting is senior transportation engineer with the Insurance Institute for Highway Safety (IIHS) in Arlington, VA, USA. His areas of specialization include intersection safety, traffic control devices, roadside hazards, pedestrian safety, speed studies and automated traffic enforcement. He holds an M.S. in transportation planning and engineering from Polytechnic University of New York. Prior to joining ITE in 1990, he served as deputy assistant commissioner for the New York City Department of Transportation. He is a past chair of the ITE Transportation Safety Council and is a member of ITE. Charles M. Farmer, Ph.D., is director of Statistical Services with IIHS in Arlington, VA. He holds a Ph.D. in statistics from Iowa State University. He has published in many areas of highway safety, including vehicle technology, regulation and refinement of driver behavior and roadway design. Susan A. Ferguson, Ph.D., is senior vice president for Research with IIHS in Arlington, VA. She holds a B.A. in psychology and a Ph.D. in experimental psychology from The George Washington University. She has published papers on a variety of highway safety research topics, including airbag performance, alcohol-impaired driving, teenage driving and child occupant protection. Helen B. Weinstein is a research associate with Preusser Research Group Inc. in Trumbull, CT, USA. She holds a master s degree from Simmons College. Her research interests are in the areas of crash analysis, pedestrian safety and child passenger safety. Advertise Your Positions Available Through ITE To Advertise a Position in ITE Journal or on the Web Visit the ITE Web site at jobs.ite.org. You can easily post an ad in the Journal or on the Web with the click of your mouse. The deadline to post an ad in ITE Journal is the 8th of the month before publication date (for example, May 8 for the June issue). The magazine is mailed the first week of the month, with subscribers receiving it sometime in the second week. Web ads run for 30 days and begin as soon as payment is received. Web ads can be modified, deleted or renewed at any time. For details on pricing, discounts, posting and more, please contact Christina Garneski, Marketing Sales Manager at 202-289-0222 ext. 128, or cgarneski@ite.org, or visit the Web site today! ITE Journal / December 2006 23