Evaluation of Alternative Methods for Identifying High Collision Concentration Locations Raghavan Srinivasan 1 Craig Lyon 2 Bhagwant Persaud 2 Carol Martell 1 Jongdae Baek 1 Meeting with CALTRANS, December 7 and 8, UNC Highway Safety Research Center 2 Persaud and Lyon, Inc. 1

Background States and local agencies have limited budgets Need an efficient process to identify sites with potential for safety improvement Conventional methods have problems: –Potential bias due to regression to the mean Sites with a randomly high count may be incorrectly identified –Use of crash rates implicitly assume crash frequency and traffic volume are linearly related (shown to be incorrect quite often) Meeting with CALTRANS, December 7 and 8,

Objectives Evaluate various methods using data from California Identify methods that are optimal for identifying locations for further review Assess the recently released software product (SafetyAnalyst) for network screening Meeting with CALTRANS, December 7 and 8,

Overview of Methods Table C method Level of Service (LOSS) method Methods based on the empirical Bayes (EB) procedure Continuous Risk Profile (CRP) method Screening based on high proportions of specific crash types Meeting with CALTRANS, December 7 and 8,

Table C Method Compare observed accident frequency with critical crash frequency –Average accident rate (base rate + AADT factor) –Computed for different rate groups (i.e., type of facilities) –For roadway segments, use sliding window of 0.2 miles with 0.02 mile increments –Intersections (250 feet radius) –Time periods: 3, 6, and 12 months –Select windows where observed frequency is greater than critical crash frequency Meeting with CALTRANS, December 7 and 8,

Table C Method, contd. Does not account for potential bias due to Regression to the Mean (RTM) –Can be a major issue since a maximum of 1 year of data is used Uses crash rates –Incorrectly assumes that crash frequency is proportional to traffic volume No ranking of sites Meeting with CALTRANS, December 7 and 8,

Level of Service (LOSS) Method Develop Safety Performance Functions (SPFs) –Equations that relate expected crash frequency to site characteristics –Predict average accident frequencies Compare crash frequency with average frequencies from SPFs Accounts for non-linear relationship between crash frequency and volume Does not account for RTM No automatic ranking of sites Meeting with CALTRANS, December 7 and 8,

Methods based on the EB procedure Expected crashes for a site (E) = w*(SPF prediction) +(1-w)*(Observed frequency) Expected Excess for a site = E – SPF prediction Implementation –Sliding Window –Peak Search Meeting with CALTRANS, December 7 and 8,

Sliding Window Fixed window that moves in defined increments A window can include portions from multiple segments Each segment is characterized by the maximum value calculated at any window position in or overlapping the segment Meeting with CALTRANS, December 7 and 8,

Peak Search Incrementally growing window lengths are selected No windows span multiple segments Window starts at left boundary of a segment and moves incrementally until it reaches the end Test of significance based on the coefficient of variation Meeting with CALTRANS, December 7 and 8,

Methods based on the EB procedure, contd. Regression to the Mean (RTM) is accounted for Accounts for non-linear relationship between traffic volume and crash counts Windows of varying length in the peak search method allow a more detailed assessment of crash risk Sites are ranked based on EB Expected or EB Expected Excess Meeting with CALTRANS, December 7 and 8,

Continuous Risk Profile (CRP) Compute cumulative count of accidents Subtract cumulative expected number of accidents from cumulative count leading to a cumulative excess Compute moving average of cumulative excess using a sliding window If moving average is negative, a value of zero is assigned; positive values remain the same Plot revised moving average with distance Meeting with CALTRANS, December 7 and 8,

CRP, contd. Does not account for RTM because only observed crashes are used Does not account for traffic volume Uses constant window size – same limitation as other sliding window approaches No procedure for ranking of sites Meeting with CALTRANS, December 7 and 8,

Screening Based on High Proportions Ranks locations that have a proportion of a specific accident type higher than a threshold value Could reveal existence of accident patterns susceptible to correction Uses only accident data RTM is not accounted for Does not account for traffic volume Meeting with CALTRANS, December 7 and 8,

Methods Selected for Evaluation Expected Crashes based on EB procedure Expected Excess Crashes based on EB procedure LOSS method Table C method High proportion method (as an illustration for intersections) Meeting with CALTRANS, December 7 and 8,

Evaluation Approach How does each method rank high those locations that are more likely to have large number of crashes in the future? How does each method rank high the locations that are identified as needing improvement (among the ones that were selected for review by Table C)? Compare the characteristics of top ranked locations in each method Meeting with CALTRANS, December 7 and 8,

Data California roadway, intersection, and crash files obtained from the Highway Safety Information System (HSIS) for HSIS staff linked the location of ramps and intersections to roadway segment file –Determine roadway segments within intersections Crashes within 250 feet of the center of intersections were assigned to intersections –Interchange location is not available: 0.3 miles on either side of a ramp was used as a proxy for interchange influence area Meeting with CALTRANS, December 7 and 8,

Data Preparation, contd. Caltrans provided excel file called ‘TableCInv6yr’ –Sites selected from Table C for investigation –Sites identified as ‘improvement recommended’ Roadway segments –Rural two lane roads –Urban freeways Intersections –Rural 4 leg minor stop controlled intersections –Rural 4 leg signalized Meeting with CALTRANS, December 7 and 8,

Safety Performance Functions Two types of SPFs developed with HSIS data –Type 1 SPF (similar to default SPFs in SafetyAnalyst) AADT is the only term for segments Major and Minor road AADT are the only terms for intersections –Type 2 SPF Other site characteristics in addition to AADT –Roadway segments (shoulder width, median width, etc.) –Intersections (presence of right turn or left turn lanes, etc.) For freeways, separate SPFs developed for within and outside interchange influence area SPFs developed for many crash types Meeting with CALTRANS, December 7 and 8,

Conclusions and Recommendations Methods based on EB procedure generally identify sites with higher AADTs and higher expected crashes. Top ranked sites based on the EB methods have more crashes in the future compared to the Table C method. When data on investigated sites was used, Table C did quite well (as expected). However, other methods did well in many cases. Future research could consider using cost effectiveness data. Meeting with CALTRANS, December 7 and 8,

Conclusions and Recommendations, contd If sufficient samples are available, directly estimating SPFs from CA data are better. –This study estimated SPFs for two intersection types and two roadway types –Future research could develop SPFs for other facility types Methods from EB procedure work better with longer segments. With expanded lengths, an entire segment could be flagged instead of just the window. Meeting with CALTRANS, December 7 and 8,

Conclusions and Recommendations, contd Future research could examine the possibility of coming up with weights that reflect potential cost benefit values. The ‘proportions’ method could be used in combination with EB methods as a diagnostic tool. Future research could investigate if combination of methods would provide better results. Meeting with CALTRANS, December 7 and 8,

Conclusions and Recommendations, contd Develop a database of interchange locations along with a link to the ramps from each interchange. Compile data on lengths of individual ramps. Obtain more recent and accurate minor road AADT data. Develop a database with detailed information about treatments implemented. Meeting with CALTRANS, December 7 and 8,