Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

University Transportation Centers Program2019PDFJournal ArticleHasani, MahdieJahangiri, ArashSener, Ipek NeseMunira, SirajumOwens, Justin MAppleyard, Bruce S.Ryan, SherryTurner, Shawn M.Machiani, Sahar GhanipoorSan Diego State University. Department of Civil, Construction, and Environmental EngineeringTexas A&M Transportation InstituteVirginia Tech Transportation InstituteSan Diego State University. School of Public AffairsSafety through Disruption (Safe-D) University Transportation Center (UTC)United States. Department of Transportation. University Transportation Centers (UTC) ProgramJohn Wiley & Sons Ltd.United StatesBicyclingCrash dataCrash injuriesCrash ratesHigh risk locationsInjury severityRisk assessmentTraffic safetyWalking01-00369A3551747115This is an open access article under the terms of the Creative Commons Attribution-Noncommercial-No Derivatives License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.Citation: M. Hasani, A. Jahangiri, I. N. Sener, S. Munira, J. M. Owens, B. Appleyard, S. Ryan, S. M. Turner and S. Ghanipoor Machiani (2019), "Identifying High-Risk Intersections for Walking and Bicycling using Multiple Data Sources in the City of San Diego", Journal of Advanced Transportation, 2019.Open Access; Creative Commons Attribution License (CC-BY)Over the last decade, demand for active transportation modes such as walking and bicycling has increased. While it is desirable to provide high levels of safety for these eco-friendly modes of travel, unfortunately, the overall percentage of pedestrian and bicycle fatalities increased from 13% to 18% of total road-related fatalities in the last decade. In San Diego County, although the total number of pedestrian and bicyclist fatalities decreased over the same period of time, a similar trend with a more drastic change is observed; the overall percentage of pedestrian and bicycle fatalities increased from 19.5% to 31.8%. This study aims to estimate pedestrian and bicyclist exposure and identify signalized intersections with highest risk for walking and bicycling within the city of San Diego, California, USA. Multiple data sources such as automated pedestrian and bicycle counters, video cameras, and crash data were utilized. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. Cluster analysis coupled with stratification was employed to select a representative sample of intersections for data collection. Automated pedestrian and bicycle counting models utilized in this study reached a high accuracy, provided certain conditions exist in video data. Results from exposure modeling showed that pedestrian and bicyclist volume was characterized by transportation network, population, traffic generators, and land use variables. There were both similarities and differences between pedestrian and bicycle models, including different spatial scales of influence by mode. Additionally, the study quantified risk incorporating injury severity levels, frequency of victims, distance crossed, and exposure into a single equation. It was found that not all intersections with the highest number of pedestrian and bicyclist victims were identified as high-risk after exposure and other factors such as crash severity were taken into account.136