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

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dc.contributor.authorHasani, Mahdieen
dc.contributor.authorJahangiri, Arashen
dc.contributor.authorSener, Ipek Neseen
dc.contributor.authorMunira, Sirajumen
dc.contributor.authorOwens, Justin M.en
dc.contributor.authorAppleyard, Bruceen
dc.contributor.authorRyan, Sherryen
dc.contributor.authorTurner, Shawn M.en
dc.contributor.authorMachiani, Sahar Ghanipooren
dc.date.accessioned2019-06-24T11:53:50Zen
dc.date.available2019-06-24T11:53:50Zen
dc.date.issued2019-06-16en
dc.date.updated2019-06-23T07:00:39Zen
dc.description.abstractOver 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.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMahdie Hasani, Arash Jahangiri, Ipek Nese Sener, et al., “Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego,” Journal of Advanced Transportation, vol. 2019, Article ID 9072358, 15 pages, 2019. doi:10.1155/2019/9072358en
dc.identifier.doihttps://doi.org/10.1155/2019/9072358en
dc.identifier.urihttp://hdl.handle.net/10919/90413en
dc.language.isoenen
dc.publisherHindawien
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderCopyright © 2019 Mahdie Hasani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleIdentifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diegoen
dc.title.serialJournal of Advanced Transportationen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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