Sangam: A Confluence of Knowledge Streams

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

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dc.creator Hasani, Mahdie
dc.creator Jahangiri, Arash
dc.creator Sener, Ipek Nese
dc.creator Munira, Sirajum
dc.creator Owens, Justin M.
dc.creator Appleyard, Bruce
dc.creator Ryan, Sherry
dc.creator Turner, Shawn M.
dc.creator Machiani, Sahar Ghanipoor
dc.date 2019-06-24T11:53:50Z
dc.date 2019-06-24T11:53:50Z
dc.date 2019-06-16
dc.date 2019-06-23T07:00:39Z
dc.date.accessioned 2023-03-01T18:53:16Z
dc.date.available 2023-03-01T18:53:16Z
dc.identifier Mahdie 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:10.1155/2019/9072358
dc.identifier http://hdl.handle.net/10919/90413
dc.identifier https://doi.org/10.1155/2019/9072358
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/281707
dc.description 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.
dc.description Published version
dc.format application/pdf
dc.format application/pdf
dc.format text/xml
dc.language en
dc.publisher Hindawi
dc.rights Creative Commons Attribution 4.0 International
dc.rights http://creativecommons.org/licenses/by/4.0/
dc.rights Copyright © 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.
dc.title Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego
dc.title Journal of Advanced Transportation
dc.type Article - Refereed
dc.type Text


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