Urban Transportation Infrastructure and Cyclist and Pedestrian Safety
The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the targe...
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ftlouisianastuir:oai:digitalcommons.lsu.edu:transet_pubs-1123 2023-06-11T04:16:16+02:00 Urban Transportation Infrastructure and Cyclist and Pedestrian Safety Sharif, Hatim Dessouky, Samer 2021-10-01T07:00:00Z application/pdf https://digitalcommons.lsu.edu/transet_pubs/123 https://digitalcommons.lsu.edu/context/transet_pubs/article/1123/viewcontent/Final_Report_20SAUTSA35.pdf unknown LSU Digital Commons https://digitalcommons.lsu.edu/transet_pubs/123 https://digitalcommons.lsu.edu/context/transet_pubs/article/1123/viewcontent/Final_Report_20SAUTSA35.pdf Publications Crashes Pedestrians Safety Bicyclist Safety Transportation Engineering text 2021 ftlouisianastuir 2023-05-28T18:33:22Z The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the target area, calculation of crash counts and rates, identifying road segments and intersections with highly concentrated bicycle and pedestrian crashes, and the development of effective safety countermeasures. Several variables and factors were analyzed, including driver characteristics such as age and gender, road-related factors, and environmental factors such as weather conditions and time of the day. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian/bicyclist crashes. Geospatial analysis was used to investigate crash frequency and severity. High-risk locations were identified through heat maps and hotspot analysis. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. The strongest predictors of severe injury include lighting condition, road class, road speed limit, traffic control, collision type, and the age and gender of the pedestrian/bicyclist. Fatal and incapacitating injury risk increased substantially when the pedestrian/bicyclist was at fault. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, and raised medians, and the use of leading pedestrian/bicyclist interval and hybrid beacons are recommended. Text Refuge Islands LSU Digital Commons (Louisiana State University) Refuge Islands ENVELOPE(-67.166,-67.166,-68.350,-68.350) |
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LSU Digital Commons (Louisiana State University) |
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Crashes Pedestrians Safety Bicyclist Safety Transportation Engineering |
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Crashes Pedestrians Safety Bicyclist Safety Transportation Engineering Sharif, Hatim Dessouky, Samer Urban Transportation Infrastructure and Cyclist and Pedestrian Safety |
topic_facet |
Crashes Pedestrians Safety Bicyclist Safety Transportation Engineering |
description |
The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the target area, calculation of crash counts and rates, identifying road segments and intersections with highly concentrated bicycle and pedestrian crashes, and the development of effective safety countermeasures. Several variables and factors were analyzed, including driver characteristics such as age and gender, road-related factors, and environmental factors such as weather conditions and time of the day. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian/bicyclist crashes. Geospatial analysis was used to investigate crash frequency and severity. High-risk locations were identified through heat maps and hotspot analysis. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. The strongest predictors of severe injury include lighting condition, road class, road speed limit, traffic control, collision type, and the age and gender of the pedestrian/bicyclist. Fatal and incapacitating injury risk increased substantially when the pedestrian/bicyclist was at fault. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, and raised medians, and the use of leading pedestrian/bicyclist interval and hybrid beacons are recommended. |
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Text |
author |
Sharif, Hatim Dessouky, Samer |
author_facet |
Sharif, Hatim Dessouky, Samer |
author_sort |
Sharif, Hatim |
title |
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety |
title_short |
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety |
title_full |
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety |
title_fullStr |
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety |
title_full_unstemmed |
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety |
title_sort |
urban transportation infrastructure and cyclist and pedestrian safety |
publisher |
LSU Digital Commons |
publishDate |
2021 |
url |
https://digitalcommons.lsu.edu/transet_pubs/123 https://digitalcommons.lsu.edu/context/transet_pubs/article/1123/viewcontent/Final_Report_20SAUTSA35.pdf |
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ENVELOPE(-67.166,-67.166,-68.350,-68.350) |
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Refuge Islands |
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Refuge Islands |
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Refuge Islands |
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https://digitalcommons.lsu.edu/transet_pubs/123 https://digitalcommons.lsu.edu/context/transet_pubs/article/1123/viewcontent/Final_Report_20SAUTSA35.pdf |
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