Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas
Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian in...
Published in: | Sustainability |
---|---|
Main Authors: | , , |
Format: | Text |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2021
|
Subjects: | |
Online Access: | https://doi.org/10.3390/su13126610 |
id |
ftmdpi:oai:mdpi.com:/2071-1050/13/12/6610/ |
---|---|
record_format |
openpolar |
spelling |
ftmdpi:oai:mdpi.com:/2071-1050/13/12/6610/ 2023-08-20T04:09:26+02:00 Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas Khondoker Billah Hatim O. Sharif Samer Dessouky agris 2021-06-10 application/pdf https://doi.org/10.3390/su13126610 EN eng Multidisciplinary Digital Publishing Institute Sustainable Transportation https://dx.doi.org/10.3390/su13126610 https://creativecommons.org/licenses/by/4.0/ Sustainability; Volume 13; Issue 12; Pages: 6610 pedestrian motor vehicle crashes fatalities logistic regression bivariate analysis Text 2021 ftmdpi https://doi.org/10.3390/su13126610 2023-08-01T01:55:16Z Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. 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, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended. Text Refuge Islands MDPI Open Access Publishing Refuge Islands ENVELOPE(-67.166,-67.166,-68.350,-68.350) Sustainability 13 12 6610 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
pedestrian motor vehicle crashes fatalities logistic regression bivariate analysis |
spellingShingle |
pedestrian motor vehicle crashes fatalities logistic regression bivariate analysis Khondoker Billah Hatim O. Sharif Samer Dessouky Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas |
topic_facet |
pedestrian motor vehicle crashes fatalities logistic regression bivariate analysis |
description |
Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. 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, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended. |
format |
Text |
author |
Khondoker Billah Hatim O. Sharif Samer Dessouky |
author_facet |
Khondoker Billah Hatim O. Sharif Samer Dessouky |
author_sort |
Khondoker Billah |
title |
Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas |
title_short |
Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas |
title_full |
Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas |
title_fullStr |
Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas |
title_full_unstemmed |
Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas |
title_sort |
analysis of pedestrian–motor vehicle crashes in san antonio, texas |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/su13126610 |
op_coverage |
agris |
long_lat |
ENVELOPE(-67.166,-67.166,-68.350,-68.350) |
geographic |
Refuge Islands |
geographic_facet |
Refuge Islands |
genre |
Refuge Islands |
genre_facet |
Refuge Islands |
op_source |
Sustainability; Volume 13; Issue 12; Pages: 6610 |
op_relation |
Sustainable Transportation https://dx.doi.org/10.3390/su13126610 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/su13126610 |
container_title |
Sustainability |
container_volume |
13 |
container_issue |
12 |
container_start_page |
6610 |
_version_ |
1774722396950888448 |