Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation i...

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Main Authors: Xu, Xuecai, Xie, Siqi, Wong, Sze Chun, Xu, Pengpeng, Huang, Helai, Pei, Xin
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2017
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Online Access:https://hdl.handle.net/10356/81615
http://hdl.handle.net/10220/42276
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-816152020-03-07T11:43:32Z Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model Xu, Xuecai Xie, Siqi Wong, Sze Chun Xu, Pengpeng Huang, Helai Pei, Xin School of Civil and Environmental Engineering Signalized intersection Pedestrian injury severity The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash-severity models. The data from 1889 pedestrian-related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross-intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness-of-fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two-way carriageway, and those that occurred near tram or light-rail transit stops. 2017-04-17T08:16:59Z 2019-12-06T14:35:00Z 2017-04-17T08:16:59Z 2019-12-06T14:35:00Z 2017 2017 Journal Article Xu, X., Xie, S., Wong, S. C., Xu, P., Huang, H., & Pei, X. (2017). Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model. Journal of Advanced Transportation, 50(8), 2015-2028. 0197-6729 https://hdl.handle.net/10356/81615 http://hdl.handle.net/10220/42276 10.1002/atr.1442 197594 en Journal of Advanced Transportation © 2017 John Wiley & Sons, Ltd.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Signalized intersection
Pedestrian injury severity
spellingShingle Signalized intersection
Pedestrian injury severity
Xu, Xuecai
Xie, Siqi
Wong, Sze Chun
Xu, Pengpeng
Huang, Helai
Pei, Xin
Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model
description The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash-severity models. The data from 1889 pedestrian-related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross-intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness-of-fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two-way carriageway, and those that occurred near tram or light-rail transit stops.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Xu, Xuecai
Xie, Siqi
Wong, Sze Chun
Xu, Pengpeng
Huang, Helai
Pei, Xin
format Article
author Xu, Xuecai
Xie, Siqi
Wong, Sze Chun
Xu, Pengpeng
Huang, Helai
Pei, Xin
author_sort Xu, Xuecai
title Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model
title_short Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model
title_full Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model
title_fullStr Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model
title_full_unstemmed Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model
title_sort severity of pedestrian injuries due to traffic crashes at signalized intersections in hong kong: a bayesian spatial logit model
publishDate 2017
url https://hdl.handle.net/10356/81615
http://hdl.handle.net/10220/42276
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