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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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. |
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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 |
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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. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Xu, Xuecai Xie, Siqi Wong, Sze Chun Xu, Pengpeng Huang, Helai Pei, Xin |
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Article |
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Xu, Xuecai Xie, Siqi Wong, Sze Chun Xu, Pengpeng Huang, Helai Pei, Xin |
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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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1681042940458172416 |