Spatial-temporal analysis of pedestrian injury severity with geographically and temporally weighted regression model in Hong Kong

Xu, Xuecai; Luo, Xiangjian; Ma, Changxi; Xiao, Daiquan

doi:10.1016/j.trf.2020.02.003

Cited by 33 publications

(16 citation statements)

References 60 publications

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“…The same conclusion was obtained by Klassen et al [45], who used a spatial random intercept model to explore bicycle-motor vehicle crashes. In addition, there are many other studies to determine the risk factors of injury outcomes by establishing statistical functions with spatial error term [46][47][48]. That research could provide inspiration for this research.…”

Section: Spatial Correlation In Crash Analysismentioning

confidence: 96%

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Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

et al. 2021

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The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

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Section: Spatial Correlation In Crash Analysismentioning

confidence: 96%

“…In most cases, the existence of spatial correlation is reasonable because the adjacent segments will have similar geometric features and environments [47,54]. This may lead to certain factors shared among adjacent crashes [11].…”

Section: Spatial Random Parameters Logit Modelmentioning

confidence: 99%

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

et al. 2021

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“…Wang et al focused on mountainous expressways and proposed a partial proportional odds model to determine the determinants of truckinvolved crash injury severity [15]. Xu et al attempted to investigate pedestrian-involved crash injury severity by using geographically and temporally weighted regression model taking into account spatial-temporal correlation [16].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model

Yuan

Yan

et al. 2021

Journal of Advanced Transportation

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Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.

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“…The definition of safe traffic is ambiguous in the literature. Some researchers focus on the real crash-related scenarios (Xu et al, 2014;Yuan et al, 2020;Xu et al, 2020), while most of the studies concentrate on the potential crash of which the risk level is normally quantified by SSMs, e.g., gap time (Mahmud et al, 2017), time-to-collision (TTC) (Hayward, 1971) and time-integrated time-to-collision (TIT) (Minderhoud and Bovy, 2001).…”

Section: Definition Of Traffic Stability and Safetymentioning

confidence: 99%

“…2) the fact that traffic stability is normally analysed with theoretical methods, e.g., the transfer function-based method (Sun et al, 2018;Zhou and Zhu, 2021), but traffic safety analysis mainly adopts data-based methods (Xu et al, 2020) or simulationbased methods (Sharma et al, 2021;Deluka Tibljaš et al, 2018). Few studies have studied the specific relationship between traffic safety and stability.…”

Section: Introductionmentioning

confidence: 99%

Investigation of traffic safety and stability through simulation and theoretical modelling

Dong¹

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Words cannot express my gratitude to my supervisor, Assistant Professor Zhu Feng, for his invaluable patience and feedback. I also could not have undertaken this journey without his help on knowledge and expertise. I would also like to thank Associate Professor Wong Yiik Diew and Associate Professor Wang Zhiwei, David for taking the time to be my Thesis Advisory Committee (TAC) members and providing insightful comments and suggestions for my research. I have benefited a lot from the TAC meeting every year. Their immense knowledge and plentiful experience have encouraged me in all the time of my academic research and daily life.Thanks should also go to the librarians, research assistants, and study participants from the university, who impacted and inspired me. I am also grateful to Ng Soo Ching, who is the senior executive of school. In the past four years, she has been patient and meticulous, helping me deal with a lot of administrative affairs.I need to thank colleagues who make my Ph.D journey memorable. Special thanks to my group members, Zhou Jiazu, Zhu Yang, Xu Xuecai, Wu Yuanyuan, Zhu Siying and Wan Jianwu, for the insightful suggestions and discussions on my Ph.D. research during our weekly group meeting. I really appreciate those valuable comments although they are sometimes critical. My thanks also go to Zhang Qun, Ding Ruiyu, Xiong Daiqi and Tian Qingyun who have helped me a lot during this study. In addition, I appreciate my twelve-year friend, Xu Borui. She always warms and heals me like the sun. Most importantly, a big thank you to my beloved, Zhou Jiazu, for his unconditional help and support. He has given me a lot of strength and warmth through his persistent companionship during many difficult times.In addition, I would like to thank my parents, Dong Zhijun and Guo Suyan, for their understanding and support. They are always there for me. Without their support, I would not be able to spend all my efforts on my research and study.

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Spatial-temporal analysis of pedestrian injury severity with geographically and temporally weighted regression model in Hong Kong

Cited by 33 publications

References 60 publications

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model

Investigation of traffic safety and stability through simulation and theoretical modelling

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