Cost of travel delays caused by traffic crashes

IF 12.5 Q1 TRANSPORTATION Communications in Transportation Research Pub Date : 2024-04-16 DOI:10.1016/j.commtr.2024.100124
Ting Lian , Becky P.Y. Loo
{"title":"Cost of travel delays caused by traffic crashes","authors":"Ting Lian ,&nbsp;Becky P.Y. Loo","doi":"10.1016/j.commtr.2024.100124","DOIUrl":null,"url":null,"abstract":"<div><p>This study proposes a method for measuring travel delays caused by traffic crashes based on taxi GPS data and other open-source spatial data. Travel delays caused by traffic crashes are quantified according to the difference between the post-crash and typical travel speeds on affected road segments. Based on multiple sources of data in Hong Kong, we also develop a generalized linear model with explanatory variables including crash characteristics, temporal attributes, road network features, traffic indicators, and built environment features, to unveil the relationship between travel delays and these factors. The findings show that crash characteristics alone inadequately explain variations in delays. The model performance improves after factors about the built environment and the dynamic road conditions are incorporated. This underscores the importance of urban factors in traffic delay analysis. Furthermore, we estimate the total travel delays caused by traffic crashes in the city. It is estimated that Hong Kong has suffered from a total delay of 713,877 vehicle-hours in 2021. The associated economic loss amounts to US$11.02 million. This study contributes to methodological advances in estimating crash-induced travel delays. The explanatory model considers factors which help policy makers and planners to identify risky factors and hot spots for devising more targeted and effective strategies of shortening crash-induced traffic congestion in the future. In addition, the findings highlight the significance and magnitude of another negative externality of traffic crashes – traffic delays – in a complex urban road network.</p></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772424724000076/pdfft?md5=7dd2e7443178cff7cac1d1f954f1b6e8&pid=1-s2.0-S2772424724000076-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Transportation Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772424724000076","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0

Abstract

This study proposes a method for measuring travel delays caused by traffic crashes based on taxi GPS data and other open-source spatial data. Travel delays caused by traffic crashes are quantified according to the difference between the post-crash and typical travel speeds on affected road segments. Based on multiple sources of data in Hong Kong, we also develop a generalized linear model with explanatory variables including crash characteristics, temporal attributes, road network features, traffic indicators, and built environment features, to unveil the relationship between travel delays and these factors. The findings show that crash characteristics alone inadequately explain variations in delays. The model performance improves after factors about the built environment and the dynamic road conditions are incorporated. This underscores the importance of urban factors in traffic delay analysis. Furthermore, we estimate the total travel delays caused by traffic crashes in the city. It is estimated that Hong Kong has suffered from a total delay of 713,877 vehicle-hours in 2021. The associated economic loss amounts to US$11.02 million. This study contributes to methodological advances in estimating crash-induced travel delays. The explanatory model considers factors which help policy makers and planners to identify risky factors and hot spots for devising more targeted and effective strategies of shortening crash-induced traffic congestion in the future. In addition, the findings highlight the significance and magnitude of another negative externality of traffic crashes – traffic delays – in a complex urban road network.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
交通事故造成的旅行延误成本
本研究基于出租车 GPS 数据和其他开源空间数据,提出了一种测量交通事故造成的旅行延误的方法。交通事故造成的出行延误根据事故后受影响路段的行车速度与典型行车速度之间的差异进行量化。基于香港的多种数据来源,我们还建立了一个广义线性模型,其解释变量包括交通事故特征、时间属性、路网特征、交通指标和建筑环境特征,以揭示出行延误与这些因素之间的关系。研究结果表明,仅凭碰撞特征不足以解释延误的变化。在纳入建筑环境和动态路况因素后,模型的性能有所改善。这凸显了城市因素在交通延误分析中的重要性。此外,我们还估算了市内交通事故造成的总行程延误。据估计,2021 年香港因交通事故造成的总延误时间为 713,877 车时。相关经济损失达 1,102 万美元。本研究在估算交通事故导致的行车延误方面取得了方法上的进步。解释性模型考虑的因素有助于政策制定者和规划者识别风险因素和热点,以便在未来制定更有针对性和更有效的策略,缩短车祸导致的交通拥堵。此外,研究结果还强调了交通事故的另一个负面外部效应--交通延误--在复杂的城市道路网络中的重要性和严重程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
15.20
自引率
0.00%
发文量
0
期刊最新文献
Harnessing multimodal large language models for traffic knowledge graph generation and decision-making Controllability test for nonlinear datatic systems Intelligent vehicle platooning transit A multi-functional simulation platform for on-demand ride service operations Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1