交通事故造成的旅行延误成本

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
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引用次数: 0

摘要

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。
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Cost of travel delays caused by traffic crashes

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.

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