Zone-level traffic crash analysis with incorporated multi-sourced traffic exposure variables using Bayesian spatial model
Zone-level traffic crash analysis with incorporated multi-sourced traffic exposure variables using Bayesian spatial model
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使用贝叶斯空间模型结合多源交通暴露变量进行区域级交通事故分析
DOI：
10.1080/19439962.2022.2164815
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发表时间：
2023-01
影响因子：
2.6
通讯作者：
Wei Yin
中科院分区：
文献类型：
--
作者：
Hao Zhang;Jie Bao;Qiong Hong;Lv Chang;Wei Yin
摘要 本研究的主要目的是从一些新数据源中发现交通暴露变量，并探讨这些新数据源及其组合如何影响区域级碰撞模型的性能。分别从 Twitter 和出租车 GPS 记录中推断出七种类型的签到活动和五种类型的出租车行程。然后，采用贝叶斯空间模型进行区域级交通事故分析。结果表明，一些特定的签到活动和推断的出租车行程与区域级事故计数密切相关，从而证实了将新数据源纳入区域级事故模型的好处。比较分析进一步表明，作为交通事故空间分析中交通暴露的代理，推特签到活动比推断的出租车行程表现得更好，并且隐藏在新数据源中的详细行程目的信息比简单地聚合每个区域中的位置点更有利于区域级事故模型。研究结果表明，各个大数据源都有其突出的用户群体和空间区域覆盖范围，它们的组合可以作为传统暴露变量的有效补充信息，以提高区域级事故模型的性能，更好地揭示人类活动对交通事故的空间影响。本研究结果可以帮助交通主管部门制定更有针对性的交通需求调整策略，有效降低区域级碰撞风险。
Abstract The primary objective of this study is to discover traffic exposure variables from some new data sources and explore how these new data sources and their combination affects the performance of zone-level crash models. Seven types of check-in activities and five types of taxi trips are inferred from Twitter and taxi GPS records, respectively. Then, Bayesian spatial models are employed to conduct zone-level traffic crash analysis. The results suggest that some specific check-in activities and inferred taxi trips are closely related with zone-level crash counts, and thereby confirms the benefits of incorporating new data sources into zone-level crash models. The comparative analyses further indicate that twitter check-in activities perform better than inferred taxi trips as a proxy for traffic exposures on spatial analyses of traffic crashes, and detailed trip purpose information hidden in new data sources greatly benefit zone-level crash models than simply aggregating location points in each zone. The results of this study reveal that each big data source has its prominent coverage of user groups and spatial areas, and their combination can serve as effective supplementary information to traditional exposure variables to improve the performance of zone-level crash models and better reveal the spatial impacts of human activities on traffic crashes. The findings of this study can help transportation authority develop more targeted traffic demand adjustment strategies to effectively reduce zone-level crash risks.
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影响因子：
6.1
作者：
Li, Zhibin;Wang, Wei;Ragland, David R.
通讯作者：
Ragland, David R.
DOI：
--
发表时间：
2015
期刊：
--
影响因子：
--
作者：
Carson Qing;S. Parfenov;Lee-Jung Kim
通讯作者：
Carson Qing;S. Parfenov;Lee-Jung Kim
影响因子：
1.7
作者：
A. Tarko;K. Sinha;O. Farooq
通讯作者：
A. Tarko;K. Sinha;O. Farooq
影响因子：
2.6
作者：
Romi Satria;Jonathan Aguero-Valverde;M. Castro
通讯作者：
Romi Satria;Jonathan Aguero-Valverde;M. Castro
DOI：
10.1016/j.aap.2017.06.012
发表时间：
2017-09
期刊：
Accident; analysis and prevention
影响因子：
--
作者：
Jie Bao;Pan Liu;Hao Yu;Chengcheng Xu
通讯作者：
Jie Bao;Pan Liu;Hao Yu;Chengcheng Xu