利用实时交通和天气大数据建立考虑到未观察到的异质性的优先级的基于危害的持续时间模型

Songha Lee, Juneyoung Park, Mohamed Abdel-Aty
{"title":"利用实时交通和天气大数据建立考虑到未观察到的异质性的优先级的基于危害的持续时间模型","authors":"Songha Lee, Juneyoung Park, Mohamed Abdel-Aty","doi":"10.1177/03611981241255905","DOIUrl":null,"url":null,"abstract":"Traffic crash-post management is very important for transportation agencies. Delays in clearing the scene after a crash can directly increase the likelihood of a secondary crash and cause more serious traffic congestion. To optimize the management strategies for non-recurrent congestion, it is important to understand the factors that affect incident clearance times. This paper develops a model to analyze the duration time on highways using various types of datasets, including real-time data at the time of or immediately before the crash, detailed time variables, and crash type, with an accelerated failure time model. The model includes the three parametric distributions and assumed randomness, which is called unobserved heterogeneity, and can parametrically estimate the time to hazard to provide the conditional probability that the crash will be resolved. The results show that the Weibull distribution model with random parameters was suitable for both injury and non-injury crashes. Specifically, factors such as whether a truck was involved, temporal speed difference, rain, and rollover status are related to the increase in the duration time. Also, when the weighted length of the response time and detection time are applied to the duration time, the shorter the response time, the shorter the duration time for injury crashes. If there are no injuries, the faster it will be detected and help arrive at the scene. On this result, it is expected that it will be possible to develop a highly accurate clearance time prediction model with artificial intelligence techniques by using more data samples or high-resolution vehicle trajectory data.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hazards-Based Duration Time Model with Priorities Considering Unobserved Heterogeneity Using Real-Time Traffic and Weather Big Data\",\"authors\":\"Songha Lee, Juneyoung Park, Mohamed Abdel-Aty\",\"doi\":\"10.1177/03611981241255905\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traffic crash-post management is very important for transportation agencies. Delays in clearing the scene after a crash can directly increase the likelihood of a secondary crash and cause more serious traffic congestion. To optimize the management strategies for non-recurrent congestion, it is important to understand the factors that affect incident clearance times. This paper develops a model to analyze the duration time on highways using various types of datasets, including real-time data at the time of or immediately before the crash, detailed time variables, and crash type, with an accelerated failure time model. The model includes the three parametric distributions and assumed randomness, which is called unobserved heterogeneity, and can parametrically estimate the time to hazard to provide the conditional probability that the crash will be resolved. The results show that the Weibull distribution model with random parameters was suitable for both injury and non-injury crashes. Specifically, factors such as whether a truck was involved, temporal speed difference, rain, and rollover status are related to the increase in the duration time. Also, when the weighted length of the response time and detection time are applied to the duration time, the shorter the response time, the shorter the duration time for injury crashes. If there are no injuries, the faster it will be detected and help arrive at the scene. On this result, it is expected that it will be possible to develop a highly accurate clearance time prediction model with artificial intelligence techniques by using more data samples or high-resolution vehicle trajectory data.\",\"PeriodicalId\":309251,\"journal\":{\"name\":\"Transportation Research Record: Journal of the Transportation Research Board\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Record: Journal of the Transportation Research Board\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/03611981241255905\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Record: Journal of the Transportation Research Board","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/03611981241255905","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

交通事故岗亭管理对于交通机构来说非常重要。交通事故后清理现场的延误会直接增加发生二次交通事故的可能性,并造成更严重的交通拥堵。为了优化非经常性拥堵的管理策略,了解影响事故清理时间的因素非常重要。本文建立了一个模型,利用各种类型的数据集(包括碰撞发生时或碰撞前的实时数据、详细的时间变量和碰撞类型),采用加速故障时间模型来分析高速公路上的持续时间。该模型包括三种参数分布和假定的随机性,即未观察到的异质性,可以参数估计危害时间,从而提供碰撞解决的条件概率。结果表明,带有随机参数的 Weibull 分布模型适用于伤害性和非伤害性碰撞事故。具体来说,是否涉及卡车、时间速度差、雨水和翻车状态等因素与持续时间的增加有关。此外,如果将响应时间和检测时间的加权长度应用于持续时间,则响应时间越短,受伤碰撞事故的持续时间就越短。如果没有人员受伤,则检测和救援到达现场的速度就越快。根据这一结果,通过使用更多的数据样本或高分辨率车辆轨迹数据,有望利用人工智能技术开发出高精度的清除时间预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hazards-Based Duration Time Model with Priorities Considering Unobserved Heterogeneity Using Real-Time Traffic and Weather Big Data
Traffic crash-post management is very important for transportation agencies. Delays in clearing the scene after a crash can directly increase the likelihood of a secondary crash and cause more serious traffic congestion. To optimize the management strategies for non-recurrent congestion, it is important to understand the factors that affect incident clearance times. This paper develops a model to analyze the duration time on highways using various types of datasets, including real-time data at the time of or immediately before the crash, detailed time variables, and crash type, with an accelerated failure time model. The model includes the three parametric distributions and assumed randomness, which is called unobserved heterogeneity, and can parametrically estimate the time to hazard to provide the conditional probability that the crash will be resolved. The results show that the Weibull distribution model with random parameters was suitable for both injury and non-injury crashes. Specifically, factors such as whether a truck was involved, temporal speed difference, rain, and rollover status are related to the increase in the duration time. Also, when the weighted length of the response time and detection time are applied to the duration time, the shorter the response time, the shorter the duration time for injury crashes. If there are no injuries, the faster it will be detected and help arrive at the scene. On this result, it is expected that it will be possible to develop a highly accurate clearance time prediction model with artificial intelligence techniques by using more data samples or high-resolution vehicle trajectory data.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信