多尺度因素对高速公路工作区驾驶员并线行为交互影响的规律性变化

Lan Huang, Zhibin Ren, Xianghai Meng
{"title":"多尺度因素对高速公路工作区驾驶员并线行为交互影响的规律性变化","authors":"Lan Huang, Zhibin Ren, Xianghai Meng","doi":"10.1177/03611981231215334","DOIUrl":null,"url":null,"abstract":"This study aimed to explore the impacting mechanism of macro- and micro-factors and multi-scale synergy on drivers’ merging behaviors in the highway work zone, and then to facilitate future merging prediction research. The merging behavior, requiring drivers to detect adjacent vehicles and real-time traffic conditions simultaneously, is a complex cognitive process. Previous studies have mainly focused on the stable impacts of limited factors on merging probability, but ignored the varying states and drivers’ performance. Status-changing positions in the whole merging process were first identified by constructing and analyzing the relationship between running speed and distance from the construction area. Subsequently, the interaction analysis was conducted among the multi-scale traffic factors, utilizing optimized logistic models and interaction estimates, thus establishing macro- and micro-factor connections. Besides, the marginal effect was calculated to analyze the fluctuation degree of these connections. Finally, a multilevel identification framework was proposed, whose effectiveness and practicality were validated using 744 naturalistic vehicular trajectories from a real highway work area. At different positions, drivers are affected by various factors to varying degrees. While approaching the construction area, drivers become more passive, thus trying to avoid rear-ending the lead vehicle but ignoring the lag vehicle. Besides, traffic volume also affects drivers’ merging decisions by confusing their cognition toward the time headway. This research indicates that dynamic interaction effects between multi-scale factors could provide far-reaching benefits for lane-changing prediction. The findings provide a basis for formulating traffic management policies and constructing driving assistance systems.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Changing Regularity of the Interaction Effects of Multi-Scale Factors on Drivers’ Merging Behaviors in the Highway Work Zone\",\"authors\":\"Lan Huang, Zhibin Ren, Xianghai Meng\",\"doi\":\"10.1177/03611981231215334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aimed to explore the impacting mechanism of macro- and micro-factors and multi-scale synergy on drivers’ merging behaviors in the highway work zone, and then to facilitate future merging prediction research. The merging behavior, requiring drivers to detect adjacent vehicles and real-time traffic conditions simultaneously, is a complex cognitive process. Previous studies have mainly focused on the stable impacts of limited factors on merging probability, but ignored the varying states and drivers’ performance. Status-changing positions in the whole merging process were first identified by constructing and analyzing the relationship between running speed and distance from the construction area. Subsequently, the interaction analysis was conducted among the multi-scale traffic factors, utilizing optimized logistic models and interaction estimates, thus establishing macro- and micro-factor connections. Besides, the marginal effect was calculated to analyze the fluctuation degree of these connections. Finally, a multilevel identification framework was proposed, whose effectiveness and practicality were validated using 744 naturalistic vehicular trajectories from a real highway work area. At different positions, drivers are affected by various factors to varying degrees. While approaching the construction area, drivers become more passive, thus trying to avoid rear-ending the lead vehicle but ignoring the lag vehicle. Besides, traffic volume also affects drivers’ merging decisions by confusing their cognition toward the time headway. This research indicates that dynamic interaction effects between multi-scale factors could provide far-reaching benefits for lane-changing prediction. The findings provide a basis for formulating traffic management policies and constructing driving assistance systems.\",\"PeriodicalId\":309251,\"journal\":{\"name\":\"Transportation Research Record: Journal of the Transportation Research Board\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-03\",\"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/10.1177/03611981231215334\",\"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/10.1177/03611981231215334","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

本研究旨在探讨宏观和微观因素及多尺度协同作用对高速公路工作区驾驶员并线行为的影响机制,进而促进未来的并线预测研究。并线行为要求驾驶员同时检测相邻车辆和实时交通状况,是一个复杂的认知过程。以往的研究主要关注有限因素对并线概率的稳定影响,却忽视了状态的变化和驾驶员的表现。通过构建和分析运行速度与施工区域距离之间的关系,首先确定了整个并线过程中的状态变化位置。随后,利用优化的逻辑模型和交互估计,对多尺度交通因素之间的交互作用进行分析,从而建立宏观和微观因素之间的联系。此外,还计算了边际效应,以分析这些联系的波动程度。最后,提出了一个多层次识别框架,并利用实际高速公路工作区的 744 个自然车辆轨迹验证了该框架的有效性和实用性。在不同的位置,驾驶员受到各种因素不同程度的影响。在接近施工区域时,驾驶员会变得更加被动,从而尽量避免与前方车辆追尾,却忽略了后方车辆。此外,交通流量也会混淆驾驶员对时间间隔的认知,从而影响其并线决策。这项研究表明,多尺度因素之间的动态交互效应可为变道预测带来深远的益处。研究结果为制定交通管理政策和构建驾驶辅助系统提供了依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Changing Regularity of the Interaction Effects of Multi-Scale Factors on Drivers’ Merging Behaviors in the Highway Work Zone
This study aimed to explore the impacting mechanism of macro- and micro-factors and multi-scale synergy on drivers’ merging behaviors in the highway work zone, and then to facilitate future merging prediction research. The merging behavior, requiring drivers to detect adjacent vehicles and real-time traffic conditions simultaneously, is a complex cognitive process. Previous studies have mainly focused on the stable impacts of limited factors on merging probability, but ignored the varying states and drivers’ performance. Status-changing positions in the whole merging process were first identified by constructing and analyzing the relationship between running speed and distance from the construction area. Subsequently, the interaction analysis was conducted among the multi-scale traffic factors, utilizing optimized logistic models and interaction estimates, thus establishing macro- and micro-factor connections. Besides, the marginal effect was calculated to analyze the fluctuation degree of these connections. Finally, a multilevel identification framework was proposed, whose effectiveness and practicality were validated using 744 naturalistic vehicular trajectories from a real highway work area. At different positions, drivers are affected by various factors to varying degrees. While approaching the construction area, drivers become more passive, thus trying to avoid rear-ending the lead vehicle but ignoring the lag vehicle. Besides, traffic volume also affects drivers’ merging decisions by confusing their cognition toward the time headway. This research indicates that dynamic interaction effects between multi-scale factors could provide far-reaching benefits for lane-changing prediction. The findings provide a basis for formulating traffic management policies and constructing driving assistance systems.
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