Mark Franz PhD, Sara Zahedian PhD, Dhairya Parekh, Tahsin Emtenam PhD, Greg Jordan
{"title":"Exploring Commercial Vehicle Detouring Patterns through the Application of Probe Trajectory Data","authors":"Mark Franz PhD, Sara Zahedian PhD, Dhairya Parekh, Tahsin Emtenam PhD, Greg Jordan","doi":"arxiv-2407.17319","DOIUrl":null,"url":null,"abstract":"Understanding motorist detouring behavior is critical for both traffic\noperations and planning applications. However, measuring real-world detouring\nbehavior is challenging due to the need to track the movement of individual\nvehicles. Recent developments in high-resolution vehicle trajectory data have\nenabled transportation professionals to observe real-world detouring behaviors\nwithout the need to install and maintain hardware such as license plate reading\ncameras. This paper investigates the feasibility of vehicle probe trajectory\ndata to capture commercial motor vehicle (CMV) detouring behavior under three\nunique case studies. Before doing so, a validation analysis was conducted to\ninvestigate the ability of CMV probe trajectory data to represent overall CMV\nvolumes at well-calibrated count stations near virtual weigh stations (VWS) in\nMaryland. The validation analysis showed strong positive correlations (above\n0.75) at all VWS stations. Upon validating the data, a methodology was applied\nto assess CMV detour behaviors associated with CMV enforcement activities,\ncongestion avoidance, and incident induced temporary road closures.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computational Engineering, Finance, and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.17319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding motorist detouring behavior is critical for both traffic
operations and planning applications. However, measuring real-world detouring
behavior is challenging due to the need to track the movement of individual
vehicles. Recent developments in high-resolution vehicle trajectory data have
enabled transportation professionals to observe real-world detouring behaviors
without the need to install and maintain hardware such as license plate reading
cameras. This paper investigates the feasibility of vehicle probe trajectory
data to capture commercial motor vehicle (CMV) detouring behavior under three
unique case studies. Before doing so, a validation analysis was conducted to
investigate the ability of CMV probe trajectory data to represent overall CMV
volumes at well-calibrated count stations near virtual weigh stations (VWS) in
Maryland. The validation analysis showed strong positive correlations (above
0.75) at all VWS stations. Upon validating the data, a methodology was applied
to assess CMV detour behaviors associated with CMV enforcement activities,
congestion avoidance, and incident induced temporary road closures.