应用探测器轨迹数据探索商用车辆绕行模式

Mark Franz PhD, Sara Zahedian PhD, Dhairya Parekh, Tahsin Emtenam PhD, Greg Jordan
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引用次数: 0

摘要

了解驾驶者的绕行行为对于交通运营和规划应用都至关重要。然而,由于需要跟踪单个车辆的移动,测量真实世界中的绕行行为具有挑战性。高分辨率车辆轨迹数据的最新发展使交通专业人员能够观察真实世界中的绕行行为,而无需安装和维护车牌读取摄像头等硬件。本文研究了车辆探测轨迹数据在三个独特案例研究中捕捉商用机动车(CMV)绕行行为的可行性。在此之前,还进行了验证分析,以调查 CMV 探头轨迹数据在马里兰州虚拟称重站(VWS)附近校准良好的计数站代表 CMV 总流量的能力。验证分析表明,所有虚拟称重站都存在较强的正相关性(高于 0.75)。在验证数据后,应用一种方法来评估与 CMV 执法活动、拥堵规避和事故诱发的临时道路关闭相关的 CMV 绕行行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Commercial Vehicle Detouring Patterns through the Application of Probe Trajectory Data
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.
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