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首页> 外文期刊>Journal of Intelligent Transportation Systems >Determining optimal sensor locations under uncertainty for a truck activity monitoring system on California freeways
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Determining optimal sensor locations under uncertainty for a truck activity monitoring system on California freeways

机译：在加州高速公路上的卡车活动监测系统不确定性下确定最佳传感器位置

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摘要

A new hybrid sensor technology integrating existing weigh-in-motion axle configuration data combined with inductive signature data obtained from advanced inductive loop detectors is gaining interest due to its potential to provide detailed classification of truck body types as well as anonymous tracking of truck movements on freeways. However, selecting optimal deployment locations for the hybrid sensors has been persistently challenging because implementing new technologies state-wide can demand significant capital investment and logistics preparation. This article investigates two proposed strategies for optimally deploying this new technology on California freeways based on actual truck GPS trajectories: (i) A flow-interception approach to maximize the total amount of net origin-destination flows; and (ii) a truck re-identification approach to maximize insights into origins and destinations of sampled truck trips, as well as routes of those trips. The flow-interception model is capable of selecting locations emphasizing different body types with flow-based weight factors. The truck re-identification model investigates the best locations to identify heavy truck movement by selecting pairwise locations, and is shown to be sensitive to re-identification performance uncertainty.
机译：一种新的混合传感器技术，整合了现有的动作轴配置数据与从高级感应回路检测器获得的电感签名数据相结合的是由于其潜力提供了对卡车身体类型的详细分类以及卡车运动的匿名跟踪来获得兴趣高速公路。然而，选择用于混合传感器的最佳部署位置一直持续具有挑战性，因为实现了全面的新技术可以要求大量资本投资和物流准备。本文根据实际的卡车GPS轨迹调查了两种拟议的策略，以便基于实际的卡车GPS轨迹在加州高速公路上最佳地部署这项新技术：（i）流动拦截方法，以最大限度地提高净原产目的地流量的总量; （ii）一种卡车重新识别方法，可以最大限度地探讨采样卡车旅行的起源和目的地，以及这些旅行的路线。流动拦截模型能够选择具有基于流量的权力因子的不同体型的位置。卡车重新识别模型通过选择成对位置来调查最佳位置，以识别重型卡车运动，并显示对重新识别性能不确定性敏感。

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