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Unscented Kalman filter for urban link travel time estimation with mid-link sinks and sources

机译:Unscented Kalman滤波器用于估计带有中间链路汇和源的城市链路旅行时间

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To estimate link travel time, the classical analytical procedure uses vehicles counts at upstream and downstream locations. This procedure is vulnerable in urban networks mainly due to significant flow to and from mid-link sinks and sources. One of the important developments recently done on this topic has yielded to the CUPRITE methodology. This method is derived from the classical analytical procedure. It integrates probe vehicle data to correct deterministically the upstream cumulative plot to match the information of probe vehicles travel times, whilst the downstream cumulative plot is kept unchanged. The algorithm proposed and validated in this research estimates urban links travel times based on an unscented Kalman filter (UKF). This algorithm integrates stochastically the vehicle count data from underground loop detectors at the end of every link and the travel time from probe vehicles. The proposed methodology, which can be used for travel time estimation in real-time, is compared to the classical analytical procedure and to the CUPRITE method in case of mid-link perturbation. Along to its lower sensitivity than CUPRITE, the UKF algorithm makes it possible detection and exclusion of outliers from both data sources.
机译:为了估算路段的行驶时间,经典分析程序使用上游和下游位置的车辆计数。此过程在城市网络中很容易受到攻击,这主要是由于大量流入和流出中间链路汇和源。最近在此主题上进行的一项重要开发已屈服于CUPRITE方法。该方法源自经典的分析程序。它集成了探测车数据,以确定性地校正上游累计图以匹配探测车行驶时间的信息,而下游累计图则保持不变。本研究中提出并验证的算法基于无味卡尔曼滤波器(UKF)估算城市路段旅行时间。该算法随机地集成了每个链路末端的地下环路检测器的车辆计数数据和探测车辆的行驶时间。所提出的方法可用于实时旅行时间估计,并与经典分析程序和中链扰动情况下的CUPRITE方法进行了比较。 UKF算法的灵敏度低于CUPRITE,因此可以检测和排除两个数据源中的异常值。

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