Significance of Sensor Location in Real-time Traffic State Estimation☆
Significance of Sensor Location in Real-time Traffic State Estimation☆
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DOI：
10.1016/j.proeng.2014.07.012
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Afzal Ahmed;D. Watling;D. Ngoduy
中科院分区：
文献类型：
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作者：
Afzal Ahmed;D. Watling;D. Ngoduy
由于交通事故或其他道路环境因素造成的短期拥堵，大大降低了路段的交通通行能力，成为出行时间延误的主要部分。准确可靠地估计实时流量状态对于在不可预测的事件期间优化网络性能至关重要。对当前交通状态的不准确估计会产生不可靠的行程时间估计，从而导致交通事故期间交通管理策略无效。本研究强调了当交通流预测模型未提供有关事件持续时间和事件对路段交通流量影响的信息时交通状态估计的准确性和可靠性。信元传输模型 (CTM) 用于预测交通状态，传感器的测量结果在扩展卡尔曼滤波器 (EKF) 中进行组合，以最大限度地减少预测和测量的交通状态之间的误差平方。使用简单的链接来突出实际交通状态和使用朴素预测模型估计的交通状态之间的差异，以进行实时交通状态估计。仿真结果分析表明，当测量传感器下游发生事件时，测量传感器上游小区的交通状态估计是可靠且准确的。而当事件位置位于测量传感器的上游时，测量传感器下游小区的估计交通状态更接近实际交通状况。
Short-term congestion caused due to traffic incidents or other road environment factors significantly reduces traffic flow capacity of a link which forms a major part of travel time delays. Accurate and reliable estimate of real-time traffic state is essential for optimizing network performance during unpredictable events. Inaccurate estimate of current traffic state produces unreliable travel-time estimations which lead to ineffective traffic management strategies during traffic incident.This study highlights the accuracy and reliability of traffic state estimate when a traffic flow prediction model is not provided with information about duration and impact of the incident on traffic flow capacity of the link. Cell Transmission Model (CTM) is used for prediction of traffic state and measurements from the sensor are combined in Extended Kalman Filter (EKF) to minimize square of error between predicted and measured traffic state. A simple link is used to highlight the difference between actual traffic state and estimated traffic state using a naive prediction model for real-time traffic state estimation. Analysis of simulation results shows that estimate of traffic state is reliable and accurate for cells upstream of the measurement sensor when incident occurred downstream of measurement sensor. Whereas when incident location is upstream of measurement sensor, the estimated traffic state for downstream cells of measurement sensor is more close to actual traffic condition.