Cycle-to-Cycle Queue Length Estimation from Connected Vehicles with Filtering on Primary Parameters
Cycle-to-Cycle Queue Length Estimation from Connected Vehicles with Filtering on Primary Parameters
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DOI：
10.1016/j.ijtst.2021.04.009
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
G. Comert;N. Begashaw
中科院分区：
文献类型：
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作者：
G. Comert;N. Begashaw
联网车辆的估计模型通常假设低水平参数，例如已知的到达率和市场渗透率，或者实时估计它们。在低的市场渗透率，这样的参数估计产生大的误差，估计队列长度的控制或操作应用程序效率低下。为了提高低层参数估计的准确性，研究了连接车辆信息过滤对排队长度估计模型的影响。滤波器被用作多级实时估计器。准确性测试对已知的到达率和市场渗透率的情况下，使用微观模拟。为了了解短期或动态过程的有效性，到达率和市场渗透率每15分钟改变一次。结果表明，卡尔曼和粒子滤波器，参数估计能够在15分钟内找到真实值，并满足和超过已知参数的准确性，特别是低市场渗透率的情况。此外，当不存在联网车辆时使用最后已知的估计队列长度比输入平均估计值的性能更好。此外，研究表明，这两种滤波算法都适合于需要小于0.1秒的计算时间的实时应用。
Estimation models from connected vehicles often assume low level parameters such as arrival rates and market penetration rates as known or estimate them in real-time. At low market penetration rates, such parameter estimators produce large errors making estimated queue lengths inefficient for control or operations applications. In order to improve accuracy of low level parameter estimations, this study investigates the impact of connected vehicles information filtering on queue length estimation models. Filters are used as multilevel real-time estimators. Accuracy is tested against known arrival rate and market penetration rate scenarios using microsimulations. To understand the effectiveness for short-term or for dynamic processes, arrival rates, and market penetration rates are changed every 15 min. The results show that with Kalman and Particle filters, parameter estimators are able to find the true values within 15 min and meet and surpass the accuracy of known parameter scenarios especially for low market penetration rates. In addition, using last known estimated queue lengths when no connected vehicle is present performs better than inputting average estimated values. Moreover, the study shows that both filtering algorithms are suitable for real-time applications that require less than 0.1 second computational time.