论文标题
循环到周期的队列长度长度估计与主要参数过滤的连接车辆
Cycle-to-Cycle Queue Length Estimation from Connected Vehicles with Filtering on Primary Parameters
论文作者
论文摘要
来自连接车辆的估计模型通常假设较低的参数，例如已知的到达率和市场渗透率，或实时估算它们。在低市场渗透率下，这种参数估计器会产生较大的错误，从而使估计的队列长度无效地控制或操作应用程序。为了提高低级参数估计的准确性，本研究研究了连接车辆信息过滤对队列长度估计模型的影响。过滤器用作多级实时估计器。使用微拟合测试了已知到达率和市场渗透率方案的准确性。要了解短期或动态过程的有效性，每15分钟就会更改每15分钟的到达率和市场渗透率。结果表明，使用卡尔曼和粒子过滤器，参数估计器能够在15分钟内找到真实值，并满足并超过已知参数方案的准确性，尤其是对于低市场渗透率。此外，使用最后已知的估计队列长度在没有连接的车辆时的表现要好于输入平均估计值。此外，研究表明，两种过滤算法都适用于需要小于0.1秒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 minutes. The results show that with Kalman and Particle filters, parameter estimators are able to find the true values within 15 minutes 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.
