We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from connected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various penetration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetration rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous prediction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.

中文翻译：

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使用卡尔曼滤波算法预测连接车辆环境中的短期交通流量

我们基于从连接的车辆获得的数据，开发了一种Kalman滤波器，用于预测城市动脉的交通流量。所提出的算法计算效率高，并且提供了实时预测，因为它恰好在预测周期之前调用了连接的车辆数据。此外，它可以预测各种连接车辆的渗透率（连接车辆数与车辆总数之比）的交通流量。首先，针对不同的穿透率，不同的车辆到达波动率以及各种信号设置，使用从Vissim交通模拟器获得的数据对Kalman滤波器方程进行校准。然后针对在Vissim模拟器中生成的各种流量情况评估过滤器。我们使用一些统计方法评估了几种交通情况下不同渗透率的算法性能。尽管许多先前的预测方法高度依赖于固定传感器（即环路探测器和摄像机）的数据，这些数据与巨大的安装和维护成本相关联，但本研究仅针对基于短期流量预测的低成本方法提供了一种方法。在连接的车辆数据上。