A Multisource Data Approach for Estimating Vehicle Queue Length at Metered On-Ramps

Abstract

Queue length information is a critical input for ramp metering management. Based on accurate and reliable queue length, the inflow rate can be optimized to maximize the benefit of ramp metering. This paper proposes a queue length estimation method for metered on-ramps. In the proposed method, multiple data sources including INRIX data, controller event-based data, and loop detector data are used. The proposed method is based on the resilient back-propagation neural network model. In addition, the proposed method is enhanced by two techniques. The first technique is implementing the decision tree to determine whether or not the queue length is larger than zero and the second technique is checking whether or not the queue length reaches the ramp queue capacity by using the loop occupancy rate data. Three ramps along the SR-51 freeway in Phoenix, Arizona, were selected to evaluate the proposed method. The proposed method is compared with the Kalman filter (KF)-based method that has been proposed in previous research. The results show that the average improvements over the KF-based method are 46.82% and 63.08% for the estimated mean absolute error and root-mean-square error, respectively.