Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

Abstract

With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection’s queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively.

Queue Length Estimation Based on Probe Vehicle Data at Signalized Intersections

“…Assumption (1) states that the information of intersections is known, and we will not derive incorrect intersection information (signal timing, etc. ), resulting in incorrect model establishment.…”

“…How to improve the efciency of signalized intersections becomes the key to solve trafc congestion. Queue length is an important index to evaluate the operational efciency of signalized intersections [1]. Timely and accurate acquisition of queue length can provide efective data basis for the management and optimization of signal timing at intersections [2], with the queue length minimization as the optimization objective, the phase and timing of the signal can be optimized accordingly [3], and signal optimization based on queue length can solve the problem of queue overfow caused by the failure of traditional signal control strategies in dealing with oversaturation at intersections.…”