A Real-Time Queue Length Estimation Method Based on Probe Vehicles in CV Environment

The real-time traffic status estimation in urban signalized intersections is highly valuable for modern traffic control and management. This paper presents a real-time queue length estimation method based on probe vehicles' data in the connected vehicle (CV) environment. The probe data are used to identify the stopping states of CVs. Based on this, a queue length time series referring to the stopping time and the positions of CVs is built for describing the queuing process at an intersection. Considering the statistical average traffic rate, queue length time series in historical cycles, and the stopping states for real-time CV arrival features in the current cycle, the critical queuing time is forecasted based on the linear fitting method and the real-time queue length is estimated based on the Markov model. The overall scheme is thoroughly tested and demonstrated in a realistic scenario at different penetration rates. Under different conditions, the stationarity of the queue length series is tested by the augmented Dickey-Fuller. Two Markov models based on the transition matrices of the current cycle and both the current and historical cycles are verified, respectively. The results demonstrate the high accuracy in the real-time queue length estimation, and the proposed method shows good performance in handling the randomness, especially when the CV penetration rate is low.