Cycle-Based Estimation on Lane-Level Queue Length at Isolated Signalized Intersection Using License Plate Recognition Data

Queue length is an important parameter to evaluate the congestion level of urban intersections. Abundant methods based on different data sources, such as loop detector data and mobile sensor data, for estimating queue length have received wide attention. A license plate recognition (LPR) system recording vehicle information can be used as a data source to estimate lane-level queue length. In this study, an improved method is proposed for cycle-based queue length estimation at an isolated signalized intersection using LPR data, considering the residual queued vehicles under oversaturated conditions. First, a modified interpolation method is developed to infer the travel time of unmatched vehicles. Then, the complete arrival and departure information are processed as three characteristic parameters of vehicles, which are the input to the maximum probability function for queue length estimation at each lane. Finally, the proposed model is validated using actual cycle maximum queue length collected from a video camera and LPR data in Changsha city, China. The numerical results showed that the proposed model can achieve accurate estimation for lane-level queue length.