Estimating Queue Length at Undersaturated and Oversaturated Signalized Intersections Using Multi-Source Data: A Shockwave Theory Approach

With the progress of information and sensing technologies, estimating vehicular queue length at signalized intersections becomes feasible and has attracted considerable attention. The existing studies provided a solid theoretical foundation for the estimation, however, the studies have some restrictions or limitations more or less. This paper presents a new methodology for estimating vehicular queue length at signalized intersections using multi-source data under both undersaturated and oversaturated conditions. The methodology applies the shockwave theory to model queue evolution over time and space. Using data from probe vehicles and point detectors, analytical formulations for calculating the maximum and minimum (residual) queue lengths of each cycle are developed. Ground truth data was collected from numerical experiments conducted at two intersections in Shanghai, China to verify the proposed methodology. It is found that the methodology has mean absolute percentage errors of 17.09% and 12.28% respectively for maximum queue length estimation in two tests, which are reasonably effective. However, the methodology is unsatisfactory in estimating the residual queue length. Other limitations of the proposed models and algorithms are also discussed in the paper.