Analytical Model for Queue Length Estimation at Signalized Intersections from Travel Time

Queue formation and dissipation are a major feature of urban road networks and estimation of queue lengths is important in traffic flow modeling. In this study, we propose a methodology for estimating the queue length at signalized intersections using only travel time data and signal timing information. We use analytical equations to trace the trajectory of the vehicle and estimate the location of the vehicle in queue. We compare the performance of our Queue Location Estimation Using Travel Time data (QLE-UTT) with another methodology which used travel time data from multiple detectors and kinematic equations for vehicle location estimation. The performances of both methodologies were found to be comparable; however, the limited data requirement and simplicity of QLE-UTT makes the methodology attractive for practical implementation. The vehicle locations estimated from QLE-UTT was used to estimate the maximum queue length in a signal cycle using a probabilistic approach. This methodology for queue length estimation was tested both on a simulated dataset, for undersaturated, saturated, and oversaturated traffic conditions, and the real-world NGSIM dataset. The methodology combining QLE-UTT and the probabilistic approach was found to estimate the vehicle trajectories and queue lengths with good accuracy.