Estimating Maximum Queue Length for Traffic Lane Groups Using Travel Times from Video-Imaging Data

Queue length is an important measure that helps traffic managers collect and evaluate feedback about traffic signal control. Some mobile-sensor-based approaches developed in recent years can help identify critical points for understanding the queue process. This involves using sample data related to travel time or trajectory. The latest video imaging detectors facilitate the collection of significant lane-based travel time data, from detectors installed at fixed locations. This paper presents a method to estimate lane-based queue length using the travel time data collected by these new detectors. The first vehicle leaving the downstream stop line is deli ned as the leading vehicle in each signal cycle. The key premise underlying this new method is that the maximum queue length in the first cycle, when the leading vehicle is queued, is related to the leading vehicle's delay time and the duration of the red light in each cycle. Queue length in the current cycle is derived by analyzing the recursive formula of maximum queue lengths across different cycles. Finally, the new model's precision is evaluated using a field survey. The results show that the Hell - method has a higher precision compared to the existing method based in a similar concept, with maximum and average deviations of 39.36% and 12.25% respectively, over twenty. cycles. The findings of this paper can be applied to it on traffic signal control systems.