Dynamic Analysis of Service Level at Signalized Intersections Based on License Plate Recognition Data

Abstract

Accurately analyzing the level of service (LOS) of signalized intersections is vital for evaluating signal control strategies. Currently, most research on LOS focuses on the factors that influence commuters’ perceptions. However, these methods lack immediacy and universality, making them challenging to apply to dynamic and real-time scenarios. This study proposes an innovative framework for dynamically analyzing the LOS at signalized intersections using license plate recognition data (LPR data). This framework integrates free-flow speed estimation, average vehicle delay calculation from LPR data, unreasonable delay correction within windows, and dynamic LOS evaluation at signalized intersections according to Highway Capacity Manual (HCM). Specifically, Gaussian mixture models are employed for estimating free travel time at signalized intersections and calculating average vehicle delays using LPR data. The day is segmented into 144 10-min windows for LOS assessment, with each window’s average vehicle delay determining the intersection’s LOS according to HCM. The Local Outlier Factor algorithm is used to detect unreasonable abrupt changes in delays, then smoothing with Loess regression, ultimately achieving dynamic intersection delays calculation and LOS evaluation throughout the day. The method’s efficacy and universality are validated using simulation data from SUMO and LPR data from three signalized intersections with different traffic patterns in Xiaoshan District, Hangzhou, China. The method’s effectiveness in dynamically calculating intersection delays and evaluating service levels throughout the day underscores its potential for widespread use in traffic management and control strategies. Level of service is an important indicator for measuring the traffic performance of a signalized intersection. This paper, based on license plate recognition data, achieves a dynamic evaluation of the level of service of an intersection. We used license plate recognition data to perform travel time calculation, data cleaning, free travel time fitting, and delay calculation. Then, using the standards from Highway Capacity Manual, we achieved a dynamic evaluation of the all-day service level for the intersection. We validated the efficacy and universality of our method through simulation and real-world data. Our method can be directly applied to intersections equipped with license plate recognition devices, allowing for dynamic evaluation of traffic performance throughout the day. Other practical applications include identifying peak hours, evaluating signal strategies, and depicting the traffic evolution process, etc. If real-time license plate recognition data can be obtained, our method can also calculate delays and evaluate service level in real time, demonstrating potential for widespread use in traffic management and evaluation.