Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

Abstract

Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time in order to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adjust the signal phasing and timings for different traffic movements at a signalized intersection with ASCS. The accuracy of the queue length prediction model varies based on the many factors, such as the stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and saturated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to develop short-term queue length prediction models for signalized intersections that can be leveraged by adaptive traffic signal control systems using four variations of Grey systems: (i) the first order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections (EGVM). The efficacy of the Grey models is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they are able to adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with adaptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models were compared with linear, nonlinear time series models, and long short-term memory (LSTM) neural network. Based on our analyses, we found that EGVM reduces the prediction error over closest competing models (i.e., LSTM and Additive Autoregressive (AAR) time series models) in predicting average and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error (RMSE), and 51% and 50%, respectively, in terms of Mean Absolute Error (MAE).

A Simple Traffic Signal Control Using Queue Length Information

“…With known queue lengths, adaptive signal control was also studied in a reinforcement learning scheme (Genders & Razavi (2016); Gao et al (2017)). If true QLs can be obtained as feedback, Comert et al (2019) showed that QLs for next time interval can be predicted within 5 meters (m) accuracy. Assuming Poisson arrivals, Zhang and Wang (Zhang & Wang (2011)) developed a method to optimize min green and max green parameters of an actuated control using real-time QL information (i.e., utilizing the information in the constraint of the optimization).…”

Queue Length Estimation at Traffic Signals: Connected Vehicles with Range Measurement Sensors

“…In basic form, there is no feedback from environment, thus, true queue length values are not known. This makes critical difference with the prediction formulations with series of true queue lengths are available where researchers used filtering, fit models, and deep learning methods (Tiaprasert et al (2015); Yin et al (2018); Gao et al (2020); Comert et al (2019)).…”