Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control
This work addresses traffic congestion at signalized intersections for urban traffic management, offering an incremental improvement in prediction accuracy.
The study tackled short-term queue length prediction for adaptive traffic signal control by developing Grey models, which reduced prediction errors by up to 51% compared to existing methods like LSTM and time series models.
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 patterns in real-time in order to reduce congestion. Real-time prediction of queue lengths can be used to adjust the phasing and timings for different movements at an intersection with ASCS. The accuracy of the prediction varies based on the factors, such as the stochastic nature of the vehicle arrival rates, time of the day, weather and driver characteristics. In addition, accurate prediction for multilane, undersaturated and saturated traffic scenarios is challenging. Thus, the objective of this study is to develop queue length prediction models for signalized intersections that can be leveraged by ASCS 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; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections. The efficacy of the GM 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 ASCS on a calibrated roadway network in Lexington, South Carolina. GM 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 time series models) in predicting average and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error, and 51% and 50%, respectively, in terms of Mean Absolute Error.