Traffic-light control in urban environment exploiting drivers' reaction to the expected red lights duration. (December 2022)

• Record Type: Journal Article
• Authors: Scandella, Matteo; Ghosh, Arnob; Bin, Michelangelo; Parisini, Thomas
• Is Part Of: Transportation research. Volume 145(2022)
• Publication Date: 2022-12
• Subjects: Traffic light control -- Traffic network -- Congestion; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103910
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: Traffic congestion in urban environment is one of the most critical issue for drivers and city planners for both environment and efficiency reasons. Traffic lights are one of the main tools used to regulate traffic by diverting the drivers between different paths. Rational drivers, in turn, react to the traffic light duration by evaluating their options and, if necessary, by changing direction in order to reach their destination quicker. In this paper, we introduce a macroscopic traffic model for urban intersections that incorporates this rational behavior of the drivers. Then, we exploit it to show that, by providing additional information about the expected red-time duration to the drivers, one can decrease the amount of congestion in the network and the overall length of the queues at the intersections. Additionally, we develop a control policy for the traffic lights that exploits the reaction of the drivers in order to divert them to a different route to further increase the performances. These claims are supported by extensive numerical simulations. Highlights: A traffic-flow macroscopic model of the drivers' rational decision-making. It considers origin-destination pairs and the ability to show the red time duration. Numerical simulations illustrate the effectiveness of showing the red time duration. A novel control policy that exploit the drivers' reaction to the red time duration. The increased efficiency of the controller is validated with extensive simulations.