Optimizing Traffic Flow Efficiency by Controlling Lane Changes: Collective, Group, and User Optima

Abstract

Lane changes can lead to disturbances in traffic flow, and the uneven distribution of traffic over different lanes as a result of lane changes can also lead to instability and congestion in one specific lane. Advice on lane changing can therefore be beneficial to both individual drivers and traffic in the network. The many variations in the content and objectives of advice, however, may affect the effectiveness of the advice. This paper focuses on the optimization of traffic flow through the performance of specific lane changes. Traffic flow is modeled on a two-lane stretch, and lane change time instants of a subset of vehicles are considered as decision variables. Optimizations with three objectives are constructed: a collective optimum, a group optimum, and a user optimum. These optima are found through the minimization of the total travel delay of different vehicle groups. To solve the problems, a genetic algorithm is implemented as a heuristic method. Each optimum leads to different lane changes. Specifically, through the proposed algorithm, vehicles receive suggestions to change lanes in bigger gaps to improve collective or group efficiency and are supposed to overtake as many vehicles as they can by changing lanes for their own benefit. The algorithm can be further extended to a more effective in-car advice system, which can improve traffic efficiency through communication with partly automated vehicles.