A variable tolling scheme for highway tolls based on traffic forecasting

Abstract

Highway variable toll collection is an important means of traffic governance in modern society. A reasonable variable tolling scheme can adjust the traffic flow through price, and further develop the bearing capacity of highway on the basis of existing infrastructure to improve its traffic efficiency and service level. In the past, the formulation of variable tolling schemes is usually based on various assumed models, which belongs to the knowledge-driven method. In this paper, a data-driven variable tolling scheme is proposed, which uses the traffic flow prediction model integrating attention mechanism, recurrent neural network and graph neural network to mine the historical traffic information data on the road. In this paper, the traffic flow information on the future road is forecasted, and the information is used to replace some assumptions of bottleneck theory which are not completely in line with the actual situation. Taking the forecast information of traffic flow as the new constraint condition of dynamic congestion pricing in bottleneck theory can help us develop a more scientific and reasonable variable tolling scheme.