Abstract
In this paper we propose a novel approach for alleviating traffic congestion in freeways with multiple access locations through the use of dynamic toll pricing. The pricing strategy is determined using model-based feedback control, with the underlying model derived using a combination of both traffic flow modeling and driver behavior. The traffic segment we focus on is a suburban freeway with multiple access locations. A model derived from the cell transmission method was utilized to develop the traffic flow model, with past traffic information from on-road sensors utilized for determining the model parameters. The behavior of the driver with respect to the choice of whether or not to enter the freeway segment is modeled using utility theory and the Value of Time (VOT) relative to the toll value. The proposed toll-pricing scheme is tested with traffic data from Portuguese freeway A5 and with different hypothesis on the driver’s VOT distribution, showing a significant improvement of the overall traffic conditions. The algorithm developed here provides an opportunity to improve on existing toll policy by guaranteeing more stable traffic conditions for the freeway users and optimizing the overall traffic throughput.
Acknowledgements
C.L. would like to acknowledge his collegues Yue Guan and Vineet Jagadeesan Nair at MIT - AAC Lab for several useful discussions. The authors give a special thank to Lara Trigueiro Moura, Research and Innovation Manager at A-to-Be, for providing them with the A5 traffic data of 2017, and to Instituto Nacional de Estatística, IP - Portugal for the data of 2017 Mobility Survey.
Disclosure statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Notes
1 Source: Instituto Nacional de Estatística, IP - Portugal (Matriz do número de deslocações/dia por município de origem e destino, por escalão horário (hora de partida), Inquérito à Mobilidade nas Áreas Metropolitanas do Porto e de Lisboa, 2017)
2 https://www.google.com/maps (accessed: 10.12.2020)
3 https://www.google.com/maps (accessed: 10.12.2020)