DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation


  • Haipeng Chen Nanyang Technological University
  • Bo An Nanyang Technological University
  • Guni Sharon University of Texas at Austin
  • Josiah Hanna University of Texas at Austin
  • Peter Stone University of Texas at Austin
  • Chunyan Miao Nanyang Technological University
  • Yeng Soh Nanyang Technological University




Dynamic road pricing, Sequential planning, Policy gradient


To alleviate traffic congestion in urban areas, electronic toll collection (ETC) systems are deployed all over the world. Despite the merits, tolls are usually pre-determined and fixed from day to day, which fail to consider traffic dynamics and thus have limited regulation effect when traffic conditions are abnormal. In this paper, we propose a novel dynamic ETC (DyETC) scheme which adjusts tolls to traffic conditions in realtime. The DyETC problem is formulated as a Markov decision process (MDP), the solution of which is very challenging due to its 1) multi-dimensional state space, 2) multi-dimensional, continuous and bounded action space, and 3) time-dependent state and action values. Due to the complexity of the formulated MDP, existing methods cannot be applied to our problem. Therefore, we develop a novel algorithm, PG-beta, which makes three improvements to traditional policy gradient method by proposing 1) time-dependent value and policy functions, 2) Beta distribution policy function and 3) state abstraction. Experimental results show that, compared with existing ETC schemes, DyETC increases traffic volume by around 8%, and reduces travel time by around 14:6% during rush hour. Considering the total traffic volume in a traffic network, this contributes to a substantial increase to social welfare.




How to Cite

Chen, H., An, B., Sharon, G., Hanna, J., Stone, P., Miao, C., & Soh, Y. (2018). DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11337



Computational Sustainability and Artificial Intelligence