Learning and managing stochastic network traffic dynamics: an iterative and interactive approach

This study examines the potential of an iterative and interactive approach to learn network traffic dynamics and optimise tolling strategies considering time-varying stochastic traffic. A tractable ‘truth model’ based on the stochastic Macroscopic Fundamental Diagram is developed to represent the transportation system to be learned and managed. A ‘twin model’ that mirrors the truth model is formulated and calibrated for testing and optimising tolling adjustment strategies with the help of reinforcement learning. The optimised prices are then put into the ‘truth model’ to evaluate network efficiency improvement. The above procedure is iterative and interactive, which can be applied for congestion management in the period-to-period tolling adjustment fashion. Numerical studies show that the proposed iterative and interactive pricing strategy is able to enhance network efficiency even under limited information and/or inaccurate learning of the system. This illustrates the great potential of utilising iterative and interactive frameworks for congestion management.