Key origin–destination pairs perception reasoning approach

Abstract

This paper proposes a key origin–destination (OD) pairs perception reasoning (KODPR) approach for route guidance (RG) in urban traffic networks with numerous OD pairs. First, to reduce a real-world RG problem's complexity with large OD sizes, a long-term perception module is developed to identify a few critical OD pairs, making real-world application feasible. Second, the issue of multi-OD cooperation and system resource allocation is addressed through the cooperative perception reasoning method that performs a sequential action update mechanism among agents. Additionally, a balanced reward function is designed in the Markov decision process framework for optimizing dynamic RG strategies. Experimental results using a real-world road network in Hangzhou, China, within a simulation of urban mobility-based simulation platform, demonstrate the superior performance of the proposed approach. The KODPR achieves optimization results close to dynamic user equilibrium by adjusting only 30% of the OD pairs in the network, significantly outperforming comparison methods. Its ability to coordinate extensive OD pairs in densely populated urban environments presents a promising solution for urban traffic RG.