Design analysis of a decentralized equilibrium-routing strategy for intelligent vehicles

Abstract

Intelligent vehicle technologies are opening new possibilities for decentralized vehicle routing systems, suitable for regulating large traffic networks, and at the same time, capable of providing customized advice to individual vehicles. In this study, we perform a rigorous simulation-based analysis of an in-vehicle routing strategy that aims to achieve a user-equilibrium distribution in traffic. Novel features of the approach include: a mechanism based on forward propagation of individual vehicle decisions to anticipate future traffic dynamics; time-dependent prediction of route travel times with neural network-based link predictors; and a stochastic routing policy for in-vehicle decision-making based on predicted travel times. However, for an effective application of the approach, design choices need to be made regarding the accuracy of the link predictors, and some control settings. These choices may depend on the network size and structure. We investigate the impact of two important design aspects: sequentially using link-level predictors for route travel time estimation, and the control parameter values, on the equilibrium performance at the network-level. The results suggest functional scalability of the approach, in terms of the prediction model accuracy and routing performance. Overall, the work contributes to a qualitative and quantitative understanding of emergent performance from the given routing approach.