Pricing is All You Need to Improve Traffic Routing

TL;DR

This paper develops a stochastic dynamical system model using Markov chains to design pricing policies that improve traffic routing adherence and optimize throughput in a network with a corridor and local street.

Contribution

It introduces a novel Markov chain-based approach to model driver compliance and formulates a nonlinear stochastic system for better traffic management.

Findings

Pricing policies can significantly influence driver compliance.

Stability analysis provides bounds on network throughput.

Optimal tolls can be derived to maximize traffic flow.

Abstract

We investigate the design of pricing policies that enhance driver adherence to route guidance, ensuring effective routing control. The major novelty lies in that we adopt a Markov chain to model drivers' compliance rates conditioned on both traffic states and tolls. By formulating the managed traffic network as a nonlinear stochastic dynamical system, we can quantify in a more realistic way the impacts of driver route choices and thus determine appropriate tolls. Specially, we focus on a network comprised of one corridor and one local street. We assume that a reasonable routing policy is specified in advance. However, drivers could be reluctant to be detoured. Thus a fixed toll is set on the corridor to give drivers incentives to choose the local street. We evaluate the effectiveness of the given routing and pricing policies via stability analysis. We suggest using the stability and instability conditions to establish lower and upper bounds on throughput. This allows us to select suitable tolls that maximize these bounds.