Pricing is All You Need to Improve Traffic Routing
Yu Tang, Kaan Ozbay, Li Jin
TL;DR
The paper addresses stabilizing traffic routing under driver non-compliance by modeling compliance as a Markov chain conditioned on traffic state and tolls, and by casting the managed network as a nonlinear stochastic dynamical system. It develops stability and instability conditions via Foster–Lyapunov theory and Markov transience, enabling the computation of lower and upper throughput bounds through semi-infinite programs and toll optimization. Numerical experiments on a two-parallel-link network show how toll levels influence stability and suggest an optimal toll near $5$ $/veh$ for maximizing throughput bounds, while also highlighting the impact of compliance variance. The results provide a principled pricing design tool to maintain routing effectiveness and improve throughput in congested networks with uncertain driver responses.
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.