Choice-driven bilevel optimization for multiclass traffic congestion management via eco-routing incentives

Abstract

This study contributes to sustainable transportation modeling by proposing a user-centric approach to incentivize eco-routing travel behavior. We propose a novel reward credit scheme to provide path-based commuter incentives with the goal of reducing $C{O}_2$ emissions and the total system travel time. The scheme takes into account multiple classes of commuters in the network that differ by their value of time and their vehicle energy type. Users subscribing to the scheme may earn monetary reward credits which act as incentives to promote sustainable mobility. Two types of reward credits are considered: subscription- and path-based credits. A discrete choice model is embedded within a traffic assignment model to capture the endogenous impact of commuters’ scheme adoption onto network congestion effects. We introduce a bilevel optimization formulation to determine optimal non-additive, path-based reward credits and subscription-based reward credits within a predefined budget under traffic equilibrium conditions. In this formulation, the follower problem is a parameterized multi-class user equilibrium traffic assignment problem with non-additive path costs and incorporates a logit choice model for scheme adoption. The leader represent the network regulator whose goal is to maximize social welfare by minimizing the total system travel time and total $C{O}_2$ emissions. We develop a single-level Karush–Kuhn–Tucker reformulation and propose a customized branch-and-bound algorithm to solve this bilevel optimization problem. Numerical experiments demonstrate the potential of eco-routing incentives to promote sustainable urban mobility and highlight the benefits of combining subscription- and path-based reward credits for traffic congestion management.