Maximizing Social Welfare in Selfish Multi-Modal Routing using Strategic Information Design for Quantal Response Travelers

TL;DR

This paper introduces LoRI, a strategic information design algorithm that improves social welfare in multi-attribute selfish routing systems with boundedly-rational travelers, outperforming traditional methods in simulations.

Contribution

It models multi-attribute costs and bounded rationality in travelers, proposing a novel approximate algorithm for strategic information design to enhance network efficiency.

Findings

LoRI persuades travelers towards socially optimal routes 66.66% of the time on average.

LoRI outperforms SSSP in system utility, especially with motive mismatches.

Tradeoff observed between system performance and runtime in experiments.

Abstract

Traditional selfish routing literature quantifies inefficiency in transportation systems with single-attribute costs using price-of-anarchy (PoA), and provides various technical approaches (e.g. marginal cost pricing) to improve PoA of the overall network. Unfortunately, practical transportation systems have dynamic, multi-attribute costs and the state-of-the-art technical approaches proposed in the literature are infeasible for practical deployment. In this paper, we offer a paradigm shift to selfish routing via characterizing idiosyncratic, multi-attribute costs at boundedly-rational travelers, as well as improving network efficiency using strategic information design. Specifically, we model the interaction between the system and travelers as a Stackelberg game, where travelers adopt multi-attribute logit responses. We model the strategic information design as an optimization problem, and develop a novel approximate algorithm to steer Logit Response travelers towards social welfare using strategic Information design (in short, LoRI). We demonstrate the performance of LoRI on a Wheatstone network with multi-modal route choices at the travelers. In our simulation experiments, we find that LoRI outperforms SSSP in terms of system utility, especially when there is a motive mismatch between the two systems and improves social welfare. For instance, we find that LoRI persuades a traveler towards a socially optimal route for 66.66% of the time on average, when compared to SSSP, when the system has 0.3 weight on carbon emissions. However, we also present a tradeoff between system performance and runtime in our simulation results.