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Traditional selfish routing literature quantifies inefficiency intransportation 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 transportationsystems have dynamic, multi-attribute costs and the state-of-the-art technicalapproaches proposed in the literature are infeasible for practical deployment.In this paper, we offer a paradigm shift to selfish routing via characterizingidiosyncratic, multi-attribute costs at boundedly-rational travelers, as wellas improving network efficiency using strategic information design.Specifically, we model the interaction between the system and travelers as aStackelberg game, where travelers adopt multi-attribute logit responses. Wemodel the strategic information design as an optimization problem, and developa novel approximate algorithm to steer Logit Response travelers towards socialwelfare using strategic Information design (in short, LoRI). We demonstrate theperformance of LoRI on a Wheatstone network with multi-modal route choices atthe travelers. In our simulation experiments, we find that LoRI outperformsSSSP in terms of system utility, especially when there is a motive mismatchbetween the two systems and improves social welfare. For instance, we find thatLoRI persuades a traveler towards a socially optimal route for 66.66% of thetime on average, when compared to SSSP, when the system has 0.3 weight oncarbon emissions. However, we also present a tradeoff between systemperformance and runtime in our simulation results.

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