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We study the problem of optimally routing plug-in electric and conventionalfuel vehicles on a city level. In our model, commuters selfishly aim tominimize a local cost that combines travel time, from a fixed origin to adesired destination, and the monetary cost of using city facilities, parking orservice stations. The traffic authority can influence the commuters' preferredrouting choice by means of personalized discounts on parking tickets and on theenergy price at service stations. We formalize the problem of designing thesemonetary incentives optimally as a large-scale bilevel game, where constraintsarise at both levels due to the finite capacities of city facilities andincentives budget. Then, we develop an efficient decentralized solution schemewith convergence guarantees based on BIG Hype, a recently-proposedhypergradient-based algorithm for hierarchical games. Finally, we validate ourmodel via numerical simulations over the Anaheim's network, and show that theproposed approach produces sensible results in terms of traffic decongestionand it is able to solve in minutes problems with more than 48000 variables and110000 constraints.

Designing Optimal Personalized Incentive for Traffic Routing using BIG Hype algorithm