We study the problem of optimally routing plug-in electric and conventional fuel vehicles on a city level. In our model, commuters selfishly aim to minimize a local cost that combines travel time, from a fixed origin to a desired destination, and the monetary cost of using city facilities, parking or service stations. The traffic authority can influence the commuters' preferred routing choice by means of personalized discounts on parking tickets and on the energy price at service stations. We formalize the problem of designing these monetary incentives optimally as a large-scale bilevel game, where constraints arise at both levels due to the finite capacities of city facilities and incentives budget. Then, we develop an efficient decentralized solution scheme with convergence guarantees based on BIG Hype, a recently-proposed hypergradient-based algorithm for hierarchical games. Finally, we validate our model via numerical simulations over the Anaheim's network, and show that the proposed approach produces sensible results in terms of traffic decongestion and it is able to solve in minutes problems with more than 48000 variables and 110000 constraints.

本研究通过优化路线的方式，以私人汽车通勤者为中心，考虑了能源成本和停车成本等因素，通过设置个性化的优惠券来引导交通流量。研究祖先了设计这些货币激励的最优方案的问题，并提出了一种分布式解决方案，证明其在Anaheim的道路网络上能够缓解交通堵塞问题，并能在几分钟内解决超过48000个变量和110000个约束条件的问题。

利用BIG Hype算法设计最佳个性化交通路由激励方案