# Designing Optimal Personalized Incentive for Traffic Routing using BIG Hype algorithm

arXiv: https://arxiv.org/abs/2304.12004 · HTML: https://mlsanity.com/paper/2304.12004/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning](https://mlsanity.com/paper/1909.10300/) (2019)
- [BIG Hype: Best Intervention in Games via Distributed Hypergradient Descent](https://mlsanity.com/paper/2303.01101/) (2023)
- [Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks](https://mlsanity.com/paper/1511.03611/) (2015)
- [Local Stackelberg equilibrium seeking in generalized aggregative games](https://mlsanity.com/paper/2006.03916/) (2020)
- [Incentive-Based Electric Vehicle Charging for Managing Bottleneck Congestion](https://mlsanity.com/paper/2111.05600/) (2021)
- [A Stackelberg game for incentive-based demand response in energy markets](https://mlsanity.com/paper/2204.08730/) (2022)
- [Smart Routing of Electric Vehicles for Load Balancing in Smart Grids](https://mlsanity.com/paper/1705.03805/) (2017)
- [Coupled Charging-and-Driving Incentives Design for Electric Vehicles in Urban Networks](https://mlsanity.com/paper/2001.11758/) (2020)
- [Hierarchical coupled routing-charging model of electric vehicles, stations and grid operators](https://mlsanity.com/paper/2012.06392/) (2020)

## Built on by (most cited first, 1 of 1)

- [Urban traffic congestion control: a DeePC change](https://mlsanity.com/paper/2311.09851/) (2023)

## Abstract (arXiv)

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.