# Congestion Reduction in EV Charger Placement Using Traffic Equilibrium Models

arXiv: https://arxiv.org/abs/2512.12081 · HTML: https://mlsanity.com/paper/2512.12081/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [Traffic Modeling with SUMO: a Tutorial](https://mlsanity.com/paper/2304.05982/) (2023)
- [Flows over time in time-varying networks](https://mlsanity.com/paper/1401.6053/) (2014)
- [Optimizing the Deployment of Electric Vehicle Charging Stations Using Pervasive Mobility Data](https://mlsanity.com/paper/1511.00615/) (2015)
- [Population Games With Erlang Clocks: Convergence to Nash Equilibria For Pairwise Comparison Dynamics](https://mlsanity.com/paper/2204.00593/) (2022)

## Abstract (arXiv)

Growing EV adoption can worsen traffic conditions if chargers are sited without regard to their impact on congestion. We study how to strategically place EV chargers to reduce congestion using two equilibrium models: one based on congestion games and one based on an atomic queueing simulation. We apply both models within a scalable greedy station-placement algorithm. Experiments show that this greedy scheme yields optimal or near-optimal congestion outcomes in realistic networks, even though global optimality is not guaranteed as we show with a counterexample. We also show that the queueing-based approach yields more realistic results than the congestion-game model, and we present a unified methodology that calibrates congestion delays from queue simulation and solves equilibrium in link-space.