Congestion Reduction in EV Charger Placement Using Traffic Equilibrium Models

TL;DR

This paper presents a scalable method for strategically placing EV chargers to reduce traffic congestion, using equilibrium models based on congestion games and queueing simulations, with the queueing approach providing more realistic results.

Contribution

It introduces a unified, scalable methodology combining queueing simulations with equilibrium models for EV charger placement to effectively reduce congestion.

Findings

Greedy placement scheme achieves near-optimal congestion reduction.

Queueing-based model yields more realistic congestion estimates.

Unified calibration method links queue simulation with equilibrium analysis.

Abstract

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.