A Simulation Framework for Ride-Hailing with Electric Vehicles

TL;DR

This paper introduces a scalable, flexible simulation framework for electric vehicle ride-hailing systems, enabling optimization of fleet operations, charging strategies, and algorithm testing in urban environments.

Contribution

The authors develop a novel, process-driven simulation platform that efficiently models EV fleet dynamics and supports customization for diverse urban mobility scenarios.

Findings

The framework handles peak demand scenarios with thousands of trips in minutes.

A case study on NYC taxi data evaluates multiple dispatching and charging strategies.

The adaptive power-of-d dispatch policy improves throughput and demand matching.

Abstract

This research presents a Python-based simulation framework designed to model electric vehicle (EV) on-demand transportation systems, with a focus on optimizing urban fleet operations. Built on a process-driven architecture, the system efficiently simulates EV fleet dynamics, including passenger matching, vehicle dispatching, and charging strategies, while enabling customization to address critical challenges such as charger placement, fleet management, and algorithm performance. We overcome the challenge of high dimensional state-space and non-Markovian system dynamics by executing processes asynchronously using SimPy and updating only the states that are affected by employing object-oriented programming. As a result, our simulation framework is capable of handling peak demand scenarios involving thousands of trips and completing multi-day scenarios in minutes. The modular design enables users to experiment with parameters, test algorithms, and integrate custom datasets, making the tool highly adaptable for diverse urban contexts. By providing a realistic and extensible platform, this adaptable, scalable, and open-source framework advances the optimization of EV fleet operations and offers a valuable resource for decision-makers and city planners navigating the transition to sustainable urban mobility solutions. We also present a case study using the NYC taxi dataset evaluating various dispatching algorithms, including closest vehicle dispatch, closest available vehicle dispatch, and power-of-d vehicle dispatch, and exploring charging approaches like continuous and nighttime charging. We propose a novel adaptive power-of-d dispatch policy, which dynamically adjusts to real-time conditions and demonstrates high throughputs when combined with adaptive charging policies that interrupt charging to meet demand during the peak and delay some of the charging to the nighttime.