/EcoRideScheduler

Primary LanguagePython

A Reward-Based, Scalable Charging Scheduling Algorithm for Community-Based Ride-Sharing Services

Overview

This repository contains the code implementation for the paper "A Reward-Based, Scalable Charging Scheduling Algorithm for Community-Based Ride-Sharing Services". The algorithm integrates multiple constraints including energy usage, vehicle distribution, route planning, passenger-to-vehicle matching, and charging station availability to optimise the number of trips served and improve energy efficiency.

Table of Contents

  1. Introduction
  2. Installation
  3. Usage
  4. Datasets
  5. License
  6. Contact

Introduction

The SmartMobility algorithm operates on a proactive, community-based, carbon-neutral ride-sharing model. It leverages reward-based mechanisms and integrates a vehicle routing algorithm with a vehicle charging algorithm. The system is designed to be highly scalable and efficient, handling large volumes of trip petitions and electric vehicles (EVs) in urban environments.

Installation

Prerequisites

  • Python 3.7 or higher

Setup

  1. Clone the repository:

    git clone https://github.com/Nasheor/EcoRideScheduler.git
cd ride_sharing_framework/3_Code

  2. Create a virtual environment:

    python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Usage

Running the Simulation

  1. Execute the Ride-Sharing Algorithm: Run the ride-sharing algorithm to generate initial EV schedules and the charging scheduling algorithm to optimise EV routes with strategic charging decisions: 
    python run_experiments.py

Output

  • The simulation results, including the initial and optimised schedules, will be saved in the output directory.
  • Detailed logs and performance metrics will be available in the logs directory.

Datasets

The repository includes scripts to preprocess and adapt public datasets for the simulation:

  1. Google Hashcode Dataset:

    • 10,000 trip petitions
    • 400 EVs

  2. NYC Taxi Dataset:

    • 50,000 trip petitions
    • 1,000 EVs

The datasets are parameterised to fit the community-based ride-sharing context and can be accessed or generated through the data_preprocessing.py script.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions, issues, or contributions, please contact the project maintainers:

We appreciate your feedback and contributions to improve this project.