Eco-Friendly Vehicle Routing: Extending the Solomon Dataset for CO2, Energy, Noise, and Distance in Smart Cities
- DOI
- 10.2991/978-94-6463-787-8_17How to use a DOI?
- Keywords
- Eco-Friendly Vehicle Routing; Solomon Benchmark Dataset; CO₂ Emissions; Energy Efficiency; Smart Cities
- Abstract
We commonly use Solomon Benchmark Dataset to study vehicle routing in transport and logistics. In this paper we have extended the Solomon dataset by adding CO₂ emissions, energy use, noise, and distance to focus on eco-friendly transport. The purpose is to make it suitable for smart cities & EVs by resolving traffic, charging and pollution issues. We have tested the extended dataset using existing eco-friendly routing methods in MATLAB. The Eco-VRP with EVs method reduced emissions, energy use and costs however it maintains travel time reasonably. The best results we got from Eco-VRP with Smart Charging, in which we used renewable energy and smart charging. The Eco-VRP with Smart Charging gives least distance, emissions, energy use and cost, making it the best green option. This study demonstrates the importance of sustainability factors in vehicle routing for smart cities.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Amrita Prakash AU - Md. Amir Khusru Akhtar PY - 2025 DA - 2025/07/17 TI - Eco-Friendly Vehicle Routing: Extending the Solomon Dataset for CO2, Energy, Noise, and Distance in Smart Cities BT - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025) PB - Atlantis Press SP - 190 EP - 200 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-787-8_17 DO - 10.2991/978-94-6463-787-8_17 ID - Prakash2025 ER -