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Leveraging Geospatial Analysis and Machine Learning for Optimal Green Vehicle Assignment
Gamez-Perez,Karla M. Velázquez-Martínez,Josué C. Barkah,Ade Palacios-Argüello,Laura Fransoo,Jan C.
Gamez-Perez,Karla M.
Velázquez-Martínez,Josué C.
Barkah,Ade
Palacios-Argüello,Laura
Fransoo,Jan C.
Abstract
The transportation sector has been the main contributor to emissions growth in the last decade. The type of truck and its delivery characteristics largely explain the transportation CO2 emissions and carbon intensity factors. This article introduces a novel methodology for the allocation of a fleet of vehicles to certain regions aimed at minimizing total transportation-related CO2 emissions. Our methodology employs geospatial analysis and machine learning to assess the fuel efficiency and CO2 emissions performance of a vehicle f leet by analyzing historical GPS data, cargo, and fuel use. Subsequently, we include these variables into a mathematical model to obtain an optimal allocation that minimizes total transportation CO2 emissions. Our approach extends the current literature by considering detailed data for operation, such as gradient variability (road hilliness), vehicle speed, elevation / altitude, and distance between stops. We applied our methodology in Coppel, one of the largest retailers in Mexico, which operates its own fleet. Our results showed that by exchanging 10 vehicles for one month, we observed 8% savings in fuel efficiency and transportation CO2 emissions.
Description
Date
2026-01-22
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
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Journal Issue
Keywords
SDG 11 - Sustainable Cities and Communities, SDG 13 - Climate Action
Citation
Gamez-Perez, K M, Velázquez-Martínez, J C, Barkah, A, Palacios-Argüello, L & Fransoo, J C 2026, 'Leveraging Geospatial Analysis and Machine Learning for Optimal Green Vehicle Assignment', Informs Journal on Applied Analytics.
License
info:eu-repo/semantics/openAccess