Citation

Muñoz-Villamizar A, Faulin J, Reyes-Rubiano L, Henriquez-Machado R, Solano-Charris E. Transp. Res. Proc. 2024; 78: 32-39.

Copyright

(Copyright © 2024, Elsevier Publications)

DOI

10.1016/j.trpro.2024.02.005

PMID

unavailable

Abstract

Freight transportation is the backbone of urban economies and plays a critical role in the smooth functioning of cities. As such, devising efficient methods for freight transportation planning is of paramount importance. One of the most crucial aspects affecting the efficiency of freight transport is the variability in travel speeds, impacted by factors such as traffic congestion. While traditional approaches often rely on GPS technologies and associated routing services--which can be expensive--for planning, these methods also necessitate frequent re-optimization due to ever-changing traffic conditions. To address these challenges, we introduce a novel solution that integrates real-time traffic data into daily vehicle route planning. Specifically, our method incorporates Google Maps API for traffic congestion estimation and utilizes a Mixed Integer Linear Programming (MILP) model to determine optimal routes for an entire day. We tested our methodology in a major U.S. city and found that it outperforms conventional approaches by up to 18% in terms of routing time, underscoring its practical relevance and efficiency.

Language: en

Keywords

Case Study; City Logistics; Google Maps API; Vehicle Routing Problem