Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities
Name
44327_2025_Article_112.pdf
Size
3.1 MB
Format
Adobe PDF
Checksum (MD5)
41ce8743e3afae1d5fe4f6d262736e9c
Author(s)
Rabelo, Luis
Rincón-Guio, Cristian
Laynes, Valeria
Gutierrez-Franco, Edgar
Bhat, Vasanth
Zamora-Aguas, Juan
Elkamel, Marwen
Date Issued
August 17, 2025
Journal
Discover Cities
Publisher
Springer International Publishing
Citation
Rabelo, L., Rincón-Guio, C., Laynes, V. et al. Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities. Discov Cities 2, 69 (2025).
Version
Final published version
Abstract
This paper presents a framework for effective last-mile delivery in underdeveloped megacities by combining social media, machine learning, and reinforcement learning. Leveraging a Graph Convolutional Networks and a Long Short-Term Memory model for traffic prediction, the framework incorporates multimodal data sources, such as social media sentiment analysis, to provide real-time insights into traffic dynamics. By framing the delivery problem as a Markov Decision Process, reinforcement learning dynamically adapts routing decisions to obtain delivery efficiency, reduce delays, and minimize fuel consumption. A case study in Bogotá demonstrates the framework’s effectiveness in mitigating urban traffic challenges. This work highlights the transformative potential of integrating adaptive learning technologies to address urban logistics’ environmental, economic, and operational complexities. Future research explores advanced methodologies, including multi-agent systems and transformer-based architectures, to further enhance scalability and adaptability in dynamic urban environments.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s44327-025-00112-z
