Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities

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.