The proliferation of e-commerce in recent years has been driven in part by the increasing ease of making purchases online and having them delivered directly to the consumer. However, these last-mile delivery logistics have become complex due to external factors (traffic, weather, etc.) affecting the delivery routes’ optimization. Intelligent Transportation Systems (ITS) also have a challenge that contributes to the need of delivery companies for traffic sensors in urban areas. The main purpose of this paper is to propose a framework that closes the gap on accurate traffic prediction tailored for last-mile delivery logistics, leveraging social media analysis along with traditional methods. This work can be divided into two stages: (1) traffic prediction, which utilizes advanced deep learning techniques such as Graph Convolutional and Long-Short Term Memory Neural Networks, as well as data from sources such as social media check-ins and Collaborative Innovation Networks (COINs); and (2) experimentation in both short- and long-term settings, examining the interactions of traffic, social media, weather, and other factors within the model. The proposed framework allows for the integration of additional analytical techniques to further enhance vehicle routing, including the use of simulation tools such as agent-based simulation, discrete-event simulation, and system dynamics.

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.

COVID-19 pandemic demanded swift and adaptive operational responses to ensure public health and safety. This case study presents the rapid design, modeling, and implementation of a drive-through COVID-19 testing and housing check-in system at the University of Central Florida during the Fall 2020 move-in period. Leveraging a spiral system development methodology, combined with queuing theory (QT) and discrete-event simulation (DES), the project iteratively refined the system design through six simulation-based iterations. These models addressed key decision-making questions regarding resource allocation, process configuration, and throughput estimation under tight constraints. Simulation results projected average time-in-system values of 6.81 minutes for vehicles and 10.10 minutes for pedestrians. Post-deployment, real-world data were collected to validate the simulation outputs using the Kolmogorov-Smirnov test. This study demonstrates how simulation-driven design, in tandem with agile development methods, can effectively guide the deployment of complex, time-sensitive systems. The findings offer a transferable framework for rapidly implementing scalable, data-informed solutions in crisis contexts.