Keywords
time series prediction, social media, traffic prediction, deep learning
Abstract
Over the past decade, people have been spending more time online. Almost anything can be done from a laptop or cellphone. This is one of the reasons why e-commerce has been in a constant boom, as it is easier to buy something online and have it delivered to the front door than to go to the store. As more people engage in this activity, e-commerce platforms' challenges are more complicated and need to be addressed faster. However, these challenges escape the company's scope when external factors influence the objective of optimized deliveries, for example, traffic issues or bad weather during the last mile, pushing the company to fill the gap of developing different types of routes depending on the area. On the other hand, Intelligent Transportation Systems (ITS) also have a budget challenge that interferes with the need for delivery companies for traffic sensors in urban areas. This research aims to investigate a solution that closes the gap for accurate traffic prediction tailored for last-mile delivery logistics using social media analysis. The novel proposed methodology can be divided into two stages: (1) social media analysis: to get an idea of the overall sentiment around the city regarding traffic, and (2) traffic prediction: uses deep learning tools like Graph Convolutional and Long-Short Term Memory Neural Networks and data from social media and other influential factors.
Completion Date
2025
Semester
Spring
Committee Chair
Rabelo, Luis
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Industrial Engineering and Management Systems
Identifier
DP0029337
Document Type
Dissertation/Thesis
Campus Location
Orlando (Main) Campus
STARS Citation
Laynes Fiascunari, Valeria, "A Deep Learning Framework for Last-Mile Delivery Enhancement Using Social Media" (2025). Graduate Thesis and Dissertation post-2024. 169.
https://stars.library.ucf.edu/etd2024/169