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

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