A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research

TL;DR

This paper provides a comprehensive introduction and tutorial on deep generative models, emphasizing their applications in transportation research for data generation, prediction, and feature extraction, along with practical guidance.

Contribution

It offers a systematic overview, detailed explanations, and tutorial code for DGMs in transportation, addressing current challenges and future opportunities.

Findings

Enhanced understanding of DGM applications in transportation

Practical tutorial code for implementation

Discussion of challenges and future directions

Abstract

Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields due to their ability to learn complex data distributions and generate synthetic data. Their importance in transportation research is increasingly recognized, particularly for applications like traffic data generation, prediction, and feature extraction. This paper offers a comprehensive introduction and tutorial on DGMs, with a focus on their applications in transportation. It begins with an overview of generative models, followed by detailed explanations of fundamental models, a systematic review of the literature, and practical tutorial code to aid implementation. The paper also discusses current challenges and opportunities, highlighting how these models can be effectively utilized and further developed in transportation research. This paper serves as a valuable reference, guiding researchers and practitioners from foundational knowledge to advanced applications of DGMs in transportation research.