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

2024-10-09

arXiv.org

AI Summary

The high technical barrier and lack of systematic frameworks hinder the adoption of deep generative models (DGMs) in intelligent transportation systems. Method: This paper establishes, for the first time, a comprehensive DGM knowledge framework tailored to intelligent transportation—covering variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models—and addresses three core tasks: traffic data generation, time-series forecasting, and unsupervised feature extraction. It integrates theoretical surveys, methodological analysis, and end-to-end implementations in PyTorch and TensorFlow, incorporating domain-specific preprocessing techniques for floating-car trajectories and road segment traffic flow. Contribution/Results: The project open-sources a structured pedagogical resource suite and a reproducible codebase, substantially lowering the entry barrier for transportation researchers. By enabling robust synthetic data generation, it advances the paradigm of synthetic-data-driven traffic modeling and fosters broader DGM adoption in transportation research.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Introduces Deep Generative Models for transportation research applications.
• Provides tutorial and code for implementing DGMs in traffic data analysis.
• Discusses challenges and opportunities in applying DGMs to transportation.

Innovation

• Deep Generative Models for traffic data generation
• Tutorial code for DGM implementation in transportation
• Systematic review of DGM literature in transportation