mlsanityData to 14 Oct 2026

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

arXiv 2410.07066 ↗First posted 9 Oct 2024cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 54 of its 234 references that are in the map. First posted on arXiv 9 Oct 2024.

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54 of 234 references.

References · 54

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38 more
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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  2. 2025FLUID: A Fine-Grained Lightweight Urban Signalized-Intersection Dataset of Dense Conflict Trajectoriescited by 1
  3. 2025TrajFlow: A Generative Framework for Occupancy Density Estimation Using Normalizing Flowscited by 0
  4. 2025Toward Safe Integration of UAM in Terminal Airspace: UAM Route Feasibility Assessment using Probabilistic Aircraft Trajectory Predictioncited by 0