手把手教交通研究者用生成模型模拟数据、预测交通流
A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research
- 从基础原理到代码实现，系统讲解生成模型在交通中的应用
- 梳理近年文献，揭示生成模型在交通数据生成与预测中的潜力
- 适合刚入门的研究者，也适合想拓展交通建模方法的工程师
深度生成模型（DGMs）近年来快速发展，因其能够学习复杂数据分布并生成合成数据，已成为多个领域的关键技术。在交通研究中，其重要性日益凸显，尤其在交通数据生成、预测与特征提取方面展现出广泛应用前景。本文提供一份全面的DGM入门指南与教程，涵盖生成模型的基本概念、核心模型详解、文献系统综述及可运行的实践代码，助力研究者快速上手。同时探讨当前挑战与未来机遇，强调如何有效应用并进一步发展这些模型于交通研究中。本文可作为从基础到高级应用的权威参考，为研究人员和从业者提供完整指引。
原文摘要 · Abstract (English)
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.
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