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Accidents, Traffic, and Efficiency: AI for Transportation and Logistics
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This paper provides a comprehensive overview of deep generative models (DGMs) and their applications within transportation research. It begins by outlining the fundamental principles and concepts of DGMs, focusing on various model types such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and Diffusion Models. The paper then examines the effectiveness of DGMs in diverse transportation research areas, including data generation, estimation and prediction, and unsupervised representation learning. It also includes practical tutorial codes to guide researchers in implementing these models for real-world transportation applications, highlighting current challenges and opportunities for future development.
Read the paper: https://arxiv.org/abs/2410.07066
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