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

• URL: http://arxiv.org/abs/2410.07066v2
• Date: Thu, 10 Oct 2024 19:47:04 GMT
• Title: A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research
• Authors: Seongjin Choi, Zhixiong Jin, Seung Woo Ham, Jiwon Kim, Lijun Sun,
• Abstract summary: Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields. 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.
• Score: 21.66278922813198
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report [3.4632900249241874]
  This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture combines generative capabilities of Large Language Models (LLMs) with the precision of information retrieval. The practical implications of this research lie in enhancing the reliability of generative AI systems in various sectors.
  arXiv  Detail & Related papers  (2024-10-21T12:21:49Z)
• Deep Learning based Visually Rich Document Content Understanding: A Survey [8.788354139674789]
  Visually Rich Documents (VRDs) are essential in academia, finance, medical fields, and marketing. Deep learning has revolutionized this process, introducing models that leverage multimodal information vision, text, and layout. These models have achieved state-of-the-art performance across various downstream tasks.
  arXiv  Detail & Related papers  (2024-08-02T14:19:34Z)
• Human-artificial intelligence teaming for scientific information extraction from data-driven additive manufacturing research using large language models [3.0061386772253784]
  Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. It requires substantial effort and time to extract scientific information from these works. We propose a framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature.
  arXiv  Detail & Related papers  (2024-07-26T15:43:52Z)
• A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) [57.30228361181045]
  This survey connects key advancements in recommender systems using Generative Models (Gen-RecSys) It covers: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges.
  arXiv  Detail & Related papers  (2024-03-31T06:57:57Z)
• Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond [58.63558696061679]
  Trajectory computing is crucial in various practical applications such as location services, urban traffic, and public safety. We present a review of development and recent advances in deep learning for trajectory computing (DL4Traj) Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold potential to augment trajectory computing.
  arXiv  Detail & Related papers  (2024-03-21T05:57:27Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• Learn From Model Beyond Fine-Tuning: A Survey [78.80920533793595]
  Learn From Model (LFM) focuses on the research, modification, and design of foundation models (FM) based on the model interface. The study of LFM techniques can be broadly categorized into five major areas: model tuning, model distillation, model reuse, meta learning and model editing. This paper gives a comprehensive review of the current methods based on FM from the perspective of LFM.
  arXiv  Detail & Related papers  (2023-10-12T10:20:36Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)
• A Survey of Knowledge Tracing: Models, Variants, and Applications [70.69281873057619]
  Knowledge Tracing is one of the fundamental tasks for student behavioral data analysis. We present three types of fundamental KT models with distinct technical routes. We discuss potential directions for future research in this rapidly growing field.
  arXiv  Detail & Related papers  (2021-05-06T13:05:55Z)
• An Introduction to Deep Generative Modeling [8.909115457491522]
  Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions. We provide an introduction to DGMs and a framework for modeling the three most popular approaches. Our goal is to enable and motivate the reader to contribute to this proliferating research area.
  arXiv  Detail & Related papers  (2021-03-09T02:19:06Z)
• From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability [10.27718355111707]
  This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes. Grounded in this described data modeling pipeline for ITS, wedefine the characteristics, engineering requisites and intrinsic challenges to its three compounding stages, namely, data fusion, adaptive learning and model evaluation.
  arXiv  Detail & Related papers  (2020-02-06T12:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.