Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems

• URL: http://arxiv.org/abs/2306.01282v1
• Date: Fri, 2 Jun 2023 05:50:57 GMT
• Title: Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems
• Authors: Hongde Wu, Sen Yan, Mingming Liu
• Abstract summary: The chapter presents background information on the key technical challenges for ITS design, along with a review of research methods. We provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications.
• Score: 7.335098632782098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks. This integration of various components such as roads, vehicles, and communication systems, is expected to improve efficiency and safety by providing better information, services, and coordination of transportation modes. In recent years, graph-based machine learning has become an increasingly important research focus in the field of ITS aiming at the development of complex, data-driven solutions to address various ITS-related challenges. This chapter presents background information on the key technical challenges for ITS design, along with a review of research methods ranging from classic statistical approaches to modern machine learning and deep learning-based approaches. Specifically, we provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications. Additionally, two case studies of graph-based ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain.

Related papers

• Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models [0.0]
  We propose a novel approach based on knowledge graphs to provide timely access to structured information. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable.
  arXiv  Detail & Related papers  (2024-09-30T07:08:28Z)
• Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey [67.7834898542701]
  We focus on reviewing privacy-preserving techniques of graph machine learning. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information.
  arXiv  Detail & Related papers  (2023-07-10T04:30:23Z)
• Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportation [0.0]
  This thesis investigates how different optimization algorithms and machine learning techniques can be leveraged to improve system performance. In the first topic, we propose an optimal transmission frequency management scheme using decentralized ADMM-based method. In the second topic, we leverage graph neural network (GNN) for demand prediction for shared bikes. In the last topic, we consider a highway traffic network scenario where frequent lane changing behaviors may occur with probability.
  arXiv  Detail & Related papers  (2022-10-13T11:45:30Z)
• Intelligent Traffic Monitoring with Hybrid AI [78.65479854534858]
  We introduce HANS, a neuro-symbolic architecture for multi-modal context understanding. We show how HANS addresses the challenges associated with traffic monitoring while being able to integrate with a wide range of reasoning methods.
  arXiv  Detail & Related papers  (2022-08-31T17:47:22Z)
• 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)
• Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview [72.79089075263985]
  Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors.
  arXiv  Detail & Related papers  (2022-02-28T06:28:44Z)
• Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions [58.220137936626315]
  This paper extensively discusses automated graph machine learning approaches. We introduce AutoGL, our dedicated and the world's first open-source library for automated graph machine learning. Also, we describe a tailored benchmark that supports unified, reproducible, and efficient evaluations.
  arXiv  Detail & Related papers  (2022-01-04T18:31:31Z)
• Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems [6.699060157800401]
  Internet of Vehicles (IoV) is transformed into an Intelligent Transportation System (ITS) To address these challenges, Federated Learning, a collaborative and distributed intelligence technique, is suggested. With a multitude of use cases and benefits, Federated Learning is a key enabler for ITS and is poised to achieve widespread implementation in 5G and beyond networks and applications.
  arXiv  Detail & Related papers  (2021-02-19T20:07:17Z)
• Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions [0.0]
  Big data analytics, machine learning (ML), and deep learning (DL) plays a key role when it comes to the analysis of this massive amount of data and generation of valuable insights. This paper explores and surveys the Smart grid architectural elements, machine learning, and deep learning-based applications and approaches in the context of the Smart grid.
  arXiv  Detail & Related papers  (2020-10-16T01:40:24Z)
• Graph signal processing for machine learning: A review and new perspectives [57.285378618394624]
  We review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. We discuss exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. We provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.
  arXiv  Detail & Related papers  (2020-07-31T13:21:33Z)

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.