[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118763-en":3,"doc-seo-118763-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118763,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems","Intelligent Transportation Systems (ITS) strengthen modern transportation infrastructure by combining communication, information processing, and control to coordinate roads, vehicles, and communication components. Graph-based machine learning has become a central research direction for building complex, data-driven solutions to ITS challenges. This chapter reviews key technical problems in ITS design, surveys methods from classical statistics to modern machine and deep learning, and focuses on graph concepts, graph data representation, and graph neural network architectures in relation to ITS.","Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation  \nSystems  \nHongde Wu§, Sen Yan§, Mingming Liu†  \nSchool of Electronic Engineering and Insight SFI Centre for Data Analytics, Dublin City University, Dublin9, Ireland.  \n§Both authors contributed to the work equally and are joint first authors † [Corresponding author E-mail: ](Corresponding author E-mail: mingming.liu@dcu.ie)[mingming.liu@dcu.ie](Corresponding author E-mail: mingming.liu@dcu.ie)  \nAbstract:  \nThe 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, datadriven 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 architecturesand their relation to ITS applications. Additionally, two case studies of graphbased ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain.  \nAbbreviations  \n\n| ADAS\u003Cbr>AKF\u003Cbr>ARIMA\u003Cbr>AST-GCN\u003Cbr>CNN\u003Cbr>ConvLSTM\u003Cbr>DCRNN\u003Cbr>DL\u003Cbr>FC-LSTM\u003Cbr>GAT | Advanced Driver Assistance Systems\u003Cbr>Adaptive Kalman Filter\u003Cbr>AutoRegressive Integrated Moving Average\u003Cbr>Attention-based Spatial-Temporal Graph Convolutional Network Convolutional Neural Networks\u003Cbr>Convolutional Long Short-Term Memory Diffusion Convolutional Recurrent Neural Network Deep Learning\u003Cbr>Fully Connected Long Short-Term Memory\u003Cbr>Graph Attention Network |\n| --- | --- |\n\n\n| GCN\u003Cbr>GNN\u003Cbr>ITS\u003Cbr>LSTM\u003Cbr>ML\u003Cbr>MLE\u003Cbr>PSO\u003Cbr>RF\u003Cbr>STDN\u003Cbr>STGCN\u003Cbr>ST-ResNet\u003Cbr>SVM | Graph Convolutional Network Graph Neural Network Intelligent Transportation Systems Long Short-Term Memory Machine Learning\u003Cbr>Maximum Likelihood Estimation Particle Swarm Optimization\u003Cbr>Random Forest\u003Cbr>Spatial-Temporal Dynamic Network\u003Cbr>Spatial-Temporal Graph Convolutional Network\u003Cbr>Spatial-Temporal Residual Network Support Vector Machine |\n| --- | --- |\n\n1. Introduction  \nIn recent years, due to the acceleration of urbanization, many people are moving to cities rapidly. In many countries around the world, especially developing ones, the growing demand for public transport services and the growing number of private vehicles are putting enormous pressure on existing transport systems. Frequent traffic accidents, serious traffic congestion, longer commuting time and other problems greatly reduce the efficiency of urban operations and affect the travel experience of passengers. To address these challenges, an increasing number of cities in the world have been developing intelligent transportation systems (ITS) to facilitate efficient traffic management by optimizing the utilization of system resources. Typically, an intelligent transportation system can leverage a set of technologies and tools including connected sensors, real-time communication, advanced control and optimization methods to allow a variety of road users to efficiently share information in the road networks. For instance, a roadside camera can be facilitated in a highway network to timely track moving vehicles by using computer vision techniques. Such a system can be used to ","cbCaiuATa1dYknT1","https://ap.wps.com/l/cbCaiuATa1dYknT1","pdf",472283,1,20,"English","en",105,"# Introduction\n## Traffic congestion and event detection","[{\"question\":\"What role does Intelligent Transportation Systems (ITS) play in urban transport?\",\"answer\":\"ITS manages transportation networks by integrating communication technology, information processing, and control systems. This improves efficiency and safety through better information sharing and coordination of transport modes.\"},{\"question\":\"Why has graph-based machine learning gained interest in the ITS field?\",\"answer\":\"Graph-based methods can effectively capture traffic data structured as graphs. This supports solutions to complex, previously hard-to-tackle ITS problems using complex datadriven modeling.\"},{\"question\":\"What topics does the chapter cover regarding graph-based learning?\",\"answer\":\"The chapter reviews ITS design challenges and surveys approaches from classical statistics to machine and deep learning. It then provides an in-depth review of graph basics, graph data representation, and graph neural network architectures, linking them to ITS applications.\"}]","Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems | PDF",1785720100,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"recent-advances-in-graph-based-machine-learning-for-applications-in-smart-urban-transportation-systems","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/recent-advances-in-graph-based-machine-learning-for-applications-in-smart-urban-transportation-systems/118763/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What role does Intelligent Transportation Systems (ITS) play in urban transport?","Question",{"text":75,"@type":76},"ITS manages transportation networks by integrating communication technology, information processing, and control systems. This improves efficiency and safety through better information sharing and coordination of transport modes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why has graph-based machine learning gained interest in the ITS field?",{"text":80,"@type":76},"Graph-based methods can effectively capture traffic data structured as graphs. This supports solutions to complex, previously hard-to-tackle ITS problems using complex datadriven modeling.",{"name":82,"@type":73,"acceptedAnswer":83},"What topics does the chapter cover regarding graph-based learning?",{"text":84,"@type":76},"The chapter reviews ITS design challenges and surveys approaches from classical statistics to machine and deep learning. 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