[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119385-en":3,"doc-seo-119385-105":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},119385,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Overview of Machine Learning-Based Traffic Flow Prediction","Traffic flow prediction is a core task in intelligent transportation systems, and rapid advances in machine learning plus unprecedented data availability have driven major progress. The article surveys traffic flow prediction research, outlining current challenges and introducing a literature classification approach. It organizes studies by the prediction preparation stage and by how prediction models are built, including whether spatial dependencies are explicitly modeled, and summarizes innovative modules and future research directions.","REVIEW  \n\n| Open Access [https://doi.org/10.48130/DTS-2023-0013](https://doi.org/10.48130/DTS-2023-0013)\u003Cbr>Digital Transportation and Safety 2023, 2(3):164−175\u003Cbr>Overview of machine learning-based traffic flow prediction\u003Cbr>ZhiboXing, Mingxia Huang* and Dan Peng\u003Cbr>Department of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China\u003Cbr>* Corresponding author, [E-mail: mingxia@sjzu.edu.cn](E-mail: mingxia@sjzu.edu.cn) |\n| --- |\n| Abstract\u003Cbr>Traffic flow prediction is an important component of intelligent transportation systems. Recently, unprecedented data availability and rapid development of machine learning techniques have led to tremendous progress in this field. This article first introduces the research on traffic flow prediction and the challenges it currently faces. It then proposes a classification method for literature, discussing and analyzing existing research on using machine learning methods to address traffic flow prediction from the perspectives of the prediction preparation process and the construction of prediction models. The article also summarizes innovative modules in these models. Finally, we provide improvement strategies for current baseline models and discuss the challenges and research directions in the field of traffic flow prediction in the future.\u003Cbr>Keywords: Traffic flow prediction; Machine learning; Intelligent transportation; Deep learning |\n| Citation: Xing Z, Huang M, Peng D. 2023. Overview of machine learning-based traffic flow prediction. Digital Transportation and Safety 2(3):164−175\u003Cbr>[https://doi.org/10.48130/DTS-2023-0013](https://doi.org/10.48130/DTS-2023-0013) |\n\nIntroduction  \nIn recent years, intelligent transportation systems have gradually developed, involving multiple aspects of traffic management, rail transportation, smart highways, and operation management, providing many conveniences for people's daily lives. Short-term traffic flow prediction, as a prerequisite for real-time traffic signal control, traffic allocation, path guidance, automatic navigation, and determination of residential travel connection schemes in intelligent transportation systems, is currently a research hotspot in the transportation field[1] . The goal of traffic flow prediction is to estimate the future traffic conditions of the traffic network based on historical observations. According to the prediction time span, traffic prediction can be divided into short-term prediction and long-term prediction. As shown in Fig. 1, traffic flow prediction has significant application value in reducing road congestion, optimizing vehicle dispatch[2], formulating traffic control measures[3], reducing environmental pollution, and so on.  \nShort-term traffic flow prediction research poses certain challenges. On the one hand, due to the randomness and uncertainty of traffic flow changes, the shorter the prediction period, the more difficult the prediction becomes. On the other hand, traffic flow has a complex temporal and spatial dependence[4] . For example, regarding the traffic flow on road A, in terms of temporal dynamics, sudden accidents or rush hour periods on the road can form an unstable traffic flow time series. In terms of spatial correlation, as shown in Fig. 2, the traffic flow of adjacent upstream and downstream roads b/c in the same direction as the target prediction section a will exhibit stronger correlation with a because of their closer Euclidean distance, whereas the traffic flow on the opposing road d with a similar Euclidean distance to a may exhibit weaker correlation. Moreover, a region in the road network is usually spatially dependent on another region through various non-Euclidean  \nrelationships, such as spatial adjacency, point-of-interest (POI), and semantic information. Therefore, how to model these dependency relationships remains a challenge.  \nWith the development of Intelligent Transportation Systems (ITS) related technologies, traffic informat","cbCaipZPQJ5M1Q9F","https://ap.wps.com/l/cbCaipZPQJ5M1Q9F","pdf",5721865,1,12,"English","en",105,"# Introduction\n## Short-term traffic flow prediction challenges\n## Data-driven shift in traffic prediction\n## Literature search and classification method\n# Machine learning overview\n## Machine learning and deep learning within ITS","[{\"question\":\"What is the main goal of traffic flow prediction in intelligent transportation systems?\",\"answer\":\"The goal is to estimate future traffic conditions of a traffic network using historical observations. Prediction supports real-time control, allocation, guidance, and planning decisions.\"},{\"question\":\"Why is short-term traffic flow prediction difficult?\",\"answer\":\"It is harder because traffic flow changes are random and uncertain, and because traffic flow exhibits complex temporal and spatial dependencies. Sudden incidents and rush-hour dynamics can destabilize time series.\"},{\"question\":\"How does the article classify existing research on machine learning-based traffic flow prediction?\",\"answer\":\"It first separates studies into prediction preparation and model establishment processes. It further discusses models based on whether spatial dependencies are modeled and summarizes external modules that improve accuracy.\"}]","Overview of Machine Learning-Based Traffic Flow Prediction | PDF",1785724040,30,{"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},"overview-of-machine-learning-based-traffic-flow-prediction","",{"@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/overview-of-machine-learning-based-traffic-flow-prediction/119385/",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 is the main goal of traffic flow prediction in intelligent transportation systems?","Question",{"text":75,"@type":76},"The goal is to estimate future traffic conditions of a traffic network using historical observations. Prediction supports real-time control, allocation, guidance, and planning decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is short-term traffic flow prediction difficult?",{"text":80,"@type":76},"It is harder because traffic flow changes are random and uncertain, and because traffic flow exhibits complex temporal and spatial dependencies. Sudden incidents and rush-hour dynamics can destabilize time series.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the article classify existing research on machine learning-based traffic flow prediction?",{"text":84,"@type":76},"It first separates studies into prediction preparation and model establishment processes. 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