[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119221-en":3,"doc-seo-119221-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119221,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning in Vehicle Travel Time Estimation - A Brief Technological Perspective and Review","A precise Estimated Time of Arrival (ETA) is essential for navigation and logistics, directly influencing trip experience and operational efficiency. Machine learning has increasingly been used for ETA prediction, with approaches commonly grouped into route-based and origin-destination-based methods. Route-based methods estimate travel time by predicting segment durations, while origin-destination-based methods rely on limited inputs such as origin, departure time, and other natural features. This paper reviews recent studies to identify necessary model inputs, key influencing factors, and suitable learning approaches, and it highlights promising directions such as time-series formulations, uncertainty modeling, and ensemble learning.","Proceedings of the 57th Hawaii International Conference on System Sciences | 2024  \nMachine Learning in Vehicle Travel Time Estimation: A Brief Technological  \nPerspective and Review  \nSon Pham Universitt der Bundeswehr M¨unchen, Germany  [son.pham@unibw.de](son.pham@unibw.de)  \nGerschberger Markus University of Applied Sciences Upper Austria  \n[markus.gerschberger@fh-steyr.at](markus.gerschberger@fh-steyr.at)  \nMarian Sorin Nistor Universitt der Bundeswehr M¨unchen, Germany[sorin.nistor@unibw.de](sorin.nistor@unibw.de)  \nMaximilian Moll Universitt der Bundeswehr M¨unchen, Germany [maximilian.moll@unibw.de](maximilian.moll@unibw.de)  \nLoi Cao  \nLe Quy Don Technical University, Vietnam [loi.cao@lqdtu.edu.vn](loi.cao@lqdtu.edu.vn)  \nMilani Rudy Universitt der Bundeswehr M¨unchen, Germany [rudy.milani@unibw.de](rudy.milani@unibw.de)  \nAbstract  \nA precise Estimated Time of Arrival (ETA) finds applications in various domains, such as navigation and logistics systems. This problem has gained a lot of attention from the research community. Machine learning has recently been applied and has shown promising results for ETA. Machine learning approaches can be divided into two categories, which are route-based and origin-destination-based methods. The first one divides the route into segments and predicts the ETA based on the information of these segments. The last one predicts ETA based on a few natural information, such as the origin, the estimation, and the departure time. In this paper, we aim to review recent studies of the mentioned machine learning approaches for ETA to determine the necessary input for an ETA forecasting model, the critical factors, and suitable approaches for ETA. Furthermore, we will discuss promising research directions to improve ETA, such as formulating ETA as a time series forecasting problem, including uncertainty or using ensemble learning models.  \nKeywords: ETA, Machine Learning, Route-based ETA, Origin-Destination-based ETA  \n1. Introduction  \n”One of the most critical location-based services (LBS) is the Estimated Time of Arrival (ETA) or vehicle Travel Time Estimation (TTE) (Z. Wang et al., 2018)”. As a crucial component of many systems, such as navigation and intelligent transportation systems, it is becoming increasingly important and prevalent (Liet al., 2018) . Applications for ETA can be found in ride-hailing, logistics, and shipping, where the duration  \nof the trip substantially impacts the service’s quality. A precise ETA will also improve the transportation system’s efficiency in reducing negative externalities such as user travel costs, energy usage, and motor vehicle pollution. ETA has consequently created a crucial element that affects decision-makers (Z. Wanget al., 2018) .  \nRoute-based and origin-destination-based methods are the two main approaches for estimating the arrival time for road transport (Li et al., 2018; Z. Wang et al., 2018) . There are publications on ETA prediction in maritime and air transport but this paper focuses only on road transport. The selected publication are some of the most recent and impactful one.  \nOn the one hand, route-based techniques need route knowledge to develop a forecast. A route can be considered as a collection of successive segments. The total travel time on a route is the summation of the duration spent on each segment.  \nOn the other hand, origin-destination-based approaches can forecast travel time even without route data. For forecasting ETA, just a few numbers of raw input features are provided, including the origin, destination, and departure time (Li et al., 2018) .  \nIn this report, we aim to review recent studies of the above approaches to clarify the following research questions:  \n• What kinds of data are needed as the input for the ETA forecasting model?  \n• What might impact an ETA?  \n• What are suitable approaches for ETA? The paper is structured as follows. Section 2 discusses the ETA forecasting approaches based on machine lea","cbCaipI3gbJVnGx2","https://ap.wps.com/l/cbCaipI3gbJVnGx2","pdf",248377,1,6,"English","en",105,"# Introduction\n# Machine Learning Approaches for ETA\n## Stand-alone Route-based Approach\n## Hybrid Route-based Approach\n## Origin-Destination-based Approach\n# Promising Future Research Directions\n# Summary","[{\"question\":\"What two categories of machine learning approaches are used for ETA estimation in this paper?\",\"answer\":\"The paper divides machine learning approaches into route-based and origin-destination-based methods.\"},{\"question\":\"How do route-based methods estimate vehicle travel time compared with origin-destination-based methods?\",\"answer\":\"Route-based methods predict travel time for each route segment and then sum segment times, while origin-destination-based methods forecast ETA using limited inputs such as origin, destination and departure time without requiring detailed route data.\"},{\"question\":\"What future research directions does the paper suggest to improve ETA forecasting?\",\"answer\":\"The paper discusses improvements such as formulating ETA as a time series forecasting problem, incorporating uncertainty, and using ensemble learning models.\"}]","Machine Learning in Vehicle Travel Time Estimation - A Brief Technological Perspective and Review | PDF",1785723158,15,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-in-vehicle-travel-time-estimation-a-brief-technological-perspective-and-review","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-in-vehicle-travel-time-estimation-a-brief-technological-perspective-and-review/119221/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What two categories of machine learning approaches are used for ETA estimation in this paper?","Question",{"text":76,"@type":77},"The paper divides machine learning approaches into route-based and origin-destination-based methods.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do route-based methods estimate vehicle travel time compared with origin-destination-based methods?",{"text":81,"@type":77},"Route-based methods predict travel time for each route segment and then sum segment times, while origin-destination-based methods forecast ETA using limited inputs such as origin, destination and departure time without requiring detailed route data.",{"name":83,"@type":74,"acceptedAnswer":84},"What future research directions does the paper suggest to improve ETA forecasting?",{"text":85,"@type":77},"The paper discusses improvements such as formulating ETA as a time series forecasting problem, incorporating uncertainty, and using ensemble learning models.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]