[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117864-en":3,"doc-seo-117864-105":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},117864,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Estimating Passenger Demand Using Machine Learning Models - A Systematic Review","This article systematically reviews machine learning models for estimating passenger demand and clarifies how such models are used to infer passenger trip behaviour. Prior research on demand estimation and the underlying methodologies is fragmented, motivating a coordinated review across three major online databases. From 911 unique records, 102 articles are screened and 21 full-text papers are extracted, then analysed through thematic research questions on data collection, interventions, and intervention performance. Findings highlight the importance of mobility records, LSTM-based modelling, and performance metrics, while also identifying evaluation limitations and suggesting improvements.","Estimating Passenger Demand Using Machine Learning Models: A Systematic Review  \nAdjei Boateng1, Charlse AnumAdams1,*, and Emmanuel Kofi Akowuah2,**  \n1Regional Transport Research and Education Centre Kumasi, Civil Engineering Department, KNUST, Kumasi, Ghana  \n2Computer Engineering Department, College of Engineering, KNUST, Kumasi, Ghana  \nAbstract. This article investigated machine learning models used to estimate passenger demand. These models have the potential to provide valuable insights into passenger trip behaviour and other inferences.  \nThe estimate of passenger demand using machine learning model research and the methodologies used are fragmented. To synchronise these studies, this paper conducts a systematic review of machine learning models to estimate passenger demand. The review investigates how passenger demand is estimated using machine learning models. A comprehensive search strategy is conducted across the three main online publishing databases to locate 911 unique records. Relevant record titles, abstracts, and publication information are extracted, leaving 102 articles. Furthermore, articles are evaluated according to eligibility requirements. This procedure yields 21 full-text papers for data extraction. 3 research thematic questions covering passenger data collection techniques, passenger demand interventions, and intervention performance are reviewed in detail. The results of this study suggest that mobility records, LSTM-based models, and performance metrics play a critical role in conducting passenger demand prediction studies.  \nThe model evaluation was mostly restricted to 3 performance metrics which needs improved metric for evaluation. Furthermore, the review determined an overreliance on the long-and short-term memory model to estimate passenger demand. Therefore, minimising the limitation of the LSTM model will generally improve the estimation models. Furthermore, having an acceptable trainset to avoid overfitting is crucial. In  \naddition, it is advisable to consider multiple metrics to have a more comprehensive evaluation.  \n1 Introduction  \nGetting to a location to participate in activities such as work, recreation, and socialisation is a necessity for human survival. Transport enables these activities to be carried out. Individuals use private transportation, usually self-owned vehicles. The public uses a system of shared transportation units provided by entities. More people patronise public transport. Therefore, its impacts are immediately felt when it is effective. The demand for transportation services continues to increase as a result of increasing urbanisation. In London, more than two billion passenger trips were made in 2009 (W. Wang et al., 2011) . The increasing demand for passengers threatens the safety and quality of transportation services. Public transport operators must optimise operations by accurately estimating passenger demands, fleet, and income (Hänseler et al., 2017) . Estimating passenger demand is the foundation of an efficient transportation system. It is challenging if operators do not use modern technologies and mathematical models in managing transportation (Sbai & Ghadi, 2018) . The deployment of new transportationrelated technology has resulted in an exponential increase in the availability of data on passenger movements. Furthermore, recent strides in machine learning research have resulted in numerous applications of machine learning (Hillel et al., 2021) .  \n* Corresponding authors:  [carladams1702@yahoo.com](carladams1702@yahoo.com)  \n􀀍􀀍 [emekoahh@gmail.com](emekoahh@gmail.com)  \nThis paper provides a systematic review for the estimation of passenger demands using machine learning techniques. The review focusses on three thematic research questions covering passenger data collection techniques, passenger demand interventions, and intervention performance.  \n1.1 Public Transport Operation  \nPublic transport refers to a mobility service that is used by the pub","cbCaicQBjk6j9p2k","https://ap.wps.com/l/cbCaicQBjk6j9p2k","pdf",1437923,1,12,"English","en",105,"# Introduction\n## Public Transport Operation\n## Previous passenger demand estimation","[{\"question\":\"What problem does the review address in estimating passenger demand?\",\"answer\":\"Research methods for estimating passenger demand using machine learning are described as fragmented. The review coordinates studies to examine how passenger demand is estimated using these models.\"},{\"question\":\"How were studies selected for the systematic review?\",\"answer\":\"A comprehensive search across three main online databases produced 911 unique records. After screening titles, abstracts, and publication information, 102 articles remained, and eligibility checks yielded 21 full-text papers for data extraction.\"},{\"question\":\"What are the main factors highlighted by the review for passenger demand prediction studies?\",\"answer\":\"The review suggests that mobility records, LSTM-based models, and performance metrics are critical. It also notes overreliance on long-and short-term memory models and limited use of only three performance metrics, recommending broader evaluation and mitigation of LSTM limitations.\"}]","Estimating Passenger Demand Using Machine Learning Models - A Systematic Review | PDF",1785680062,30,{"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},"estimating-passenger-demand-using-machine-learning-models-a-systematic-review","",{"@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/estimating-passenger-demand-using-machine-learning-models-a-systematic-review/117864/",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","2026-08-02",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 problem does the review address in estimating passenger demand?","Question",{"text":75,"@type":76},"Research methods for estimating passenger demand using machine learning are described as fragmented. The review coordinates studies to examine how passenger demand is estimated using these models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were studies selected for the systematic review?",{"text":80,"@type":76},"A comprehensive search across three main online databases produced 911 unique records. After screening titles, abstracts, and publication information, 102 articles remained, and eligibility checks yielded 21 full-text papers for data extraction.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main factors highlighted by the review for passenger demand prediction studies?",{"text":84,"@type":76},"The review suggests that mobility records, LSTM-based models, and performance metrics are critical. 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