[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120704-en":3,"doc-seo-120704-105":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},120704,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models","Predicting passenger flow within a city is central to intelligent transportation management, particularly under urban development pressures, post-pandemic policy shifts, and infrastructure upgrades. This study compares a selective machine learning approach with traditional simulation methods using Oslo, Norway, as the test case. Sensitivity and scenario analyses assess model parameters and city characteristics. Results show the traditional spatial interaction model is interpretable with fewer parameters but less able to reproduce real flow structures and with higher variability. Statistical evidence questions the ML model’s robustness yet indicates potential for scenario-based trend identification and decision support for planners.","Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models  \nDownloaded from: [https://research.chalmers.se](https://research.chalmers.se), 2026-08-02 19:46 UTC  \nCitation for the original published paper (version of record):  \nParishwad, O., Jiang, S., Gao, K. (2023). Investigating machine learning for simulating urban transport patterns: A comparison with  \ntraditional macro-models. Multimodal Transportation, 2(3) .  \n[http://dx.doi.org/10.1016/j.multra.2023.100085](http://dx.doi.org/10.1016/j.multra.2023.100085)  \nN. B. When citing this work, cite the original published paper.  \nresearch.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004. research.chalmers.se is administrated and maintained by Chalmers Library  \n(article starts on next page)  \nMultimodal Transportation 2 (2023) 100085  \nContents lists available at ScienceDirect  \nMultimodal Transportation  \njournal [homepage: www.elsevier.com/locate/multra](homepage: www.elsevier.com/locate/multra)  \n| Full Length Article\u003Cbr>Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models\u003Cbr>Omkar Parishwad a,∗, Sida Jiangb, Kun Gao a\u003Cbr>a Chalmers Institute of Technology, Architecture and Civil Engineering, Gothenburg, 41296, Sweden b WSP,Transport Advisory, Globen, Stockholm, 12188, Sweden |  |  |  |\n| --- | --- | --- | --- |\n| a r t i c l e i n f o |  | a b s t r a c t |  |\n| Keywords:\u003Cbr>Intelligent transportation systems Passenger flow prediction Spatial interaction model Traffic simulation\u003Cbr>Sensitivity analysis Transport planning Norway |  | Predicting passenger flow within a city is crucial for intelligent transportation management systems, especially in the context of urban development, post-pandemic policy changes, and infrastructure improvements. Traditional macro models have limitations in accurately capturing the complex structure of real traffic flows, and recent advancements in machine learning offer promising approaches for improving transportation simulations. This research aims to compare the effectiveness of traditional simulation models with a selective machine learning (ML) model for traffic flow prediction in Oslo, Norway. Sensitivity and scenario analyses are conducted to examine the models’ parameters and derive the city’s characteristics.\u003Cbr>Results substantiate that the traditional Spatial Interaction model (SIM), although interpretable and requiring fewer parameters, has limitations in accurately capturing real flow structures and exhibits greater variability compared to the ML model. Statistical analyses support these findings and raise questions about the validity of the ML model’s results over the SIM. The research highlights the potential of ML models to identify trends in passenger flows and simulate traffic flows in different scenarios related to city development. Overall, the research presents a decision support system for planners and policymakers to predict traffic flow accurately and efficiently. It highlights the benefits and drawbacks of both the traditional SIM and ML models, contributing to the ongoing discussion of the role of machine learning in transportation modeling. |  |\n\n1. Introduction  \nThe Land Use Transport Interaction (LUTI) models have been theorized upon correlating factors by transport and city planners to explain the existing flows within the city (Wegener, 2004). Various endogenous phenomena metrics, such as measurement of workplace accessibility (Waddell, 2002), long-term implications of policy decisions, transportation infrastructure, and trip production activity distribution, real-estate and housing supply, goods and passenger transport distribution, incorporated into urban transportation or public services, were based on the classic four-stage sequential approach","cbCaivSg9XSFzops","https://ap.wps.com/l/cbCaivSg9XSFzops","pdf",3532763,1,16,"English","en",105,"# Abstract\n## Introduction\n## Literature review","[{\"question\":\"What is the main goal of this research on urban transport modeling?\",\"answer\":\"To compare the effectiveness of traditional simulation models with a selective machine learning model for traffic flow prediction, using passenger flow in Oslo, Norway as the application case.\"},{\"question\":\"What modeling methods are compared in the study?\",\"answer\":\"The study contrasts a traditional Spatial Interaction Model (SIM) with a selective machine learning model for passenger-flow and traffic-flow prediction.\"},{\"question\":\"What do the results suggest about the Spatial Interaction Model and the ML model?\",\"answer\":\"The traditional SIM is interpretable and uses fewer parameters but captures real flow structures less accurately and shows greater variability. The statistical analyses also raise concerns about the ML model’s validity, while highlighting ML potential for identifying trends and simulating scenarios.\"}]","Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models | PDF",1785731627,40,{"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},"investigating-machine-learning-for-simulating-urban-transport-patterns-a-comparison-with-traditional-macro-models","",{"@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/investigating-machine-learning-for-simulating-urban-transport-patterns-a-comparison-with-traditional-macro-models/120704/",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 this research on urban transport modeling?","Question",{"text":75,"@type":76},"To compare the effectiveness of traditional simulation models with a selective machine learning model for traffic flow prediction, using passenger flow in Oslo, Norway as the application case.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What modeling methods are compared in the study?",{"text":80,"@type":76},"The study contrasts a traditional Spatial Interaction Model (SIM) with a selective machine learning model for passenger-flow and traffic-flow prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results suggest about the Spatial Interaction Model and the ML model?",{"text":84,"@type":76},"The traditional SIM is interpretable and uses fewer parameters but captures real flow structures less accurately and shows greater variability. The statistical analyses also raise concerns about the ML model’s validity, while highlighting ML potential for identifying trends and simulating scenarios.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]