[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127788-en":3,"doc-seo-127788-105":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},127788,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Prediction of destination choice in transport modelling with mobile phone data and machine learning models - Experiences from Tønsberg","Transportation infrastructure planning is a prolonged and complex process requiring costly investment, and transport modelling supports decisions by predicting future traffic flows for appropriate infrastructure dimensioning. The study investigates destination choice prediction in the four-step model (4SM) by combining mobile phone data with machine learning algorithms. Algorithms evaluated include Logistic Regression, Support Vector Machine, Random Forest, and Naïve Bayes. Results use five-fold cross-validation and MCDA criteria, showing robust performance, with Random Forest achieving prediction accuracy of 0.943.","Master’s Thesis 2024 30 ECTS  \nFaculty of Science and Technology  \nPrediction of destination choice in transport modelling with mobile phone data and machine learning models: Experiences from Tønsberg  \nKristian Olsen  \nIndustrial Economics  \nAbstract  \nThe development of transportation infrastructure is a prolonged and complex process that requires in depth planning. The planning process is costly, and further investments for construction of infrastructure is even larger. Transport modelling predicts future traffic flow to appropriately dimension the required infrastructure. The study addresses the prediction of destination choice in the four-step model (4SM) in transport modelling with combining mobile phone data and machine learning algorithms.  \nTraditionally, destination choice models relied on theory-based discrete choice models using data from travel surveys. However, emerging of Big Data and advances in artificial intelligence have facilitated the use of more representative data, possible for enhancing predictive accuracy and potentially reducing costs and risks associated with the over-or under-construction of infrastructure. Previous research has focused on either the application of mobile phone data or artificial intelligence independently in transport modelling. This research aims to investigate the performance by combining these two novel approaches in transport modelling.  \nThe selection of algorithms tested includes Logistic Regression, Support Vector Machine, Random Forest and Naïve Bayes. They are tested within a basic analytical pipeline to assess performance. Performance evaluations were conducted using a five-fold cross-validation on performance metrics, and through a multi-criteria decision analysis (MCDA) to consider both qualitative and quantitative criteria for model performance. Results indicate robust performance across all algorithms on mobile phone data, with Random Forest performing best, considering both quantitative and qualitative metrics, achieving a prediction accuracy of 0.943. Naïve Bayes and Support Vector Machine followed with 0.925 and 0.895, respectively, while Logistic Regression achieved 0.832. Simpler hyperparameters yielded better results for Support Vector Machine and Logistic Regression, whereas Random Forest utilized more complex hyperparameters for best performance.  \nThese findings suggest that integrating mobile phone data with machine learning algorithms holds substantial promise for enhancing the prediction of destination choices. Future research should explore the model’s applicability across different geographic contexts and further steps towards its implementation and deployment.  \nKeywords: transport modelling, travel demand, mobile phone data, machine learning, artificial intelligence, Big Data, destination choice.  \nSammendrag  \nUtviklingen av transportinfrastruktur er en langvarig og kompleks prosess som krever grundig planlegging. Planleggingen av infrastruktur er svært kostnadskrevende, og ytterligere investeringer kreves til konstruksjon av infrastrukturen. Transportmodellering forutsier fremtidig trafikkflyt for å dimensjonere den nødvendige infrastrukturen på en passende måte. Denne oppgaven tar for seg prediksjon av destinasjonsvalg i firestrinnsmodellen (4SM) i transportmodellering ved å kombinere mobildata og maskinlæringsalgoritmer.  \nTradisjonelt har modeller for valg av destinasjon støttet seg på teoribaserte, diskrete valgmodeller som bruker data fra reisevaneundersøkelser. Imidlertid har fremveksten av nye, store datakilder og fremskritt innen kunstig intelligens muliggjort bruken av mer representative data, noe som kan bedreprediksjonsnøyaktiget og potensielt redusere kostnader forbundet med over- eller underdimensjonering av infrastruktur. Tidligere forskning i transportmodellering har satt søkelys på enten bruk av mobildata eller kunstig intelligens uavhengig av hverandre. Denne forskningen siktermot å undersøke ytelsen ved å kombinere disse to nye tiln","cbCaio0ezQfDI1Iy","https://ap.wps.com/l/cbCaio0ezQfDI1Iy","pdf",3397835,1,86,"English","en",105,"# Abstract\n## Study objective and approach\n## Algorithms and evaluation method\n## Results and implications\n# Keywords\n## Transportation modelling concepts","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"The thesis aims to predict destination choice in transport modelling by combining mobile phone data with machine learning algorithms within the four-step model (4SM).\"},{\"question\":\"Which machine learning algorithms are tested?\",\"answer\":\"Logistic Regression, Support Vector Machine, Random Forest, and Naïve Bayes are evaluated in a basic analytical pipeline. Random Forest performs best overall in the reported results.\"},{\"question\":\"How are model performances evaluated?\",\"answer\":\"Performance is assessed using five-fold cross-validation with performance metrics and through multi-criteria decision analysis (MCDA) to consider both qualitative and quantitative criteria.\"}]","Prediction of destination choice in transport modelling with mobile phone data and machine learning models - Experiences from Tønsberg | PDF",1785941674,217,{"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},"prediction-of-destination-choice-in-transport-modelling-with-mobile-phone-data-and-machine-learning-models-experiences-from-tonsberg","",{"@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/prediction-of-destination-choice-in-transport-modelling-with-mobile-phone-data-and-machine-learning-models-experiences-from-tonsberg/127788/",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","2026-08-24","2026-08-05",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 is the main goal of the thesis?","Question",{"text":76,"@type":77},"The thesis aims to predict destination choice in transport modelling by combining mobile phone data with machine learning algorithms within the four-step model (4SM).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are tested?",{"text":81,"@type":77},"Logistic Regression, Support Vector Machine, Random Forest, and Naïve Bayes are evaluated in a basic analytical pipeline. Random Forest performs best overall in the reported results.",{"name":83,"@type":74,"acceptedAnswer":84},"How are model performances evaluated?",{"text":85,"@type":77},"Performance is assessed using five-fold cross-validation with performance metrics and through multi-criteria decision analysis (MCDA) to consider both qualitative and quantitative criteria.","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},[94,98,102,106,111,116,121,124,129,132,136],{"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":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]