[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127491-en":3,"doc-seo-127491-105":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},127491,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development","Sustainable transportation planning depends on improving public transport attractiveness while aligning with Sustainable Development Goals. This study proposes a machine-learning-based predictive modeling framework for metro ridership using station-area built-environment variables derived from urban land use policies. Twelve inputs include time of day, day of week, station identity, and nine land-use density types. A time-series database supports model training and testing using multiple ML regressors tuned with Bayesian optimization and grid search under 10-fold cross-validation. Results show a decision-tree model with R² = 87.4% and identify government, education facilities, and mixed-use densities as most influential for ridership decisions.","sustainability   \nArticle  \nPredictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of  \nTransit-Oriented Development  \nAya Hasan AlKhereibi 1, Tadesse G. Wakjira 2, Murat Kucukvar 1 and Nuri C. Onat 3, *  \nCitation: AlKhereibi, A.H.; Wakjira, T.G.; Kucukvar, M.; Onat, N.C. Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development. Sustainability 2023, 15, 1718. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)su15021718  \nAcademic Editor: Jingxu Chen  \nReceived: 9 November 2022  \nRevised: 18 December 2022  \nAccepted: 22 December 2022  \nPublished: 16 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Industrial and Systems Engineering, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar  \n2 Civil and Architectural Engineering, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar  \n3 Qatar Transportation and Traffic Safety Center, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar  \n* Correspondence: [onat@qu.edu.qa](onat@qu.edu.qa)  \nAbstract: The endeavors toward sustainable transportation systems are a key concern for planners and decision-makers where increasing public transport attractiveness is essential. In this paper, a machine-learning-based predictive modeling approach is proposed for metro ridership prediction, considering the built environment around the stations; it is in the best interest of sustainable transport planning to ultimately contribute to the achievement of Sustainable Development Goals (UN-SDGs) . A total of twelve parameters are considered as input features including time of day, day of the week, station, and nine types of land use density. Hence, a time-series database is used for model development and testing. Several machine learning (ML) models were evaluated for their predictive performance: ridge regression, lasso regression, elastic net, k-nearest neighbor, support vector regression, decision tree, random forest, extremely randomized trees, adaptive boosting, gradient boosting, extreme gradient boosting, and stacking ensemble learner. Bayesian optimization and grid search are combined with 10-fold cross-validation to tune the hyperparameters of each model. The performance of the developed models was validated based on the test dataset using five quantitative performance measures. The results demonstrated that, among the base learners, the decision tree showed the highest performance with an R2 of 87 .4% on the test dataset. KNN and SVR were the second and third-best models among the base learners. Furthermore, the feature importance investigation explains the relative contribution of each type of land use density to the prediction of the metro ridership. The results showed that governmental land use density, educational facilities land use density, and mixed-use density are the three factors that play the most critical role in determining total ridership. The outcomes of this research could be of great help to the decision-making process for the best achievement of sustainable development goals in relation to sustainable transport and land use.  \nKeywords: sustainable transportation; metro ridership; time series models; machine learning; urban planning; land use policy; sustainable development  \n1. Introduction  \nSustainable transport includes the application of the sustainable development concept in the process of planning and development of transport infrastructure. Refs. [1–3] suggested that sustainable transportation systems evolve the application of sustainable developmen","cbCaihDhr8Ct4wOQ","https://ap.wps.com/l/cbCaihDhr8Ct4wOQ","pdf",8761899,1,20,"English","en",105,"# Introduction\n# Methodology: Data and Predictive Modeling\n## Feature set and time-series database\n## Model selection and hyperparameter tuning\n# Results and Model Performance\n## Comparative predictive accuracy\n## Feature importance for land-use densities\n# Conclusions and Decision-Making Implications","[{\"question\":\"What factors are used to predict metro ridership in this study?\",\"answer\":\"The model uses 12 inputs: time of day, day of week, station, and nine types of land-use density describing the built environment around stations.\"},{\"question\":\"Which machine learning algorithms are evaluated for ridership prediction?\",\"answer\":\"The study evaluates ridge, lasso, elastic net, k-nearest neighbor, support vector regression, decision tree, random forest, extremely randomized trees, adaptive boosting, gradient boosting, extreme gradient boosting, and a stacking ensemble learner.\"},{\"question\":\"What model performs best, and how are land-use densities interpreted?\",\"answer\":\"The decision tree achieves the highest test performance with R² of 87.4%. Feature-importance analysis shows that governmental land-use density, educational facilities density, and mixed-use density are the most critical drivers of total ridership.\"}]","Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development | PDF",1785939438,50,{"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},"predictive-machine-learning-algorithms-for-metro-ridership-based-on-urban-land-use-policies-in-support-of-transit-oriented-development","",{"@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/predictive-machine-learning-algorithms-for-metro-ridership-based-on-urban-land-use-policies-in-support-of-transit-oriented-development/127491/",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","2026-08-05",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 factors are used to predict metro ridership in this study?","Question",{"text":75,"@type":76},"The model uses 12 inputs: time of day, day of week, station, and nine types of land-use density describing the built environment around stations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are evaluated for ridership prediction?",{"text":80,"@type":76},"The study evaluates ridge, lasso, elastic net, k-nearest neighbor, support vector regression, decision tree, random forest, extremely randomized trees, adaptive boosting, gradient boosting, extreme gradient boosting, and a stacking ensemble learner.",{"name":82,"@type":73,"acceptedAnswer":83},"What model performs best, and how are land-use densities interpreted?",{"text":84,"@type":76},"The decision tree achieves the highest test performance with R² of 87.4%. Feature-importance analysis shows that governmental land-use density, educational facilities density, and mixed-use density are the most critical drivers of total ridership.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"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":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]