{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T07:55:58Z","timestamp":1776498958325,"version":"3.51.2"},"reference-count":100,"publisher":"Tech Science Press","issue":"2","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":328,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.058888","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T08:21:29Z","timestamp":1730362889000},"page":"2161-2194","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":6,"title":["Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice\u2014A Systematic Review"],"prefix":"10.32604","volume":"81","author":[{"given":"Mujahid","family":"Ali","sequence":"first","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/B978-0-12-381984-0.10034-7","author":"Stradling","year":"2011","journal-title":"Handbook of Traffic Psychology"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1016\/j.retrec.2020.100966","article-title":"The role of personal norms in the choice of mode for commuting","volume":"83","author":"Ababio-Donkor","year":"2020","journal-title":"Res. Transp. Econ."},{"key":"ref3","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1177\/0361198119854091","article-title":"Connectivity, accessibility, and mobility relationships in the context of low-volume road networks","volume":"2673","author":"Labi","journal-title":"Transp. Res. Rec."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.trb.2005.09.008","article-title":"The impact of stop-making and travel time reliability on commute mode choice","volume":"40","author":"Bhat","year":"2006","journal-title":"Trans. Res. Part B: Methodol."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.1016\/j.trpro.2017.05.150","article-title":"Effects of cost adjustment on travel mode choice: Analysis and comparison of different logit models","volume":"25","author":"Bai","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.3390\/su132112226","article-title":"Time-use and spatio-temporal variables influence on physical activity intensity, physical and social health of travelers","volume":"13","author":"Ali","year":"2021","journal-title":"Sustainability"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"117","DOI":"10.3141\/1894-13","article-title":"Travel mode choice of the elderly: Effects of personal, household, neighborhood, and trip characteristics","volume":"1894","author":"Kim","year":"2004","journal-title":"Transp. Res. Rec."},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/j.cstp.2021.05.002","article-title":"Exploring the effect of the built environment, weather condition and departure time of travel on mode choice decision for different travel purposes: Evidence from Isfahan, Iran","volume":"9","author":"Mirzaei","year":"2021","journal-title":"Case Stud. Trans. Pol."},{"key":"ref9","first-page":"386","article-title":"Perceived accessibility, mobility, and connectivity of public transportation systems","volume":"77","author":"Cheng","year":"2015","journal-title":"Trans. Res. Part A: Policy Pract."},{"key":"ref10","article-title":"Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis","volume":"20","author":"Abulibdeh","year":"2023","journal-title":"Trans. Res. Interdiscip. Perspect."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.jtrangeo.2010.07.005","article-title":"Determinants of transport mode choice: A comparison of Germany and the USA","volume":"19","author":"Buehler","year":"2011","journal-title":"J. Transp. Geogr."},{"key":"ref12","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.trpro.2023.02.175","article-title":"The impact of parking pricing on mode choice","volume":"69","author":"Vidovic","year":"2023","journal-title":"Transp. Res. Procedia"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/BF02125333","article-title":"Parking subsidies and travel choices: Assessing the evidence","volume":"17","author":"Willson","year":"1990","journal-title":"Transportation"},{"key":"ref14","article-title":"Promoting walking and cycling as an alternative to using cars: Systematic review","volume":"329","author":"Ogilvie","year":"2004","journal-title":"BMJ"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.trd.2013.03.001","article-title":"Limits to active transport substitution of short car trips","volume":"22","author":"Beckx","year":"2013","journal-title":"Trans. Res. Part D: Transp. Environ."},{"key":"ref16","doi-asserted-by":"crossref","DOI":"10.1186\/s12544-018-0311-y","article-title":"Determinants of urban public transport efficiency: Case study of the Czech Republic","volume":"10","author":"Fitzov\u00e1","year":"2018","journal-title":"Eur. Transp. Res. Rev."},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1016\/j.jtrangeo.2020.102766","article-title":"Determinants of transport mode choice in metropolitan areas the case of the metropolitan area of the Valley of Mexico","volume":"87","author":"Harbering","year":"2020","journal-title":"J. Transp. Geogr."},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.3390\/urbansci3030082","article-title":"Determinants of transport mode choice for non-commuting trips: The roles of transport, land use and socio-demographic characteristics","volume":"3","author":"Convery","year":"2019","journal-title":"Urban Sci."},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.3390\/su16145908","article-title":"Travel mode choice prediction to pursue sustainable transportation and enhance health parameters using R","volume":"16","author":"Ali","year":"2024","journal-title":"Sustainability"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1016\/j.jth.2024.101927","article-title":"Health enhancement through activity travel participation and physical activity intensity","volume":"39","author":"Ali","year":"2024","journal-title":"J. Transp. Health"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.jprocont.2007.08.003","article-title":"Fault detection and identification with a new feature selection based on mutual information","volume":"18","author":"Verron","year":"2008","journal-title":"J. Process Control"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.3390\/app112411916","article-title":"Classification of imbalanced travel mode choice to work data using adjustable SVM model","volume":"11","author":"Qian","year":"2021","journal-title":"Appl. Sci."},{"key":"ref23","author":"Guthrie","year":"2020","journal-title":"NIST\/SEMATECH e-Handbook of Statistical Methods (NIST Handbook 151)"},{"key":"ref24","article-title":"Nonlinear relationships between vehicle ownership and household travel characteristics and built environment attributes in the US using the XGBT algorithm","volume":"14","author":"Ma","year":"2022","journal-title":"Sustainability"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/0361198118773556","article-title":"Machine learning travel mode choices: Comparing the performance of an extreme gradient boosting model with a multinomial logit model","volume":"2672","author":"Wang","year":"2018","journal-title":"Transp. Res. Rec."},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)UP.1943-5444.0000830","article-title":"Assessing the performance of gradient-boosting models for predicting the travel mode choice using household survey data","volume":"148","author":"Pineda-Jaramillo","year":"2022","journal-title":"J. Urban Plan. Dev."},{"key":"ref27","article-title":"Targeting sustainable transportation development: The support vector machine and the bayesian optimization algorithm for classifying household vehicle ownership","volume":"14","author":"Xu","year":"2022","journal-title":"Sustainability"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"241","DOI":"10.2174\/1874447802115010241","article-title":"Travel mode choice modeling: Predictive efficacy between machine learning models and discrete choice model","volume":"15","author":"Ali","year":"2021","journal-title":"Open Transp. J."},{"key":"ref29","article-title":"How sustainable is people travel to reach public transit stations to go to work? A machine learning approach to reveal complex relationships","volume":"14","author":"Tang","year":"2022","journal-title":"Sustainability"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"19762","DOI":"10.1109\/ACCESS.2023.3247448","article-title":"On hyperparameter optimization of machine learning methods using a bayesian optimization algorithm to predict work travel mode choice","volume":"11","author":"Aghaabbasi","year":"2023","journal-title":"IEEE Access"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.tbs.2022.07.003","article-title":"Predicting the travel mode choice with interpretable machine learning techniques: A comparative study","volume":"29","author":"Tamim Kashifi","year":"2022","journal-title":"Travel Behav. Soc."},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/6685004","article-title":"Analysis of travel mode choice in seoul using an interpretable machine learning approach","volume":"2021","author":"Kim","year":"2021","journal-title":"J. Adv. Transport."},{"key":"ref33","unstructured":"X. Zhao, X. Yan, and P. Van Hentenryck, \u201cModeling heterogeneity in mode-switching behavior under a mobility-on-demand transit system: An interpretable machine learning approach,\u201d 2019, arXiv:1902.02904."},{"key":"ref34","article-title":"Understanding travel mode choice behavior: Influencing factors analysis and prediction with machine learning method","volume":"15","author":"Zhang","year":"2023","journal-title":"Sustainability"},{"key":"ref35","first-page":"93","article-title":"The determinants of travel mode choice: The case of \u0141\u00f3d\u017a, Poland","volume":"44","author":"W\u00f3jcik","year":"2019","journal-title":"Bull. Geography. Soc.-Eco. Ser."},{"key":"ref36","first-page":"65","article-title":"Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance","volume":"100","author":"Ding","year":"2017","journal-title":"Transp. Res. Part A: Policy Pract."},{"key":"ref37","first-page":"65","volume":"907","author":"Ali","year":"2024","journal-title":"Advanced Solutions for Mobility in Urban Areas, Lecture Notes in Networks and Systems"},{"key":"ref38","author":"Dora","year":"2000","journal-title":"Transport, Environment and Health (No. 89)"},{"key":"ref39","first-page":"131","author":"Aijaz","year":"2022","journal-title":"Smart Technologies for Energy and Environmental Sustainability"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.jth.2016.07.003","article-title":"Travel behaviour and health: A conceptual model and research agenda","volume":"3","author":"van Wee","year":"2016","journal-title":"J. Transp. Health"},{"key":"ref41","series-title":"Proc. 13th World Conf. Transp. Res.","first-page":"15","article-title":"Urban forms and health promotion: An evaluation based on health-related QOL indicators","author":"Zhang","year":"Jul. 15\u201318, 2013"},{"key":"ref42","doi-asserted-by":"crossref","DOI":"10.1016\/j.asej.2024.103050","article-title":"Interaction of activity travel, GHG emissions, and health parameters using R\u2014A step towards sustainable transportation system","author":"Ali","year":"2024","journal-title":"Ain Shams Eng. J."},{"key":"ref43","year":"2022","journal-title":"NextGen National Household Travel Survey"},{"key":"ref44","doi-asserted-by":"crossref","DOI":"10.1016\/j.trd.2020.102534","article-title":"Greenhouse gas emissions of electric vehicles in Europe considering different charging strategies","volume":"87","author":"Xu","year":"2020","journal-title":"Transp. Res. Part D: Transp. Environ."},{"key":"ref45","doi-asserted-by":"crossref","first-page":"129","DOI":"10.2495\/UT110121","volume":"116","author":"Hollevoet","year":"2011","journal-title":"Urban Transport XVII: Urban Transport and the Environment in the 21st Century"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1016\/j.envint.2011.02.003","article-title":"Improving health through policies that promote active travel: A review of evidence to support integrated health impact assessment","volume":"37","author":"de Nazelle","year":"2011","journal-title":"Environ. Int."},{"key":"ref47","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1068\/b33118","article-title":"Dynamic accessibility analysis in location-based service using an incremental parallel algorithm","volume":"35","author":"Huang","year":"2008","journal-title":"Environ. Plann. B: Plann. Des."},{"key":"ref48","doi-asserted-by":"crossref","DOI":"10.3390\/su14042436","article-title":"An advanced machine learning approach to predicting pedestrian fatality caused by road crashes: A step toward sustainable pedestrian safety","volume":"14","author":"Tao","year":"2022","journal-title":"Sustainability"},{"key":"ref49","first-page":"605","article-title":"The main determinants of the demand for public transport: A comparative analysis of England and France using shrinkage estimators","volume":"37","author":"Bresson","year":"2003","journal-title":"Transp. Res. Part A: Policy Pract."},{"key":"ref50","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/j.trpro.2015.09.036","article-title":"The role of accessibility and connectivity in mode choice. A structural equation modeling approach","volume":"10","author":"Papaioannou","year":"2015","journal-title":"Transp. Res. Procedia"},{"key":"ref51","doi-asserted-by":"crossref","DOI":"10.1016\/j.cities.2022.104050","article-title":"The effect of neighbourhood and urban center structures on active travel in small cities","volume":"132","author":"Wolday","year":"2023","journal-title":"Cities"},{"key":"ref52","author":"Sultana","year":"2019","journal-title":"Factors Affecting Parents\u2019 Choice of Active Transport Modes for Children\u2019s Commute to School: Evidence from 2017 NHTS Data"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"26","DOI":"10.53623\/csue.v1i1.28","article-title":"Investigating the use of active transportation modes among university employees through an advanced decision tree algorithm","volume":"1","author":"Aghaabbasi","year":"2021","journal-title":"Civil Sustain. Urban Eng."},{"key":"ref54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-009-0198-y","article-title":"Boosting support vector machines for imbalanced data sets","volume":"25","author":"Wang","year":"2010","journal-title":"Knowl. Inf. Syst."},{"key":"ref55","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/TFUZZ.2010.2042721","article-title":"FSVM-CIL: Fuzzy support vector machines for class imbalance learning","volume":"18","author":"Batuwita","year":"2010","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref56","series-title":"ICML 2003 Workshop Learn. Imbalanced Data Sets II","first-page":"49","article-title":"Class-boundary alignment for imbalanced dataset learning","author":"Wu","year":"2003"},{"key":"ref57","article-title":"Machine learning techniques for evaluating the nonlinear link between built-environment characteristics and travel behaviors: A systematic review","volume":"33","author":"Aghaabbasi","year":"2023","journal-title":"Travel Behav. Soc."},{"key":"ref58","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1016\/j.istruc.2023.06.131","article-title":"Artificial intelligent techniques for prediction of rock strength and deformation properties\u2014A review","volume":"55","author":"Ali","year":"2023","journal-title":"Structures"},{"key":"ref59","first-page":"2359","article-title":"Assessing impacts of the built environment on mobility: A joint choice model of travel mode and duration","volume":"50","author":"Yang","year":"2023","journal-title":"Environ. Plann."},{"key":"ref60","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.tbs.2022.08.011","article-title":"A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions","volume":"30","author":"Xia","year":"2023","journal-title":"Travel Behav. Soc."},{"key":"ref61","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.procs.2023.03.079","volume":"220","author":"Noorbakhsh","year":"2023","journal-title":"Procedia Computer Science"},{"key":"ref62","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.ijtst.2022.03.008","article-title":"Mode shift behaviour and user willingness to adopt the electric two-wheeler: A study based on Indian road user preferences","volume":"12","author":"Murugan","year":"2023","journal-title":"Int. J. Trans. Sci. Tech."},{"key":"ref63","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2023.104318","article-title":"A prediction and behavioural analysis of machine learning methods for modelling travel mode choice","volume":"156","author":"Mart\u00edn-Baos","year":"2023","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"ref64","article-title":"How rail transit makes a difference in people\u2019s multimodal travel behaviours: An analysis with the XGBoost method","volume":"12","author":"Liu","year":"2023","journal-title":"Land"},{"key":"ref65","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1080\/19427867.2022.2130150","article-title":"Testing and enhancing spatial transferability of artificial neural networks based travel behavior models","volume":"15","author":"Koushik","year":"2023","journal-title":"Transp. Lett."},{"key":"ref66","doi-asserted-by":"crossref","first-page":"12648","DOI":"10.1109\/ACCESS.2023.3241627","article-title":"Non-linear associations between the urban built environment and commuting modal split: A random forest approach and SHAP evaluation","volume":"11","author":"Hatami","year":"2023","journal-title":"IEEE Access"},{"key":"ref67","article-title":"Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach","volume":"33","author":"Bei","year":"2023","journal-title":"Travel Behav. Soc."},{"key":"ref68","doi-asserted-by":"crossref","DOI":"10.1016\/j.jtrangeo.2022.103482","article-title":"Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms","volume":"105","author":"Yousefzadeh Barri","year":"2022","journal-title":"J. Transp. Geogr."},{"key":"ref69","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116253","article-title":"A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity","volume":"193","author":"Salas","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref70","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph192416844","article-title":"Application of machine learning to child mode choice with a novel technique to optimize hyperparameters","volume":"19","author":"Naseri","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref71","first-page":"A187","article-title":"Modeling the behavior in choosing the travel mode for long-distance travel using supervised machine learning algorithms","volume":"24","author":"Momin","year":"2022","journal-title":"Commun. Sci. Lett. Univ. Zilina."},{"key":"ref72","doi-asserted-by":"crossref","DOI":"10.1016\/j.jth.2022.101362","article-title":"The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier: An evaluation for active commuting behavior","volume":"25","author":"Mohd Ali","year":"2022","journal-title":"J. Transp. Health"},{"key":"ref73","doi-asserted-by":"crossref","first-page":"3578","DOI":"10.1007\/s12205-022-1281-0","article-title":"Transportation mode detection by using smartphones and smartwatches with machine learning","volume":"26","author":"Hasan","year":"2022","journal-title":"KSCE J. Civil Eng."},{"key":"ref74","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.trpro.2022.02.047","article-title":"A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling","volume":"62","author":"Garc\u00eda-Garc\u00eda","year":"2022","journal-title":"Transp. Res. Procedia"},{"key":"ref75","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2021.103050","article-title":"ResLogit: A residual neural network logit model for data-driven choice modelling","volume":"126","author":"Wong","year":"2021","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"ref76","article-title":"Transportation mode choice behavior with recommender systems: A case study on Beijing","volume":"11","author":"Sun","year":"2021","journal-title":"Transp. Res. Interdiscip. Perspect."},{"key":"ref77","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106882","article-title":"Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory","volume":"218","author":"Gao","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref78","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s12652-020-02855-6","article-title":"Using neural nets to predict transportation mode choice: Amsterdam network change analysis","volume":"12","author":"Buijs","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref79","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.tbs.2020.02.003","article-title":"Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models","volume":"20","author":"Zhao","year":"2020","journal-title":"Travel Behav. Soc."},{"key":"ref80","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.procs.2020.03.022","volume":"170","author":"Slik","year":"2020","journal-title":"Procedia Computer Science"},{"key":"ref81","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1080\/01441647.2019.1704307","article-title":"Machine learning applications in activity-travel behaviour research: A review","volume":"40","author":"Koushik","year":"2020","journal-title":"Transp. Rev."},{"key":"ref82","doi-asserted-by":"crossref","first-page":"190964","DOI":"10.1109\/ACCESS.2020.3032328","article-title":"Clustering life course to understand the heterogeneous effects of life events, gender, and generation on habitual travel modes","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref83","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.procs.2020.03.015","article-title":"Using neural nets to predict transportation mode choice: An Amsterdam case study","volume":"170","author":"Buijs","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref84","unstructured":"W. Yan, W. Zhou, C. Tan, and L. Fan, \u201cEmployee ridesharing: Reinforcement learning and choice modeling,\u201d presented at the 25th Am. Conf. Inf. Syst., AMCIS 2019, Jun. 15\u201319, 2019."},{"key":"ref85","doi-asserted-by":"crossref","DOI":"10.1186\/s12966-019-0904-4","article-title":"Gender and active travel: A qualitative data synthesis informed by machine learning","volume":"16","author":"Haynes","year":"2019","journal-title":"Int. J. Behav. Nutr. Phys. Act."},{"key":"ref86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tbs.2018.09.002","article-title":"Applying a random forest method approach to model travel mode choice behavior","volume":"14","author":"Cheng","year":"2019","journal-title":"Travel Behav. Soc."},{"key":"ref87","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1080\/23249935.2019.1620380","article-title":"Travel mode choice: A data fusion model using machine learning methods and evidence from travel diary survey data","volume":"15","author":"Chang","year":"2019","journal-title":"Transportmetrica A Transport Sci."},{"key":"ref88","doi-asserted-by":"crossref","DOI":"10.3390\/su11164484","article-title":"Travel-to-school mode choice modelling employing artificial intelligence techniques: A comparative study","volume":"11","author":"Assi","year":"2019","journal-title":"Sustainability"},{"key":"ref89","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1007\/s11116-017-9770-6","article-title":"A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice","volume":"45","author":"Zhu","year":"2018","journal-title":"Transportation"},{"key":"ref90","series-title":"2018 21st Int. Conf. Intell. Transp. Syst. (ITSC)","first-page":"749","article-title":"Modelling latent travel behaviour characteristics with generative machine learning","author":"Wong","year":"2018"},{"key":"ref91","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.eswa.2017.01.057","article-title":"A comparative study of machine learning classifiers for modeling travel mode choice","volume":"78","author":"Hagenauer","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref92","doi-asserted-by":"crossref","DOI":"10.3390\/su152316338","article-title":"Influence of activity-travel participation, travel mode choice, and multitasking activities on subjective well-being using R","volume":"15","author":"Ali","year":"2023","journal-title":"Sustainability"},{"key":"ref93","doi-asserted-by":"crossref","first-page":"5","DOI":"10.20858\/tp.2023.18.4.01","article-title":"The influence of travel mode choice on subjective wellbeing\u2014A case study","volume":"18","author":"Ali","year":"2023","journal-title":"Transp. Probl.: An Int. Sci. J."},{"key":"ref94","first-page":"352","article-title":"Mode choice of mothers travelling with young children in malaysia","volume":"20","author":"Harumain","year":"2022","journal-title":"Plann. Malays."},{"key":"ref95","series-title":"REAL CORP 2017-PANTA RHEI\u2013A World Constant Motion.","first-page":"723","article-title":"Private or public transport? The determinants of travel behaviour in post-industrial city-the case of \u0141\u00f3d\u017a","author":"W\u00f3jcik","year":"Sep. 13, 2017"},{"key":"ref96","doi-asserted-by":"crossref","DOI":"10.1016\/j.jtrangeo.2024.103828","article-title":"Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach","volume":"116","author":"Ashik","year":"2024","journal-title":"J. Transp. Geogr."},{"key":"ref97","doi-asserted-by":"crossref","first-page":"25","DOI":"10.5507\/tots.2023.016","article-title":"Examination of the effects of the pandemic process on the E-scooter Usage Behaviours of individuals with machine learning","volume":"14","author":"Kuskapan","year":"2023","journal-title":"Trans. Transp. Sci."},{"key":"ref98","doi-asserted-by":"crossref","first-page":"32","DOI":"10.15446\/dyna.v86n211.79743","article-title":"A review of Machine Learning (ML) algorithms used for modeling travel mode choice","volume":"86","author":"Pineda-Jaramillo","year":"2019","journal-title":"Dyna"},{"key":"ref99","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1007\/s11116-023-10375-3","article-title":"Exploring passengers\u2019 choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach","volume":"51","author":"Ren","year":"2023","journal-title":"Transportation"},{"key":"ref100","first-page":"4026","article-title":"Travel behaviour and health: Interaction of activity-travel pattern, travel parameter and physical intensity","volume":"63","author":"Ali","year":"2020","journal-title":"Solid State Technol."}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/files\/cmc\/2024\/TSP_CMC-81-2\/TSP_CMC_58888\/TSP_CMC_58888.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T04:25:17Z","timestamp":1741321517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v81n2\/58684"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":100,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.058888","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2024-09-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-18","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}