[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125691-en":3,"doc-seo-125691-105":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},125691,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Travel Mode Choice Prediction Using Imbalanced Machine Learning","Travel mode choice prediction underpins travel demand prediction, guiding transport resource allocation and policy decisions. Travel mode data often show strong class imbalance, causing minority modes to be under-predicted and biasing demand forecasts when models are trained and evaluated with metrics unsuited to extreme imbalance. This paper presents an evaluation framework that systematically combines six over/undersampling techniques with three prediction methods, showing on the London Passenger Mode Choice dataset that oversampling/undersampling boosts minority-class F1 without substantially degrading overall performance or model interpretability.","This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.  \nIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1  \nTravel Mode Choice Prediction Using Imbalanced  \nMachine Learning  \nHuanfa Chen and Yan Cheng  \nAbstract—Travel mode choice prediction is critical for travel demand prediction, which influences transport resource allocation and transport policies. Travel modes are often characterised by severe class imbalance and inequality, which leads to the inferior predictive performance of minority modes and bias in travel demand prediction. In existing studies, the class imbalance in travel mode prediction has not been addressed with a general approach. Basic resampling methods were adopted without much investigation, and the performance was assessed by commonly used metrics (e.g., accuracy), which is not suitable for predicting highly imbalanced modes. To this end, this paper proposes an evaluation framework to systematically investigate the combination of six over/undersampling techniques and three prediction methods. In a case study using the London Passenger Mode Choice dataset, results show that applying over/undersampling techniques on travel mode substantially improves the F1 score (i.e., the harmonic mean of precision and recall) of minority classes, without considerably downgrading the overall prediction performance or model interpretation. These findings suggest that combining over/undersampling techniques and statistical/machine-learning methods is appropriate for predicting travel mode, which effectively mitigates the influence of class imbalance while achieving high predictive accuracy and model interpretation. In addition, the combination of over/undersampling techniques and prediction methods enriches the model options for predicting mode choice, which would better support transport planning.  \nIndex Terms—Class imbalance, machine learning, oversampling, undersampling, travel mode choice.  \nI. INTRODUCTION  \nTRAVEL mode choice prediction is an essential step of  \ntravel demand prediction. It affects not only resource allocation in transport planning and operation, but also transport policy-making with goals such as improving mobility and decarbonisation. Travel mode choice prediction aims for accurate aggregate prediction (i.e., prediction of market shares) as well as precise disaggregate prediction (i.e., prediction of modes of individual trips) . Traditionally, travel mode prediction is approached using discrete choice models (DCM),  \nManuscript received 26 August 2021; revised 25 March 2022 and 8 September 2022; accepted 29 December 2022 . The Associate Editor for this article was T. Q. Dinh. (Corresponding author: Yan Cheng.)  \nHuanfa Chen is with the Centre for Advanced Spatial Analysis, University College London, W1T 4TJ London, U.K.  \nYan Cheng is with the Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China, also with the Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China, and also with the Centre for Transport Studies, University College London, WCE1 6BT London, U.K. (e-mail: [yan_cheng@tongji.edu.cn](yan_cheng@tongji.edu.cn)).  \nThis article has supplementary downloadable material available at [https://doi.org/10.1109/TITS.2023.3237681](https://doi.org/10.1109/TITS.2023.3237681), provided by the authors.  \nDigital Object Identifier 10.1109/TITS.2023.3237681  \nincluding the multinomial logit model and its variants [1],[2] . Recently, there is a growing interest in using machine learning methods for modelling travel mode choice, including support vector machine (SVM), deep neural network (DNN), and extreme gradient boosting (XGB) . It is reported that XGBand DNN methods have higher predictive power than discrete choice models in predicting travel mode [3], [4], [5], [6] .  \nIn travel m","cbCaihKtKaIWT5gv","https://ap.wps.com/l/cbCaihKtKaIWT5gv","pdf",3503106,1,14,"English","en",105,"# Introduction\n## Travel demand prediction and mode choice\n## Discrete choice models and machine learning approaches\n# Literature\n## Class imbalance in travel mode prediction","[{\"question\":\"Why is travel mode choice prediction important for transportation planning?\",\"answer\":\"It supports travel demand prediction, influencing transport resource allocation and transport policy-making, including goals like improving mobility and decarbonisation.\"},{\"question\":\"What problem does class imbalance cause in travel mode prediction?\",\"answer\":\"Class imbalance leads models to focus on majority modes and under-estimate minority modes, severely compromising estimation and predictive performance.\"},{\"question\":\"What does the proposed evaluation framework in the paper do?\",\"answer\":\"It systematically combines six over/undersampling techniques with three prediction methods and evaluates their suitability for highly imbalanced datasets using an appropriate performance assessment.\"}]","Travel Mode Choice Prediction Using Imbalanced Machine Learning | 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is travel mode choice prediction important for transportation planning?","Question",{"text":75,"@type":76},"It supports travel demand prediction, influencing transport resource allocation and transport policy-making, including goals like improving mobility and decarbonisation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does class imbalance cause in travel mode prediction?",{"text":80,"@type":76},"Class imbalance leads models to focus on majority modes and under-estimate minority modes, severely compromising estimation and predictive performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the proposed evaluation framework in the paper do?",{"text":84,"@type":76},"It systematically combines six over/undersampling techniques with three prediction methods and evaluates their suitability for highly imbalanced datasets using an appropriate performance 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