{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:49:33Z","timestamp":1787017773426,"version":"build-2736575974"},"reference-count":72,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T00:00:00Z","timestamp":1660176000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52172311"],"award-info":[{"award-number":["52172311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,8,11]]},"abstract":"<jats:p>The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examined as the base models, and three ensemble models are further constructed by using various ensemble methods including Random Forest (bagging), AdaBoost (boosting), and Linear Regression (stacking). A data-driven bus single-trip time prediction framework is then proposed, which consists of three phases including traffic data analysis, feature extraction, and ensemble model prediction. Finally, the data features and the proposed ensembled models are analyzed using real-world datasets that are collected from the Beijing Transportation Operations Coordination Center (TOCC). Through comparing the predicting results, the following conclusions are drawn: (1) the accuracy of predicting by using the three ensemble models constructed is better than the corresponding prediction results by using the five sub-models; (2) the Random Forest ensemble model constructed based on the bagging method has the best prediction accuracy among the three ensemble models; and (3) in terms of the five sub-models, the prediction accuracy of LR is better than that of the other four models.<\/jats:p>","DOI":"10.1155\/2022\/6831167","type":"journal-article","created":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T18:35:08Z","timestamp":1660242908000},"page":"1-24","source":"Crossref","is-referenced-by-count":12,"title":["Bus Single-Trip Time Prediction Based on Ensemble Learning"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5337-2834","authenticated-orcid":true,"given":"Haifeng","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4761-4187","authenticated-orcid":true,"given":"Lei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongjia","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Information Management, School of Management, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Ai","sequence":"additional","affiliation":[{"name":"Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1061\/JTEPBS.0000029"},{"issue":"11","key":"2","first-page":"23","article-title":"Network-scale traffic modeling and forecasting with graphical lasso and neural networks","volume":"138","author":"D Subhe","year":"2022","journal-title":"Journal of Transportation Engineering"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)TE.1943-5436.0000312"},{"key":"4","first-page":"95","article-title":"A method of road traffic flow prediction based on ARIMA model","volume-title":"Proceedings of the Aeit 2012: 2012 2nd International Conference on Aerospace Engineering and Information Technology Hong Kong Education Soc: Tseung Kwan O","author":"L. 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