{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T20:47:08Z","timestamp":1775422028331,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Project of National Natural Science Foundation of China","award":["U1908212"],"award-info":[{"award-number":["U1908212"]}]},{"name":"Key Project of National Natural Science Foundation of China","award":["1653137155953"],"award-info":[{"award-number":["1653137155953"]}]},{"name":"Key Project of National Natural Science Foundation of China","award":["2021jh1\/10400006"],"award-info":[{"award-number":["2021jh1\/10400006"]}]},{"name":"Central Government\u2019s Guided Local Science and Technology Development Fund Project","award":["U1908212"],"award-info":[{"award-number":["U1908212"]}]},{"name":"Central Government\u2019s Guided Local Science and Technology Development Fund Project","award":["1653137155953"],"award-info":[{"award-number":["1653137155953"]}]},{"name":"Central Government\u2019s Guided Local Science and Technology Development Fund Project","award":["2021jh1\/10400006"],"award-info":[{"award-number":["2021jh1\/10400006"]}]},{"name":"Liaoning Province\u2019s \u201cTakes the Lead\u201d Science and Technology Research Project","award":["U1908212"],"award-info":[{"award-number":["U1908212"]}]},{"name":"Liaoning Province\u2019s \u201cTakes the Lead\u201d Science and Technology Research Project","award":["1653137155953"],"award-info":[{"award-number":["1653137155953"]}]},{"name":"Liaoning Province\u2019s \u201cTakes the Lead\u201d Science and Technology Research Project","award":["2021jh1\/10400006"],"award-info":[{"award-number":["2021jh1\/10400006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Providing accurate and real-time bus travel time information is crucial for both passengers and public transportation managers. However, in the traditional bus travel time prediction model, due to the lack of consideration of the influence of different bus drivers\u2019 driving styles on the bus travel time, the prediction result is not ideal. In the traditional bus travel time prediction model, the historical travel data of all drivers in the entire bus line are usually used for training and prediction. Due to great differences in individual driving styles, the eigenvalues of drivers\u2019 driving parameters are widely distributed. Therefore, the prediction accuracy of the model trained by this dataset is low. At the same time, the training time of the model is too long due to the large sample size, making it difficult to provide a timely prediction in practical applications. However, if only the historical dataset of a single driver is used for training and prediction, the amount of training data is too small, and it is also difficult to accurately predict travel time. To solve these problems, this paper proposes a method to predict bus travel times based on the similarity of drivers\u2019 driving styles. Firstly, the historical travel time data of different drivers are clustered, and then the corresponding types of drivers\u2019 historical data are used to predict the travel time, so as to improve the accuracy and speed of the travel time prediction. We evaluated our approach using a real-world bus trajectory dataset collected in Shenyang, China. The experimental results show that the accuracy of the proposed method is 13.4% higher than that of the traditional method.<\/jats:p>","DOI":"10.3390\/fi15070222","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T01:49:32Z","timestamp":1687398572000},"page":"222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bus Travel Time Prediction Based on the Similarity in Drivers\u2019 Driving Styles"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7724-225X","authenticated-orcid":false,"given":"Zhenzhong","family":"Yin","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110819, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"ref_1","unstructured":"Jeong, R., and Rilett, L.R. 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