{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:42:59Z","timestamp":1767339779290,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T00:00:00Z","timestamp":1563494400000},"content-version":"vor","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":["41701444"],"award-info":[{"award-number":["41701444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses\u2019 GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error (      M A P E  \u00af     ) and root mean square error (      R M S E  \u00af     ) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles.<\/jats:p>","DOI":"10.3390\/e21070709","type":"journal-article","created":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T03:14:54Z","timestamp":1563765294000},"page":"709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Peak Traffic Congestion Prediction Method Based on Bus Driving Time"],"prefix":"10.3390","volume":"21","author":[{"given":"Zhao","family":"Huang","sequence":"first","affiliation":[{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Information Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Jizhe","family":"Xia","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Fan","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Qingquan","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.tra.2010.04.001","article-title":"A framework for evaluating the dynamic impacts of a congestion pricing policy for a transportation socioeconomic system","volume":"44","author":"Liu","year":"2010","journal-title":"Transp. 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