{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:39:56Z","timestamp":1790249996342,"version":"4.1.0"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T00:00:00Z","timestamp":1491868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["41271441"],"award-info":[{"award-number":["41271441"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the great development of intelligent transportation systems (ITS), travel time prediction has attracted the interest of many researchers, and a large number of prediction methods have been developed. However, as an unavoidable topic, the predictability of travel time series is the basic premise for travel time prediction, which has received less attention than the methodology. Based on the analysis of the complexity of the travel time series, this paper defines travel time predictability to express the probability of correct travel time prediction, and proposes an entropy-based method to measure the upper bound of travel time predictability. Multiscale entropy is employed to quantify the complexity of the travel time series, and the relationships between entropy and the upper bound of travel time predictability are presented. Empirical studies are made with vehicle trajectory data in an express road section to shape the features of travel time predictability. The effectiveness of time scales, tolerance, and series length to entropy and travel time predictability are analyzed, and some valuable suggestions about the accuracy of travel time predictability are discussed. Finally, comparisons between travel time predictability and actual prediction results from two prediction models, ARIMA and BPNN, are made. Experimental results demonstrate the validity and reliability of the proposed travel time predictability.<\/jats:p>","DOI":"10.3390\/e19040165","type":"journal-article","created":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T11:41:42Z","timestamp":1491910902000},"page":"165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An Entropy-Based Approach for Evaluating Travel Time Predictability Based on Vehicle Trajectory Data"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8993-9481","authenticated-orcid":false,"given":"Tao","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China"},{"name":"School of Geographic Sciences, East China Normal University, Shanghai 200241, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianrui","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China"},{"name":"School of Geographic Sciences, East China Normal University, Shanghai 200241, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5814-0805","authenticated-orcid":false,"given":"Yujie","family":"Hu","sequence":"additional","affiliation":[{"name":"Kinder Institute for Urban Research, Rice University, Houston, TX 77005, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China"},{"name":"School of Geographic Sciences, East China Normal University, Shanghai 200241, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TITS.2004.837813","article-title":"Travel-time prediction with support vector regression","volume":"5","author":"Wu","year":"2005","journal-title":"IEEE Trans. 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