{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T04:22:10Z","timestamp":1771302130948,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Department of Transportation (USDOT)","award":["69A3551747133"],"award-info":[{"award-number":["69A3551747133"]}]},{"name":"U.S. Department of Transportation (USDOT)","award":["ZR2020MA049"],"award-info":[{"award-number":["ZR2020MA049"]}]},{"name":"U.S. Department of Transportation (USDOT)","award":["S202010431014"],"award-info":[{"award-number":["S202010431014"]}]},{"name":"National Science Foundation of Shandong Province","award":["69A3551747133"],"award-info":[{"award-number":["69A3551747133"]}]},{"name":"National Science Foundation of Shandong Province","award":["ZR2020MA049"],"award-info":[{"award-number":["ZR2020MA049"]}]},{"name":"National Science Foundation of Shandong Province","award":["S202010431014"],"award-info":[{"award-number":["S202010431014"]}]},{"name":"Department of Higher Education, Chinese Ministry of Education","award":["69A3551747133"],"award-info":[{"award-number":["69A3551747133"]}]},{"name":"Department of Higher Education, Chinese Ministry of Education","award":["ZR2020MA049"],"award-info":[{"award-number":["ZR2020MA049"]}]},{"name":"Department of Higher Education, Chinese Ministry of Education","award":["S202010431014"],"award-info":[{"award-number":["S202010431014"]}]},{"name":"Texas Southern University","award":["69A3551747133"],"award-info":[{"award-number":["69A3551747133"]}]},{"name":"Texas Southern University","award":["ZR2020MA049"],"award-info":[{"award-number":["ZR2020MA049"]}]},{"name":"Texas Southern University","award":["S202010431014"],"award-info":[{"award-number":["S202010431014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Three different types of entropy weight methods (EWMs), i.e., EWM-A, EWM-B, and EWM-C, have been used by previous studies for integrating prediction models. These three methods use very different ideas on determining the weights of individual models for integration. To evaluate the performances of these three EWMs, this study applied them to developing integrated short-term traffic flow prediction models for signalized intersections. At first, two individual models, i.e., a k-nearest neighbors (KNN)-algorithm-based model and a neural-network-based model (Elman), were developed as individual models to be integrated using EWMs. These two models were selected because they have been widely used for traffic flow prediction and have been approved to be able to achieve good performance. After that, three integrated models were developed by using the three different types of EWMs. The performances of the three integrated models, as well as the individual KNN and Elman models, were compared. We found that the traffic flow predicted with the EWM-C model is the most accurate prediction for most of the days. Based on the model evaluation results, the advantages of using the EWM-C method were deliberated and the problems with the EWM-A and EWM-B methods were also discussed.<\/jats:p>","DOI":"10.3390\/e24070849","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T21:55:19Z","timestamp":1655848519000},"page":"849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction"],"prefix":"10.3390","volume":"24","author":[{"given":"Wenrui","family":"Qu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Changqing District, Jinan 250353, China"}]},{"given":"Jinhong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Changqing District, Jinan 250353, China"}]},{"given":"Wenting","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Changqing District, Jinan 250353, China"}]},{"given":"Xiaoran","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Changqing District, Jinan 250353, China"}]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Changqing District, Jinan 250353, China"}]},{"given":"Hanlin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Changqing District, Jinan 250353, China"}]},{"given":"Yanfei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Changqing District, Jinan 250353, China"}]},{"given":"Qun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Transportation Studies, Texas Southern University, 3100 Cleburne Street, Houston, TX 77004-9986, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6314-2626","authenticated-orcid":false,"given":"Yi","family":"Qi","sequence":"additional","affiliation":[{"name":"Department of Transportation Studies, Texas Southern University, 3100 Cleburne Street, Houston, TX 77004-9986, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"key":"ref_1","unstructured":"Zhao, Y., Kong, L., and He, G. 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