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Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme.<\/jats:p>","DOI":"10.1155\/2018\/4570493","type":"journal-article","created":{"date-parts":[[2018,3,19]],"date-time":"2018-03-19T19:31:33Z","timestamp":1521487893000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5068-5162","authenticated-orcid":true,"given":"Darong","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, 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