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Classifying the traffic state of urban expressways: A machine-learning approach

  • Published:2020-07 Issue: Volume:137 Page:411-428
  • ISSN:0965-8564
  • Container-title:Transportation Research Part A: Policy and Practice
  • language:en
  • Short-container-title:Transportation Research Part A: Policy and Practice

Author:

Cheng Zeyang,Wang Wei,Lu Jian,Xing Xue

Funder

Jiangsu Province Science and Technology Project

Scientific Research Foundation of Graduate School of Southeast University

Publisher

Elsevier BV

Subject

Management Science and Operations Research,Transportation,Civil and Structural Engineering

Reference46 articles.

1. Categorizing freeway flow conditions by using clustering methods;Azimi;Transp. Res. Rec.,2010

2. Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes;Austin;J. Clin. Epidemiol.,2013

3. Dynamic data-driven local traffic state estimation and prediction;Antoniou;Transp. Res. Part C: Emerg. Technol.,2013

4. Urban traffic state estimation: fusing point and zone-based data;Bhaskar;Transp. Res. Part C: Emerg. Technol.,2014

5. Traffic state identification for urban expressway based on projection pursuit dynamic cluster model;Bing;J. Southwest Jiaotong Univ.,2015
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