A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy

This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization in GACE is made. These range from a pure GA to a pure CE, passing through different weights for each of the combined techniques. The results prove that GACE is more accurate than GA or CE alone for predicting short-term traffic congestion.

• Record URL:
  https://doi.org/10.1109/TITS.2015.2491365
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Lopez-Garcia, Pedro
  • Onieva, Enrique
  • Osaba, Eneko
  • Masegosa, Antonio D
  • Perallos, Asier
• Publication Date: 2016-2

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 557-569
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 17
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Fuzzy systems; Genetic algorithms; Intelligent transportation systems; Mathematical prediction; Traffic congestion; Traffic simulation
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01594925
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Mar 30 2016 9:48AM