DYNAMIC TRAFFIC PATTERN CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS

Because of the difficulty of modeling the traffic conditions on a roadway network, little has been achieved to date in area control using dynamic traffic volume. The most commonly practiced method for timing control of area signals that takes into account traffic volume changes is "time-interval-dependent control". This type of control strategy assumes that the traffic volume on each roadway of a network is constant over each time interval; it then determines different optimal sets of control parameters for each interval. Such a control strategy requires a procedure for determining appropriate time intervals. According to this investigation, one possible approach for determining proper time intervals for traffic control purposes is the dynamic programming (DP) method. This paper introduces an artificial neural network architecture called adaptive resonance theory (ART), which has demonstrated successful results when applied to different pattern classification problems. ART1 is applied to dynamic traffic pattern classification to determine appropriate time intervals and the starting times for those intervals. The results of a case study clearly demonstrate the feasibility of ART1 for time interval determination using network-level traffic patterns. A comparative conceptual analysis of the DP method and the ART1 neural network is also included. The computational experience describing the advantages and disadvantages of ART1 for general traffic pattern recognition and classification problems is summarized, and the conclusion that the neural network approach is feasible and efficient for network-level traffic pattern classification is reached. The methodology introduced in this paper may be applied to other transportation problems.

• Record URL:
  http://onlinepubs.trb.org/Onlinepubs/trr/1993/1399/1399-003.pdf
• Record URL:
  https://scholar.google.com/scholar_lookup?title=DYNAMIC+TRAFFIC+PATTERN+CLASSIFICATION+USING+ARTIFICIAL+NEURAL+NETWORKS&author=J.+Hua&author=A.+Faghri&publication_year=1993
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  • Find a library where document is available. Order URL: http://worldcat.org/isbn/0309054710
• Supplemental Notes:
  • This paper appears in Transportation Research Record No. 1399, Artificial Intelligence. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved
• Authors:
  • Hua, Jiuyi
  • Faghri, Ardeshir
• Publication Date: 1993

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: p. 14-19
• Monograph Title: Artificial intelligence
• Serial:
  • Transportation Research Record
  • Issue Number: 1399
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Alternatives analysis; Classification; Dynamic programming; Neural networks; Pattern recognition systems; Traffic signal timing; Travel patterns
• Uncontrolled Terms: Adaptive resonance theory; Traffic patterns
• Subject Areas: Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 00637878
• Record Type: Publication
• ISBN: 0309054710
• Files: TRIS, TRB, ATRI
• Created Date: Oct 21 1993 12:00AM