A Naive Bayesian Classifier based Algorithm for Freeway Traffic Incident Detection
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Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University,Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University,Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University,Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University,Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University

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U492.8+5; TP391.9

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    Abstract:

    This paper presents a naive Bayesian classifier based algorithm for freeway non recurrent traffic incident detection to enhance the accuracy and learning ability of intelligent traffic incident detection algorithm. The traffic wave theory is employed to establish a conceptual characteristic model of traffic incident, continuous characteristic variables are transferred into discrete characteristic variables via sub discretization, and the naive bayesian based traffic incident classifier is designed by regarding traffic incident detection as “0 1” classification problems. An experiment is carried on a section of a typical freeway, and the performance of the presented model and algorithm is validated via VISSIM simulation. Extensive simulation results show that the algorithm in freeway traffic incident detection system is of high accuracy and strong robustness even if the traffic volumes increase.

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ZHANG Lun, YANG Wenchen, LIU Tuo, SHI Yicheng. A Naive Bayesian Classifier based Algorithm for Freeway Traffic Incident Detection[J].同济大学学报(自然科学版),2014,42(4):0558~0563

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History
  • Received:May 21,2013
  • Revised:June 28,2013
  • Adopted:September 02,2013
  • Online: April 17,2014
  • Published:
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