• DocumentCode
    29786
  • Title

    Improved adaptive neuro fuzzy inference system car-following behaviour model based on the driver–vehicle delay

  • Author

    Khodayari, Alireza ; Ghaffari, Aboozar ; Kazemi, Riza ; Alimardani, Fatemeh ; Braunstingl, Reinhard

  • Author_Institution
    Mech. Eng. Dept., Islamic Azad Univ., Tehran, Iran
  • Volume
    8
  • Issue
    4
  • fYear
    2014
  • fDate
    Jun-14
  • Firstpage
    323
  • Lastpage
    332
  • Abstract
    In the past decades, different forms of car-following behaviour model have been intensively studied, proposed and implemented. These models are increasingly used by transportation experts to utilise for appropriate intelligent transportation systems. Unlike previous works, where the reaction delay is considered to be fixed, an improved adaptive neuro fuzzy inference system (ANFIS) model is proposed to simulate and predict the car-following behaviour based on the reaction delay of the driver-vehicle unit. An idea is proposed to calculate the reaction delay. In this model, the reaction delay is used as an input and other inputs-outputs of the model are chosen with respect to this parameter. Using the real-world data, the performance of the model is evaluated and compared with the responses of other existing ANFIS car-following models. The simulation results show that the proposed model has a very close compatibility with the real-world data and reflects the situation of the traffic flow in a more realistic way. Also, the comparison shows that the error of the proposed model is smaller than that in the other models.
  • Keywords
    automated highways; automobiles; fuzzy neural nets; inference mechanisms; traffic engineering computing; ANFIS model; adaptive neuro fuzzy inference system car following behaviour model; driver vehicle delay; driver vehicle unit; intelligent transportation systems; reaction delay; transportation experts;
  • fLanguage
    English
  • Journal_Title
    Intelligent Transport Systems, IET
  • Publisher
    iet
  • ISSN
    1751-956X
  • Type

    jour

  • DOI
    10.1049/iet-its.2012.0111
  • Filename
    6824008