IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0155697.html
   My bibliography  Save this article

Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder

Author

Listed:
  • Shahaboddin Shamshirband
  • Lejla Banjanovic-Mehmedovic
  • Ivan Bosankic
  • Suad Kasapovic
  • Ainuddin Wahid Bin Abdul Wahab

Abstract

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Suggested Citation

  • Shahaboddin Shamshirband & Lejla Banjanovic-Mehmedovic & Ivan Bosankic & Suad Kasapovic & Ainuddin Wahid Bin Abdul Wahab, 2016. "Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-12, May.
    • Handle: RePEc:plo:pone00:0155697
    DOI: 10.1371/journal.pone.0155697
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155697
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0155697&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0155697?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dia, Hussein, 2001. "An object-oriented neural network approach to short-term traffic forecasting," European Journal of Operational Research, Elsevier, vol. 131(2), pages 253-261, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    2. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    3. Yang Yue & Anthony Gar-On Yeh, 2008. "Spatiotemporal Traffic-Flow Dependency and Short-Term Traffic Forecasting," Environment and Planning B, , vol. 35(5), pages 762-771, October.
    4. Hongxia Ge & Siteng Li & Rongjun Cheng & Zhenlei Chen, 2022. "Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    5. Lu, Xijin & Ma, Changxi & Qiao, Yihuan, 2021. "Short-term demand forecasting for online car-hailing using ConvLSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    6. Chrobok, R. & Kaumann, O. & Wahle, J. & Schreckenberg, M., 2004. "Different methods of traffic forecast based on real data," European Journal of Operational Research, Elsevier, vol. 155(3), pages 558-568, June.
    7. Sohani Liyanage & Hussein Dia, 2020. "An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services," Sustainability, MDPI, vol. 12(10), pages 1-20, May.
    8. Zhao, Jiangjiang & Ma, Tieju, 2016. "Optimizing layouts of initial AFV refueling stations targeting different drivers, and experiments with agent-based simulations," European Journal of Operational Research, Elsevier, vol. 249(2), pages 706-716.
    9. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).
    10. Yikang Rui & Yannan Gong & Yan Zhao & Kaijie Luo & Wenqi Lu, 2023. "Predicting Traffic Flow Parameters for Sustainable Highway Management: An Attention-Based EMD–BiLSTM Approach," Sustainability, MDPI, vol. 16(1), pages 1-17, December.
    11. Alireza Ermagun & David Levinson, 2017. "Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending," Working Papers 000166, University of Minnesota: Nexus Research Group.
    12. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    13. Matheus S Quessada & Rickson S Pereira & William Revejes & Bruno Sartori & Euclydes N Gottsfritz & Douglas D Lieira & Marco AC da Silva & Geraldo P Rocha Filho & Rodolfo I Meneguette, 2020. "ITSMEI: An intelligent transport system for monitoring traffic and event information," International Journal of Distributed Sensor Networks, , vol. 16(10), pages 15501477209, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0155697. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.