IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p16322-d995563.html
   My bibliography  Save this article

Nature-Inspired Cloud–Crowd Computing for Intelligent Transportation System

Author

Listed:
  • Vandana Singh

    (Birla Institute of Technology Mesra, Ranchi 835215, India)

  • Sudip Kumar Sahana

    (Birla Institute of Technology Mesra, Ranchi 835215, India)

  • Vandana Bhattacharjee

    (Birla Institute of Technology Mesra, Ranchi 835215, India)

Abstract

Nowadays, it is crucial to have effective road traffic signal timing, especially in an ideal traffic light cycle. This problem can be resolved with modern technologies such as artificial intelligence, cloud and crowd computing. We hereby present a functional model named Cloud–Crowd Computing-based Intelligent Transportation System (CCCITS). This model aims to organize traffic by changing the phase of traffic lights in real-time based on road conditions and incidental crowdsourcing sentiment. Crowd computing is responsible for fine-tuning the system with feedback. In contrast, the cloud is responsible for the computation, which can use AI to secure efficient and effective paths for users. As a result of its installation, traffic management becomes more efficient, and traffic lights change dynamically depending on the traffic volume at the junction. The cloud medium collects updates about mishaps through the crowd computing system and incorporates updates to refine the model. It is observed that nature-inspired algorithms are very useful in solving complex transportation problems and can deal with NP-hard situations efficiently. To establish the feasibility of CCCITS, the SUMO simulation environment was used with nature-inspired algorithms (NIA), namely, Particle Swarm Optimization (PSO), Ant Colony Optimization and Genetic Algorithm (GA), and found satisfactory results.

Suggested Citation

  • Vandana Singh & Sudip Kumar Sahana & Vandana Bhattacharjee, 2022. "Nature-Inspired Cloud–Crowd Computing for Intelligent Transportation System," Sustainability, MDPI, vol. 14(23), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16322-:d:995563
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/16322/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/23/16322/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Le, Tung & Vu, Hai L. & Walton, Neil & Hoogendoorn, Serge P. & Kovács, Péter & Queija, Rudesindo N., 2017. "Utility optimization framework for a distributed traffic control of urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 539-558.
    2. Divya Jayakumar Nair & Flavien Gilles & Sai Chand & Neeraj Saxena & Vinayak Dixit, 2019. "Characterizing multicity urban traffic conditions using crowdsourced data," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    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. Vinayak Dixit & Divya Jayakumar Nair & Sai Chand & Michael W Levin, 2020. "A simple crowdsourced delay-based traffic signal control," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-12, April.
    2. Rey, David & Levin, Michael W., 2019. "Blue phase: Optimal network traffic control for legacy and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 105-129.
    3. Joshua Ezekiel Rito & Neil Stephen Lopez & Jose Bienvenido Manuel Biona, 2021. "Modeling Traffic Flow, Energy Use, and Emissions Using Google Maps and Google Street View: The Case of EDSA, Philippines," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    4. Sai Chand & Emily Moylan & S. Travis Waller & Vinayak Dixit, 2020. "Analysis of Vehicle Breakdown Frequency: A Case Study of New South Wales, Australia," Sustainability, MDPI, vol. 12(19), pages 1-14, October.
    5. Li, Li & Jabari, Saif Eddin, 2019. "Position weighted backpressure intersection control for urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 435-461.
    6. Sai Chand & Zhuolin Li & Abdulmajeed Alsultan & Vinayak V. Dixit, 2022. "Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    7. S. Travis Waller & Sai Chand & Aleksa Zlojutro & Divya Nair & Chence Niu & Jason Wang & Xiang Zhang & Vinayak V. Dixit, 2021. "Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    8. Maurizio Faccio & Serena Finco & Ilenia Zennaro, 2021. "Sustainable People Home-Work Logistics: An Integrated Model of Circular Economy in the Chiampo Valley," Sustainability, MDPI, vol. 13(21), pages 1-13, October.

    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:gam:jsusta:v:14:y:2022:i:23:p:16322-:d:995563. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.