IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i21p11772-d664236.html
   My bibliography  Save this article

Formal Modeling of Responsive Traffic Signaling System Using Graph Theory and VDM-SL

Author

Listed:
  • Afifa Nawaz

    (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan)

  • Nazir Ahmad Zafar

    (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan)

  • Eman H. Alkhammash

    (Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

Internet of things (IoT) is playing a major role in smart cities to make a digital environment. Traffic congestion is a serious road issue because of an increasing number of vehicles in urban areas. Some crucial traffic problems include accidents and traffic jams that cause waste of fuel, health diseases, and a waste of time. Present traffic signaling systems are not efficient in resolving congestion problems because of the lack of traffic signals. Nowadays, traffic signaling systems are modeled with fixed time intervals in which no proper mechanism for emergency vehicles is available. Such traffic mechanisms failed to deal with traffic problems effectively. The major objective is to establish a robust traffic monitoring and signaling system that improves signal efficiency by providing a responsive scheme; appropriate routes; a mechanism for emergency vehicles and pedestrians in real-time using Vienna Development Method Specification Language (VDM-SL) formal method and graph theory. A formal model is constructed by considering objects, such as wireless sensors and cameras that are used for collecting information. Graph theory is used to represent the network and find appropriate routes. Unified Modeling Language is used to design the system requirements. The graph-based framework is converted into a formal model by using VDM-SL. The model has been validated and analyzed using many facilities available in the VDM-SL toolbox.

Suggested Citation

  • Afifa Nawaz & Nazir Ahmad Zafar & Eman H. Alkhammash, 2021. "Formal Modeling of Responsive Traffic Signaling System Using Graph Theory and VDM-SL," Sustainability, MDPI, vol. 13(21), pages 1-29, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11772-:d:664236
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/21/11772/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/21/11772/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Farzad Tahriri & Ali Azadeh, 2018. "Lean Traffic Control (LTC) for Emergency Vehicles Applied in Developing Countries: Tehran Transport Planning," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 4(2), pages 57-63.
    2. Bahrami, Sina & Roorda, Matthew J., 2020. "Optimal traffic management policies for mixed human and automated traffic flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 130-143.
    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. Sun, Mingmei, 2023. "A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    2. Cong-Jian Liu & Fang-Kai Wang & Zhuang-Zhuang Wang & Tao Wang & Ze-Hao Jiang, 2022. "Autonomous Vehicles for Enhancing Expressway Capacity: A Dynamic Perspective," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    3. Bowen Gong & Fanting Wang & Ciyun Lin & Dayong Wu, 2022. "Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model," Sustainability, MDPI, vol. 14(10), pages 1-18, May.

    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:13:y:2021:i:21:p:11772-:d:664236. 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.