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Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey

Author

Listed:
  • Akbar Ali

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan)

  • Nasir Ayub

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan)

  • Muhammad Shiraz

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan
    Department of Computer Science, Allama Iqbal Open University, Islamabad 44000, Pakistan)

  • Niamat Ullah

    (Department of Computer science, University of Buner, Bunir 19281, Pakistan)

  • Abdullah Gani

    (Faculty of Computing and Informatics, University Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Malaysia)

  • Muhammad Ahsan Qureshi

    (Faculty of Computing and Information Technology, University of Jeddah, Khulais 21959, Saudi Arabia)

Abstract

The population is increasing rapidly, due to which the number of vehicles has increased, but the transportation system has not yet developed as development occurred in technologies. Currently, the lowest capacity and old infrastructure of roads do not support the amount of vehicles flow which cause traffic congestion. The purpose of this survey is to present the literature and propose such a realistic traffic efficiency model to collect vehicular traffic data without roadside sensor deployment and manage traffic dynamically. Today’s urban traffic congestion is one of the core problems to be solved by such a traffic management scheme. Due to traffic congestion, static control systems may stop emergency vehicles during congestion. In daily routine, there are two-time slots in which the traffic is at peak level, which causes traffic congestion to occur in an urban transportation environment. Traffic congestion mostly occurs in peak hours from 8 a.m. to 10 a.m. when people go to offices and students go to educational institutes and when they come back home from 4 p.m. to 8 p.m. The main purpose of this survey is to provide a taxonomy of different traffic management schemes for avoiding traffic congestion. The available literature categorized and classified traffic congestion in urban areas by devising a taxonomy based on the model type, sensor technology, data gathering techniques, selected road infrastructure, traffic flow model, and result verification approaches. Consider the existing urban traffic management schemes to avoid congestion and to provide an alternate path, and lay the foundation for further research based on the IoT using a Mobile crowd sensing-based traffic congestion control model. Mobile crowdsensing has attracted increasing attention in traffic prediction. In mobile crowdsensing, the vehicular traffic data are collected at a very low cost without any special sensor network infrastructure deployment. Mobile crowdsensing is very popular because it can transmit information faster, collect vehicle traffic data at a very low cost by using motorists’ smartphone or GPS vehicular embedded sensor, and it is easy to install, requires no special network deployment, has less maintenance, is compact, and is cheaper compared to other network options.

Suggested Citation

  • Akbar Ali & Nasir Ayub & Muhammad Shiraz & Niamat Ullah & Abdullah Gani & Muhammad Ahsan Qureshi, 2021. "Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey," Sustainability, MDPI, vol. 13(23), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13068-:d:688015
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    References listed on IDEAS

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    1. Christopher R. Bennett & Hernán De Solminihac & Alondra Chamorro, 2006. "Data Collection Technologies for Road Management," World Bank Publications - Reports 11776, The World Bank Group.
    2. repec:ipt:iptwpa:jrc47967 is not listed on IDEAS
    3. Zhang, Shaojun & Wu, Ye & Liu, Huan & Huang, Ruikun & Yang, Liuhanzi & Li, Zhenhua & Fu, Lixin & Hao, Jiming, 2014. "Real-world fuel consumption and CO2 emissions of urban public buses in Beijing," Applied Energy, Elsevier, vol. 113(C), pages 1645-1655.
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    Cited by:

    1. Jiayi Li & Zhaocheng He & Jiaming Zhong, 2022. "The Multi-Type Demands Oriented Framework for Flex-Route Transit Design," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
    2. Francisco Melo Pereira & Rute C. Sofia, 2022. "An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories," Future Internet, MDPI, vol. 15(1), pages 1-19, December.

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