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Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility

Author

Listed:
  • Fatema Elwy

    (Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Raafat Aburukba

    (Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • A. R. Al-Ali

    (Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Ahmad Al Nabulsi

    (Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Alaa Tarek

    (Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Ameen Ayub

    (Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Mariam Elsayeh

    (Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

Abstract

Shared mobility is one of the smart city applications in which traditional individually owned vehicles are transformed into shared and distributed ownership. Ensuring the safety of both drivers and riders is a fundamental requirement in shared mobility. This work aims to design and implement an adequate framework for shared mobility within the context of a smart city. The characteristics of shared mobility are identified, leading to the proposal of an effective solution for real-time data collection, tracking, and automated decisions focusing on safety. Driver and rider safety is considered by identifying dangerous driving behaviors and the prompt response to accidents. Furthermore, a trip log is recorded to identify the reasons behind the accident. A prototype implementation is presented to validate the proposed framework for a delivery service using motorbikes. The results demonstrate the scalability of the proposed design and the integration of the overall system to enhance the rider’s safety using machine learning techniques. The machine learning approach identifies dangerous driving behaviors with an accuracy of 91.59% using the decision tree approach when compared against the support vector machine and K-nearest neighbor approaches.

Suggested Citation

  • Fatema Elwy & Raafat Aburukba & A. R. Al-Ali & Ahmad Al Nabulsi & Alaa Tarek & Ameen Ayub & Mariam Elsayeh, 2023. "Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility," Future Internet, MDPI, vol. 15(10), pages 1-18, October.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:333-:d:1256627
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    References listed on IDEAS

    as
    1. Raafat Aburukba & A. R. Al-Ali & Ahmed H. Riaz & Ahmad Al Nabulsi & Danayal Khan & Shavaiz Khan & Moustafa Amer, 2021. "Fog Computing Approach for Shared Mobility in Smart Cities," Energies, MDPI, vol. 14(23), pages 1-19, December.
    2. Shaheen, Susan PhD & Martin, Elliot Phd & Bansal, Apaar, 2018. "Peer-To-Peer (P2P) Carsharing: Understanding Early Markets, Social Dynamics, and Behavioral Impacts," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7s8207tb, Institute of Transportation Studies, UC Berkeley.
    3. Guo, Yuhan & Zhang, Yu & Boulaksil, Youssef, 2021. "Real-time ride-sharing framework with dynamic timeframe and anticipation-based migration," European Journal of Operational Research, Elsevier, vol. 288(3), pages 810-828.
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