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Exploring the Synergy of Blockchain, IoT, and Edge Computing in Smart Traffic Management across Urban Landscapes

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Abstract

In the ever-evolving landscape of smart city transportation, effective traffic management remains a critical challenge. To address this, we propose a novel Smart Traffic Management System (STMS) Architecture algorithm that combines cutting-edge technologies, including Blockchain, IoT, edge computing, and reinforcement learning. STMS aims to optimize traffic flow, minimize congestion, and enhance transportation efficiency while ensuring data integrity, security, and decentralized decision-making. STMS integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with Blockchain technology to enable secure and transparent data sharing among traffic-related entities. Smart contracts are deployed on the Blockchain to automate the execution of predefined traffic rules, ensuring compliance and accountability. Integrating IoT sensors on vehicles, roadways, and traffic signals provides real-time traffic data, while edge nodes perform local traffic analysis and contribute to optimization. The algorithm’s decentralized decision-making empowers edge devices, traffic signals, and vehicles to interact autonomously, making informed decisions based on local data and predefined rules stored on the Blockchain. TD3 optimizes traffic signal timings, route suggestions, and traffic flow control, ensuring smooth transportation operations. STMSs holistic approach addresses traffic management challenges in smart cities by combining advanced technologies. By leveraging Blockchain’s immutability, IoT’s real-time insights, edge computing’s local intelligence, and TD3’s reinforcement learning capabilities, STMS presents a robust solution for achieving efficient and secure transportation systems. This research underscores the potential for innovative algorithms to revolutionize urban mobility, ushering in a new era of smart and sustainable transportation networks.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

Fund project: Supported by Projects in Humanities and Social Sciences of Training Programme for 1,000 Core Young and Middle-aged Teachers of Guangxi University: Research on the Institutional Development Mechanism of Non-governmental Old-age Care Institutions under the Healthy Guangxi Strategy (2021QGRW021).

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Authors and Affiliations

  1. School of Humanities and Social Science, Guangxi Medical University, Guangxi Nanning, 530021, China

    Yu Chen, Yilun Qiu, Shuling Long, Lingfeng Zhao & Zhong Tang

  2. Department of Computer Engineering, University of California, Irvine, Irvine, CA, 92697, USA

    Zhenyu Tang

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  1. Yu Chen
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  2. Yilun Qiu
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  3. Zhenyu Tang
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  4. Shuling Long
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Contributions

Yu Chen: Conceptualization, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing.

Yilun Qiu: Validation, Resources, Data Curation, Resources, Writing—review & editing.

Zhenyu Tang: Validation, Resources, Data Curation, Resources, Writing.

Shuling Long: Validation, Resources, Data Curation, Resources, Writing.

Lingfeng Zhao: Validation, Resources, Data Curation, Resources, Writing.

Zhong Tang: Resources, Data Curation, Resources, Writing—review & editing.

Corresponding author

Correspondence to Zhong Tang.

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Chen, Y., Qiu, Y., Tang, Z. et al. Exploring the Synergy of Blockchain, IoT, and Edge Computing in Smart Traffic Management across Urban Landscapes. J Grid Computing 22, 45 (2024). https://doi.org/10.1007/s10723-024-09762-6

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