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Smart city article details
| Title | Smart Vehicle Scenarios In Urban Transportation Through Blockchain And Advanced Machine Learning Techniques |
|---|---|
| ID_Doc | 51697 |
| Authors | Meshram K. |
| Year | 2024 |
| Published | Smart Vehicle Scenarios in Urban Transportation Through Blockchain and Advanced Machine Learning Techniques |
| DOI | http://dx.doi.org/10.1002/9781394228416.ch14 |
| Abstract | The contemporary urban transportation landscape is increasingly reliant on digital networks, rendering traditional traffic management systems insufficient to address the intricate challenges of security and efficiency. Existing models for secure traffic analysis, predominantly rooted in conventional techniques, exhibit notable limitations in the context of evolving smart vehicle ecosystems. These models often struggle with scalability, adaptability, and precision in threat detection, particularly under diverse and dynamic traffic conditions. To bridge this gap, this chapter introduces a novel approach, integrating deep Dyna-Q learning with the grey wolf optimizer, specifically tailored for securing smart vehicle scenarios in urban transportation systems. This innovative model leverages the strengths of deep Dyna-Q learning to dynamically adapt to changing traffic patterns, ensuring robust decision-making in real-time traffic management. The integration of the grey wolf optimizer enhances the model’s capability to efficiently navigate the complex optimization landscape of urban traffic scenarios. This combination not only aids in precise threat detection but also optimizes traffic flow, ensuring a balance between security and efficiency levels. Empirical evidence from deployments in both European and American road systems substantiates the superiority of this approach. The model demonstrates a 4.9% increase in energy efficiency and a 4.5% enhancement in the speed of operation compared to existing methods. Additionally, it exhibits a 3.9% improvement in throughput and a 3.4% increase in packet delivery performance. Notably, the approach reduces jitter by 4.3% and augments precision in attack analysis by 4.5%, showcasing its efficacy in maintaining network integrity and mitigating cyber threats. The implications of this work are profound, signaling a paradigm shift in urban transportation management. By addressing the limitations of existing traffic analysis models, this approach not only enhances traffic efficiency and security but also sets a new benchmark for future research and development in the field. It paves the way for more resilient, adaptable, and efficient urban transportation systems, aligning with the evolving needs of smart cities. © 2024 Scrivener Publishing LLC. |
| Author Keywords | Q learning; smart city; Traffic management; Traffic pattern |
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