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October 23, 2025Open Access

An Integrated AI-Driven Framework for Smart Urban Traffic Management: Towards Sustainable, Efficient, and Safe Cities

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Authors

MGMehdi Tamaddon GoharMSMahdi Shahrjerdi

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Overview

Analysis demonstrates reduced traffic congestion and emissions in cities, suggesting AI enhances urban efficiency.

Key Points

  • Average travel time reduced by 34%, showing notable impact on urban mobility.
  • Evaluation across real-world datasets from Tehran and Barcelona highlights the framework's effectiveness.
  • Integrated AI-driven approach combines deep learning and reinforcement learning for optimal traffic control.
  • Modular design supports scalability, indicating potential for broader application in smart city initiatives.

Cite This Study

Gohar et al. (2025) studied this question.

synapsesocial.com/papers/68f9bad6d7353cfcfc68f2cahttps://doi.org/10.20944/preprints202510.1585.v1
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Also Consider

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  1. 1ENHANCING URBAN MOBILITY THROUGH MACHINE LEARNING-DRIVEN TRAFFIC MANAGEMENT IN SMART CITIES2024
  2. 2Hybrid Intelligent Control Framework for Sustainable Smart Cities2025
  3. 3Advancing Urban Planning with Deep Learning: Intelligent Traffic Flow Prediction and Optimization for Smart Cities2025
  4. 4ANALYSIS OF AI-ENABLED ADAPTIVE TRAFFIC CONTROL SYSTEMS FOR URBAN MOBILITY OPTIMIZATION THROUGH INTELLIGENT ROAD NETWORK MANAGEMENT2025 · 8 citations
  5. 5Real-time traffic signal optimization for urban mobility: a reinforcement learning-enhanced framework with application to Kuwait City2025