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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

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Smart and Sustainable Transportation Based on Artificial Intelligence in Big Cities : A case study of Isfahan, Iran2025
  2. 2ENHANCING URBAN MOBILITY THROUGH MACHINE LEARNING-DRIVEN TRAFFIC MANAGEMENT IN SMART CITIES2024
  3. 3AI-Enhanced Traffic Signal Optimization Using Microscopic Simulation Models for Congestion and Emissions Reduction in Mid-Sized U.S. Urban Corridors2026 · 1 citations
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  5. 5Smart Traffic Control System Using AI2024 · 2 citations