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October 15, 2025International Journal For Multidisciplinary ResearchOpen Access

AI-Powered Multi-Objective Dynamic Route Optimization for Smart Urban Mobility and Logistics

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Authors

NKN KumareshNVN. Suthanthira VanithaJawaharlal Nehru Technological University, Kakinada

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Implication

This framework improves travel time and emissions in urban mobility, suggesting novel routing strategies.

Key Points

  • The proposed framework achieves up to 18% reduction in travel time while optimizing fuel consumption and CO₂ emissions.
  • Results showed a 20% improvement in on-time delivery rates compared to baseline shortest-path algorithms.
  • The solution incorporates real-time traffic data and logistics constraints, enabling continuous route adjustments.
  • Challenges include computational overhead and scalability, indicating a need for future integration with edge computing.

Cite This Study

Kumaresh et al. (2025) studied this question.

synapsesocial.com/papers/68efbd16d61273c8652d7f48https://doi.org/10.36948/ijfmr.2025.v07i05.57665
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Also Consider

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  1. 1Machine Learning-Based Route Optimization for Smart Urban Transportation Systems2025 · 1 citations
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  5. 5Integrated fleet sizing and routing optimization for shared electric vehicles under energy and charging constraints2026 · 1 citations