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

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

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.

Kumaresh et al. (2025) studied this question.

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