فهرست نشریات علمی دسترسی آزاد ایران

This study proposes a multi-objective framework for optimizing urban shortest paths by simultaneously considering traffic congestion and air quality. The objective is to minimize a weighted combination of travel time and pollutant exposure, with binary decision variables representing route selection. Real-world air quality data (SO₂, NO₂, CO) were collected from IoT sensors on 500 vehicles and 50 fixed stations across Tehran, integrated with traffic data via spatio-temporal synchronization using inverse distance weighting and 500m grid mapping. The transportation network was extracted from OpenStreetMap (14,287 nodes, 32,456 edges). The Incremental C-means Adaptive Weights (ICAW) algorithm was applied for real-time clustering of congestion and pollution hotspots, with optimal clusters (c=5) determined via the elbow method. Route optimization was performed using a hybrid Genetic Algorithm (population=200, tournament selection) with adaptive edge weights updated dynamically. The framework was evaluated across three pollution scenarios with 100 routing instances per scenario. Results demonstrate statistically significant improvements (p<0.01): pollutant exposure reduced by 18.7% (S1: good downtown air), 23.4% (S2: moderate pollution), and 31.6% (S3: severe pollution), with travel time reductions of 12.4%, 10.8%, and 8.9%, while distance increased by only 3.2-6.9%. Comparative analysis shows ICAW-GA achieves 28% lower computational time than NSGA-II and 42% faster convergence than PSO. The framework successfully balances transportation efficiency and environmental sustainability, providing real-time adaptive routing suitable for smart city deployment.