Modeling the synergistic impact of intelligent transport systems and citizen engagement on urban outcomes: A simulation-based analysis using artificial neural networks

Abstract

This study shows the synergistic effects of Intelligent Transportation Systems (ITS) and citizen engagement on key urban outcomes—namely, traffic congestion, air quality, and economic activity—within the context of smart city development. While conceptual links between smart transportation and urban performance are acknowledged, quantitative analyses of the interplay between technological infrastructure and social factors remain scarce. We developed a simulation-based approach using an Artificial Neural Network (ANN) model trained on a synthetically generated dataset comprising over 50 scenarios that represent varying levels of ITS implementation and citizen engagement (0–100%). The ANN architecture consisted of two input neurons, one hidden layer with five neurons, and three output neurons, which was validated through k-fold cross-validation. Results demonstrate positive synergies: both factors contribute to improvements, but citizen engagement has a disproportionately strong influence on air quality and economic activity. Sensitivity analysis confirms the robustness, while a comparison with linear regression validates the ANN's superior performance in capturing nonlinear relationships. We propose specific target thresholds for planners and discuss implications for sustainable urban mobility, particularly in developing contexts where rapid urbanization demands efficient resource allocation. The study concludes that smart transportation policies must couple technological deployment with robust community participation strategies to maximize urban outcomes. The results show significant synergistic effects, as the ANN model achieved high predictive accuracy (R²: 0.92, 0.89, 0.85) and suggest a potential inflection point near 60% citizen engagement, beyond which the simulated economic and environmental benefits accelerate more markedly.