Environmental Impact Assessment of Urban Transport Scenarios Using AI-Based Modeling

Urban transportation systems significantly influence environmental quality in modern cities. This study aims to assess the environmental impacts of different urban transport development scenarios using artificial intelligence-based modeling techniques. The main objective is to compare scenarios such as status quo, public transport expansion, vehicle electrification, and private car restriction, in terms of their effectiveness in reducing air pollutants and energy consumption. A hybrid machine learning framework combining Random Forest and Artificial Neural Networks was employed to model the complex relationship between transportation parameters and key environmental indicators including CO₂, NOx, PM۲.۵, and fuel usage. The proposed model was trained and tested using urban traffic, emissions, land-use, and meteorological datasets. Scenario simulations were applied to a metropolitan case study, where the predictive model demonstrated high accuracy in estimating environmental outputs under different planning strategies. The results reveal that electrification of the public and private vehicle fleets leads to the most significant reduction in emissions, followed by increased investment in public transit infrastructure. On the other hand, the scenario with minimal intervention shows a continuous upward trend in pollutant levels. The findings of this research highlight the value of AI-based modeling in supporting urban planners and policymakers to design sustainable and environmentally sound transport strategies. The proposed framework can be adapted to other urban areas with appropriate local data inputs.

Keywords:

urban transportation , environmental impact , artificial intelligence , scenario modeling , Machine Learning , Air Pollution