Investigation of Road Transport-Based Greenhouse Gas Prediction Models and the Use of Intelligent Transportation Systems for Emission Reduction

Based on the modeling results, the carbon equivalent values for each pollutant were calculated. In cases where transportation-related emissions exceed regulatory thresholds, an ITS-based traffic management strategy was proposed to redirect vehicles to alternative routes. Transportation sector is among the critical domains where effective public interventions and adaptive strategies are essential to mitigate CO₂ emissions. In this context, artificial intelligence (AI)-based models are increasingly utilized to support emission reduction efforts. This study focuses on developing a predictive model for road transport-related greenhouse gas emissions in Dilovası, a district of Kocaeli Province known for its high levels of air pollution. Two AI-based approaches, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN), were employed to model pollutant emissions. At the same time, the potential contribution of Intelligent Transportation Systems (ITS) to emission mitigation and climate change adaptation was also examined. Initially, NO _x and CO emissions from light and heavy vehicles were modeled using ANFIS and ANN, and the results were compared with outputs from the COPERT 4 (Calculations of Emissions from Road Transport) software. The high adaptability of the ANFIS model allowed for a more accurate representation of the influence of environmental variables and vehicle counts on emission levels.