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Traffic-related pollution significantly contributes to environmental degradation. The escalating demand for vehicles, coupled with consumer preferences for larger utility vehicles, poses a challenge to achieving targeted carbon emission reductions in common fleet vehicles. To address the inefficiencies in existing route planning methods, this paper introduces a novel eco-routing approach known as Visual Eco-Routing (VER). VER is designed to understand the non-linear relationships between road scenes and emissions data, providing a comprehensive insight into the real-time dynamics of roads and their influence on vehicle performance characteristics. On-road experimental cycles are conducted to gather data, creating a new dataset called the Vehicle Activity Dataset (VAD). To assess viability of the VER approach, a model named VER-XGB based on eXtreme Gradient Boosting (XGBoost) is proposed. Performance comparisons are made by individual training and benchmarking three selected models, both without VER association and separately with VER association. The comparison reveals significantly lower prediction errors in models with VER, with VER-XGB exhibiting enhanced reliability, yielding MAPE of 4.83% with VAD. Additionally, an aggregate factor termed the emission factor is introduced to explore the correlation between emission gases and distinct groups of visual features defined in the study. The analysis indicates a high correlation between infrastructure features such as traffic signals and stop signs on the road and vehicle emissions. Concluding the study, a qualitative examination is undertaken to evaluate the real-world applicability of the model by predicting an eco-route for a given origin and destination pair. The MAPE for this route for predictions from VER-XGB is found to be 6.21%, affirming the practical utility of the proposed VER-XGB model in real-world scenarios. |
