Fuel Consumption Estimation Using Spatio-Temporal Modeling and Traffic Flow Predictions: A Comparative Analysis

Effective traffic management and energy-saving techniques are increasingly needed as metropolitan areas grow and traffic volumes rise. This work estimates fuel consumption over three selected routes in an urban context using spatio-temporal modeling essentially building on a previously developed approach in traffic prediction and forecasting. A weighted adjacency matrix for a Graph Neural Network (GNN) is constructed in the original approach which combines graph theory frameworks with travel times obtained from average speeds and distances between traffic count stations. Next, the traffic flow estimate uncertainty is measured using Adaptive Conformal Prediction (ACP) to provide a more reliable forecast. This work predicts fuel consumption under different scenarios by utilizing Monte Carlo simulations based on the expected traffic flows providing insights into energy efficiency and the best routes to take. The study compares passenger vehicles' and heavy-duty trucks' mean fuel consumption under morning and evening traffic conditions. For passenger vehicles, the predicted fuel consumption showed a maximum error of 5.6% when compared to observed values, while for heavy-duty trucks, the maximum error was 9.5%. The model's capacity to effectively represent temporal fluctuations in traffic patterns and their effects on fuel economy is demonstrated by this comparative analysis. The study shows the practical applicability of this approach for energy-efficient route planning and urban traffic management by validating the estimated fuel consumption against real-world data. This gives transportation planners a comprehensive tool to help them make decisions that minimize environmental impacts and maximize fuel efficiency.