FAFGAN: A Fuzzy-Aware Fuel Generative Adversarial Network Approach to Road-level Fuel Estimation Considering Drive-style

• Propose a novel generative framework FAFGAN for fuel consumption estimation. • FAFGAN estimates road-level fuel consumption using only macroscopic traffic observations. • A differentiable fuzzy logic module is developed to infer driving style for fuel estimation. • FAFGAN outperforms classic methods with more than 70% reduction in training time. For estimating the road-level fuel consumption, the existing models rely on data collected from high-precision detectors and rarely incorporate behavioral factors such as the impact of driving styles, which limits the accuracy and the scalability of the estimation models for real-world applications. To this end, this paper proposes a novel generative framework that estimates fuel consumption at the road level with low cost and high behavioral consistency, using only macroscopic traffic observations. The model consists of three key modules: a speed generation module conditioned on macroscopic inputs to reconstruct vehicle trajectory data, a differentiable fuzzy logic module to capture the uncertainty and heterogeneity of driving styles, and a style-aware fusion module that embeds behavioral semantics into the fuel estimation process. We tested the proposed model against the baseline models on the NGSIM and HighD datasets. The results show that FAFGAN achieves the best road-level estimation accuracy with RMS E avg , outperforming advanced generative methods including LCM, HRF, and SiT by 3% to 14%, while achieving comparable accuracy to GaGAN with 72% less training time.