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Given an on-board diagnostics (OBD) dataset and a physics-based emissionsprediction model, this paper aims to develop an accurate andcomputational-efficient AI (Artificial Intelligence) method that predictsvehicle emissions. The problem is of societal importance because vehicularemissions lead to climate change and impact human health. This problem ischallenging because the OBD data does not contain enough parameters needed byhigh-order physics models. Conversely, related work has shown that low-orderphysics models have poor predictive accuracy when using available OBD data.This paper uses a divergent window co-occurrence pattern detection method todevelop a spatiotemporal variability-aware AI model for predicting emissionvalues from the OBD datasets. We conducted a case study using real-world OBDdata from a local public transportation agency. Results show that the proposedAI method has approximately 65% improved predictive accuracy than a non-AIlow-order physics model and is approximately 35% more accurate than a baselinemodel.

Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary Results