Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary
  Results
  Given an on-board diagnostics (OBD) dataset and a physics-based emissions
prediction model, this paper aims to develop an accurate and
computational-efficient AI (Artificial Intelligence) method that predicts
vehicle emissions. The problem is of societal importance because vehicular
emissions lead to climate change and impact human health. This problem is
challenging because the OBD data does not contain enough parameters needed by
high-order physics models. Conversely, related work has shown that low-order
physics models have poor predictive accuracy when using available OBD data.
This paper uses a divergent window co-occurrence pattern detection method to
develop a spatiotemporal variability-aware AI model for predicting emission
values from the OBD datasets. We conducted a case study using real-world OBD
data from a local public transportation agency. Results show that the proposed
AI method has approximately 65% improved predictive accuracy than a non-AI
low-order physics model and is approximately 35% more accurate than a baseline
model.
