Optimizing Vehicle Emission Estimation of On-Road Vehicles Using Deep Learning Frameworks

Egemen Belge, Rıdvan Keskin, Senol Hakan Kutoglu

Vehicle, industrial, and urban emissions remain major contributors to air quality degradation, affecting public health and the level of environmental cleanliness. Cost-effective specific pollutant estimation models, i.e., for carbon monoxide CO, carbon dioxide CO2, and ammonia NH3, are essential to tackle the practical challenge of high-resolution monitoring for reducing vehicle emissions in traffic. Existing model design methods, however, may be insufficient, particularly for peak time estimations, since such models are typically designed using gridding-based vehicle-specific power polynomial and non-optimized artificial neural networks. In this paper, we propose vehicle emission models of pollutants based on a Bayesian Monte Carlo (MC) Dropout-based robust data-driven gated recurrent unit (BMC-GRU) method to enhance estimation robustness and mitigate the overfitting problem in the deep learning network. Bayesian optimization determines the optimal architecture by efficiently and probabilistically searching the hyperparameters of the network, while MC-Dropout quantifies epistemic uncertainty through multiple stochastic forward passes during testing. Therefore, the proposed method improves the models’ calibrations and robustness to distribution shifts. For benchmarking, least squares-based first- and fourth-order polynomials, conventional long-short term memory (LSTM), and bidirectional LSTM (BiLSTM)-based estimation models are designed. The proposed method outperforms the mentioned state-of-the-art methods with strong robust estimation performance. The experimental results on multiple real-world vehicle datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches. The method presents a promising solution for uncertainty-aware vehicle emission modeling that is applicable to transportation systems.