NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning

This significant contribution makes it a critical sector for climate change mitigation, as reducing emissions from transportation is essential for achieving global climate goals. The sector's transformation through electrification, automation, and intelligent infrastructure offers promising avenues for substantial emissions reductions (Sciarretta et al., 2020; International Energy Agency, 2023; McKinsey Center for Future Mobility, 2023). However, the success of these innovations is critically dependent on the availability of suitable and accurate emission estimation models to guide the design and deployment of new technologies. Motor Vehicle Emission Simulation (MOVES) (U.S. Environmental Protection Agency, 2022), one of the most well-established emission estimation models, serves as the official and state-of-the-art emission estimation model in the U.S., provided, enforced, and maintained by the U.S. Environmental Protection Agency (EPA). Despite its technical certification, MOVES' processing and software is tailored for two specific governmental uses: State Implementation Plans and Conformity Analyses U.S. Environmental Protection Agency (2021), which are for states to achieve and maintain air quality standards; and its use beyond trained practitioners and these specific analyses poses two main limitations. First, a steep learning curve, computational demands, and complex inputs make it difficult for researchers and practitioners to use. In particular, MOVES has rigid input requirements, including a combination of toggle-based settings within its GUI and structured input files in specific formats. Second, MOVES is tailored for macroscopic analysis and is unsuitable for microscopic applications, such as control and optimization, which commonly require second-by-second emission calculations for individual actions and vehicles.