NeuralMOVES：一种基于逆向工程与代理学习的轻量级微观车辆排放估计模型
NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning
AI总结：
本文提出NeuralMOVES，通过逆向工程MOVES并结合神经网络构建轻量级代理模型，在保持高精度的同时将模型压缩至2.4 MB，支持实时微观车辆CO2排放估计。
摘要：
交通运输部门是温室气体排放的重要来源，因此需要精确的排放模型来指导减排策略。尽管行业标准的机动车排放模拟器（MOVES）经过了实地验证和认证，但它仍面临使用复杂、计算需求高以及不适合微观实时应用等挑战。为了解决这些局限性，我们提出了NeuralMOVES，这是一套面向车辆CO2排放的高性能、轻量级代理模型。NeuralMOVES基于逆向工程和神经网络开发，在涵盖超过两百万个场景（包含多样化的轨迹以及环境和车辆相关因素）的广泛测试中，相对于MOVES取得了6.013%的平均绝对百分比误差。NeuralMOVES仅占2.4 MB，将原始MOVES和逆向工程得到的MOVES大幅压缩为紧凑表示，同时保持了高精度。因此，NeuralMOVES在保持MOVES精度的同时显著提升了可访问性，简化了交通分析中的CO2评估，并能在不依赖复杂软件或大量计算资源的情况下实现跨多种场景的实时微观应用。此外，本文首次提供了一个专门针对交通场景的工业级软件逆向工程框架，其适用范围超越了MOVES本身。代理模型可在https://github.com/edgar-rs/neuralMOVES获取。
英文摘要：
The transportation sector significantly contributes to greenhouse gas emissions, necessitating accurate emission models to guide mitigation strategies. Despite its field validation and certification, the industry-standard Motor Vehicle Emission Simulator (MOVES) faces challenges related to complexity in usage, high computational demands, and its unsuitability for microscopic real-time applications. To address these limitations, we present NeuralMOVES, a comprehensive suite of high-performance, lightweight surrogate models for vehicle CO2 emissions. Developed based on reverse engineering and Neural Networks, NeuralMOVES achieves a remarkable 6.013% Mean Average Percentage Error relative to MOVES across extensive tests spanning over two million scenarios with diverse trajectories and the factors regarding environments and vehicles. NeuralMOVES is only 2.4 MB, largely condensing the original MOVES and the reverse engineered MOVES into a compact representation, while maintaining high accuracy. Therefore, NeuralMOVES significantly enhances accessibility while maintaining the accuracy of MOVES, simplifying CO2 evaluation for transportation analyses and enabling real-time, microscopic applications across diverse scenarios without reliance on complex software or extensive computational resources. Moreover, this paper provides, for the first time, a framework for reverse engineering industrial-grade software tailored specifically to transportation scenarios, going beyond MOVES. The surrogate models are available at https://github.com/edgar-rs/neuralMOVES.
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