Abstract：
Air pollution is a primary concern, causing around 7 million premature deaths annually, with traffic-related sources contributing 23 %-45 % of emissions. While several studies have surveyed vehicle emission models, they are either outdated or focus on specific data-driven models. This paper systematically reviews vehicle emission prediction models, comparing traditional approaches with data-driven emission models. The traditional emission models can be divided into average-speed, modal, and other models, noting their reliance on empirical assumptions and parameters that may not be universally applicable. In contrast, we delve into data-driven models utilizing dynamometer and on-road test data for time-series and spatial-temporal predictions. The application of these models is discussed across various scenarios, highlighting the progress and gap. We observed that traditional models, primarily estimating total traffic emissions in study regions, lack micro-level detail crucial for tailored decisions. The direct link between road emission model accuracy and input data quality poses challenges in disaggregating on-road vehicle emission inventories. Due to unique transportation instruments, traffic fleet components, and patterns, exploring the effects of emission-reduction policies in specific cities or regions is urgent. Vehicle characteristics, environmental conditions, traffic scenarios, and prediction scales are common effect factors, while instantaneous driving profiles prove effective in model calibration. In data-driven models, ANN outperforms in estimating emissions and performance of low-power diesel engines with errors not exceeding 5 %. However, no single data-driven method performed excellently in predicting all pollutants. Besides, integrated methods utilizing LSTM, GRU, and RNN outperform individual models. To enhance prediction accuracy considering the inherent connectivity of road networks and spatiotemporal variation patterns of vehicle emissions, GCN is an emerging approach for capturing spatial-temporal relationships based on remote sensing data. Moreover, limited data-driven studies have been performed to forecast particle matter emissions, the main contributors to urban pollution, calling for more attention for future research.
摘要：
空气污染是首要问题，每年导致约700万人过早死亡，与交通相关的来源占排放量的23%-45%。虽然一些研究已经调查了车辆排放模型，它们要么过时，要么专注于特定的数据驱动模型。本文系统地回顾了汽车排放预测模型,将传统方法与数据驱动的排放模型进行比较。传统的排放模型可以分为平均速度，模态,和其他型号，注意到他们依赖于经验假设和参数，这些假设和参数可能并不普遍适用。相比之下,我们深入研究了利用测功机和道路测试数据进行时间序列和时空预测的数据驱动模型。这些模型的应用在各种场景中进行了讨论，突出进步和差距。我们观察到传统模型，主要估算研究区域的总交通排放量，缺乏对量身定制的决策至关重要的微观细节。道路排放模型准确性与输入数据质量之间的直接联系在分解道路车辆排放清单方面提出了挑战。由于独特的运输工具，交通车队组件，和模式，探索减排政策在特定城市或地区的效果迫在眉睫。车辆特性,环境条件,交通场景，预测尺度是共同的影响因素，而瞬时驱动曲线在模型校准中被证明是有效的。在数据驱动模型中，ANN在估算低功率柴油发动机的排放和性能方面表现出色，误差不超过5％。然而,没有单一的数据驱动方法在预测所有污染物方面表现优异。此外,利用LSTM的集成方法，GRU,和RNN优于单个模型。考虑到路网的固有连通性和车辆排放的时空变化模式，提高预测精度。GCN是一种基于遥感数据捕获时空关系的新兴方法。此外，已经进行了有限的数据驱动研究来预测颗粒物的排放，城市污染的主要贡献者，呼吁对未来的研究给予更多关注。
