Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts.

To further improve air quality in China, especially ozone and fine particulate matter (PM_2.5), additional reductions of volatile organic compounds (VOCs) are needed. Evaporative emissions from internal combustion engines (ICE) and hybrid vehicles are known to be an important source of VOCs in urban cities, which can be efficiently reduced through the introduction of more stringent regulatory standards. The most stringent global standards for evaporative emissions are the U.S. Tier 3 light-duty vehicle standards. This study compares the evaporative emissions of light-duty ICE vehicles designed to China 6 and U.S. Tier 3 regulations for both China and U.S. test conditions. Data are analyzed from manufacturer testing of full vehicle evaporative emissions conducted in the laboratory following U.S. Tier 3 and China 6 certification test methods utilizing Sealed Housing for Evaporative Determination (SHED) chambers equipped with Flame Ionization Detection (FID) analyzers. Vehicles designed to U.S. Tier 3 standards are observed to have 64% lower diurnal and hot soak evaporative emissions and 98% lower canister bleed emissions relative to vehicles designed to China 6 standards. U.S. Tier 3, U.S. Tier 2, and China 6 light-duty evaporative emission certification values submitted by manufacturers are also compared. The average of all current U.S. Tier 3 certifications is, on average, 52.4% lower than the average of all China 6 certifications. The results from the testing and data analysis of five vehicles, with comparison to the certification data, suggest that the introduction of standards equivalent in stringency to U.S. Tier 3 in China can significantly reduce evaporative emissions relative to China 6.

Carbon emissions are currently a hot topic in the international community. CO₂ reduction from heavy-duty commercial vehicles plays a significant role in slowing down the global greenhouse effect and promoting sustainable development. To control carbon emissions, many countries have tightened CO₂ emission regulations and policy requirements for heavy-duty commercial vehicles in recent years. Various CO₂ emission simulation models have been developed, such as the Greenhouse Gas Emissions Model (GEM) in the United States and the Vehicle Energy Consumption Calculation Tool (VECTO) in the European Union, to evaluate the real CO₂ emission levels of commercial vehicles and provide a scientific basis for formulating corresponding emission reduction policies and control measures. This paper systematically analyzes the CO₂ emission regulations and policy requirements for heavy-duty commercial vehicles in the United States, the European Union, China, and other developed countries. It also analyzes the GEM software in the United States, the VECTO software used in Europe, and the energy consumption simulation software for commercial vehicles in China. The influencing factors of CO₂ emission simulation are explored in detail. This study found that, although GEM and VECTO software are recognized for their high accuracy, their applications are still dependent on local policies. In other countries and regions, VECTO software has broader applicability. On the other hand, China’s commercial vehicle energy consumption simulation software and other reported studies have only been validated for specific vehicle types. The accuracy and generalizability of these models should be further promoted and verified.