To investigate the emission status and predict the future trends of heavy-duty diesel passenger buses in Hainan Province, the technical level distribution, activity characteristics, and operating conditions of heavy-duty diesel passenger buses were statistically analyzed. The emissions of CO, CO2, NOX, and PM of the province’s heavy-duty diesel passenger buses in 2017 were calculated by the COPERT model. The Portable Emission Measurement System was applied to the calibration of emission factors calculated by the model to improve the accuracy of emission predictions. The prediction of emission trends sets three different scenarios: baseline scenarios (BAS), emission reduction standard scenario (ERS), and emission reduction standard and replacement by electric vehicle scenario (ERS and REV). The gray model was used to predict the number of heavy-duty diesel passenger buses in the three scenarios and combined with the calibrated emission factors to predict the emission trends under different scenarios. Results show that the ERS will reduce CO, CO2, NOX, and PM emissions by approximately 23%, 12%, 23%, and 46% respectively, in 2025 compared with BAS. ERS and REV will reduce CO, CO2, NOX, and PM emissions by approximately 38%, 33%, 38%, and 50% for the three emissions, compared with the BAS.

Predicting the future trajectories of multiple pedestrians in certain scenes has become a key task for ensuring that autonomous vehicles, socially interactive robots and other autonomous mobile platforms can navigate safely. The social interactions between people and the multimodal nature of pedestrian movement make pedestrian trajectory prediction a challenging task. In this paper, the problem is solved using a generative adversarial network (GAN) and a graph attention network (GAT) based on the spatiotemporal interaction information about pedestrians. Our method, STI-GAN, is based on an end-to-end GAN model that simulates the pedestrian distribution to capture the uncertainty of the predicted paths and generate more reasonable future trajectories. The complex interactions between people are modeled by a GAT, and spatiotemporal interaction information is used to improve the performance of trajectory prediction. We verify the robustness and improvement of our framework by evaluating its results on various datasets and comparing them with the results of several existing baselines. Compared with the existing pedestrian trajectory prediction methods, our method reduces the average displacement error (ADE) and final displacement error (FDE) by 21.9% and 23.8% respectively.

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In this study, reconstruction of flame chemiluminescence distribution with complicated structure was numerically investigated and experimentally validated. The ill-conditioned equations were constructed using the quasi-Monte Carlo method and solved by an algebraic reconstruction technique, where the convergence criterion was the Euclidean norm of the dimensionless displacement vector. Results of a phantom study revealed that the number of camera angles is the main restriction on reconstruction accuracy, and increase of the flame's nonhomogeneity improves the sensibility of reconstruction accuracy to image resolution. Results of experimental reconstruction showed the CH* distribution in a Meker burner flame. This work provides a better understanding in how to establish experimental systems for complicated flame reconstruction.

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