This study examines the public acceptance of platoon control for connected and automated vehicles (CAVs) and analyzes it from a sustainability perspective. A questionnaire survey was conducted targeting a diverse social group to collect data on public attitudes, acceptability, and factors related to the environment, social responsibility, personal economy, and behavior. Factor analysis was then performed to reduce these data into three factors: “attitudes and acceptance”, “environment and social responsibility”, and “personal economy and behavior”. Further, multiple regression analysis was employed to investigate the relationship between these three factors and the willingness to accept platooning control, as well as the willingness to actively adopt it. The findings indicate that the ”attitude and acceptance” factor significantly influences the public’s acceptance of platoon control. Moreover, the ”environment and social responsibility” and ”personal economy and behavior” factors also have a certain influence on decision-making. This study not only contributes to understanding public attitudes towards CAVs’ platoon control but also explores how to promote the development of sustainable intelligent transportation systems. By gaining a better understanding of public attitudes, policymakers and relevant stakeholders can take measures to enhance the acceptance of CAVs, thereby driving the sustainable development of transportation systems.

Pulse and glide (PnG) has limited application in urban traffic flows, particularly in emerging mixed traffic flows comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), as well as at signalized intersections. In light of this, green wave coordination is applied to the urban network of multiple signalized intersections. Under perception asymmetries, HDVs lack environmental perception capabilities, while CAVs are equipped with perception sensors of varying performance. CAVs could activate the PnG mode and set its average speed based on signal phase and safety status, enabling assessment of fuel savings and safety. The findings reveal that (i) excluding idling fuel consumption, when the traffic volume is low and market penetration rate (MPR) of CAVs exceeds 70%, CAVs could significantly reduce regional average fuel consumption by up to 8.8%. (ii) Compared to HDVs, CAVs could achieve a fuel saving rate (FSR) ranging from 7.1% to 50%. In low-traffic-volume conditions, CAVs with greater detection ranges could swiftly activate the PnG mode to achieve fuel savings, while in higher-traffic-volume conditions, more precise sensing aids effectiveness. (iii) the PnG mode could ensure safety for CAVs and HDVs, with CAVs equipped with highly precise sensing exhibiting particularly robust safety performance.

Pulse and Glide (PnG), as a fuel-saving technique, has primarily been applied to manual transmission vehicles. So, its effectiveness when integrated with a novel vehicle type like connected and automated vehicles (CAVs) remains largely unexplored. On the other hand, CAVs have evidently received less attention regarding energy conservation, and their prominent perception capabilities clearly exhibit individual variations. In light of this, this study investigates the impacts of PnG combined with CAVs on energy conservation and safety within the emerging mixed traffic flow composed of CAVs with varying sensing accuracies. The results indicate the following: (i) compared to the traditional driving modes, the PnG can achieve a maximum fuel-saving rate of 39.53% at Fuel Consumption with Idle (FCI), reducing conflicts by approximately 30% on average; (ii) CAVs, equipped with sensors boasting a greater detection range, markedly enhance safety during vehicle operation and contribute to a more uniform distribution of individual fuel consumption; (iii) PnG modes with moderate acceleration, such as 1–2 m/s², can achieve excellent fuel consumption while ensuring safety and may even slightly enhance the operational efficiency of the intersection. The findings could provide a theoretical reference for the transition of transportation systems toward sustainability.

Pansharpening plays an important role in remote sensing image processing. Its purpose is to fuse a high-spatial-resolution panchromatic (PAN) image and a low-spatial-resolution multispectral (LRMS) image, thereby reconstructing a high-resolution multispectral (HRMS) image with both high spatial clarity and high spectral fidelity. In recent years, diffusion models have shown great potential in image generation. However, existing diffusion-based pansharpening methods usually adopt a fixed denoising strategy, making it difficult to adapt to the stage-wise changes in the denoising process and complex degradation distributions. Based on this, we propose PanDiM, an efficient generative framework for pansharpening. Specifically, we reformulate pansharpening as a high-frequency residual restoration process constrained by multimodal conditions. To improve the response accuracy of the model in complex regions, we design a Degradation-Posterior Guidance Module (DPGM), which extracts dual-scale physical detail priors from the PAN image, explicitly infers the degradation posterior, and converts it into dynamic control variables to adaptively regulate the state evolution of Mamba. In addition, we propose a time-aware mechanism, which allows temporal information to directly intervene in posterior estimation and state-space modeling, so as to accurately match the modeling requirements of different denoising stages. Considering the characteristics of residual reconstruction, we further propose a frequency-decoupled loss (FDL), which separates low- and high-frequency components in the frequency domain and applies targeted constraints. This significantly enhances the model’s ability to represent textures and achieves more robust spectral fidelity. Extensive experiments on three benchmark datasets, including WorldView-3, GaoFen-2, and QuickBird, show that PanDiM significantly outperforms existing mainstream methods in both reduced-resolution and full-resolution evaluations, providing a new solution for high-fidelity pansharpening in complex scenarios.