This paper evaluates the amount of noise reduction when using different pavement materials in two adjacent lanes, where the close-proximity (CPX) method is introduced to analyze the tire/road noise before and after pavement maintenance. We consider four types of pavement materials, including ECA-10, PUC-10, PAC-13, and double-layer porous asphalt pavement (PAC-13+PUC-10), where these materials and their combinations are paved on two adjacent lanes. We measure the tire/road noise with the CPX method using a two-wheel trailer that can install two types of tires in different tests. This study provides some guidelines on controlling traffic noise pollution by using the combination of low noise pavement materials. From the experimental and theoretical results, one can see that the highest amount of noise reduction can be obtained when both the inner and outer lanes use the double-layer porous asphalt pavement. To make a balance between the noise reduction performance and the road maintenance cost, one can have the suboptimal choice, where the inner lane uses PUC-10 and the outer lane uses the double-layer porous asphalt pavement.

Unmanned aerial vehicle (UAV) image stitching refers to the process of combining multiple UAV images into a single large-format, wide-field image, and the stitched image often contains large irregular boundaries and multiple stitching seams. Usually, irregular boundaries are addressed using grid-constrained methods, while seams are optimized through the design of energy functions and penalty terms applied to the pixels at the seams. The above-mentioned two solutions can only address one of the two issues individually and are often limited to pairwise stitching of images. To the best of our knowledge, there is no unified approach that can handle both seams and irregular boundaries in the context of multi-image stitching for UAV images. Considering that addressing irregular boundaries involves completing missing information for regularization and that mitigating seams involves generating images near the stitching seams, both of these challenges can be viewed as instances of a mask-based image completion problem. This paper proposes a UAV image stitching method based on a diffusion model. This method uniformly designs masks for irregular boundaries and stitching seams, and the unconditional score function of the diffusion model is then utilized to reverse the process. Additional manifold gradient constraints are applied to restore masked images, eliminating both irregular boundaries and stitching seams and resulting in higher perceptual quality. The restoration maintains high consistency in texture and semantics. This method not only simultaneously addresses irregular boundaries and stitching seams but also is unaffected by factors such as the number of stitched images, the shape of irregular boundaries, and the distribution of stitching seams, demonstrating its robustness.

With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has become one of the most important measurements for evaluating road traffic pollution. There are subjective experimental methods and objective prediction methods to assess the annoyance level of traffic noise: the subjective experimental method usually uses social surveys or listening experiments in laboratories to directly assess the subjective annoyance level, which is highly reliable, but often requires a lot of time and effort. The objective method extracts acoustic features and predicts the annoyance level through model mapping. Combining the above two methods, this paper proposes a deep learning model-based objective annoyance evaluation method, which directly constructs the mapping between the noise and annoyance level based on the listening experimental results and realizes the rapid evaluation of the noise annoyance level. The experimental results show that this method has reduced the mean absolute error by 30% more than the regression algorithm and neural network, while its performance is insufficient in the annoyance interval where samples are lacking. To solve this problem, the algorithm adopts transfer learning to further improve the robustness with a 30% mean absolute error reduction and a 5% improvement in the correlation coefficient between the true results and predicted results. Although the model trained on college students’ data has some limitations, it is still a useful attempt to apply deep learning to noise assessment.

The ability to localize a sound source is very important in our daily life, specifically to analyze auditory scenes in complex acoustic environments. The concept of minimum audible angle (MAA), which is defined as the smallest detectable difference between the incident directions of two sound sources, has been widely used in the research fields of auditory perception to measure localization ability. Measuring MAAs usually involves a reference sound source and either a large number of loudspeakers or a movable sound source in order to reproduce sound sources at a large number of predefined incident directions. However, existing MAA test systems are often cumbersome because they require a large number of loudspeakers or a mechanical rail slide and thus are expensive and inconvenient to use. This study investigates a novel MAA test method using virtual sound source synthesis and avoiding the problems with traditional methods. We compare the perceptual localization acuity of sound sources in two experimental designs: using the virtual presentation and real sound sources. The virtual sound source is reproduced through a pair of loudspeakers weighted by vector-based amplitude panning (VBAP). Results show that the average measured MAA at 0° azimuth is 1.1° and the average measured MAA at 90° azimuth is 3.1° in a virtual acoustic system, meanwhile the average measured MAA at 0° azimuth is about 1.2° and the average measured MAA at 90° azimuth is 3.3° when using the real sound sources. The measurements of the two methods have no significant difference. We conclude that the proposed MAA test system is a suitable alternative to more complicated and expensive setups.

The transportation industry is one of the largest consumers of fossil fuels and sources of carbon dioxide emission, with highway transportation accounting for more than 70% of the total. In order to promote efficient, clean, diversified, and intelligent use of energy, this study proposes an innovative application technology model for integrating highway transportation and energy to improve the utilization of clean energy and reduce the proportion of fossil energy and carbon emissions in highway transportation. This study addresses the current problems of green energy development in China’s highways, and combines green low-carbon development policies in international transportation with inspirations based on experience. Based on the principles of safety, efficiency, environmental friendliness, wisdom, and economy, this study systematically analyzes the current situation of green energy application in China’s highway transportation. It proposes one core objective, two major roles, and five major security systems of development measures for green energy application China’s highway transportation with the aim of promoting the transformation and upgrade of China’s highway transportation industry. This study can also help to achieve green and low-carbon development of highway transportation and provides a decision-making reference for the ecological civilization construction goals of the industry.

To enhance the logistics scheduling efficiency of automated guided vehicles (AGVs) in automated ports and achieve the orderly charging and battery swapping of AGVs as well as self-sufficient clean energy, this paper proposes an integrated optimization method. The method first utilizes graph theory to construct a theoretical model that includes AGVs, the port road network, and charging and battery-swapping stations, in order to analyze the optimal logistics scheduling and charging and swapping strategies. Subsequently, for the multi-objective optimization problems in AGV logistics scheduling and charging and swapping, a fast solution method based on the immune optimization algorithm is proposed, with scheduling time and the self-sufficiency rate of clean energy for port AGVs as the constraint conditions. Finally, the effectiveness of the proposed model and algorithm is verified through a simulation scenario. The results show that in the simulated port logistics scenario, after optimization, the total operation time of AGVs is significantly reduced. Compared with the cases that only consider scheduling time, the charging strategy, or wind and solar output, the average clean energy self-sufficiency rate under the proposed strategy increased by 82.7%, 27.5%, and 53.9%, respectively. In addition, as the weight of the self-sufficiency rate increases, both the total driving time and the total clean energy self-sufficiency rate of AGVs show an upward trend and are approximately linearly related. Within the specified maximum scheduling time, the actual scheduling time and self-sufficiency rate can be flexibly coordinated, with significant carbon reduction benefits.