As one of the early manifestations of road pavement structure degradation, road cracks will accelerate the deterioration of the road if not detected and repaired in time. Aiming at the problems of low recall and incomplete crack detection in current road detection, based on the U-Net network, this paper proposed an Attention-Dynamic Snake Convolution U-Net (ADSC-U-Net) network. Firstly, the dynamic snake-shaped convolution was added to the normal downsampling process to make the network adaptively focus on the slender and curved local features, which can solve the problem of low accuracy of small crack detection. Secondly, the attention mechanism was used to pay better attention to the significant features of positive samples under the condition of a large proportion gap between positive and negative samples, which solved the problem of the poor crack integrity detection effect. Finally, the dataset was expanded by random vertical and horizontal flip operations, which solved the problem of network training overfitting caused by the small-scale datasets. The experimental results showed that, when the input image had a resolution of 480 × 320, evaluation indices P, R, and F1 of ADSC-U-Net on the self-built dataset were 74.44%, 68.77%, and 69.42%, respectively. Compared to SegNet, DeepLab, and DeepCrack, the P was improved by 1.90%, 2.49%, and 11.64%, respectively; the R was improved by 8.01%, 4.70%, and 59.58%, respectively; and the comprehensive evaluation index F1 was improved by 5.73%, 4.02%, and 55.87%, respectively, which proves the effectiveness of the proposed method.

This study presents a multi-objective optimal framework for deploying traffic monitoring cameras at road networks. Compared with previous studies that focused on addressing single traffic problem such as OD estimation, link flow observation, path flow reconstruction, and travel time estimation, this study aims to address a comprehensive traffic management problem, including crash prevention, traffic violation governance, and traffic efficiency improvement. First, a potential principle for selecting the location of traffic monitoring deployment is determined, taking into account the key signalized intersections, areas prone to traffic congestion, crash-prone spots, and areas prone to traffic violations. Then, a multi-objective optimal model is developed to minimize the ATFM (i.e., average traffic volume of each five minutes), TCF (i.e., traffic crash frequency), and TVF (i.e., traffic violation frequency) while adhering to cost constraints. Finally, RVEA and NSGA-II algorithms are used to solve the multi-objective optimal model, respectively, and a comprehensive metric is proposed to evaluate the deployment schemes. The case study results demonstrate that the solutions obtained by the RVEA algorithm outperform those of the NSGA-II algorithm, and the best traffic monitoring deployment rate is 62.79%, under cost constraints. In addition, the comparison using the FAHP method also illustrates that the RVEA scheme is superior to the NSGA-II scheme. The above research results could potentially be used to optimize the locations of traffic cameras in road networks, which help to improve traffic management.

Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this study, wireless charging technology is employed to construct a complex driving scenario including urban roads and dynamic wireless charging facilities. A combination of variable-scale elements consisting of vehicles, roads, and the environment is analysed hierarchically to develop a wireless charging scheme for urban transport systems. Using passage efficiency, energy consumption, and passenger comfort as the joint optimisation objectives, an optimal driving model for CAEFVs in wireless charging scenarios at signalised intersections combining scenario boundaries and vehicle dynamic constraints is proposed. Considering the differentiated charging needs of vehicles, this model is divided into a time priority strategy (TPS), balance priority strategy (BPS), and charging priority strategy (CPS). The obtained results reveal that the CPS is superior to the TPS in terms of the charging benefits but requires a longer travel time. Meanwhile, the BPS increases the charging benefits and passing efficiency. This study provides guidance for the deployment of wireless charging lanes with a high application value.

Patching is a common technology used in repairing asphalt-pavement potholes. Due to the differences in material properties between patched- and unpatched-asphalt mixtures, significant strain and stress concentrations could be induced; thus, further cracks and interfacial debonding distress could be caused. As a remedy, the strain and stress concentrations can be alleviated by utilizing optimum patching shapes. Therefore, this paper employed finite element methods (FEM) to deeply analyze the mechanical performance of patched-asphalt pavements embedded with different patching shapes. Three patching shapes, these being rectangular, stair, and trapezoid, were considered for use in pavement pothole repairs based on two- and three-dimensional finite element models. In the two-dimensional models, Top-Down and Bottom-Up crack propagations were simulated to assess the anti-damage performance of the patched pavements with different patching shapes. In addition, the thermal stress behaviors within patched-asphalt pavements were simulated using the two-dimensional model to analyze the performance of the patched pavements during the cooling process in construction. In addition, interface-debonding performance was simulated for the patched-asphalt pavements using three-dimensional models. In light of the simulation results, engineers are expected to better understand the mechanism within patched pavements and to improve the quality of the pavement patching.

Connected and automated vehicles (CAVs) represent a significant development in the transport industry owing to their intelligent and interconnected features. Potential field theory has been extensively used to model CAV driving behaviour owing to its objectivity, universality, and measurability. However, existing car-following models do not consider the impact of time delays and the influence of information from multiple vehicles ahead and behind. This paper focuses on the driving-safety risks associated with CAVs, aiming to enhance vehicle safety and reliability during travelling. We developed a multi-vehicle car-following model based on safety potential fields (MIDM-SPF), taking into account the characteristics of multi-vehicle connected information and time delays. To enhance the model’s precision, real-world data from urban roads were employed, alongside an improved optimisation algorithm to fine-tune the car-following model. The simulation experiment revealed that MIDM-SPF significantly reduces stop-and-go traffic, thereby improving traffic flow stability in urban areas. Additionally, we validated the stability of our model under varying market penetration rates in large-scale mixed traffic. Our findings indicate that increasing the CAV proportion improves the stability of mixed traffic flows, which has important implications for alleviating traffic congestion and guiding the large-scale implementation of autonomous driving in the future.