Due to the impact of scale variation of vehicle targets and changes in traffic environments in large-scale traffic monitoring systems, vehicle target detection methods often face challenges. To improve the adaptability of detection methods to these variations, we proposed an enhanced YOLOv7 for traffic systems (ETS-YOLOv7). To mitigate the effects of complex environments, we introduced the convolutional block attention module (CBAM) into the YOLOv7 framework, which filters important features in both channel and spatial dimensions, thereby enhancing the model’s capability to recognize traffic object features. To address the influence of aspect ratio variations in vehicle targets, we replaced the original complete intersection over union (CIoU) with wise intersection over union v3 (WIoUv3), eliminating the aspect ratio consistency loss and enhancing the model’s ability to generalize and its overall performance. Additionally, we employed the compact layer aggregation networks (CLAN) module to replace the efficient layer aggregation networks (ELAN) module, reducing redundant computations and improving computational efficiency without compromising model accuracy. The proposed method was validated on the large-scale traffic monitoring dataset UA-DETARC, achieving a mean average precision (mAP0.5–0.95) of 90.2%, which is a 3% improvement over the original YOLOv7. The frames per second (FPS) reached 149, demonstrating that the proposed model is highly competitive in terms of detection efficiency and vehicle detection accuracy compared to other advanced object detection methods.

The rapid growth of electric vehicle (EV) adoption poses significant challenges for the rational planning of charging infrastructure, where economic efficiency and service quality are inherently conflicting. To support scientific decision-making in charging station siting, this study proposes an integrated multi-objective optimization and decision-support framework that combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an entropy-weighted TOPSIS method. A bi-objective siting model is developed to simultaneously minimize total operator costs and maximize user satisfaction. User satisfaction is explicitly characterized by a nonlinear charging distance perception function and a queuing-theoretic waiting time model, enabling a more realistic representation of user service experience. To enhance convergence performance and solution diversity, the NSGA-II algorithm is improved through variable-wise random chaotic initialization, opposition-based learning, and adaptive crossover and mutation operators. The resulting Pareto-optimal solutions are further evaluated using an improved entropy-weighted TOPSIS approach to objectively identify representative compromise solutions. Simulation results demonstrate that the proposed framework achieves superior performance compared with the standard NSGA-II algorithm in terms of operating cost reduction, user satisfaction improvement, and multi-objective indicators, including hypervolume, inverted generational distance, and solution diversity. The findings confirm that the proposed NSGA-II–TOPSIS framework provides an effective, robust, and interpretable decision-support tool for EV charging station planning under conflicting objectives.

With rapid urbanization and surging traffic volumes, traffic accident data have become high-dimensional, multi-source, heterogeneous, and spatiotemporally dynamic, posing challenges for traditional statistical methods and machine learning models to simultaneously account for data heterogeneity and nonlinear interactions. Knowledge graphs, by constructing structured semantic networks that integrate accident events, participants, environmental factors, and other multidimensional elements, inherently support multi-source information fusion and reasoning. In this study, following a top-down ontology design principle, we construct a California Traffic Accident Knowledge Graph (TAKG) encompassing over one hundred elements, and propose an enhanced TransD embedding model. Our model introduces entity–attribute projection vectors into the dynamic mapping mechanism to explicitly encode domain attributes, and designs a dual-limit scoring loss function to independently regulate the positive and negative sample boundaries. Experimental results demonstrate that our method significantly outperforms traditional translation-based models on the self-built TAKG as well as on the FB15K-237 and WN18RR benchmark datasets. This research provides a solid data foundation and algorithmic support for downstream traffic accident risk prediction and intelligent traffic safety management.

In order to effectively solve the problem of electric taxi charging load prediction and reasonable charging behaviour discrimination, in this paper, we use taxi GPS trajectory data to mine the probability of operation behaviour in each area of the city, simulate the operation behaviour of a day by combining it with reinforcement learning ideas, obtain the optimal operation strategy through training, and count the spatial and temporal distributions and power values at the time of charging decision making, so as to predict the charging load of electric taxis. Experiments are carried out using taxi travel data in Shenzhen city centre. The results show that, in terms of taxi operation behaviour, the operation behaviour optimized by the DQN algorithm shows the optimal effect in terms of the passenger carrying time, mileage, and daily net income; in terms of the charging load distribution, the spatial charging demand of electric taxis in each area shows obvious differences, and the charging demand load located in the city centre area and close to the traffic hub is higher. In time, the peak charging demand is distributed around 3:00 to 4:00 and 14:00 to 15:00. Compared with the operating habits of drivers based on the Monte Carlo simulation, the DQN algorithm is able to optimise the efficiency and profitability of taxi drivers, which is more in line with the actual operating habits of drivers formed through accumulated experience, thus achieving a more accurate charging load distribution.

Considering the problems of randomness and fuzziness in evaluating indexes from maritime search and rescue emergency management capabilities, and the shortcoming of classic gray whitening-weight function indealing with complex problem, a novel grey cloud clustering comprehensive evaluation model based on improved grey cloud whitening weight function and improved analysis hierarchy process (IAHP)was presented. The weight of each index is determined by using the IAHP, which is good for decision makers to improve accuracy in the judgment process of any two index. A model of integrated the advantages of grey cloud model and an IAHP are established to evaluate the guangzhou maritime search and rescue emergency management capability by improved grey variable weight clustering theory. From the evaluation result of the model, the proposed model increases the reliability of emergency management capability assessment for maritime search and rescue and provides a new approach to evaluate emergency management capability of maritime search and rescue.

Against the backdrop of global energy transition and carbon emission reduction, the scientific siting of electric vehicle (EV) charging stations has become a key issue constraining the sustainable development of the industry. To address the common shortcomings of existing research, such as single-objective bias and the tendency of traditional optimization algorithms to fall into local optima, this paper proposes a multi-objective siting optimization method that couples an improved NSGA-II algorithm with an improved TOPSIS model. First, a charging station location model is established with the dual objectives of minimizing total operator costs and maximizing user satisfaction, where user satisfaction comprehensively incorporates factors such as charging distance and queuing time. Second, at the algorithmic level, chaotic mapping, opposition-based learning, and adaptive crossover–mutation operators are introduced to enhance global search capability and solution diversity. Then, an improved entropy-weighted TOPSIS model is used to select the optimal compromise solution from the Pareto set, achieving objective weight determination and stabilized ranking outcomes. Finally, simulation experiments show that the proposed method outperforms the standard NSGA-II algorithm in both operating cost reduction and user satisfaction improvement, while also exhibiting superior performance in hypervolume (HV), inverted generational distance (IGD), and diversity metrics. The results verify that the integrated improved NSGA-II–TOPSIS framework provides an efficient, scientific, and interpretable decision-support tool for the planning of EV charging infrastructure.