Research on the underground logistics system (ULS) has been carried out for nearly 30 years, but the description of the global research network, research trends, and the discussion of advanced theories and practices have not been systematically reviewed so far. The concept of public freight has expanded a new development direction: metro-based underground logistics system (M-ULS). The purpose of this paper is to analyze this new model by taking underground logistics and public freight as the research object. We performed statistical analyses of 222 references and constructed three kinds of visualized bibliographic information experiments for regional collaboration, authorship collaboration, and keywords co-occurrence. Based on the scientometrics results, the evolution path of the literature evolution was drawn. We used cluster-analysis-based taxonomy to structure the recent relevant literature. According to the comprehensive review, the research stays in the theoretical research stage, mainly focusing on system concept and planning, traffic organization, and network operation. There is still a lack of discussion on engineering quantification and application implementation. Finally, this study highlights some brief ideas, discussions, and potential suggestions for future research. This bibliometric research is expected to provide researchers and practitioners with a panoramic description and in-depth understanding of ULS and M-ULS research.

To address the challenges of selecting task schemes and ensuring secure channels duringdata collection and transmission by unmanned aerial vehicles, the study divides the secure transmission model into physical domain and social domain based on graph theory to ensure the security of unmanned aerial vehicles during task execution. Secondly, the multi to one matching problem was optimized through matching theory, achieving maximum benefits between drones and ground to ground (D2D) users. This study designed a stable matching scheme for drone task allocation based on bipartite graphs and a popular matching strategy for drone security spectrum resource allocation based on bipartite graphs. By adapting the design of a series of coherent actions of drones from information collection to information transmission, the efficiency and security of drone information transmission can be improved. The experimental results show that the average revenue of the designed algorithm for unmanned aerial vehicles is between 21 and 40, while the average revenue range for a single task is between 173 and 210. Compared with other algorithms in terms of security performance for drones and D2D users, the algorithm designed in the study shows strong advantages. Specifically, in terms of drone safety performance, the design algorithm’s performance improves as the spectrum sharing limit increases. The advantage becomes more obvious when the sharing limit value reaches 1; From the perspective of D2D users, the average worst-case safety rate of the design algorithm increases with the increase of flight cycle and average power. Compared tofixed-trajectory drones, the overall performance gain of the research and design algorithm reaches about 18.2%, and compared with fixed power drones, the overall performance gain reaches about 15.5%. In summary, the research has made significant progress in task allocation and channel security spectrum resource allocation. The design algorithm achieves the optimal effect between computational complexity and performance, and overall maximizes the benefits between drones and D2D users.

As Blockchain and Cloud computing are becoming more prevalent, the ability to adapt and maintain stability in virtual networks becomes increasingly important for socio-economic systems. The dynamic nature of VNF (virtual network functionality), which can be moved, replicated, or deleted based on network demands, requires efficient and effective algorithms for VNF adaptation and smoother functionality of socio-economic systems. Based on this, the study proposes the Migration Cost and Network Energy Consumption (MJOVME) algorithm for virtual network function adaptation based on bandwidth and other required resources, and proves its effectiveness. The experimental results reveal that the weighting factor value lies between 0.12 and 0.22 whereas the network energy loss shows diminishing trend under different bandwidths, while the migration cost loss is exhibiting an upward trend. The integration of VNF with Cloud and Blockchain environments leads to enhanced security, data integrity, scalability, and decentralized distributed management for socio-economic systems. As the number of SFC (service function chain) requests increases, the network energy loss of the MJOVME algorithm lies within 200 1200 migration cost as compared with the algorithm considered for comparative study which is approximately 0.2W104W higher than MJOVME. The MJOVME algorithm has attained an average resource utilization rate of 69.50% and a memory resource utilization rate of 66.78% which is commendable in integrated environment. The standard deviation of computing resources is 12.41% and the standard deviation of memory resources is 15.08%, lower than the comparative method. The proposed MJOVME algorithm has high effectiveness in adaptive migration of virtual network functions in integrated environment of Cloud and Blockchain and is of great significance for VNF migration when actual resource requirements are altered.

In order to reduce the excessive use of multisource traffic information collection system, a multisource traffic information collection combination optimization mode is proposed based on genetic algorithm in this paper. This model is mainly used to analyze the traffic management data in the city. According to the collected data information, the characteristics of the traffic equipment can be effectively analyzed. Basing on the market demand and supply relationship, the multisource traffic information collection combination optimization model is used to complete the reorganization and optimization of the traffic information in this paper, to acquire the main convolution feature variables of the model. The data information combination processing is performed according to the acquired feature variables, and the genetic algorithm is used to adjust the multisource traffic information. During the process of information fusion data analysis, the multisource traffic information clustering and fuzzy constraint control can be performed effectively to realize the optimization of the team’s traffic information collection combination. Finally, the simulation results show that the method proposed in this paper is more accurate in realizing the optimization process of multisource traffic information collection and combination and has a better degree of information fusion.

The current attribute reduction algorithms for information systems are difficult to handle imbalanced data with default values. Therefore, to address the shortcomings of traditional attribute reduction algorithms (ARAs) in incomplete information systems, a new algorithm is proposed by introducing intuitive fuzzy pairs (IFP). In addition, a composite minority oversampling technique TampC and Central Limit SMOTE (TampC-CL-SMOTE) is proposed to improve the pre-data sampling method of the algorithm, and its effectiveness is verified by experiments. The experimental results show that the average classification accuracy of the improved attribute reduction algorithm on the naive Bayes classifier is 82.13%, and the average classification accuracy on the support vector machine classifier is 86.48%. In the comparison of operational efficiency, the average running time of the improved attribute reduction algorithm is 5.92 seconds, and the overall consumption of running time is lower than that of the comparison algorithm. Meanwhile, the average accuracy, recall, and F-measure of the algorithm are 76.14%, 78.35%, and 77.19%, respectively. In addition, the G-means of TampC-CL-SMOTE are 2.9% and 5.3% higher than the comparison algorithm, respectively. Overall, the improved attribute reduction algorithm has high efficiency in handling imbalanced data, while the optimization of TampC-CL-SMOTE has effectiveness in practical applications and has advantages in handling high and low imbalanced data in incomplete information environments.

A mandatory lane change occurs when buses are ready to enter the station, which will easily cause a reduction of urban road capacity and induce traffic congestion. Using deep learning methods to make lane-changing decisions has become one of the research hotspots in the field of public transportation, especially with the development of the Cooperative Vehicle-Infrastructure System. Aiming at the exploration of the bus lane-changing rules and decisions during entering, we built a GRU neural network model considering bus priority by using the first real-world V2X (vehicle to everything) dataset. Firstly, we illustrated the image and point cloud data processing by coordinate transformation. Secondly, the Kalman filtering algorithm was used to evaluate the vehicle state. Combined with the bus priority rules, we propose a flexible right-of-way lane in front of the bus stop. And then, we obtain the feature variables as inputs to the model. The XGBoost algorithm was chosen to train the GRU model. Results show that the model has higher identification accuracy for lane-changing maneuvers by comparison with other models. It plays a very important role in providing a decision basis for more refined bus operation management.