With the development of science and technology, the systematization, networking and intelligentization of the social technology system gradually deepen, forming the complexity of the system. The failures of these complex systems, such as traffic jams, rumor spreading, and financial collapse, can be regarded as a kind of "1+1<2" negative emergence of system capability, which is difficult to understand directly through the reduction analysis of system components. It challenges the classical reliability theory. Research on the complex systems reliability mainly focuses on failures laws, which includes two perspectives. One is the study of system vulnerability considering failure propagation. The other is the study of system adaptability considering failure recovery. System vulnerability studies focus on exploring the internal mechanism of system collapse, namely the failure propagation mechanism. System adaptability studies focus on the capacity to adapt and recover, including the system failure recovery mechanism. Based on this, the article introduces relevant research on reliability method.

With the development of network science and the emergence of complex systems theory, scholars have embarked on in-depth research on the structural and dynamic properties of complex networks. Among the dynamic properties of complex networks, cascading failures, as one of the most important research areas, describe a situation where a fault or error in a system or process leads to the failures of other related components or links. Various models and recovery strategies have been proposed for cascading failures in complex networks. This study analyzes the mechanisms of cascading failures, provides a comprehensive summary on the development of domestic and international cascading failure in complex networks, outlines the recovery strategies for addressing cascading failures, and highlights the existing issues and shortcomings in current research, providing valuable insights for future studies.

A functional relationship was constructed between the probability of inhaling viruses and social distance to characterize the viral transmission of subway passengers at the microscopic level. Formulas for calculating the increase and decrease of viral load were constructed based on establishing the viral load evolution equation. Normalized parameters were used within this equation to describe the effect of pandemic prevention measures. The viral load of each passenger was programmed through the Anylogic software’s secondary development interface to characterize the viral load change at the pre- and post-infection phases. In the initial simulation settings, 10% of the passengers were infected with the virus, including ordinary carriers and supercarriers. The evolution of the virus under different passenger number conditions within subway carriages was simulated, which was categorized into with-control and without-control scenarios. The simulation results showed the following: as the number of passengers increases, the passenger density increases, the virus transmission increases, and the individual viral load increases rapidly. Isolating passengers with a viral load greater than a threshold of 1 000 and prohibiting them from taking the subway can reduce the viral load of all passengers by an order of magnitude.

Frequent delays of flights at large international airports can affect their smooth operation, hence, the airport apron allocation problem needs to be robustly optimized. In this study, we proposed two integer linear-programing models for solving this problem and used two algorithms for performance comparison: the hill-climbing and large-neighborhood search (LNS) metaheuristic algorithms. In addition, we used the Monte Carlo method to evaluate the effectiveness of different objective functions in dealing with flight conflicts. The final test results show that the LNS algorithm not only improves the robustness of the gate allocation scheme for large airports but also excels in speed and quality, especially, when the square of idle time is used as the objective function.

An accurate prediction of short-term subway passenger flowscan effectively alleviate traffic congestion and improve the quality of travel services for urban residents. Herein, we propose a multitask learning-based model for the prediction of short-term subway passenger flows, which uses a residual convolutional neural network (NN) and a nested long short-term memory NN to extract the spatio-temporal correlation of traffic patterns, and introduces an attention mechanism to enhance the feature extraction performance of the NNs. Considering the characteristics of subway operations, the model selects train operation features, bus stops around subway stations, and point of interest data as external features to improve the accuracy of the prediction. Based on the historical data of the Beijing Subway, experiments were conducted in multiple time granularity scenarios, such as 10, 30, and 60 min. The results showed that the methodsuccessfully modeled and analyzed the inflow-outflow interaction through multitask learning, improved the prediction performance and generalization ability of the model, and providednovel approaches for the prediction of short-term subway passenger flows.

To address the challenges related to distance measurement of an approaching vehicle in fog,we developed an experimental platform to rapid image processing and real-time distance measurement.Firstly,we down-sampled the images through the dark channel algorithm to estimate atmospheric light values. Then, we introduced a tolerance mechanism to deal with the bright regions that do not satisfy the dark channel prior. This tolerance mechanism corrected the estimate with incorrect refractive index of such regions and effectively mitigated the issues of color distortion and low contrast. Secondly, we detected the vertical edges of an approaching vehicle using the edge detection and the improved Hough transform algorithms. Finally, we measured the safe distance from the approaching vehicle using the model. The results shows that the platform developed in this study can effectively measure the distance of the approaching vehiclein fog with a visibility <100 m, and can alert drivers in a timely and effective manner.

To study the traffic flow characteristics, the traffic data is analyzed using a complex network method. A box plot-clustering algorithm model is proposed to identify and fill in missing values and outliers in the initial data. The one-dimensional data is reconstructed into network nodes using the phase space reconstruction method. Additionally, the connection threshold is selected to determine the connection relationship of network nodes to convert the traffic sequence data as a complex network and analyze the structure and quantitative indicators of the network. The result shows that the structure of the complex network of traffic data can reflect the traffic flow state of the road section to a certain extent. The research optimizes the data preprocessing method and extends the application of complex networks into traffic data research.

To meet the needs of passengers for connection and evacuation at high-speed rail stations and enhance the role of high-speed rail stations as urban comprehensive transportation hubs, a dynamic route planning model of a high-speed rail feeder bus is established based on mixed demand that includes reservation and real-time demands. Based on the reservation demand, considering the operation cost of a bus company as well as the travel time cost, the route planning model is established before the start of operation. An improved genetic algorithm was designed using niche methods to solve the problem. After the start of operation, real-time demand can be inserted into the established vehicle route with temporary stations. To realize a dynamic route planning scheme, an integer planning model is established to minimize the variable cost of the system. Using the proposed method,30 demand groups were randomly generated and solved in the Beitaipingzhuang street area, Beijing. Results show that the model can generate an optimal dynamic route planning scheme for a high-speed rail feeder bus in two periods to satisfy the mixed demand. Compared with traditional genetic algorithm, niche genetic algorithm can effectively avoid premature and obtain better results, thus confirming the feasibility of the proposed model and the niche genetic algorithm.

In this paper, an approximate model and algorithm for the throughput rate are established by studying a docked bike-sharing system (DBSS) using stochastic user demands, routing matrix, and cycling times. A DBSS with a fixed number of bikes can be considered a closed queuing network with a buffered M/M/1 queue at each station, thus establishing an approximate model and algorithm for the throughput rate of DBSS. This algorithm can calculate the average number of bikes on roads and at stations. Moreover, it can estimate the average cycling time on roads and bike dwell time at stations and further determine the optimal number of bikes achieving the maximum throughput rate in the DBSS. Additionally, this paper proposes a method to determine whether a station is a bike surplus station or a bike deficient station under given user demands, routing matrix, cycling time matrix, and dock allocation. Finally, the approximate algorithm is verified in a real-world DBSS. The results show that the throughput rate of the DBSS increases in a step-wise manner with the increasing bike input under an superior limit. When the number of bike inputs exceeds the optimal quantity, there will be idle bikes, and the spatial distribution of bike surplus stations and bike deficient stations will remain unchanged.

Unmanned aerial vehicles (UAVs) have considerable application potential in urban logistics delivery. However, there are many uncertainties in urban low-altitude airspace operation scenarios. Therefore, it is essential to build a safe and orderly logistics UAV delivery network using scientific methods. From the perspectives of delivery economy, operational safety, and features of logistics UAVs, an integer programming model of multilevel hub-and-spoke network was constructed based on the original ground logistics delivery network. A network construction method was proposed, which combines partitioning around medoids(PAM) clustering with distance restrictions and integer programming. Three evaluation indicators were selected, i.e., delivery timeliness, network security, and network structure characteristics, to compare the constructed logistics UAV delivery network with the original ground delivery network. A logistics UAV delivery network was constructed in Jiangning District of Nanjing city to verify the feasibility of the proposed network construction method. The experimental results show that the UAV delivery network constructed using this method has good delivery timeliness while taking delivery costs and safety into account.

For larger enclosed communities, it is necessary to open the existing entrances or add some entrances to allow external vehicles or pedestrians to pass through for smooth urban traffic microcirculation and alleviating traffic congestion and the mutual interference between pedestrians and motor vehicles. Considering the actual situation of a community and the traffic distribution, with the goal of minimizing the total travel time and the cost of construction to open the community as the upper level model, the existing and alternative entrances are open to external vehicles or pedestrians as decision variables, and the combined (walking and car travel) mode choice and route choice with user equilibrium model as the lower level model, a bi-level programming model of decision-making optimization for opening closed communities was established. The genetic algorithm is applied for the upper level model and Frank-Wolfe algorithm is applied for the lower level model, and a solution algorithm of the bi-level programming model was proposed. Finally, the model and algorithm were verified by a sample, discovering the setting of traffic micro circulation and optimizing the plan, the total time spent has been reduced by about 26%. This proves that the model and algorithm proposed in this article have practical engineering application value, and can effectively reduce traffic congestion and improve traffic efficiency.

In order to improve the emergency service level of the fire station and reduce the emergency response time, this paper has improved the current traditional method of manual decision-making on the supply and demand matching of rescue. Based on the full investigation of empirical data and automatic batch acquisition of geographic data, this paper proposed an optimization strategy for the supply and demand matching of fire rescue stations considering service capacity, and constructed it as a mixed integer programming model. Then, based on the actual distribution of fire rescue stations in Xicheng District of Beijing and the location of high-frequency demand nodes, this model was validated. The research results showed that compared to the manual decision only considering the service distance, the mathematical model proposed in this paper can realize the automatic matching of fire rescue facilities in a short time, fully dispatch the rescue service capacity, and provide a new solution for optimizing the emergency rescue supply and demand service matching.

To improve the utilization of highway resources and the traffic situation in parallel national and provincial roads, in this study, the interests of highway operators and users was comprehensively accounted; a differentiated toll pricing model with the upper-level objective of increasing highway operating revenues and the lower-level objective of achieving multiclass stochastic traffic network equilibrium was established; and a model incorporating the genetic algorithm, simulated annealing algorithm, and iterative weighting method was developed. Based on the analysis of the traffic characteristics of the Longqing highway and its parallel national and provincial roads in the Shandong Province, the proposed model was used to develop differential toll schemes for entrance and exit sections, time periods, and vehicle types on the highway. The results show that the optimal differentiated tolling schemes can increase the operating revenue of the Longqing highway in the north-south direction by 7861900 yuan/year and reduce the travel cost of the road network by 7 165 100 yuan/year, which confirms the practicality of the proposed model and the effectiveness of the multimode differentiated tolling schemes for highways.

The causes of railway accidents are difficult to determine as several hazards can lead to accidents. To prevent the occurrence of railway accidents, the hazards responsible for railway accidents should be analyzed, and the occurrence rules of previous railway accidents should be revealed. In this study,data mining analysis on railway accidents and hazards was conducted using the improved Apriori algorithm.Considering the severity of accident casualties, a new calculation method for support and confidence indicators was proposed to weigh and quantify railway accident factors.Furthermore, time constraints were added to explore association rules of hazards with corresponding railway accidents at different times. Using the actual UK railway accident data, the association rules between railway accidents and hazards were discovered, and effective preventive measures were formulated for actual cases. Results show that the improved Apriori algorithm can explore more association rules between railway accidents and hazards, which can play an important role in preventing railway accidents.

With the rapid growth of China's economy and the continuous urbanization, rail transit plays an increasingly important role in residents' travel. As a key factor affecting the operation efficiency and service level of urban rail transit,accurate passenger flow prediction has attracted increasing attention from operation managers and researchers. To improve the prediction accuracy of the urban rail transit passenger flow, this paper combines sparrow search algorithm (SSA) and long short-term memory network (LSTM) and proposed a SSA-LSTM combined model. Based on the passenger flow data obtained from four stations of Hangzhou Metro Line 1 and the selected factors affecting the rail transit passenger flow, we used the proposed SSA-LSTM model to predict the short-term passenger flow of relevant stations. Then, we compared the predicted results with those estimated by the LSTM, GA-LSTM, and PSO-LSTM models. Results show that the prediction accuracy of the proposed model is 16.0%, 8.8%, and 2.3%, higher than the aforementioned models, respectively; furthermore, the proposed method exhibited better performance in terms of the root mean square error. Thus, the proposed model has potential applicationin predicting the urban rail transit passenger flow. Moreover, it can assistoperation managers in improving the operation efficiency and service level of urban rail transit.

The passing capacity of several busy high-speed railway lines in China is declining, and reducing train headway can considerably improve train density.However, optimizing the control method for train control systems is important to reduce the headway. On one hand, the commonly employed “hit-hard-wall” control mode has redundancies, rendering it inefficient. On the other hand,the “hit-soft-wall” control mode cannot ensure the absolute safety of train operation. Therefore, this paper proposes a combined“hit-hard-wall” and “hit-soft-wall” control mode, which reduces train tracking interval while ensuring the absolute safety of the train. Further, basic principles for optimizing this control mode are presented herein.In addition, solutions for problems such as the inability to determine the speed of the preceding train and to fulfill control curve constraints under this mode are explored.The solutions include track circuit information-based train speed estimation and control curve generation techniques that satisfy relevant constraints. Considering the CRH380BL train as an example, a solution was developed to obtain recommended values for train control deceleration.Consequently, the train tracking interval on straight tracks was reduced by 3035 meters, and the interval tracking time was reduced by 31 seconds through the proposed control mode. This is of great significance for improving the operational efficiency and passing capacity of high-speed railway lines.

To analyze and predict the potential quality markers (Q-Marker) in Zingiberis Rhizoma based on fingerprints and network pharmacological methods. The fingerprints of 10 batches of Zingiberis Rhizoma slices were established by ultra performance liquid chromatography and the common peaks were identified; then the network diagram of active ingredient target pathway was constructed by network pharmacological method to predict the quality markers of Zingiberis Rhizoma; and the bioactivity of Q-Marker was verified by molecular docking method. Fingerprints of 10 batches of dried ginger were established, and 17 peaks were identified. Five chromatographic peaks were identified by the reference substances of Zingiberis Rhizoma, which were 6-gingerol, 8-gingerol, 10-gingerol, 6-shogaol, and 8-shogaol. The results of network pharmacology showed that these 5 components can act on 35 core targets, and 20 key pathways which play an anti-cancer, anti-inflammatory, and antioxidant role. Molecular docking showed that these 5 components had strong binding capacity with core targets and had good biological activity. It was preliminarily predicted that these five substances could be used as quality markers of dried ginger. Predicting the quality markers of Zingiberis Rhizoma by fingerprint and network pharmacology analysis will provide a reference for the quality control of Zingiberis Rhizoma and for further study on its pharmacodynamic mechanism.

This study aimed to investigate the effect of Gandouling tablet on neuroinflammation in hepatolenticular degeneration in TX mice and mechanism of generating CuCl₂-induced microglia inflammatory response based on TLR4/NF-κB/NLRP3 signaling pathway. TX mice were divided into control, model, Gandouling tablet low-dose, Gandouling tablet medium-dose, Gandouling tablet high-dose, and penicilamine groups. BV2 cells were divided into control, model, Gandouling tablet, TAK-242, and Gandouling tablet + TAK-242 groups. Hematoxylin and eosin staining was performed to detect histopathological changes in the hippocampus of mice in each group. Western blot was used to detect the expressions of TLR4, p65, NLRP3, and IL-1β in hippocampal tissue and BV2 cells of mice in each group. Enzyme-linked immunosorbent assay was performed to quantify TNF-α, IL-1β, and IL-6 levels in hippocampal tissue and BV2 cells of mice in each group. Real-time polymerase chain reaction was used to determine the expression of TLR4, p65, NLRP3, TNF-α, and IL-1β mRNA in all groups of BV2 cells. Compared with the control group, hippocampal tissue in the model group showed considerable inflammatory damage; increased protein expressions of TLR4, p65, NLRP3, and IL-1β; and significantly increased levels of TNF-α, IL-1β, and IL-6 (P<0.05). Compared with the model group, the pathological damage of hippocampal tissue improved in both Gandouling tablet and penicillamine groups, and the effect of Gandouling tablet in the Gandouling tablet high-dose group was more prominent than that in the other groups. The Gandouling tablet and TAK-242 groups inhibited the activation of BV2 cells. Additionally, the expression of TLR4, p65, NLRP3, and IL-1β protein and mRNA were significantly reduced in these two groups as compared with the model group, and TNF-α, IL-1β and IL-6 levels were significantly decreased (P < 0.05). Gandouling tablet can alleviate hippocampal inflammation and inhibit CuCl₂-induced hyperactivation of BV2 cells in TX mice probably by downregulating TLR4/NF-κB/NLRP3 signaling pathway.

This study aimed to analyze the active ingredients of Xiangsha Liujunzi Decoction and its molecular mechanism in treating Helicobacter pylori-associated gastritis using network pharmacology and molecular docking. The drug active compounds, drug target genes, and disease-related targets of H. pylori-associated gastritis in Xiangsha Liujunzi Decoction were screened using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, GeneCards database, and OMIM database, and the drug targets and disease-related targets were analyzed using Venn analysis. Cytoscape software and STRING database were used to construct drug-compound potential target interaction network and protein-protein interaction network, respectively. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed for intersection targets using the DAVID database. The key components and targets were docked using AutoDock PyMOL and other software. The apoptosis rate was determined with Jimsa staining and CCK-8 assay, and the expressions of the related target proteins were detected with western blot. Finally, 122 active compounds, such as quercetin, luteolin, and kaempferol, in Xiangsha Liujunzi Decoction were screened out. These genes may be involved in the treatment of H. pylori-associated gastritis by acting on 101 potential targets, such as STAT3, TP53, and AKT1, as well as 109 pathways, such as toll-like receptor, TNF, and T-cell receptor signaling pathways. Molecular docking showed that quercetin, β-sitosterol, and luteolin had good affinity for the target proteins STAT3, TP53, and AKT1. Compared with the model group, after treatment with Xiangsha Liujunzi Decoction, the nuclear hyperchromism of GES-1 cells was enhanced, the apoptosis rate was significantly decreased, and the expression of p-STAT3 was also significantly decreased. Based on these findings, it can be concluded that Xiangsha Liujunzi Decoction exerts antibacterial and anti-inflammatory effects in the treatment of H. pylori-associated gastritis in multiple ways via multiple components and targets.

To investigate the protective effect of quercetin on nephrotic syndrome model rats. Specific Pathogen Free (SPF) male rats were selected, and a single tail vein injection of adriamycin 6.5 mg/kg was used to induce nephrotic syndrome in the rat model. Urine was collected to determine 24 h urine protein concentrations, and the contents of blood biochemical serum total protein and albumin, total cholesterol, triglyceride levels, and renal function markers (blood urea nitrogen and serum creatinine) were analyzed. The pathological changes in renal tissue were observed by hematoxylin and eosin staining. Transmission electron microscopy was used to examine the ultrastructure of renal podocytes. Western blot was used to detect the expression of desmin, nephrin, and synaptopodin in renal tissue. The results showed that quercetin effectively alleviated 24 h urinary protein in nephrotic syndrome model rats, significantly improved blood biochemical indicators and renal function injury, and alleviated pathological changes in renal tissue and the foot process fusion of renal podocytes. Simultaneously, quercetin can reduce the expression of desmin and increase the expression of nephrin and synaptopodin. Quercetin can effectively treat nephrotic syndrome model rats induced by adriamycin, and it may play a protective role by stabilizing the normal structure and function of podocytes.

To study the influence of carbon tax on the relation between residents' commuting travel choices and social welfare in the process of optimizing customized bus fares for commuter corridors, a two-tier planning model that considers the flexible passenger flow demand and overall social welfare of corridors is established. The upper layer of the model decides the departure location and customized bus fare, and the lower layer is the flexible demand passenger flow allocation model, considering both customized bus and private carbon the commuter corridor. From the perspective of residents' travel satisfaction, the relationship between random passenger flow demand and ticket price was analyzed in the context of carbon tax. According to different passenger departure points, the passenger flow demand is refined as the input of the passenger flow allocation model of the lower elastic demand. Considering the relationship among the passenger flow demand, road congestion, passenger satisfaction, and social welfare, the welfare of corridor passenger transportation system is set as the optimization goal of the upper model. The measurement statistical analysis and particle swarm algorithm are used to solve the two-layer programming model. The calculation results show that the optimized social welfare is considerably improved, the road traffic conditions are significantly improved, and the progressive carbon tax shows positive effect on increasing the sharing rate of customized buses. Under the carbon tax setting, the optimized customized bus fares and departure locations can serve social welfare and reduce the operating costs of urban passenger transportation systems.

To address the problem of emergency evacuation of stranded passengers outside a rail station in case of its closure, this paper designs an evacuation route map, which allows passengers along the line to avail the emergency evacuation vehicles and facilitates the overall evacuation process. To minimize the total cost of vehicle operation and passenger time, this work proposes a route optimization model for emergency evacuation vehicles and improves the adaptive large neighborhood search algorithm to implement the model based on the characteristics of the problem. Finally, based on the urban traffic data of Beijing, we designed routes for emergency evacuation vehicles, analyzed their sensitivity, and verified the model and algorithm with specific examples. The results show that compared with the shortest route algorithm, the optimization results of the proposed model can reduce passengers' travelling time by 15.02%, allowing them to evacuate rapidly while ensuring their experience and improving emergency management systems in case of rail station closure.

Current studies on epidemic spreading in multilayer networks are conducted on static networks. However, most of the real-world networks exhibit temporal properties, and extensive research on epidemic spreading in dynamic multilayer networks is not yet done. This work investigates the epidemic spreading behavior of epidemic and information interactions in a two-layer network model combining dynamic and static networks. It was found that the network structure, routing strategy, information transmission probability, and degree of epidemic suppression had a significant impact on the threshold of epidemic spreading. When information completely suppresses the epidemic, the self-awareness rate can effectively control the scale of the epidemic infection. This paper also studies the interaction between epidemic spreading and traffic dynamics by adjusting the node movement speed and contact radius of the dynamic network of thee pidemic layer and finds that the epidemic spreading threshold decreases as the node movement speed increases, whereas the outbreak of the epidemic is effectively suppressed as the contact radius increases.

In this paper, the electric vehicles pickup and delivery problem considering link recharging and time windows was studied. Aims to minimize the total travel distance of vehicles, considering the constraints of vehicle capacity, time windows and battery capacity, etc., to determine a group of optimal vehicle routes. The problem is formulated as a mixed integer linear programming model; a variable neighborhood search algorithm is proposed to solve it. Numerical instances are used to verify the model properties and algorithm performance. The results show that the proposed algorithm is only 0.08% worse than the commercial solver CPLEX in small scale instances; however, in large scale instances, the algorithm can obtain high quality feasible solutions in the specified time, in contrast to the CPLEX.

Path planning for material transportation is the fundamental work for constructing power transmission line in complex terrain areas. Such paths generally comprise road and ropeway transportation paths. Based on the digital elevation model and Dijkstra algorithm, this study proposed a combined material transportation path planning algorithm for road and ropeway. Using the fast search method of no-load ropeway transportation path based on parabola, a load ropeway transportation path optimization based on catenary was implemented. Then, the road transportation path was optimally searched using Dijkstra algorithm, and the coordinated planning of road and ropeway transportation paths was conducted. The proposed algorithm was applied to the material transportation path planning of an ultra-high voltage transmission line's towers. Results showed that the combined ropeway and road transportation paths can effectively shorten the design period of material transportation path and improve the efficiency of construction material transportation.

To thoroughly investigate the traffic state of highways, Jiqing Highway was selected as the study case. By mining the gantry data, a two-stage clustering algorithm combining k-means and density-based special clustering of applications with noise (DBSCAN) algorithms were proposed. The method was used to identify vehicles entering the service area and driving abnormally. Subsequently, the filtered vehicle records were extracted to realize a traffic state index weighted by the vehicle type to analyze the traffic state of the highway in terms of spatiotemporal dimensions. Results indicate that the two-stage clustering algorithm performs very well in the identification. The traffic state index indicated three periods when the highway is defined as congested during 7:00—20:00. Furthermore, it accurately identifies the congested sections of the highway. Moreover, it shows out that the mixed rate of large vehicles and the degree of traffic congestion in a section have a close positive correlation. Finally, according to the evaluation index, the traffic state of the Jiqing Highway is divided into four levels, which provides technical support for the traffic authorities to evaluate and manage the highway sections.

To address the huge data analysis problem in the decision-making for long-term road performance maintenance, this paper introduces the deep deterministic policy gradient (DDPG) reinforcement learning model in the maintenance decision analysis. A set of scientific and effective decision-making methods for long-term performance maintenance of asphalt pavements has been established through machine learning. These methods can improve road performance and make effective use of maintenance funds. Compared with the deep Q-learning network and Q-Learning algorithms, the DDPG algorithm requires less sampling data, converges faster, performs better, and can effectively improve the evaluation efficiency of the road service performance. Therefore, the proposed model plays an important role in the development of multi-objective maintenance decision-making for asphalt pavements.

To effectively improve the traffic economic benefits of rural roads, it is crucial to quantitatively analyze the different impacts of their potential influencing factors. The current study took the rural roads of 1 791 townships across Henan Province between 2016 and 2020 as an example and selected 27 factors influencing the traffic economic benefits of rural highways from four dimensions: basic economy, transportation construction, transportation capacity, and safety and environmental protection. The partial proportional odds model was applied, and the results showed 22 significant factors of which 11 factors violated the parallel-lines assumption. Through the marginal effects of the model, further quantitative analysis of the effects of the significant factors on the traffic economic benefits of rural highways was conducted. Among the factors, the top six influencing factors were daily average travel intensity, road network density, average motor vehicle ownership per household, population density, daily average travel distance, and per capita consumption expenditure. The maximum values of their marginal effects exceeded 13%, indicating that travel demand, construction scale, and economic level had the most remarkable impact on traffic economic benefits. Other factors concerning rural highway operation service and safety and environmental protection had less impact (marginal effects were less than 11%). Based on the different effects observed, several corresponding development countermeasures are propounded to promote sound and rapid development in the traffic economy of rural highways.

The improvement of the resilience of transportation hubs is crucial to cope with the pressure brought by a sudden large passenger flow on the transportation system. Based on the layout of the internal facilities of transportation hubs, the potential passenger evacuation paths are abstracted as a network diagram comprising nodes and links. From the perspective of changing the passenger evacuation paths, an optimization model is proposed herein for sudden large passenger flow evacuation while opening the emergency channels. The emergency channels that are normally closed can be opened for evacuation when there is a sudden large passenger flow. The passenger flow between each origin and destination and the potential passenger evacuation paths are pregiven. A passenger evacuation optimization model is presented to minimize the generalized cost including the passenger evacuation time and the cost of opening the emergency channels. The chaotic ant colony algorithm is designed to solve the model and obtain the optimal passenger evacuation scheme. Finally, Beijing South Railway Station is taken as a case study to verify the model and algorithm. Results show that in case of a sudden large passenger flow, opening emergency channels with appropriate location and number can effectively shorten the evacuation time of passengers. The results obtained in this paper provide a theoretical support for improving the resilience of urban transportation hubs from the perspective of their internal facility planning and layout and the optimization of passenger evacuation.

This paper analyzes the morning peak-period commuting pattern in the one-to-many mass transit system. The cost composition is analyzed considering the heterogeneity of the passengers' sensitivity to crowding in public transport. Passengers make trade-o?s between travel time, crowding, and schedule delay costs, and make their optimal time-of-use decision to establish an equilibrium travel model. We deduce the following equilibrium properties of the morning peak-period commuting in a one-to-many mass transit system: Passengers of the same destination disperse in continuous train services and the cumulative number of passengers increase with the closeness to the optimal arrival trains; if there are people taking a specific train at two consecutive stations, then the total number of people who board a specific train at subsequent stations is constant; different passengers with the same destination can be mixed at most on one train; and passengers who are sensitive to crowding will take the train far from the expected arrival time, while passengers who are not sensitive to congestion will do the opposite. The results of the study are conducive in deepening the understanding of transit riding behavior, providing auxiliary support for transportation dispatch management and further improving the related research on the transit equilibrium model.

Autonomous transportation system (ATS) is a new generation of transportation system proposed in response to the new development trend of active intelligent transportation. To scientifically and reasonably construct the functional architecture of the ATS, an optimized Density Peak Clustering (DPC) algorithm for multiattribute text is proposed in this paper. Combined with the basic characteristics of the functional architecture of a traffic system, the algorithm converts multiattribute text into spatial dimension coordinates through improved term frequency-inverse document frequency algorithm and text vector space model. Gaussian function and decision value were used to optimize the DPC algorithm for clustering, and the clustering result was evaluated using a contour coefficient. To test the rationality of the algorithm, this paper uses the functional datasets of road-carrier operation service domain, traffic infrastructure management service domain, and traffic-safety management service domain in ATS to perform an analysis as an example and draws functional architecture diagrams according to the clustering results. The architecture diagram comprises four layers of autonomous perception, autonomous learning, autonomous decision, and autonomous response, thus forming a scientific analysis method for functional architecture in ATS application scenarios. The results of the example show that the proposed algorithm is robust and the average value of the contour coefficient of the example is 0.84. Compared with the original algorithm, the problem of difficulty in defining the clustering center in the process of clustering is solved. Compared with other architecture designs in the intelligent transportation system, the functional architecture is more hierarchical and logical. This optimization algorithm can promote the construction of the functional architecture of the new generation of transportation system and the development of the theoretical system of the ATS.

In an autonomous driving system, the system needs to accurately identify the driver's intention to help them drive safely in complex traffic scenarios. Aiming at the issue of low accuracy of driver intent recognition and lacking consideration of the influence of optimized features on the accuracy of the model currently, a driver intention recognition method based on a time series model is proposed using deep learning knowledge. The method is based on Attention mechanism and incorporates Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM), introducing own and environmental information of the vehicle as spatiotemporal input to capture the spatial interaction and temporal evolution of surrounding vehicles. The method can simultaneously predict the driver intention in both lateral and longitudinal directions of the target vehicle and is trained and verified on the actual road dataset next generation simulation. The experimental results show that the proposed CNN-LSTM-Attention model can accurately predict the driver's driving intention in the highway environment, which has obvious advantages over the LSTM and CNN-LSTM model and provides an effective guarantee for the safe operation of the automatic driving system.

In order to study the influencing factors of Category II/III operational efficiency of transportation airports, an index system regarding the factors influencing the Category II/III operation efficiency is established refering to the relevant regulations as well as airport surveys, and the group Analytic Hierarchy Process (AHP) is adopted to optimize it by improving the scoring method and k-means clustering algorithm. The comparison of the index weight results obtained using the methods before and after the improvement show that the weight obtained via the improved group AHP is considerably different, which is suitable for extracting key influencing factors. Finally, the key influencing factors, such as site transformation and lighting system upgrading, are analyzed and relevant suggestions are proposed for the benefit of Category II/III operation and construction in China.

Aiming to resolve the problem of platform congestion in some high-load node stations, this paper establishes a mathematical model to optimize the average waiting time of passengers and formulates the corresponding traffic-limiting strategies. The model takes the minimum waiting time of passengers as the optimization objective and the number of restricted passengers per unit time at the entrance of the station as the decision variable. According to the behavior characteristics of passengers at different positions in the station, three constraint conditions related to traffic restrictions are formulated:platform capacity constraint, train capacity constraint, and traffic restriction area constraint. At the same time, this paper designs an algorithm based on receding horizon control to perform simulation.The results show that compared with the general strategy of traffic restriction, the algorithm exhibits clear advantages regarding the determination of the times of overruns and the number of passengers during overruns.The algorithm can effectively lower the times of overruns, reduce the number of passengers during overruns by 9% at the platform,and decrease the total passenger travel delay time by 11% without compromising the safety at the platform. This study can provide an effective reference for rail transportation traffic restriction.

Aiming to resolve the complex traffic environment of a ten-lane expressway, considering the factors of traffic volume and proportion of trucks, a comprehensive test design method is adopted herein. Further, the VISSIM traffic simulation software is used to perform simulation research using four lane-management schemes. Using average speed, traffic capacity, and the number of conflicts as the evaluation indexes, the principal component analysis method is used to construct the lane management-scheme evaluation method. Based on the lane management-scheme scores obtained under multiple combined traffic conditions, the conditions applicable for different lane-management schemes of the ten-lane expressway are proposed. The results show that for the ten-lane expressway,when the traffic volume is≤1 000 pcu/(h·lane), trucks should be prohibited from entering the far-left lane and when the traffic volume is >1 000 pcu/(h·lane) and the proportion of trucks is ≤30%, trucks should be prohibited from entering the three lanes on the left.Further,when the traffic volume is >1 000 pcu/(h·lane) and the proportion of trucks is >30%, trucks should be prohibited from entering the two lanes on the left.

In the case of a deep aquifer, a suspended waterproof curtain is typically used in foundation pit engineering. The depth of the waterproof curtain exerts a crucial impact on the deformation of surrounding surface and buildings owing to dewatering and excavation of foundation pit. To study the impact of the dewatering and excavation of foundation pit on the deformation of adjacent high-speed railway piers under different depths of the waterproof curtain, a three-dimension finite element software was used to numerically simulate the dewatering and excavation of a subway station foundation pit in Suzhou. Results show that with increasing depth of the waterproof curtain, the settlement of piers adjacent to the foundation pits gradually decreases. When the depths of the waterproof curtain are 41 and 91 m, the pier settlements of the high-speed railway are 31.88 and 4.18 mm, respectively. Moreover, the settlement of the pier meets the control requirements (≤5 mm) only if the depth of the waterproof curtain exceeds 91 m.

To alleviate the random-parking problem of shared bicycles due to insufficient parking spaces, this paper proposes a diversion guidance strategy for parking shared bicycles and sets the corresponding reward measures according to user behavior. A survey of stated preference is designed to obtain the user behavior choice preference data. Subsequently, the Logit model is used to analyze the data and build a reward mechanism to encourage the diversion parking by shared-bicycle users. Moreover, a multiobjective optimization model is constructed for minimizing enterprise cost and maximizing transfer parking demand. Yongtaizhuang subway station in Xisanqi is used as an example to examine the practical operability of this strategy. Results show that the user acceptance of the proposed strategy is 60%, and 58.33% of the excessive parking demand is successfully transferred. This strategy can mitigate the problem of insufficient parking spaces for shared bicycles to a certain degree and provides effective suggestions for managing the parking of shared bicycles.

Based on the data of 814 accidents that caused road property losses on the southwest mountain expressway, to clarify the correlation factors of the road-property-loss severity, the road property losses caused by accidents are divided into three levels according to the amount of compensation: <4 000, 4 000~10 000, and >10 000 RMB. Further, potential factors affecting the severity of road property losses are selected from the aspects of vehicle type, number of vehicles involved, accident form and cause, and driving environment. An analysis model of the road-property-loss severity on mountain highways is established using partial advantage ratio model, and the degree of influence on road property loss due to various factors is analyzed via a pseudoelastic coefficient. The results show that 13 factors are considerably related to the degree of road property loss. Among these factors, the proportional advantage assumption is not met when the vehicles involved in accidents are small and medium-sized trucks, buses, and cars and the season is in winter, and the remaining factors meet the proportional advantage assumption. When the vehicle involved in an accident is a heavy truck, it increases the probability of a Level 2 and Level 3 road-property-loss accident; the same happens at the sections of the road with a small curve radius. In addition, smooth curve section, multivehicle accidents, weather, and straight sections will cause an increase in the probability of Level 2 and Level 3 road-property-loss accidents.

Computer interlocking system is a key subsystem of railway transportation system that requires high safety and reliability. For a long time, fully covering the test of railway signal interlocking system was difficult because of the large amount of data. This paper focuses the research and development on a rule-driven automated platform that integrates expert experience into automatic testing and can generate test scripts based on rules to cover all signal devices. It canalso protract track map and perform scripts in a single batch. Test results against an interlocking system of a special line show that the platform is especially suitable for medium- and large-scale station testing, and can be easily extended to the testing of other security systems.

This study addresses the problems associated with the storage and loss of “the first kilometer” of agricultural produce logistics in rural areas. As an example, areas with cold storage requirements are considered and the location model of a cold chain logistic center is proposed under the operation guideline of local refrigeration, direct delivery from the origin for cold chain produce logistics in rural areas. Because fresh agricultural products are perishable, requirements for transportation and refrigeration are high. Therefore, the location model introduces time constraints. The objective is to minimize the total cost of transportation, investment, construction and operation. The model is solved using the Gurobi optimizer, achieving the optimal solution that meets time-related requirements and ensuring the rationality of the warehouse location planning results. Finally, ten alternative addresses are determines. Results show that the cost of building seven cold chain storage centers is the lowest. The findings of this study confirm the feasibility of the location model of the cold chain logistic center in rural areas by considering product types, costs, and time.