With the extensive development of intelligent transportation and eco-friendly travel, a low-energy task-offloading method based on edge computing in the internet of vehicles (IoV) is proposed to address the dual challenges of low-latency service demands and energy conservation in the IoV. In the context of multivehicle single-cell scenarios on public roads, this study explores the task-offloading requirements of vehicles in motion and systematically investigates the allocation of computational resources. To fully utilize computing resources, this study not only considers the computing power of vehicles but also introduces a new approach for offloading tasks to vehicle servers traveling in the same direction or parked along the roadside as well as to edge servers in roadside units. This enables the effective integration and efficient sharing of computing resources, thereby remarkably enhancing the processing capabilities of the IoV. Furthermore, this study employs an improved particle swarm optimization algorithm to optimize offloading power and task allocation ratios. Extensive simulation tests revealed that the proposed method significantly reduced the energy consumption of vehicle tasks and improved the service quality and energy efficiency of the IoV.It helps to promote green transportation and sustainable development, and lays a solid foundation for energy optimization and efficiency improvement of future intelligent transportation systems.

Energy-saving metro train control is closely related to the vertical track alignment (VTA) design, and both have a significant impact on operating costs. To further reduce operating costs based on optimized train control, this study proposed a collaborative optimization model for the VTA design phase. This model optimizes the bidirectional train control strategy and VTA of a metro section with the goal of minimizing energy consumption and maintenance costs simultaneously, while adhering to the constraints of scheduled train control and the requirements of the "Metro Design Code." Given the numerous factors affecting the maintenance costs of wheels and rails, a train-track dynamic simulation model was developed to calculate these costs. Based on this, an algorithm combining the pseudospectral method and brute force search was designed to solve the collaborative optimization model. The effectiveness of this optimization method was validated using three sections of the Guangzhou metro line. The results indicate that, compared to the method of optimizing scheduled train control alone on the actual VTA, the collaborative optimization model is more effective in saving operating costs, reducing the average operating costs by 21% across the studied sections. This study can provide novel approaches and theoretical support to further reduce metro operating costs, which contributes to promoting sustainable development of metro.

The prediction of highway tolls is affected by complex factors such as holidays and unexpected events. Traditional prediction methods often fail to fully account for intricate interactions between these multiple factors, resulting in less-than-ideal prediction accuracy. By leveraging the self-attention mechanism, large language models can better fit complex spatiotemporal data and have enhanced feature learning capabilities, making them highly effective for precise highway toll prediction. Therefore, this study proposes a highway toll prediction model based on iTransformer. This model embeds temporal information as an independent dimension into the input sequence and reverses the roles of the self-attention mechanism and feed-forward network, thereby allowing the model to more accurately capture the dynamic features of time series and correlations between multiple variables. Case studies show that the proposed model improves the average prediction accuracy by 23.47% and 17.84% compared with the SARIMA and LSTM models, respectively, in regular scenarios. In irregular scenarios, the model demonstrates even better predictive performance, improving the accuracy by 70.92% and 45.64%, respectively. A sensitivity analysis of the proposed model indicates that it is highly sensitive to the number of feed-forward network layers and stacked encoder layers but is less sensitive to changes in the number of attention heads. Thus, this study provides a new methodological approach for addressing the challenges associated with toll prediction in complex traffic environments and has significant implications in terms of improving the accuracy of highway toll predictions.

Lane detection is a remarkable practical application of computer vision technology in the field of transportation. However, existing semantic segmentation network models still face certain challenges such as insufficient accuracy and blurred edges in road semantic segmentation tasks. To address these issues, an improved lane segmentation network architecture based on the UNet model is proposed. First, a dual attention module (DAM) is introduced in the skip connections of the UNet model, which prioritizes the importance of lane lines and effectively reduces noise interference. Additionally, dynamic snake convolution (DSConv) is employed to replace traditional convolution methods, enhancing the network’s lane detection ability. To enhance the comprehensiveness and accuracy of lane detection in underexposed or dark backgrounds, an improved adaptive Gamma correction method is introduced in the image preprocessing stage. Furthermore, atrous spatial pyramid pooling (ASPP) technology is introduced at the end of the encoder to enhance network performance. Experimental results show that this model achieves an accuracy of 98.93% on the TuSimple dataset while meeting real-time requirements. Compared to five other semantic segmentation-based lane detection algorithms, the proposed algorithm demonstrates superior recognition performance, thus validating its effectiveness.

The joint optimization of train timetable and short-turn routing under the flexible composition mode are restricted by various factors such as train timetables, passenger dynamic equations, and train composition adaptability. The coupling of constraints increases the complexity of the problem, making it difficult to solve using traditional optimization methods.This paper introduces the quantum computing method to address the problem. We built a mixed-integer nonlinear programming model to minimize the number of gathered passengers across all stations along the transit line. Furthermore, we used the real coherent Ising machine(CIM) to solve this problem. The numerical results show that the real coherent Ising machine has obvious advantages in computing efficiency and optimization performance compared with other classical algorithms.

To address the challenges posed by complex traffic scenarios, particularly congested roads where traffic objects are densely packed and often occlude each other and small-scale objects are detected inaccurately, a new object detection model called YOLO-T (You Only Look Once-Transformer) is proposed. First, the CTNet backbone network is introduced, which has a deeper network structure and multiscale feature extraction module compared with CSPDarknet53. Not only can it better learn the multilevel features of dense objects but can also improve the model’s ability to handle complex traffic scenarios. Moreover, it directs the model’s focus toward the feature information of small objects, thereby improving the detection performance for small-scale objects. Second, Vit-Block is incorporated, which integrates more features by parallelly combining convolution and Transformer. This approach balances the relevance of local and contextual information, thereby enhancing detection accuracy. Finally, the Reasonable module is added after the Neck network, introducing attention mechanisms to further improve the robustness of the object detection algorithm against complex scenarios and occluded objects. Experimental results indicate that compared with baseline algorithms, YOLO-T achieves a 1.92% and 12.78% increase in detection accuracy on the KITTI and BDD100K datasets, respectively. This enhancement effectively boosts detection performance in complex traffic scenarios and can assist drivers to better predict the behaviors of other vehicles, thus reducing the occurrence of traffic accidents.

Currently, in surveillance video groups, traditional methods for searching camera videos involve traversing and searching through all cameras or performing repetitive searches in a network topology. These approaches result in low efficiency and poor accuracy in tracking individuals. To address this issue, we propose an efficient method for selecting surveillance camera videos based on the principles of the queuing and vertex-weighted directed graph theories. In this method, we treat cameras as vertices and construct a weighted directed graph. By calculating weights, we can determine the optimal monitoring paths considering the connections and weights between cameras. The key advantage of this method is its efficient selection of surveillance camera videos. Additionally, by combining the optimal movement paths of target passengers in urban rail transit nodes with individual tracking, we use the concept of vertex-weighted directed graphs to enhance the accuracy and efficiency of person recognition. The research results show the great significance of this method in improving the performance of surveillance systems and individual tracking capabilities. By applying the queuing and vertex-weighted directed graph theories for individual tracking, we offer an innovative approach to address practical problems and enhance system performance. This method holds great importance in enhancing surveillance system performance and individual tracking capabilities.

To improve the competitiveness of the railway-passenger transportation market and increase its operational revenue, this study investigates the multiobjective system optimization issue of multimodal railway-passenger transportation fares. A mathematical model was used to describe the equilibrium relationship among the demands of different railway-passenger transportation products. Sensitivity analysis was performed to provide a calculation method for the demand elasticity of multimodal railway-passenger transportation products, and a market demand function for multimodal railway-passenger transportation was formulated. Considering multiple optimization objectives such as market demand, passenger transportation revenue, and profit of railway-passenger transportation enterprises along with passenger transportation costs, we proposed a multiobjective bi-level planning model for describing the system optimization issue of multimodal railway-passenger transportation fares. Finally, we used real passenger transportation data of the railway line between Beijing and Tianjin to validate the proposed model. The results show that the proposed method can effectively balance multiple objectives such as passenger transportation demand, passenger transportation revenue, and profit, providing reference and support for railway-passenger transportation departments to develop scientifically reasonable fare systems in different market competition stages.

To study the invulnerability of urban public transport systems, the complex network theory is used to map and analyze the metro and bus systems. First, an improved double-layer complex network construction method is proposed based on the Space-L model. This method constructs connecting edges based on actual transfer distances and uses the peak-hour passenger capacity of lines as edge weights to develop a metro-bus double-layer network. Second, the characteristics of this network and its sub-network are analyzed using indicators such as degree, intensity, and betweenness. Finally, the random attack and intentional attack models are utilized to analyze the invulnerability of the metro-bus double-layer network and its sub-networks, respectively. The results show that the developed network exhibits a scale-free property and is vulnerabe to intentioanl attacks, exhibiting different sensitivities to various intentioanl attack indicators. Thus, the results of this study provide valuable guidelines to public transport systems for responding to emergencies and improving their robustness.

This study first constructs a topological structure model of the rail transit network based on the Space L network topology method. Herein, upon analyzing network characteristics using UCINET as a basis, the maximum connected subgraph ratio and overall network efficiency are selected as indicators to analyze the resilience of the rail transit network. Then, the article comprehensively analyzes the attribute values of nodes and employs the TOPSIS method to rank the importance of nodes using the coefficient of variation for weighting. Then network resilience is analyzed by destroying individual nodes with high importance, and network recovery is achieved through indicator-based ranking restoration strategies, ultimately yielding the average resilience value of the rail transit network. Furthermore, the resilience and robustness of the rail transit network of Nanjing in 2022 is analyzed based on the established model. Results show that nodes with high degree values often have a greater impact on resilience than other indicators. Prioritizing the repair of nodes with the highest degree values leads to the greatest increase in network efficiency, whereas repairing nodes with the highest closeness to the center has less impact on network efficiency.

The highly dynamic nature of subway airport line passenger flows and their susceptibility to the influence of airport flight schedules present challenges for accurate short-term forecasting of passenger flow. This study integrates airport flight information and historical passenger flow data from airport lines to construct a short-term passenger flow forecasting model based on a stacking ensemble model. The model incorporates random forest (RF), LightGBM (light gradient boosting machine), gradient boosting decision tree (GBDT), and logistic regression algorithms to act as ensemble learners. The proposed model is validated using data from the Beijing Subway Daxing Airport Line and is compared against two baseline models, namely informer and long short-term memory (LSTM) networks. The results indicate that the dual-channel prediction, which considers flight information and historical passenger flows, outperforms the single-channel prediction solely based on historical passenger flows. The results also indicate that the stacking model demonstrates superior performance across all metrics. Particularly, the best prediction performance is achieved at a 96 step (24 h) forecast horizon, with mean absolute error of 7.66 and 4.67 for inbound and outbound passenger flow predictions, respectively. Analysis of the impact of flight information characteristics on the prediction model reveals that departure flight information is of relatively lower importance than that of arrival flights, which is attributed to large differences in advance arrival times for departing passengers.

To make comprehensive use of the passenger transportation resources in a high-speed railway hub, this study explores the division of labor among passenger stations within a high-speed railway hub. Herein, a multiobjective programming model was developed with the objectives of minimizing the total train operation time and coordinating the capacities of the stations in the hub. The augmented ε-constraint algorithm was used to solve the approximate nondominated frontier of the model. Using the Zhengzhou high-speed railway hub as a case study, the differences between the existing plan and the optimized plan and their adaptabilities were qualitatively and quantitatively compared to validate the feasibility and effectiveness of the proposed model and algorithm. The results show that the augmented ε-constraint algorithm can identify high-quality representative nondominated solutions for the proposed model. Moreover, the optimized plan can reduce the train operation time within the hub and enhance the coordination of station capacity utilization. Thus, this study provides reliable references and reasonable suggestions for decision-making regarding capacity expansion, transformation, and optimization of high-speed railway hub areas.

This study investigates vehicle scheduling and path planning problems on field roads after large equipment transportation vehicles enter construction sites. Due to road width limitations and varying task priorities, vehicles have difficulty traveling in opposite directions on the same road. Furthermore, the large equipment transportation vehicles have different priorities depending on their loads and urgency of the transportation. To address these challenges, this study constructs an integer programming model based on spatiotemporal network technology that minimizes the total travel time of all vehicles on the site by considering road restrictions and vehicle priorities. Furthermore, vehicle flow balance and meeting avoidance constraints are incorporated into the model. Moreover, a heuristic algorithm is designed to efficiently solve the model and obtain the spatiotemporal path of each vehicle, thereby providing guidance for vehicle path planning and passing each other. The effectiveness of the proposed model and algorithm is demonstrated through multiple cases based on an actual wind farm road network. The computational results show that the algorithm can quickly solve the vehicle path planning problem at various scales. Additionally, it can guarantee short waiting time to avoid vehicle meeting while eliminate spatiotemporal conflicts. Moreover, the proposed approach showed high transportation efficiency.

Based on the standard requirements of ISO 26262 Road Vehicles-Functional Safety, this study analyzes the multiaxle electro-hydraulic steering system of special vehicles to enhance the system's safety and reliability. In this study, the Simulation X software was used to establish a detailed simulation model for the multiaxle special vehicle, and simulation experiments were conducted via fault injection. The simulation results and data were analyzed to assess the severity, exposure and controllability of the faults, thereby determining the corresponding automotive safety integrity level. Thus, based on fault injection simulation, the automotive functional safety analysis method can serve as a crucial means to assess architectural safety in the early stages of system design.

In this study, we build a model for the rotors and propellers of 60 kg combined wing aircraft based on the strip and momentum theory, and circularly calculated the increment of the upcoming flow as an intermediate variable to precisely determine the propellers’ performance. By comparing the obtained results with the experimental data, we corrected the model and calculated the mechanical performance of the propeller. Result showed that the model could evaluate the thrust and shaft power with a bias of less than 5% and less than 10%, respectively. Using this method, we drew the MAP curves representing the mechanical performance as the essential parameters in the power model and built a bridge between mechanical performance and controlling model. The results can support the study of mechanical modelling of combined wing aircraft.

The issue of the pricing of airport advertising revenue, a crucial component of non-aeronautical income in airports, holds significant importance in the operational management of airports. Currently, most airports in China commonly adopt a pricing mechanism based on historical price inertia, while also making adjustments to advertising prices by appropriately considering the total passenger flow for the current year. This pricing mechanism struggles to effectively reflect the true value of advertisements in different locations. This paper proposes a pricing mechanism based on passenger traffic to assess the relative value of advertising spaces within airport terminals. Utilizing a mathematical model combined with the physical layout, and flight and passenger data of the airport, we calculate the distribution of passenger traffic and subsequently evaluate the value of advertising spaces based on this information. Additionally, we apply this approach using sample data from the Capital International Airport. The findings demonstrate that the application of this model can reveal variations in the value of airport advertising spaces with the same media format across different spatial and temporal contexts. This lays the theoretical groundwork for airport advertising management entities to further implement differential dynamic pricing strategies and flexible advertising placement policies.

The safety potential field is utilized to characterize the distribution of safety risks around a vehicle during the driving process. However, when analyzing the safety potential field formed by moving vehicles, the existing models only focus on the vehicle motion but ignore the traffic environment information perceived by drivers. This study focuses on the construction of an improved safety potential field model and its application to the car-following model. Herein, the relative state influence factor is introduced to strengthen the influence of relative speed among vehicles, and the traffic state influence factor is introduced to reflect its influence on driving safety. Moreover, the vehicle type coefficient is introduced to adjust the distance to reflect its influence on driving safety in mixed vehicle type traffic. The car-following model is developed by using the preceptive safety potential field to establish the relationship between the motion state of the front vehicle and the behavior of the following vehicle. Furthermore, the genetic algorithm is employed to calibrate the proposed model, the intelligent driver model, and the car-following model based on the safety potential field. The results show that the root mean square errors of these three models mentioned before are 6.124, 8.515 and 7.248 respectively, which proves that the model proposed in this paper can describe car-following behavior more accurately. Therefore, this study can provide theoretical support for driving risk evaluation and vehicle control under a complex environment.

To enhance the navigation efficiency of ships in inland waterway navigation facilities and increase their operational capacity, a continuous gear shifting model and algorithm for ship lock chambers are proposed. This model comprises two scenarios: considering and not considering the sequence of ships entering the lock. First, a two-dimensional packing problem model was employed to establish a continuous gear shifting model for ship lock chambers. Then, an algorithm for solving the aforementioned continuous gear shifting model based on a greedy strategy was proposed. Finally, simulated ship data for vessels arriving at the lock was generated based on the Baise Junction Project. The proposed algorithm was used to calculate the lock chamber gear arrangement. Results indicate that, in the case of randomly generated data for 90 ships, 47 lock cycles were required for the gear arrangement considering the ship arrival sequence, with an average occupancy rate of 76.424%. However, only 45 locks were needed for the gear arrangement when the ship arrival sequence was not considered, with an average occupancy rate of 76.821%. The proposed model and algorithm can effectively shift gears continuously in the ship lock chamber under various conditions.

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.