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.