This PDF file contains the front matter associated with SPIE Proceedings Volume 13421, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The incidence of pedestrian fatalities and injuries in traffic accidents remained unacceptably high. In order to understand the preferences and needs of pedestrians when crossing from a human perspective and to provide layout recommendations for intersection safety signs and facilities, this study collected pedestrian eye movement data at signalized intersections using Tobii Pro Glasses2. This study analyzed the characteristics of their attention allocation during street crossings and examined the impact of environmental factors on their attention distribution. To provide a more precise depiction of pedestrians' attention distribution on the street, this study introduced the "attention rate" index through principal component analysis. Subsequently, by employing the random forest model and nonparametric tests, this study explored how six environmental factors affected the distribution of attention rates among pedestrians. Results highlighted the pivotal role of the central area (AOI5), left area (AOI4), right area (AOI6), and lower area (AOI8) in pedestrian crossing. Participants displayed significantly different attention focuses during both the waiting and crossing stages. Furthermore, the study indicated that the nature of surrounding land, the type of crosswalk, and pedestrian warning systems significantly affected the attention distribution among pedestrians. In contrast, factors such as the duration of redlight, the light countdown, and the crossing type had relatively minor impacts on this aspect. These findings provide a valuable reference for the implementation of user-friendly crosswalk designs, the strategic placement of pedestrian warning systems, and other safety signs.

In scenarios where vehicle spatio-temporal trajectory data is available, this study proposes a real-time optimization scheme for single-point signal control based on the trajectory data of incoming vehicles, taking into account drivers' reliance on signal phase transition countdowns. The aim is to address the mismatch between signal control schemes and the randomness of traffic flow arrivals. Firstly, the concept of stage division is introduced, and a two-stage cyclic real-time control scheme is constructed. Secondly, vehicle's passing during the green phase and vehicle’s queuing during the red phase are predicted based on trajectory data. To balance vehicle throughput and delay, a method is proposed to determine the remaining green time during the variable stage of the scheme. Finally, using an actual intersection as a case study, a simulation experiment platform is built with SUMO and Python to compare the proposed signal control method with the traditional Webster timing method. Simulation results demonstrate that the proposed signal control scheme outperforms the Webster timing method in terms of average speed and delay indicators, regardless of peak or off-peak periods. These findings suggest that vehicle spatio-temporal trajectory data can facilitate the development of signal timing schemes that better align with the actual traffic conditions at intersections, offering a new approach for achieving refined control of signalized intersections.

This paper employs FLUENT to investigate of the aerodynamic characteristics of a two-stage flapping wing vehicle with 3-DoF of flapping, twisting and folding. The dynamic mesh method is used to obtain the lift and drag coefficients for different inner and outer wing span lengths, flapping frequencies, incoming flow velocities and angle of attack. The pressure contour of the surface of the wings were elucidated, and the aerodynamic mechanism of the two-stage flapping wing is further revealed. The results demonstrate that the flapping wing vehicle exhibits favorable aerodynamic characteristics with an inner wingspan of 100 mm, an outer wingspan of 150 mm, an incoming velocity of 10 m/s, a flapping frequency of 10 Hz and an angle of attack of 0°.Therefore, by constructing a three-dimensional wing dynamics model and varying the motion modes such as inner and outer wingspan, flapping frequency, incident flow velocity, and angle of attack, it can provide reference for improving the aerodynamic performance of two-stage flapping wing aircraft.

Temporal and spatial imbalances in truck flow often occur in mining operations due to fluctuating production demands and varying operational conditions. In current mining road systems, lane directions are typically unrestricted, allowing trucks to travel in both directions. However, each lane can only be occupied by one truck at a time, leading to congestion on certain roads while others remain underutilized. To address this issue, this paper proposes a novel approach that imposes directional restrictions on mine lanes and introduces operational direction schedules to optimize mixed-mine truck flow. A bi-level programming model is formulated to solve this problem, incorporating both the reconstruction of mine lanes and the scheduling of their operational directions. In the upper-level model, the number of controlled mine lanes and scheduling plans are determined to minimize the total operational cost. The lower-level model captures the behavior of mixed-mine truck flow, where both connected autonomous mine trucks (CAMTs) and connected human-driven mine trucks (CHMTs) follow a user equilibrium principle. Factors such as value of time (VOT) and fuel consumption are included in the generalized cost function. A numerical example based on a typical mining network is conducted to illustrate the effectiveness of the proposed solution. The results indicate that the introduction of operational direction schedules can significantly improve the overall efficiency of the mining transportation system.

To investigate the impact of visual composite elements, including guiding and warning elements, on drivers' visual characteristics at the entrance section of a highway tunnel, on-road vehicle experiments were conducted at a highway tunnel in Hubei Province. Visual composite elements were installed at the southbound entrance section while no elements were arranged at the northbound entrance section. Fine-grained eye-tracking data, including gaze duration and scanning angle, were collected from 18 drivers at both the southbound and northbound tunnel entrance sections to analyze differences in their visual characteristics. The results indicate that drivers had longer gaze durations and more concentrated gaze distributions at the northbound entrance section, whereas gaze durations were shorter and gaze distributions were more dispersed at the southbound entrance section. Furthermore, drivers exhibited larger scanning angle during the driving process at the southbound entrance section, while demonstrating smaller scanning angle and faster scanning speeds at the northbound entrance section. Overall, the installation of visual composite elements significantly influenced drivers' eye movement indicators, allowing drivers to more effectively gather driving-related information, reduce driving stress, and enhance visual stability while driving.

The article discusses the utilization of Building Information Modeling (BIM) technology in the design phase of liquid bulk cargo terminals with high pile structures. It employs the Analytic Hierarchy Process to analyze the key elements for addressing multi-dimensional collision issues in complex working conditions. The study includes project validation conducted on the 6th to 7th liquid chemical berth engineering of a specific international port. The findings demonstrate that BIM-based solutions offer significant advantages in multi-dimensional spatial design and intricate pipeline requirements in port engineering within complex working environments. By incorporating spatiotemporal progress simulation and 3D visualization collision analysis, the rework rate decreased by 4.3%, effectively enhancing design efficiency and engineering quality. This research provides insights for the application of BIM technology in similar engineering design phases.

The purpose of this paper is to investigate the wind field characteristics in deep gorge wind field to improve the safety and comfort of trains under the windy environment in deep gorge wind field. In this paper, a typical deep gorge wind field model is constructed to investigate the influence of canyon dimensions on the gorge wind field characteristics. The numerical simulation used in this paper was validated to be able to accurately simulate the structural characteristics of the gorge wind field by comparing it with the results of the previous mountain wind field experiments. The result shows that: the acceleration effect of airflow in the gorge increased significantly with the increase of the gorge height. The acceleration effect at the entrance decreases with the increase of the gorge width, while the acceleration effect at the center of the gorge remains the same.

In order to deeply understand the inputs and outputs of traffic safety projects, evaluate the implementation effect, cost effectiveness and impact on traffic safety of the project, this paper takes the “Starlight Project” of Tongxiang City as an example, and analyzes the performance of its traffic project by using the presence-absence comparison approach. By comparing the changes in the number of traffic accidents before and after the implementation of the “Starlight Project” and assessing the benefits of the project in terms of traffic, society and environment, it is found that the “Starlight Project” has achieved remarkable results in enhancing road traffic safety, improving night-time driving conditions and traffic sign recognition. It also found that the successful implementation of a project is closely related to its scale, investment intensity and duration. This study provides a useful reference for evaluating the performance of similar transportation projects, and provides experience and guidance for the design and implementation of future projects.

Traffic accidents occur frequently on the continuous downhill section of highway in China, and the severity of accidents is relatively high. At present, the safety evaluation of continuous downhill section mainly relies on empirical analysis and statistical analysis, and there are few quantitative comprehensive evaluation models. In this paper, based on the traffic safety characteristics of continuous downhill section, on the basis of soliciting the opinions of many experts in the industry, the evaluation index related to highway traffic safety is divided into target layer, reference layer, and factor layer, and the evaluation index structure system model is established. By using the nine-level scale method and the maximum membership method in fuzzy mathematics, the comprehensive evaluation model of continuous downhill safety is established. Through quantitative analysis, the risk factors of the continuous downhill section are determined, the comprehensive risk model of traffic safety is established, and the highway safety risk situation is quantified, which provides an effective method for the quantitative assessment of the comprehensive safety situation of the continuous downhill section, and solves the problems that are difficult to assess in the construction process. Not only has the guiding significance to the improvement of the traffic safety of the existing highway, It can also provide a basis for comparison and selection of safety advantages and disadvantages among different engineering schemes.

For the problem of excavation leakage when the reamer works, based on the finite volume method, numerical simulation is carried out by fluent simulation software, and the parameters of each factor are changed to study the concentration distribution and leakage changes in the flow field around the reamer under different working conditions, so as to make clear the influence of each factor on the flow field. The results show that: the changes of reamer rotation speed and traverse speed will have a greater impact on the flow field, and a larger disturbance will cover the influence of particle size changes on the flow field; usually a larger reamer rotation speed and traverse speed have a larger sediment absorption, but in the case of unsuitable depth of cut and tilting angle of the reamer, blindly increasing the reamer rotation speed and reamer traverse speed will lead to the decline in yield.

The purpose of this study is to explore the integrated application of BIM and UAV 3D reality modeling technology in highway engineering construction. This paper introduces the core technology of 3D reality modeling based on UAV airborne LiDAR system, summarizes the integration process of 3D reality modeling technology and BIM technology, and conducts application research of BIM and 3D reality modeling integration technology in highway engineering construction based on practical projects. The results show that the original data collected by the airborne LiDAR can be processed by 3D reality modeling technology, and the 3D reality model with accurate plane and elevation coordinate information can be generated. The combination of BIM and UAV 3D reality modeling technology performs well in terms of data integration and simulation capabilities, providing an integrated information platform that facilitates effective communication and collaboration among project participants.

In this study, a traffic safety driving simulator was employed to develop a simulation scenario of a tunnel section connecting to an interchange, design and conduct driving simulation experiments, and perform safety comparisons and selection studies on the two scenarios, namely T-type and variant T-type. Five safety evaluation indexes, including location speed, running speed, acceleration, lane change position, and driving trajectory, were designed and selected for analysis of the feasibility of the two schemes from the perspective of driving behavior. The experimental results indicate that the T-type scene is more secure. Furthermore, the connection scheme of main lanes and ramps was proposed in conjunction with engineering examples, thereby providing theoretical support for the pre-design work of highway engineering and offering new ideas and approaches for the safety evaluation of similar interchanges.

The control method of the working mode of P2.5 hybrid dual-motor heavy commercial vehicle is analyzed, and based on the vehicle power demand, battery SOC and real-time speed, the conditions for entering different working modes based on speed stratification are formulated. They are pure electric drive, driving charging, engine direct drive, series drive, parallel to oh, regenerative braking and parking mode. The control strategy ensures the effective switching between the modes, so that the P2.5 dual-motor hybrid system achieves the effect of efficient and fuel saving operation, and provides reference for the subsequent engineering developers.

With deep learning prevailing in intelligent transportation systems, pavement crack detection using object detection has aroused wide attentions in both academic research and engineering application. Unfortunately, it still a challenge to focus on pavement critical areas and perceive crack at different scales due to the complexity in traffic background. To improve feature extraction and fusion capabilities, a pavement crack detection method named YOLOv8-PCD is proposed in this paper based on enhanced feature fusion. Firstly, the Biformer attention mechanism is incorporated into the network to enhance its performance in handling pavement objects at varying scales. Secondly, the Neck network is upgraded to BiFPN to better integrate crack features from different levels, thereby improving the detection capabilities. Finally, the experiments verify the proposed method could accomplish satisfactory result in both robustness and accuracy.

In response to the changes in traffic loads brought about by the Autonomous Rail Rapid Transit (ART), an emerging medium-capacity public transportation mode, this paper proposes a co-simulation analysis method for ART and its roads based on TruckSim-Simulink-PreScan. The aim is to address the current situation of the lack of research on vehicle-road coupling effects in the promotion and application of ART. By constructing a vehicle-road coupling dynamics model, the paper builds an ART model based on TruckSim, utilizes Simulink for vehicle dynamics control optimization, and finally simulates actual road conditions in PreScan for joint simulations. The research encompasses model building, parameter setting, control optimization, and simulation testing, offering a novel research idea for the dynamic analysis of the coupling between ART and its roads. Through the simulation analysis, the research provides the theoretical foundation for the promotion and application of ART as well as its road construction standards, thereby ensuring its operational safety.

Three Gorges ship lift has been put into trial navigation since September 18, 2016, and coordinated operation with the Three Gorges ship lock. The annual throughput of Project has continued to grow steadily, and the throughput has record high consecutively for many years. This paper analyzes the cooperative operation characteristics of the Three Gorges ship lift and the Three Gorges ship lock through data statistical analysis method, and demonstrates the role of fast channel and diversion ship of Three Gorges ship lift. The results showed, Three Gorges ship lift effectively play the role of fast channel, and play the role of diversion passenger ships, public service ships, small ships, empty ships, which improve loading rate of ships passing through the Three Gorges ship lock, and the Single operation throughput promotion of the Three Gorges ship lock. It plays an important role in improving the overall passing ability of the Three Gorges project.

Affected by construction of highway reconstruction and expansion, resulting a compression of road traffic space. There are significant differences in traffic flow characteristics between the construction period and the operation period. Therefore, it is particularly important to propose appropriate traffic management strategies for targeted construction periods. This article conducts on-site research and analysis of driving speed characteristics on the traffic diversion section during the construction period of Kaiyang Expressway. Radar and video equipment are used to collect the speed of the diversion area, process and visualize the obtained traffic data, and analyze the speed characteristics of the diversion area section. Suggestions for speed limit values are proposed, providing important technical basis for the design of the diversion area line shape, speed control, and traffic control strategies during the construction period.

The space connection scheme is one of the core contents of air-rail intermodal transport. Reasonable planning of space connections helps to improve the overall efficiency of the intermodal system and optimize the travel experience for passengers. Based on the existing train operation plans and air route planning, this paper constructs an integer programming model aimed at minimizing the overall travel cost of intermodal passenger flow and the cost of air and rail enterprises. Taking the intermodal OD (Origin-Destination) served by major airports in Beijing-Tianjin-Hebei region as a research case, the results show that passengers primarily consider price advantages and transfer convenience when choosing transfer nodes, typically choosing hubs that are relatively close or can be reached by high-speed railway as transfer nodes. Shijiazhuang Zhengding Airport has the potential to develop air-rail intermodal transport because of its price advantages and convenient transfer conditions.

When the metro runs on the curve line, the vehicle will produce cyclic abrasion due to the change of the interaction between the wheel and the rail, and this abrasion may cause the vehicle and the track system to produce violent vibration, especially to make the interaction force between the wheel and the rail in the pendant direction become larger, which will cause serious damage to the track structural components. In order to study the vibration characteristics of the track under the curve conditions, a model of the wheelset and sleeper is established in the finite element software, and the modal frequencies and vibration shapes of the sleeper and wheelset are analysed within the range of the calculation frequency of 0-1200Hz. Then the curved and straight rail is established by beam unit, and the rail sleeper in the track structure is established by solid unit, so as to establish the vertical and transverse finite element model of the track. In order to take the overall vibration of the roadbed into account in the actual model, the mass unit is used to replace the roadbed to analyse the lateral and vertical response of the track under the application of transverse and vertical forces. Two types of curved tracks with different radii are modelled, and the difference between the vertical and lateral characteristics of the tracks with different curve radii is compared.

The requirements of the Energy Efficiency Existing Ship Index (EEXI) have posed significant impacts and challenges to global shipping companies. For operational vessels that do not meet EEXI requirements, determining an appropriate retrofit plan to comply with the convention, which officially takes effect in 2023, is an urgent issue that shipping companies need to address. This paper conducts an EEXI assessment on a specific 81,000 DWT bulk carrier, analyzes various retrofit options, and provides recommendations that can be referenced for the EEXI retrofit of other vessels.

The application of BIM technology in the construction industry in China has developed rapidly, and its further advancement relies on the support of BIM standards. Given the importance of BIM standards, it is necessary to review the existing research on BIM standards. First, the keyword clustering analysis method identified three key research themes: theoretical research on BIM standards, technical research, and application research. Second, for each research theme, the methods and findings of existing research were explained and summarized through a review of key literature. Then, the evolution trend of BIM standard research was analyzed using a timeline generated from keywords. The results indicate that the expansion of the IFC standard and research on IFC-based technologies and applications will remain research hotspots; at the same time, research on the framework of the BIM standards system will continue. Furthermore, future research on BIM standards should also focus on the deep integration with emerging technologies such as AI and IoT, expand adaptability in new application scenarios, and address issues of collaboration and interoperability across fields and industries. This paper contributes to the further development of BIM standards and enhances the formulation and application level of BIM standards.

Formula racing for college students does not simply emphasize speed, but should comprehensively consider safety, reliability, economy and controllability. Honda's single cylinder engine is selected as the power system of the racing car, and the frame adopts a new steel pipe truss structure. The engine model, transmission system model, braking system model, steering system model, suspension model, frame model, aerodynamic kit model and seat model are established through 3D modeling software, and the final assembly is carried out. At present, the car has been successfully developed and successfully participated in the China College Formula Auto Competition, and its power, fuel economy, braking and other comprehensive performances have been greatly improved.

With the official implementation of CNCAP2024 version, the backrest angle of the front row whip has been changed from a fixed 25° to the manufacturer's design angle, improving the flexibility of seat development by the host factory and meeting the comfort needs of a wider range of drivers. The different forms of differences in the backrest angles of the front seats will have an impact on human injury during low-speed rear end collisions. In order to study the influence of different backrest angles of the front seats on the evaluation results of the whipping test, according to CNCAP requirements, two whipping tests were conducted using different backrest angles of the front seats to analyze the differences in dummy injury caused by differences in backrest angles of the front seats. The research results indicate that in this whipping test, the pulse width and variation trend of the injury curve of the dummy are basically the same for different backrest angles of the same seat. However, different backrest angles indirectly affect the distance of the head back gap, resulting in changes in the angle of contact between the A-side of the headrest and the dummy's head and backrest, which can intuitively affect the changes in the neck force of the dummy and ultimately affect the evaluation of the whipping test. By analyzing the evaluation of the whipping test, the rationality of changing the backrest angle to the design angle in CNCAP2024 was verified. The backrest angle in the front row whipping was changed from a fixed 25° to the manufacturer's design angle, which took into account both the comfort of the driver and the accuracy of the whipping test evaluation.

The highway bridges built in the 1990s and before have increasingly serious diseases, which greatly impair the normal function of the traffic system. In this paper, bridge maintenance measures are dividing into two categories: preventive maintenance and corrective maintenance. Taking the minimum maintenance cost of bridge as the optimization goal, a mathematical model for optimal maintenance decision of highway concrete girder bridge is proposing. The optimal maintenance time and maintenance method of the concrete main girder and pier in the design service life of the case bridge are obtaining by using genetic algorithm of Matlab software.

In order to improve the operational efficiency of long downhill tunnels on mountainous expressways and ensure the safety of driving, this article takes the Taipingliang Tunnel on the Zhangshi Expressway in Hebei Province as a basis, and uses the method of laying different test sections to analyze the factors affecting the anti-skid performance of the road surface of the long downhill tunnel. Long term post observation is conducted, and continuous observation data is compared and analyzed to compare the effects of different materials and particle sizes on the use effect and service life of the tunnel pavement overlay, providing reference for similar projects.

This study investigates the impact of Augmented Reality Head-Up Display (AR-HUD) lane enhancement system on lateral control performance in fog conditions. Using Unity-based simulations, a controlled, immersive driving scenario in fog was created to assess the impact of the AR-HUD system on lateral control performance. Participants drove on a curved road in a foggy environment while their lateral control metrics, specifically steering speed (SS) and standard deviation of lateral position (SDLP), were monitored. The results showed that the AR-HUD system significantly reduced SS and SDLP in all driving segments, demonstrating improved steering accuracy and stability. Notably, the most significant improvements were observed during the initial phase when foggy conditions were encountered, i.e., the Approach Segment, highlighting the utility of the AR-HUD when transitioning to low visibility conditions.

To improve the uneven distribution of fractured rock blocks in the Aktep Tunnel in Xinjiang, the key parameters of the existing blasting plan, such as the inclination angle of the cutting hole and the spacing between surrounding holes, were optimized and analyzed through theoretical calculations using ANSYS/LSDYNA numerical simulation method; Four sets of models for cutting and smooth blasting were constructed separately, and the contour of the blasting chamber after blasting was obtained through simulation, based on which the blasting effect was analyzed. The results showed that compared with the blasting effects when the excavation angles were 70°, 75°, and 80°, when the excavation angle was 75°, while ensuring the excavation depth, the damage to the surrounding rock mass caused by blasting could be reduced, the blasting of the rock mass was more uniform, and the rock block rate was significantly reduced; Comparing the blasting effects of peripheral holes with a spacing of 50, 60, and 70 cm, as the spacing between holes increases, the damage zone between the two holes changes from through to non through. The best blasting effect can be achieved when the spacing between peripheral holes is 50 cm. The optimized blasting plan was applied to practical engineering, and the blasting effect was significantly improved, greatly enhancing construction efficiency.

To enhance tunnel maintenance and traffic safety, this study implements automatic detection and identification of tunnel defects using the YOLOv8 model. By collecting defect image data from various tunnel environments and performing data preprocessing and annotation, the YOLOv8 model was constructed for defect detection. The results demonstrate that the YOLOv8 model achieves a precision of 0.87, a recall of 0.89, and an mAP50 of 0.87 on the test set, indicating excellent detection capabilities and accuracy. The model accurately identifies and locates defect types such as cracks, damage, and spalling. Visual analysis of the model's detection results confirms its ability to precisely locate and recognize various internal tunnel defects, further validating the model's stability and practicality. This research provides significant technical support for intelligent transportation and tunnel safety management, highlighting the importance of early identification and effective treatment of tunnel defects. It contributes to improving the safety and efficiency of tunnel operations.

With the development of domestic aircraft models in China, this paper reviewed the existing airport facilities and analyzed the parameters of new aircraft design, as well as proposing airport compatibility assessment content and safety operating margins for new aircraft design. The main airport facilities include runway, taxiway, visual aids, apron, emergency rescue and clearance planning. Through both qualitative and quantitative analysis, the system and content of airport compatibility assessment were proposed. The research results is helpful to improve the reliability development of aircraft design in China.

In order to reduce the wind resistance of containers on the deck of a partially loaded ship, the layout of the containers on the container ship was adjusted, the width of two rows of containers was increased to make them full, and they were set at different positions on the deck. CFD technology was used to perform numerical simulations under various working conditions, and the wind load was studied to obtain the resistance coefficient under various working conditions. The results show that under the condition of positive headwind, most double-shell full-row layouts can reduce the resistance coefficient of containers, and the resistance reduction rate is different at different positions.

During prolonged high-speed operation, wheels can develop uneven circumferential wear, leading to out-of-round conditions that exacerbate wheel-rail interactions. As a critical component of high-speed trains, axles bear significant loads, making accurate fatigue life assessment essential for safe operation. This study examines the impact of polygonal wheel wear on the fatigue life of high-speed train axles. Using a specific high-speed train wheelset model, a rigid-flexible coupling dynamic model was established to account for wheelset flexibility. Dynamic stress at key axle positions was calculated using the modal stress recovery method and validated via the finite element method (FEM). This study investigated the influence of polygonal wheel wear on axle bending stress and assessed fatigue life at critical axle points using the nominal stress method. Results indicate that polygonal wheel wear amplitude significantly affects bending stress ranges at key axle positions. Bending stress induced by polygonal wheel wear shows nonlinear characteristics with increasing vehicle speed. Under different polygonal wear orders, axle stress variation peaks at 173 Hz, 270 Hz, 580 Hz, 690 Hz, and 930 Hz, corresponding to wheel-rail coupled vibration modes. Fatigue damage at axle points increases with the amplitude of polygonal wheel wear, with the highest damage observed in the transition zone between the gearbox seat and wheel seat. This damage value is 0.56 under a 0.2 mm polygonal wheel wear amplitude, meeting design requirements.

The stability of a high-speed train considering abnormal wheel/rail contact relationship is primarily analyzed by using a train dynamic model. In addition, aerodynamic loads of train considering different crosswind speeds and operational speed are investigated through simulation of an aerodynamic train model. Subsequently, the influences of wheel/rail contact relationship and aerodynamic loads on the hunting stability and dynamic performance of the train are further analyzed, in terms of the operating mode, ride quality and operating safety of the train. The results show that the lateral displacement of wheelset increases due to crosswind loads in the presence of low wheel/rail conicity, and even leads to the wheel flange contact. For the high wheel/rail conicity condition, the crosswind load could suppress the hunting motion of bogie system. The hunting motion of wheelset could disappear and further converges to the equilibrium position, when the crosswind speed is greater than 10 m/s. Therefore, the crosswind load could enhance the ride comfort of train in the case of bogie hunting. Whereas, with the further increased crosswind loads, the crosswind load may deteriorate the ride comfort. Finally, considering the operational safety index of train under the crosswind loads, a safety region for different wheel/rail contact relationship is determined, which could facilitate the operational safety of high-speed train in the present of cross wind.

In recent years, super multi-lane expressways such as two-way ten lanes and twelve lanes have become a new trend of highway reconstruction and expansion in our country. Due to the limitations of terrain conditions, land use scale and investment in expressway reconstruction and expansion, some adjacent interchange is set as compound interchange because of the small net distance between them. In this paper, the combination of adjacent interchange with auxiliary lanes is taken as the research scenario, and by combining theory and simulation analysis, the length selection method of the weaving area of the composite interchange of super multi-lane expressway (one-way five-lane and six-lane) is proposed, which can provide reference for related engineering design practice.

The growing adoption of electric vehicles has made the optimization of charging infrastructure an urgent priority. However, existing studies on the supply-demand balance of new energy infrastructure has often overlooked key factors such as cost, distance, convenience, and user preferences. This research proposes a method that used a Transformer-based model to calculate the selection probability of charging facilities, incorporating these critical factors. In a case study conducted in Shenzhen, the method showed that for half of the plots, the supply-demand balance was accurately reflected. However, 30% were overestimated and 20% were underestimated compared to traditional models. The main contribution of this study lies in providing a dynamic and more accurate approach to electric vehicle infrastructure planning, which could serve to better align charging supply with real-world demand.

The deterioration of asphalt highways in China, particularly due to the emergence of transverse cracks in semi-rigid base pavements, significantly undermines their structural integrity and overall performance. This study presents a comprehensive analysis conducted on a 3,000-kilometer highway network, utilizing a decade of operational, maintenance, and environmental data. The research applied Particle Swarm Optimization (PSO) and Back Propagation Neural Network (BPNN) algorithms to identify six critical factors influencing pavement crack formation: pavement layer information, layer thickness, total and surface layer service time, natural zoning, and traffic load. The findings reveal that these factors contribute differently to the occurrence and progression of pavement distresses under various conditions. Specifically, the study found that semi-arid regions exhibit a rapid increase in crack-related issues, thicker pavement layers slow down disease progression, and the extent of damage varies significantly across different service periods, with a notable acceleration observed in pavements with 4-12 years of service. Furthermore, a threshold effect was identified in the impact of traffic load, with a sharp increase in crack damage area when the Annual Average Daily Traffic (AADT) exceeds 5,000 vehicles per day. The predictive analysis using the PSO-BPNN model achieved an accuracy rate exceeding 90%, demonstrating its effectiveness in guiding maintenance decisions for highway pavements.

The FAKRA cable has emerged as a dependable and effective choice for transmitting high-frequency signals in automotive applications. However, guaranteeing signal integrity in FAKRA cable systems has become a critical challenge, particularly with the rising data rates in electric vehicle applications. This paper aims to explore the RF performances of various FAKRA cables up to 6 GHz. Additionally, a comparison is made between straight and right-angle connectors in FAKRA cable assemblies. Through a combination of simulations and measurements, it is demonstrated that the straight FAKRA connector exhibits better RF performances for high-speed data communication than the right-angle version. The study provides the physical explanation of the discrepancies between straight and right-angle FAKRA connectors. Engineers working with FAKRA system should choose cables with straight connectors to ensure optimal signal integrity for highspeed data transmission in automotive applications.

This paper discusses the integration of anti-slip design for subway station entrances and exits with innovative solutions in medium and low-speed maglev technology. Addressing the issues of wet and slippery conditions, as well as icing, at the entrances and exits of subway stations and along medium and low-speed maglev tracks during severe weather, the paper proposes a strategy that combines the design advantages of both. This strategy involves the use of new anti-slip materials, optimization of track structures, enhancement of braking system performance, and the establishment of an intelligent antislip warning system. These measures aim to improve the anti-slip performance of medium and low-speed maglev tracks and address the issues of icing and wet conditions at subway station entrances and exits. The paper, through a discussion combining silicone rubber with metal surface treatment technology, concludes that a thin layer of silicone rubber can play a role in both anti-slip and resolving icing issues at subway station entrances and exits, as well as along medium and low-speed maglev tracks.

Highway on-ramps are recurrent bottlenecks to cause serious traffic delay. This study explores the joint optimization of variable speed limits (VSL) and their locations on the highway with on-ramp bottlenecks to minimize vehicle delay. To solve the proposed model, a deep reinforcement learning method based on the Deep Q-Network (DQN) based OptionCritic framework is employed to determine real-time VSL values and locations upstream and around the on-ramp bottleneck. The model is trained in an environment with random traffic volumes of 4000-6000 veh/h, enabling it to effectively handle the uncertainties of traffic flow in real-world scenarios. A case study follows to validate the effectiveness of the proposed method. The results show that the joint optimization of VSL and their locations can significantly enhance the performance of highway on-ramp bottlenecks to reduce vehicle delay, stop time, and CO₂ emissions by 24%-60%, 30%-63%, and 4%-35%, respectively. Specifically, the joint optimization of VSL and locations performs best when the traffic flow is between 5000-5500 veh/h. This research provides insights into highway on-ramp management and helps improve traffic efficiency.

In order to better implement the statistical method of road freight volume developed by the Ministry of Transport, which is "comprehensive survey of enterprises above scale + calculation of fluctuations in households below scale", this article considers the impact of major and intermediate road maintenance conditions on the freight volume of road sections in the maintenance affected area, and studies the real-time weight adjustment problem in the statistical method. Firstly, an improved ant colony algorithm is used to simulate the missing data in the affected area. Secondly, BP neural network is used to fit the road data, and the adaptive artificial fish swarm algorithm improved by using chaotic mapping is proposed to solve the problem that the BP neural network algorithm has a high dependence on the initial value and is easy to fall into the local optimum. Finally, the trained internal connection weights of the model are used as the calculation indexes, and the entropy weight method is applied to calculate to get the adjusted weights. The improved algorithm increases the traversal and randomness by using chaotic mapping, and enhances the algorithm's global optimality seeking ability by making adaptive improvements to the field of view and step size. The example analysis shows that this model is superior to the traditional models such as BP, GA-BP and AFSA-BP, and the adjusted new weight is more in line with the demand of freight traffic statistics after major and middle road repair.

Aiming at the problem of difficult recognition of foreign object intrusion on highways under harsh weather, an improved YOLOv8 detection algorithm is proposed to enhance the accuracy of road foreign object target detection. More refined feature extraction is achieved through the SE attention mechanism, which improves the detection accuracy in the case of image blurring as well as fog and rain line occlusion in harsh weather; the backbone network improved by the sensory wild attention convolution RFA is used to enhance the ability to extract features from low-resolution and small targets; and the use of regression loss combining with focusing loss using SIoU further strengthens the network's classification and localisation ability. Experimental results show that the improved model performs well on the harsh weather traffic obstacle detection dataset, with a mAP50 of 86.8% outperforming the original YOLOv8 network at 84.2%.

To explore the rationality of the design of the top fireproof panel and side wall decorative panel for the Karnaphuli River Tunnel in Bangladesh, a numerical model considering intermediate and offset fire sources was established using the fire simulation software FDS, and the temperature field distribution of tunnel fires was studied. The results show that the high-temperature area inside the tunnel is mainly concentrated near the fire source, and the most dangerous position of the structure is located at the arch position near the fire source. The average highest temperature of the middle fire source and the offset fire source are 496°C and 561°C, respectively. The average temperature of the other areas except for the fire source does not exceed 300°C; Under both the middle fire source and the offset fire source conditions, the temperature inside the tunnel shows a distribution of higher arch temperature and lower lower lower space temperature. In the stable stage, the temperature of the side wall is not higher than 200°C. The design of the top fireproof board and side wall decorative panels of the tunnel is safe and feasible.

In order to study the effect of wheel rail impact load on fatigue damage of the structure, a vehicle track coupling dynamic model is established in the paper. Through theoretical calculation and finite element analysis methods, the P₂ resonance frequency of the wheel rail system is determined; Using the unevenness of saddle shaped welded joints as an impact load excitation model, a study is conducted on the dynamic response of the wheel rail coupling model under wheel rail impact load and the fatigue damage of the structure from the perspective of track parameters. This can provide certain assistance for future designers in selecting parameters for track systems. Based on the standards for rail polishing, as well as evaluation criteria such as wheel rail dynamic response, fatigue damage and vibration environment of the framework, the vertical amplitude polishing limit of rail welded joints is reasonably determined to be 0.3 mm. This polishing limit provides a reference for the formulation of rail welded joint polishing standards for urban rail transit.

Urban rail transit system has significant advantages, but with its rapid development, noise and vibration problems caused by wheel polygonal wear are becoming more and more prominent. When scholars study wheel polygonal abrasion, they seldom take the coupled vibration of wheel-rail system into consideration, while the P2 force resonance in the coupled vibration of wheel-rail system will have serious impact on wheel-rail system, such as wheel surface defects and rail wave abrasion, etc., and even lead to the failure of the vehicle and track components, which threatens the comfort and safety of the vehicle operation. In this paper, for the phenomenon of polygonal wheel abrasion in a certain type of underground, the mechanism of polygonal wheel abrasion caused by coupled vibration of the wheel-rail system is investigated from the analysis of rigid-flexible coupling dynamics. It was found that when the vehicle was running at a speed of 40-45 km/h, the P2 force with a frequency of about 60 Hz reached the peak value, which caused the wheel polygonal wear, resulting in the occurrence of wheel polygons of the 13th to 14th order. Meanwhile, the effects of wheel-rail parameters and track excitation on the coupled vibration of the wheel-rail system were investigated.

To identify safety hazards around railways, reduce manual workloads, and enhance the management of railway surrounding environments, a technological framework for safety protection based on high-resolution remote sensing change monitoring is developed in this study. The proposed technical routes are outlined, and key technologies involved, such as information acquisition, data storage, identification and interpretation, and change analysis, are examined. A trial verification of change monitoring using high-resolution remote sensing is performed on typical environmental safety hazards (e.g., plastic greenhouses, color steel, and dust-proof nets) within a 500 m range relative to the railway in the experimental area. This method provides a foundation for managing and remediating railway surrounding environments and offers a new technical support approach for the safety management of railway operations. This work constructs a multidimensional, multi-element, and comprehensive perception system for surrounding environmental hazards of railways, achieving all-round and refined monitoring of external environmental safety hazards of railways, which promotes the transformation of problem discovery to active intelligence.

Vehicle detection in drone aerial imagery presents challenges such as complex background environments, small target sizes, and computational complexity, which can result in missed or false detections. To address these issues, an improved YOLOv5s-ShuffleNetV2-SE-EIOU algorithm is proposed. The backbone network is optimized using the lightweight ShuffleNetV2 module, which reduces network redundancy while increasing detection speed. The SE attention module is introduced into the backbone to enhance the model's ability to capture vehicle features, thereby improving detection accuracy in complex environments. The loss function is replaced with EIOU_loss to accelerate the convergence of prediction boxes and improve regression accuracy. Experimental results show that in vehicle detection ablation experiments, this algorithm improved mAP@0.5 detection accuracy by 2.9%, mAP@0.5:0.95 detection accuracy by 3.8%, reduced training time by 0.015 hours, and decreased parameter count by 23.4%. In special weather detection experiments, the algorithm demonstrated higher accuracy.

In order to deeply analyze the influencing factors of road traffic accidents and their potential interconnections, this study selected 3,291 road traffic accidents that occurred in Yunnan Province from 2013 to 2023 as the research subjects. The study approached from five dimensions: driver, environment, road, accident participants, and accident outcomes, and applied the Apriori association algorithm in data mining algorithms to conduct an in-depth association analysis on multiple attributes such as the occurrence time, accident form, road type, and accident causes of road traffic accidents in Yunnan Province, revealing the internal connections between them. The results show that the occurrence of severe accidents is the result of the combined effects of multi-dimensional influencing factors.

Vehicle detection is a research priority in the field of transportation. How to quickly and accurately identify the category of the target to meet the real-time and high-precision requirements of vehicle detection is a problem that needs to be solved urgently. Therefore, a Swin Transformer-based urban road vehicle detection model is designed, which takes Swin Transformer as the basic framework and optimizes it from three aspects: the backbone network, the neck network, and the loss function. We construct a dataset of urban road vehicles containing 45,712 images. The experimental results show that the Precision of this model is 93.2%, Recall is 88.3%, and mAP is 92.5%. Compared with Faster R-CNN, SSD and CenterNet models, the Precision is improved by 6.5%, 5%, 2.8%, the Recall is improved by 5.8%, 4.2%, 2.9%, and the mAP is improved by 7.3%, 5.8%, and 4.4%, respectively, with better detection ability. And the AP and mAP values in the car, bus, and truck classes are higher than those of other models. The results show that the model has good accuracy and real-time performance, which can effectively improve the efficiency of traffic management and contribute to the development of traffic informatization and intelligence.

The analysis of ship avoidance maneuvers is of significant value for assessing the risk of vessel collisions and enhancing ship safety management. A new method has been proposed that utilizes the dynamic spatio-temporal data collected by the Automatic Identification System (AIS) to extract avoidance behaviors. This method integrates navigational rules and maritime practices to determine encounter situations and identify the avoidance process. Validation using actual AIS data has proven the accuracy and effectiveness of the method. It offers an efficient model for extracting encounter and avoidance behaviors from AIS data, providing support for the analysis of ship behavior and research on maritime safety.

Traditional traffic signal timing control and single-intersection control methods face challenges in addressing the complexity and dynamics of modern traffic systems. Methods such as Discrete Traffic State Code(DSTE) and image-like approaches have been employed to address difficulties in setting the state space. This paper introduces a multi-agent deep reinforcement learning algorithm based on the Double Deep Q-Network (DDQN) and proposes a state space setting approach utilizing one-hot coding to effectively reduce data dimensionality. By leveraging collaborative multi-agent reinforcement learning methods and "local centralization" strategies, issues such as dimensionality explosion and unstable training are successfully mitigated. To assess the algorithm's performance, a road network model was constructed using SUMO, demonstrating a 22% reduction in waiting time compared to the original algorithm. The results confirm that the regional signal control model and methodology presented in this paper can significantly decrease regional traffic delays and enhance the traffic efficiency of the road network.

Metropolitan area consist of cities with various sizes separated by administrative borders. Urbanization and motorization make people's daily cross-border travel become normal. However, the function of transportation system and effect in these areas still need quantitative analysis with well explanation, as the integration across these areas may be separated by the administrative borders to some extent. With household travel survey data and population/job data in Tokyo metropolitan area, this research utilize two types of gravity model to quantitatively assess the spatial characteristics of transportation and border effect. One considers both transportation impedance and latent factors (reflecting policies, cultural, and economic activities), and the other deals with only these latent factors. Comparing these models, the results reveal the border effects between Tokyo and its neighboring prefectures. Three main types of border effect from the aspects of economy, geography, and geo-policy are identified, each closely associated with specific transportation and socioeconomic characteristics that significantly influence residents' commuting patterns.

Safe and effective collision avoidance is the key for ships to achieve the autonomous navigation. This paper proposes a novel maneuvering velocity obstacle (MVO) algorithm, which considers the ship velocity area more accurately with a ship maneuvering mathematical model group (MMG) model and current navigation states. Specifically, the MVO algorithm considers ship MMG model and uses planar geometry methods to determine the reachable collision avoidance area of the velocity vector of own ship (OS). It calculates the collision avoidance velocity and course targets within a specified time step, uses a proportional integral derivative (PID) controller to execute the target instructions, and updates the states of OS to achieve dynamic collision avoidance. Three cases are carried out with different encounter situations. The results indicate that the OS using the MVO algorithm can safely avoid both static and dynamic obstacles. The collision avoidance performance is superior to that of the traditional velocity obstacle (VO) algorithm and complies with navigation rules and ship maneuvering capabilities.

This paper constructs a safe area model for unmanned vessels based on the concept of Backward Reachable Set, and combines Backward Reachable Set and model predictive control to carry out research on autonomous collision avoidance for unmanned vessels in inland river scenarios. Taking the relative position and relative dynamics system of the vessel and the target vessel as inputs, the calculated Backward Reachable Set is regarded as the dynamic safety region of the vessel, and the model predictive control (MPC) is utilized to inject the Backward Reachable Set into the low-level controller in advance through the high-level design of the preset trajectory, which enables the vessel to complete autonomous collision avoidance in a safe and effective manner, while not approaching excessively to the target vessel, and avoids the failure of decision-making in the extreme scenarios. The concept of Backward Reachable Set is introduced on the basis of the existing obstacle avoidance methods, considering the uncertain environment in the inland navigation scenario, and the practicality of the method is verified through model analysis and scenario experiments, which provides a new idea for the subsequent research on autonomous obstacle avoidance methods for inland waterway vessels.

Radar and Automatic Identification System (AIS) are important equipment for ship navigation. Obtaining ship target information through radar and AIS has the characteristics of different content, accuracy and reliability, and the integration of the two can achieve complementary advantages and improve the accuracy and reliability of navigation system, which is the trend of ship shipping technology development. This paper focuses on the research on track association, track filtering and track fusion in the process of track fusion between radar and ship automatic identification system, and the track fusion results show that the accuracy of the fused ship target track information is higher than that obtained by radar and AIS.

As a kind of porous material, aluminum honeycomb is widely used in the field of energy absorption. In this paper, the mechanical properties of aluminum honeycomb under different impact angles are analyzed. The results show that the peak collapsing force is largest in the axial impact, and it is significantly reduced in the oblique impact. When the impact angle is small, the platform force and energy absorption of aluminum honeycomb are less affected. When the impact angle increases to a certain value, the aluminum honeycomb will buckle as a whole, and the platform force and energy absorption will be significantly reduced. When the impact angle continues to increase to a specific value, the aluminum honeycomb will be tilted as a whole, and the platform force and energy absorption will be significantly reduced. This study has reference significance for the design of aluminum honeycomb buffer device.

In recent years, deep learning algorithms have been widely applied across many industries. It has also seen rapid development in the field of key point detection. This paper proposes an improved YOLOv7-tiny algorithm for detecting key points in railway workers’ operations, aiming to assist workers and reduce errors in their tasks. To enhance the model's detection capabilities, three optimization methods are proposed. Deformable Convolution (DCNv2) is introduced into the feature fusion module to enhance the model's ability to extract and fuse features. Next, to address the issues of object classification and localization, a lightweight decoupled head is introduced. Finally, to improve the accuracy of detecting targets with significant scale differences and enhance the model's convergence speed, we introduce SIoU as the loss function. Experimental results show that the improved algorithm achieves an accuracy (mAP@0.5) of 91.1% and a detection precision (P) of 87.3&percnt, representing increases of 1.5&percnt and 4.8&percnt, respectively, compared to the YOLOv7-tiny model. The detection rate is 132 FPS, which meets the requirements for practical detection.

To address the issues of poor detection performance, high false positive and missed detection rates, and low real-time capability in highway debris detection algorithms, a streamlined detection algorithm leveraging an enhanced YOLOv8n model is introduced. Firstly, the backbone HGnetv2 network from the Real-Time Detection Transformer model is integrated to optimize the detection functionality of small objects and debris in complex scenarios. Secondly, the improved CSP-PartialConv structure is used to replace C2f in the neck network, reducing parameter volume and computational demand. Additionally, the model's multi-scale feature fusion capability is improved by incorporating a weighted bidirectional feature pyramid network. Finally, the WIoU loss function is utilized for replace CIoU, optimizing the network's bounding box loss, and enhancing the model's convergence rate and accuracy in regression. The trial findings indicate that the improved model presented in this paper improves accuracy by 0.5% over the original model, while also reducing the parameter count by 24.1%, computational cost by 11%, and model size by 22.3%. This demonstrates a notable lightweight effect and improved detection performance, providing effective technical support for highway safety management.

Accurate ship object detection is crucial for navigational safety and effective maritime traffic management. Existing ship object detection models are often affected by variations in ship scale and occlusion, leading to frequent missed detections and false positives, making it challenging to achieve both high accuracy. To address these issues, this paper introduces an improved ship object detection model based on YOLOv8. First, the ReLU activation function in SEConv is replaced with the Swish activation function to enhance the model's nonlinear representation capability and its ability to handle complex backgrounds. Then, the improved SEConv is used to replace the standard convolution in C2f, forming the C2fSE module, which balances inference speed and accuracy, which is crucial for real-time object detection systems. Finally, CBAM is introduced before SPPF and C2f to concentrate the model's attention mechanism, emphasizing features that can predict ship occlusions, thus improving detection accuracy. Experimental results showed that the mAP@0.5 increased by 3.66% and mAP@0.5:.95 increased by 7.89%, while reducing the number of parameters by 2.1M, effectively balancing model parameter and detection accuracy. Especially in complex scenarios involving ship occlusions, the improved model demonstrates higher accuracy and robustness.

The continuous development of global international trade has posed significant challenges to maritime security and safe transportation, highlighting the importance of maritime regulation. Traditional ship inspection techniques can identify and locate vessels in images or videos, yet they often fall short of meeting the complex regulatory demands due to specific operational needs and risk assessment standards associated with different types of ships. Consequently, this paper independently constructs a ship image dataset and employs the YOLOv8 algorithm to further identify various types of vessels and their associated shipping companies. This approach provides richer data for maritime regulation, enhances monitoring capabilities, and helps prevent maritime accidents. Experimental results demonstrate that the average recognition accuracy and recall rates for different ship types are 0.902 and 0.909, respectively. Comparative experiments with four types of models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8n-Ghost,demonstrate that YOLOv8n performs well in terms of accuracy, recall, mAP50, and other metrics. It meets the refined needs for maritime vessel identification, facilitating better regulation of maritime traffic.

The lack of traffic control and unclear right of way result in incomplete temporal and spatial separation of motor vehicles and pedestrians at non-signalized crosswalks. Consequently, crossing decisions rely on individuals' judgments of surrounding road conditions, environment, and perceived danger, leading to frequent interactions between vehicles, pedestrians. By collecting UAV video data from these crosswalks, we extracted data on pedestrian-motor vehicle interactions. We proposed an integrated index, PVGI (Pedestrian-Vehicle Game Index), which incorporates speed, acceleration, and distance factors to characterize the pedestrian-motor vehicle interaction process. Using the Markov Chain Monte Carlo (MCMC) simulation method, we identified the critical conditions that distinguish different game modes. The results indicate that PVGI can effectively classify pedestrian-motor vehicle interaction modes at non-signalized crosswalks. The MCMC simulations identified the interaction domain as [-4.0s, 2.0s], within which the game modes split into "pedestrian yielding-vehicle dominant" and "vehicle yielding-pedestrian dominant," corresponding to the intervals [-4.0s, 0s] and [0s, 2s], respectively. This study enhances the understanding of the decision-making mechanisms of motor vehicles and pedestrians at crossings and is significant for developing autonomous driving decision-making strategies.

Similar to fast-moving consumer goods, in cigarette finished product transportation scheduling, it is necessary to solve production batch management and transport cost at the same time. This study takes transport scale and first-infirst-out of batches as core constraints, integrates batch control methods with table operation method to solve optimization industrial enterprise distributed warehouse to commercial company's transport scheduling problem, thereby constructing a transport intelligent scheduling model based on batch management and cost control optimization, and then verifying its validity by empirical test. The results show that:(1) the distribution warehouse of cigarettes meets the first-in-first-out requirement 100%; (2) the provincial market transport costs are reduced by 8.06% compared with last year. The result proves that the model has feasibility and applicability, The model will also provide insights for transportation scheduling of fast-moving consumer goods (FMCG) and fresh produce based on batch management.

This paper proposes a flight conflict detection method combining the Velocity Obstacle (VO) approach and Support Vector Machine (SVM). In a scenario with 1000 aircraft within a 1000×1000 km range and three altitude layers, ellipsoid models are used as protective zones for the aircraft. Positions and velocities of the aircraft are randomly generated, and potential conflict pairs are filtered using the VO method to train the SVM. The Radial Basis Function (RBF) kernel is employed in the SVM to identify high-probability conflict pairs, which are then confirmed by the VO method. Experimental results demonstrate that this method significantly enhances the accuracy and timeliness of conflict detection, effectively controlling both the false alarm rate and error rate.

Aiming at the problem of airport gate pre-assignment, a two-stage optimization algorithm in multi-objective programming is adopted to establish an optimization model for gate assignment with different targets in the priority way. By using the two-stage optimization algorithm in multi-objective programming, a gate assignment optimization model is established to coordinate different objectives in a priority way. The model takes the least number of soft conflicts as the premise, and aims at gate occupancy rate and the robustness of the pre-allocation scheme. Firstly, under the hard constraints, optimizing objective is to minimize the number of soft conflicts. Then, based on the results, the soft constraint conditions are relaxed. Finally, under the condition of hard constraints and relaxed soft constraints, the optimizing objectives are gate occupancy rate and the robustness of the pre-allocation scheme. According to the actual operation data of a large domestic airport, the hybrid genetic algorithm is designed to solve the optimization model; compared with the optimizier results, the validity of the model and algorithm is verified, which provides a theoretical reference for the priority assignment of various optimization objectives to solve the problem of gate assignment.

The connectivity reliability (CR) of maritime nodes reflects their transportation risk and plays a critical role in international trade transportation. In order to evaluate the CR of maritime nodes under the influence of various interrelated factors, we employ the D-vine copula to model the joint probability distribution of the interrelated factors. By incorporating the conditional probability of these factors, the overall CR is subsequently evaluated. The Strait of Malacca, the Strait of Hormuz, and the Bab el Mandeb are utilized in the case study. The research emphasizes the importance of proper and accurate estimation of the occurrence probability of interrelated factors. The results show that, overall, the CR of the Strait of Hormuz is relatively higher than the other two straits. The results demonstrate geographical variance across maritime nodes in terms of CR.

In order to explore the impact mechanism of airport operation under the role of multi-factors, and the change of the role of different levels of influence factors on airport operation over time. This paper proposes the definition of airport operation life cycle and determines the evaluation dimensions of airport operation influencing factors based on the multi-level perspective method. The structural equation model of airport operation is constructed to quantitatively analyze the path relationship between the influencing factors of airport operation, and to analyze the magnitude of the role of each level of influencing factors on airport operation. Finally, the airport clusters are categorized into two types, high development stage and low development stage, to explore the changes of airport operation impact mechanism in different development stages. The results show that the role of city economy on airport construction has the greatest influence among all paths, with a total path effect coefficient of 0.855. The city level has the greatest influence on airport operation, accounting for 54.38%, and the nation level has the least influence on airport operation, accounting for 4.33%. Airport operation in the low development stage are more dependent on city development, while airport operation in the high development stage are estranged from the city and gradually become an independent operational entity.

Static demand-responsive transit (SDRT) is an innovative service model, which provides public transport services for passengers who have similar origin and destination locations, and departure time for a certain period at generally higher price. This paper proposes a method to extract potential trip demand for SDRT from taxi order data. Passenger OD flow similarity is analysed from the perspective of spatial and geometric features, including the parameters of spatial geography, trip distance, and trip angle. OD flow similarity function is constructed, which serves as the clustering criterion within a K-nearest neighbour agglomerative hierarchical clustering framework. Thus we may locate the potential passenger OD demand to direct the efficient design of SDRT route. A case study is conducted based on taxi order data collected in Suzhou, China over one day, where the proposed method manages to extract the potential passenger demand of 22 clusters for profitable SDRT.

This paper analyzed the naturalistic driving data of speed and eye movement from Xi'an to Yulin to explore the effectiveness of guidance signs at entrances and exits on expressway. Supporters of the natural driving that it is only unfamiliar with the road must to drive through this section of the road to complete their work due to official needs. The current analysis included 20 participants, all of them have work to travel between Xi'an and Yulin, aged among 30-40 who are more likely to using a high speed, and those who owned 3 years or less driving experience is 50%. Results show that when drivers enter or exit the ramp on expressway, the line of sight were focused on the front and the right side, therefore it was effective to set the guidance sign on the right and front side. Datas show that the suitable guidance signs ensuerance drivers to obtain road information accurately and in a timely manner, which is beneficial to drivers cut down frequently change lanes and reduce the risk of traffic. This study provides strong support for the effectiveness of guidance signs on expressway. Matlab 2020b is used to simulate and analyze the evolution model array constructed by driving speed, driving behavior and guidance signs.

The evaluation of collision risks in maritime traffic is essential for guiding strategic evasive actions and decreasing the incidence of collision-related events. In this scholarly work, we introduce a maritime collision risk assessment model that employs the DBSCAN clustering methodology. This model is capable of assessing the potential for collisions in situations where numerous vessels encounter. In this model, the DBSCAN algorithm is used to obtain the collision cluster of ships, and a quantitative index based on collision avoidance rules and ship domain - danger sector is used to represent the collision risk of each ship in the case of multi-ship encounters. AIS data was applied to validate the proposed risk assessment method, and the findings demonstrated that the proposed model is proficient in discerning the likelihood of multi-vessel collisions, thereby offering a foundational theoretical framework for the surveillance of navigational safety in intricate maritime environments.

Irregular shift scheduling is closely related to high-speed rail (HSR) driver fatigue, which affects driving performance and HSR operation safety. This paper attempts to identify the difference of driver fatigue between different shifts through the research conducted on an HSR simulator. A total of 28 time-domain, frequency-domain and nonlinear heart rate variability (HRV) features are extracted. Mann-Whitney U test is used to analyze whether HRV characteristics differ in different shifts. Five classifiers (CNN, LSTM, RNN, RF and SVM) are used for fatigue ternary classification. The results show that most of the HRV features are statistically different across different shifts, and the drivers may be more tired during morning shifts driving. RF and CNN perform best in the ternary classification of driver fatigue. The results of this study are helpful to understand the time-varying difference of driver fatigue and provide reference for optimizing driver scheduling.

Intersections, as the "bottleneck" of the road network, involve a large amount of traffic flow and conflicts, posing high accident risks. Accurate risk assessments of vehicles within intersections can effectively reduce accident rates and improve intersection safety. Traditional vehicle dynamic risk assessment methods often establish models from both dynamic and static factors that affect vehicle operation. These methods have complex calculation processes, and mainly use relative speed to represent the impact of object speed on risk, which cannot accurately describe the impact of different movement trends on vehicle driving risk. This study proposes a dynamic risk assessment method for intersection vehicle operation based on the gravitational model. This model uses pseudo-velocity to characterize the impact on the driving risk of the velocity of environmental elements to the ego vehicle, and then employs the gravitational model to uniformly quantify the potential risk posed by different elements to the ego vehicle. The rationality of the model is verified through numerical simulations of typical car-following scenarios. It’s effectiveness in risk warning is then demonstrated by comparing its capabilities with the Time to Collision (TTC) method in a typical car-following scenario. The applicability of the model to typical conflict scenarios at intersections is verified through the risk assessment of vehicle merging conflict scenarios and typical left-turn merging conflict scenarios at an intersection in Suzhou, China. This model can provide a basis for vehicle warning systems, prevent traffic accidents at intersections, and enhance intersection safety.

The global popularization of Electric Vehicles (EVs) has brought great challenges for charging network. Appropriate charging recommendations therefore plays an important role in balancing charging demand with charging supply systems. In recent years, researchers have attempted to address this issue through multi-agent reinforcement learning (MARL) algorithms. However, adapting a single algorithm to suit multi-agent charging recommendations remains a significant challenge due to the complexity of coordinating multiple agents, handling the high dimensionality of the state-action space, and ensuring stability and convergence. The present work proposes a cooperative multi-agent method utilizing an enhanced Multi-Agent Cooperative Soft Actor-Critic (MAC-SAC) algorithm to tackle the complexity of charging station recommendations. Methodologically, we investigate different multi-agent reinforcement learning training approaches and highlight their respective pros and cons. The results indicate that the approach utilizing shared policy and training networks yields the best results in multi-agent charging recommendation, while other training approaches exhibit certain drawbacks, such as adversely impacting algorithm convergence and stability. Additionally, other approaches require longer training times per iteration and have lower utilization rates of experience. Our research highlights the potential of improving cooperative multi-agent methods in addressing charging recommendation challenges, providing crucial insights for the advancement of EV charging recommendation tasks.

The existing theory of systematic accident analysis is rarely used in traffic accident causation analysis. To reduce the occurrence of road freight accidents, a CAST-Accimap accident causation analysis method is proposed in this paper to analyse 17 significant freight accidents. Initially, the CAST (Causal Analysis based on System Theory) method was applied to analyse vehicle systems and road environmental factors, which served as inputs for the equipment and environmental level in the Accimap model. Subsequently, the Accimap (Accident map) method was employed to analyse the causes of highway freight accidents and their causal relationships across various levels, leading to the construction of an accident network. The Actormap method was also utilised to statistically analyse the accident participants. Further, social network analysis theory was applied to quantitatively assess the accident network, pinpointing the key factors influencing accident occurrence. In the Actormap analysis, the involvement of government policies and budgets, regulatory agencies and associations, and corporate management in technology and operations accounts for 55% of the participants in accidents. Meanwhile, in the Accimap analysis, causes unrelated to the accident process and the equipment environment constitute approximately 45%.The results demonstrate that higher-level elements such as enterprises, regulatory departments, and governments significantly influence road environments and the actions and decisions of drivers during accidents. These findings assist traffic management authorities in making informed safety decisions to prevent highway freight accidents.

Battery electric buses (BEBs) have been regarded as an environmentally friendly mode of public transportation to address the congestion and pollution problems. However, due to the limited driving ranges of BEBs, drivers frequently experience range anxiety, which has been identified as a primary obstacle to the promotion of BEBs. Accurately estimating driving distance can effectively mitigate the negative impacts of this concern. To this end, this study utilizes multi-source data collected from real-world situation to investigate the estimation of driving distance of BEBs. Firstly, based on the realworld data related to BEB operation and weather, three tree models are adopted to establish the models for driving distance estimation, including decision tree (DT), random forest (RF) and gradient boosting decision tree (GBDT). The models considers twelve impacting factors. Secondly, the performance of the tree models in estimation is assessed, revealing that they demonstrate strong capabilities in predicting driving distance, with GBDT achieving the highest accuracy among them. Lastly, the relative significance of the factors influencing the driving distance of BEBs is examined using the proposed models, followed by a discussion of valuable recommendations.

Compared to conventional optical cameras, event cameras are preferred for target tracking applications in space traffic management due to their exceptional temporal resolution and inherent advantages in capturing space targets (including low-light conditions, high dynamic range, and high-speed moving targets) in challenging spatial scenes. In this paper, we utilize event cameras as the load device for space traffic management, achieving target tracking through data collection. This enhances spatial situational awareness, improves the decision-making, planning, and reaction speed of the space traffic management system, while reducing information redundancy and system risk.

With the ongoing advancement of social and economic, it is imperative to facilitate the enhanced integration of high-quality development and high-level safety within the road transportation industry, thereby contributing to the progression of economic infrastructure. This study takes 30 provinces in China as the object, quantitatively measures the efficiency of traffic expenditure - road safety application stage from 2011 to 2020 by MPT-SUD SBM model, and analyzes the difference of technical level efficiency of each province combined with the technology gap ratio. The results show that: 1) The total performance of public-private transportation expenditure and road safety application stage in China shows an increasing trend from 2011 to 2020. 2) There are differences in the level of road safety performance among different provinces. 3) The technology gap ratio reveals that there is still a certain regional imbalance in road safety technology in China.

Flight delays have become the main research objective for airports to improve efficiency and control costs, as an important indicator to measure flight delays, departure flight normal rate (DFNR) plays a more crucial role in influencing airport planning decisions. Theoretical research mainly focuses on reducing total delays, simulation methods can not consider delays caused by non-ground operation limitations, resulting in too ideal DFNR. In order to evaluate DFNR better in the airport's planning and operational schemes, this paper proposes an evaluation method for DFNR, which models the probability distribution of flight delay caused by various uncertain factors, and combines the departure delay from AIRTOP simulation caused by the ground operational restrictions of airport. The genetic algorithm is used to solve the various parameters in the model. Taking an airport for example, the DFNRs of 10 situations is calculated. The results show that the number of delay factors has a relatively small impact on the results, the calculation results of proposed model are consistent with the expected results and can better reflect the differences of situations compared to simulation, so the calculation results of proposed model are more reasonable and can be used as a decision-making reference. Besides, the value of delay threshold has a significant impact on the calculation results of DFNR.

Due to adverse weather conditions and other meteorological factors, the arrival and departure times (ADTs) of vessels, as well as sailing times in port (STPs), exhibit significant variability during actual port operations. Scheduling schemes based on deterministic ADTs and STPs often fail to meet port production demands. This paper addresses the vessel scheduling problem under uncertain arrival and departure times (UADTs) and uncertain sailing times in port (USTPs), employing a robust optimization approach. We construct two uncertainty budget sets, one to describe the ADTs and the other to describe the STPs, and establish a robust optimization model for vessel scheduling (VS) aimed to minimizing overall times occupy by vessels in port. Leveraging model features, we propose a knowledge-driven memetic algorithm (KDMA) for solving the problem. Computational experiments are conducted at the Comprehensive port in Huanghua using various instance sizes. The results demonstrate the superiority of the proposed algorithm over others.

Fatigue driving is one of the major causes of traffic accidents. Electric vehicles become increasingly popular, considering driver fatigue during vehicle route planning poses greater challenges. In this paper, we address both the electric vehicle charging problem and driver fatigue issue within the dynamic pickup and delivery problem. We propose a mathematical model and solve it using a hybrid Adaptive Large Neighborhood Search algorithm. Numerical experiments are conducted using the real road network map of Dalian City. We find that sacrificing a small amount of traditional cost can lead to significant improvements in addressing driver fatigue.

The rapid development of China's transportation industry, in order to comply with the development trend of transportation informatization, seeks a disease coding method applicable to domestic highways, as well as the establishment method of disease library and the detection and maintenance of integrated highway disease system. In the process of inspection and maintenance of highway pavements, bridges, tunnels and other transportation structures, two main problems are often faced, one is how to find the corresponding disease quickly, and the other is the maintenance and application of the disease library. Aiming at the above problems, this paper mainly solves the problems of disease localization and disease library construction.

This paper addresses the problem of transporting people and goods in underground mining areas by establishing a mathematical model and proposing a coding and iterative process based on the Sparrow Search Algorithm optimized genetic algorithm. Test cases more aligned with the model were constructed using the CordeauMDVRP benchmark examples. Iterative comparisons were conducted between the basic genetic algorithm and the Sparrow Search Algorithm optimized genetic algorithm, revealing that the Sparrow Search Algorithm optimized genetic algorithm (GSSA) converges faster and avoids falling into local optima, especially with a larger number of nodes.

This paper presents a conflict detection and resolution model for unmanned aerial vehicles (UAVs) based on the principle of prime factorization. The goal is to achieve autonomous path planning and conflict management in a decentralized flight architecture. The model draws on the widely used prime factorization method in informatics and cryptography, applying the process of decomposing integers into prime products to UAV flight control. Based on the gridded airspace represented as a matrix, the process of integer decomposition into prime factors is utilized in UAV flight control, which possesses a certain degree of innovation. Through simulation experiments in complex air traffic environments, the model's effectiveness in identifying potential conflicts and dynamically adjusting flight altitude was validated. Results indicate that the model can ensure flight safety and efficiency in most cases and demonstrates good scalability, maintaining efficient operation even with a significant increase in the number of UAVs. This study provides a promising solution for decentralized UAV flight control, although it remains in the preliminary stages. Further research and optimization are expected to expand the scope of autonomous UAV flight. Future work will focus on improving algorithm efficiency, optimizing flight strategies, and conducting broader testing and validation in real-world environments.

With the further opening of the national low-altitude airspace, Unmanned Aerial Vehicles (UAVs) will be used in large numbers and at high density in urban low-altitude airspaces. Currently, there is a lack of accurate and efficient methods for high-density UAV conflict detection. To address this issue, this paper constructs a UAV protection zone model and a battlefield airspace grid model to extract the airspace position matrix of each UAV. By employing the Hadamard product calculation method for matrices and the properties of composite and prime factorization, the method detects UAV conflicts, identifying the UAV numbers, positions, altitudes, and other information of conflicts, enabling real-time and rapid detection of conflicts among a large number of UAVs in the airspace. The results show that compared with traditional methods, this method can detect all conflicts with a single mathematical calculation when the number of UAVs is large, with the conflict detection time for 300 UAVs controlled within 1ms, significantly improving the efficiency of conflict detection.

To explore the underlying mechanics of truck traffic accidents, identify key factors, and implement effective safety management strategies, a method based on an enhanced Apriori algorithm for determining critical causes of such accidents is proposed. This study analyzed 1839 truck accident records from a mountainous freight highway in Guangdong Province, China. Using the improved Apriori algorithm, 571 association rules were discovered among all factors and specific dimensions (time, road conditions). Findings indicate significant support for rules such as inadequate following distance, improper operation, involvement of 1-2 vehicles, clear weather, interactions between trucks and cars, slopes exceeding 2%, and curve radii under 700 meters—all suggesting drivers' unsafe behaviors leading to accidents. Moreover, association rule mining revealed elevated lift values (>1.4) for accidents involving injuries between 1-3 am, and for minor accidents during the 8-9 am and 5-6 pm periods, highlighting peak accident times. Additionally, significant accidents were linked to downhill sections with 3%-4% slopes and radii exceeding 1000 meters, emphasizing their high-risk nature. These insights into truck accident correlations provide technical support for highway improvement and safe operations in freight transport.

Haizhuwan Tunnel, as the first underwater tunnel constructed with super large shield technology in Guangzhou area, has important research value on the impact of its toll mode on the characteristics of traffic flow. This study discusses the toll station capacity of Haizhuwan Tunnel under different toll modes, and focuses on analyzing the toll station capacity based on the traffic flow characteristics under different toll modes. The results show that the combination of simple "one vehicle, one bar" and free flow toll has significant advantages in improving traffic efficiency and reducing congestion. Compared with the scheme of setting toll stations, the scheme of setting toll stations has higher safety and lower cost, and the scheme of free flow toll increases the traffic cost of defaulting users, and the road traffic efficiency is also improved.

Due to the diverse truck loading types and varied shapes of goods carried by trucks on the highway, accurately judging whether a truck is covered with a tarpaulin presents challenges. Additionally, due to the large size of trucks, occlusion issues arise, which significantly degrade detection performance. To address these challenges, this paper presents a novel YOLO-based Uncovered Truck Detection method (UTD-YOLO). Firstly, we incorporate a Squeeze Aggregated Excitation Auxiliary Information (SAEAI) module into the auxiliary branch, which focus on crucial feature information related to uncovered trucks during the feature fusion process and assists the main branch in reducing semantic information loss. Secondly, we introduce a Large Separable Kernel Attention Spatial Pyramid Pooling (LSKASPP) module into the backbone network to enhance the model's capacity to learn diverse features of uncovered trucks while maintaining lower params. The proposed method is validated using real highway freight vehicle data, and the experiment results demonstrate that UTD-YOLO achieves outstanding performance with a precision of 82.37% for mAP@0.5 and 62.84% for mAP@[0.5:0.95]. Notably, mAP@[0.5:0.95] surpasses state-of-the-art object detection methods including YOLOv9, PPYOLOE, RT-DETR, YOLOv8, YOLOv6, and YOLOv5 by 2.17%, 2.68%, 2.8%, 2.83%, 9.63%, and 5.19% respectively. UTD-YOLO also demonstrates significant advantages in model size and inference speed, making it more suitable for smart highway roadside edge applications. This method contributes to improving the warning capabilities for highway safety risks, thereby advancing the field of Intelligent Highway Traffic Monitoring System.

As a major bridge country, China's facilities are vital to the transportation network and regional economic development. However, bridges exposed to the natural environment and traffic pressure for a long time are prone to problems such as cracks and corrosion, which, if not detected and repaired promptly, may lead to structural failures, affecting safety and economic benefits. Traditional image classification methods are susceptible to interference from shadows and dirt in practical applications, leading to reduced classification accuracy and limitations in dealing with fine-grained features and small sample datasets. In this paper, based on the Transformer model, combined with AlignMixup and Omage2Token data enhancement methods, the accuracy and recognition ability of the model on fine-grained and small-sample image classification is improved.

The purpose of this study is to construct a block chain encryption federation learning model for vehicle networking scenario, in order to solve the problems of data security, privacy protection and model training efficiency in the pre-test scenario of vehicle networking traffic. The model integrates federation learning, block chain and homomorphic encryption technology, and designs a roadside unit federation learning framework based on block chain, which ensures the integrity and nonrepudiation of data transmission and solves the problem of data tampering in the process of communication. At the same time, homomorphic encryption algorithm is introduced to ensure the safe transmission of model parameters and avoid gradient leakage. Finally, the use of small batch gradient descent algorithm reduces the communication overhead and improves the training efficiency. The experimental results show that the model improves the security and efficiency of the data sharing scheme, and successfully achieves the purpose of traffic prediction.

The complexity of the existing urban road network is high, and the cost of coordinated control of the entire road network with green waves is high, green wave sections are now widely used in cities. Therefore, aiming at the limitations of traditional green wave control methods, this paper proposes a dynamic green wave coordinated control model based on DQN, which aims to optimize the traffic efficiency of green wave sections in urban road networks. Deep reinforcement learning technology is used. In the model, the intelligent body (intersection) realizes real-time perception and response to the dynamic traffic environment by learning the experience of traffic condition and historical decision making. By optimizing the phase difference and public signal period, the model is able to carry out intelligent green wave coordinated control of urban arterial roads under different time periods, traffic flows and bus demand conditions. Finally, simulations are created using SUMO to validate the offline coordinated control scheme, genetic algorithm and dynamic model proposed in this paper. The simulation results show that the dynamic control model coordinated with the green wave reduces the average vehicle deceleration by 27.8% and 8.3%, the lane occupancy by 50.8% and 44.8%, and the total number of starts and stops by 54.3% and 49% reduced upside direction compared to the two schemes. In the downstream direction, there was a reduction in average delay by 23.6% and 6.6% respectively, lane occupancy by 27.9% and 2.3%, and total starts and stops by 38.7% and 17.5%.

In order to solve the problem that BeiDou satellite navigation system (BDS) satellite signals are susceptible to interference leading to degradation of navigation performance, the complementarity between BDS and VIO systems is applied to the combined navigation system by combining the advantages of monocular visual inertial odometry (VIO), which is not easily affected by external factors and has stable output. The combined BDS/VIO navigation is usually fused and optimized using untraceable Kalman filtering (UKF). The stronger the nonlinearity of the combined navigation system, the stronger the UKF filtering performance. However, the UKF cannot be adaptively adjusted when the system model is uncertain, which reduces the accuracy of the filter; to address the above problems, this paper optimizes the mean-square error array, adjusts the gain array of the filter filter in real time by introducing an asymptotic cancellation factor, and corrects the original error array by the new state-estimated mean-square error array when the system model is interfered with, so as to improve the robustness and Utility. Finally, the improved algorithm is compared with the standard UKF algorithm, and it is verified that the improved algorithm enhances the navigation accuracy of the BDS/VIO combination.

To study the identification method of highway traffic risks under the influence of disasters, this paper uses fault tree model, correlation analysis, and Delphi method to identify and analyze the influencing factors of highway traffic operation risks under the influence of disasters. Based on the fault tree model, with "highway traffic operation risk under disasters" as the top event, a progressive analysis of risk factors in dimensions such as disasters, drivers, vehicles, roads, topography, and traffic operation is conducted, and a method for calibrating evaluation indicators for each factor is proposed. The method combines quantitative screening and qualitative analysis to screen and eliminate factors and evaluation indicators with high relevance, low importance, and poor availability, forming a quantitative evaluation indicator form for risk factors. This provides a basis for highway risk management under sudden disasters, and provides technical support for management personnel to control the development of disaster risk trends and reduce economic and property losses caused by high risks.

This paper presents an integrated Freight Traffic Assignment Model (FTAM) using a super-network approach to effectively address large regions, multiple commodities, and various transport modes. By connecting mode-specific networks, the model captures complex freight behaviors and introduces generalized impedance functions considering factors like commodity values and loading/unloading times. Applied to the Yangtze River Economic Belt, it demonstrates over 95% accuracy in predicting freight flows, with mode share errors under 5%. This model offers crucial insights for policymakers in freight transport dynamics.

Underground roads, due to their limited space and enclosed environment, impact drivers' psychology and complicate emergency rescue in the event of accidents. Accurate evaluation of traffic accidents improves the safety of underground road traffic. Based on car-following and lane-changing behavior data from a typical underground road in Shanghai, this paper combines an unsupervised feature selection model based on mutual information with the K-means clustering algorithm to classify drivers into three categories: aggressive, stable, and cautious. Considering the behavior preferences of drivers with different styles, this study modifies existing microscopic driver behavior models, including models of car-following behavior and lane-changing decisions. From the perspective of the road system, consisting of people, vehicles, roads, and environments, the study considers the influencing factors of underground road traffic accidents, including driver characteristics, traffic flow states, and the geometric alignment of underground roads. Additionally, considering various collision types in both longitudinal and lateral directions, the study establishes an evaluation index system for underground road traffic accidents and determines threshold values using the graphical method of extremum theory. Finally, the study conducts a case analysis on a specific underground road in Shanghai to identify typical high-risk sections and key factors. The findings provide technical support for the safe operation of underground roads.

With the continuous development of electric vehicles, the power battery of electric vehicles still faces the challenge of limited energy density and difficulty in achieving significant breakthroughs in the short term. However, the use of regenerative braking technology can greatly improve its energy utilization efficiency, increase the vehicle's range, and alleviate the shortcomings of electric vehicles. Therefore, this technology is gradually becoming a hot spot in the field of electric vehicle technology. This project takes the EV braking system as the research object, based on the structure and related theory of the EV braking system, and selects ideal brake force distribution as the control strategy for the EV braking system from the aspects of parallel brake force distribution, ideal brake force distribution, and optimal brakeforce distribution. Therefore, using the simulation software ADVISOR to build a vehicle model, the anti-lock function of the electric vehicle braking system control strategy is analyzed. It is found that the ideal brake force distribution control strategy can adjust the pressure according to the best slip rate control interval and has certain practical application value.

With the development of new technologies such as artificial intelligence and big data, the railroad freight industry has developed to a new level, and the safety condition of wagons, as the carrier of railroad transportation, has been more widely concerned. Limited by the data quality and algorithm effectiveness, the automatic state detection has poor monitoring accuracy of small targets. In this paper, a state inspection system for small targets on railway wagons was developed. The fusion SPD-Conv layer was used to improve the yolov8 model to realize the rope loosening detection. A context-aware network module was employed to replace the fusion layer that comes with yolov8x to realize the open wagon hitch abnormality detection. The mean average precision of rope belt loosening detection reaches 0.873, and the precision for detecting wagon hitch abnormalities reaches 0.948, proving the system's effectiveness in practice.

Analyzing synergistic evolution between the supply and demand facets of green airport development holds immense significance for achieving sustainable progress in airports and catalyzing the ecologically-driven transformation of civil aviation endeavors. To address the shortcomings of existing research in solving the supply-demand contradiction of airports from a unilateral perspective, starting from the perspective of symbiosis theory in ecology, this paper constructed an indicators system of supply and demand for green development of the airport based on the historical data of Guangzhou Baiyun International Airport from 2008 to 2019 and measured the population density of the supply and demand subsystems by using entropy-weighted linear weighting method. Further, a symbiotic evolution model was constructed from the perspectives of mutual independence and interaction of the two subsystems respectively to conduct a two-stage empirical study on the population evolution of the supply and demand subsystems of the airport’s green development, and a time-stacked logistic fitting method was designed to solve the problem. The results show that the population symbiotic evolution model under independent development is poorly fitted, that there is an interaction relationship between the supply and demand subsystems of green development at airports, and that the time-stacked logistic fitting method can realize an accurate description of the symbiotic evolution process of the supply and demand subsystems. In addition, the evolutionary pattern of symbiosis between supply and demand subsystems has shifted from the parasitic symbiosis in the first phase of 2008-2015 to the asymmetric mutually beneficial symbiosis in the second phase of 2015-2019, but it has not yet reached a synergistic state of high levels of mutually beneficial symbiosis.

To address the challenges of conducting experiments with unmanned boats under high sea conditions, a digital twin simulation experiment platform has been designed for performance testing of unmanned boat sensory equipment based on Unity and the Robot Operating System (ROS) architecture. This platform is intended to facilitate the rapid verification of performance simulation algorithms for unmanned boat LiDAR and other sensory equipment. The platform features a parametrically controlled simulation environment that can easily generate diverse sea conditions, meeting the requirements for sensory equipment under complex experimental scenarios. It enables comparative validation of the performance of commonly used unmanned boat sensors, including cameras and LiDAR, through simulation algorithms. The results demonstrate that the same sensing algorithms produce consistent results in both real and simulated environments. Consequently, data from the simulation environment can effectively validate the effectiveness and robustness of the unmanned boat sensing module in high sea states, providing an efficient experimental framework for addressing the challenges of high-sea-state experiments with unmanned boats.

Improving the accuracy of representative driving conditions has important research value for the development and certification of new energy vehicle technology, pollutant control and energy consumption prediction. Taking Beijing as an example, the driving data of 20 pure electric vehicles for six months were collected, and the driving condition test data collected on typical roads were divided in detail to obtain a large number of short trip segment. UMAP and fuzzy mean clustering were used to determine vehicle driving conditions, and suitable segments were selected by the method of minimum deviation to construct the driving conditions of electric vehicles in Beijing. The comparison of V-A distribution shows that the proposed model has high accuracy, and some characteristics of Beijing passenger car driving conditions are revealed: low average speed, high idle speed and low constant speed. These findings provide technical support for understanding and optimizing the actual driving conditions of urban electric vehicles.

Obstacle clearance is a primary task in the design of instrument flight procedures, which significantly impact fuel consumption and pollutant emissions during flights. In this paper, we introduce a green departure procedure planning method that integrates an optimization model to minimize both the risk of obstacle collisions and emissions. We have adopted the linear regression terrain fitting method to reduce computational complexity in calculation of obstacle collision risk. Additionally, the Boeing Fuel Flow Method 2 is utilized to model the emission of various pollutants. To address the path planning problem, the fallback mechanism and the direction of the search range within the A* algorithm are enhanced. These improvements have led to a reduction in computational complexity and have decreased the likelihood of planning failures. We use Xining Airport as a case study to validate the method proposed in this paper. The experimental results indicate that our proposed planning method can effectively produce a green departure flight procedure. Compared to the currently published procedures, the flight procedure generated by our method is more closely aligned with the average tracks of historical flights. This indicates that our procedure is better suited to actual flight habits. Moreover, there is a significant reduction in both the length of the flight path and total emissions

Based on the current highway event detection system false alarm rate is high, relatively single algorithm, without standard protocol, algorithm cannot iteration, considering the highway tunnel environment and the complexity, the use of a new generation of AI algorithm processing and analysis, build a new generation of intelligent analysis system, realize the acquisition of complex environment traffic parameters and detection of all kinds of traffic events, realize the highway comprehensive detection awareness, safety risk identification, accident early warning, strengthen the data access and business integration.

Transportation is the fundamental guarantee and important carrier for the implementation of the rural revitalization strategy. This article selects 189 administrative villages within 14 townships in Jiang'an County, Yibin City as the research object. Through the cost distance analysis method, a accessibility measurement system for villages and towns in Jiang'an County is constructed, and data is collected and analyzed using GIS software, Gaode Maps and other platforms. The research results indicate that there are significant differences in transportation accessibility in Jiang'an County, with higher accessibility in towns near the urban area than in towns far away from the urban area. The accessibility shows a clear direction of high-speed and trunk road networks. Among them, the village town, town city, and village city in Yibin City have basically formed a "45-60-90" minute three circle pattern of cars. Administrative villages can reach the nearest township within 45 minutes, Jiang'an County urban area within 90 minutes, and each township can reach the central urban area within 1 hour.

At present, traffic image detection technology represented by lane lines has emerged as a key area of research in the transportation. Extracting lane lines under complex backgrounds has become an urgent task for understanding road information features in technical fields such as unmanned driving. Previous studies have made great progress in feature extraction for clear target detection. However, these studies cannot be well applied to the task of accurate lane edge detection, and the accuracy of individual feature detection will be lost during the fusion training process. In view of the diverse and complex characteristics of road information within the driving field of vision, this paper designs a lane detection method that combines target classification and K-means clustering methods. To improve the lane line's longdistance detection ability, the YOLOv8, which relies on the attention mechanism, is first built to identify the lane line. Secondly, through feature extraction, the lane line accurate segmentation based on the improved K-means is constructed, with the aim of the semantic information of the lane line is fused with the local features, which further improves the reliability and swiftness of lane line recognition. According to experimental findings, the suggested approach shows certain improvements in accuracy, recall rate and F1 measure compared with the popular lane line extraction method.

When the track structural components of the intrinsic modes and wheel-rail coupling high-frequency vibration close to the resonance will occur, the resonance of the occurrence of the track components will be damaged, in order to study the intrinsic modal characteristics of the rail sleeper and the wheels, and then study the underground track components of the intrinsic modal characteristics of each component with the intrinsic modal characteristics of the components of the characteristics of the resonance occurred in the text of the establishment of a finite element model of the wheel-rail coupling, the wheel and the rail of the contact between the spring-damping unit The contact between the wheels and rails is simulated by a spring-damping unit, and the modes of the track structure under the bogie load are simulated by using a double-wheel pair model arranged according to the bogie spacing. By changing the position of the wheelset, it is concluded that the position of the wheelset will not affect the modal characteristics of the rail in the vertical and lateral directions, and the vertical modal characteristics of the rail in the frequency range of 0-1200 Hz are all manifested as the bending vibration of the rail inside the wheelset and the bending vibration of the rail outside the wheelset.

About 40% of all the trips in Shijiazhuang City are non-motorized. In practice, various channelization methods, mainly based on reservoir type, have been used, but there has been no in-depth theoretical exploration and quantitative analysis. Based on the signal phase sequence conditions, saturation conditions, accommodation rate conditions, motor vehicle flow conditions, land available area, and other factors, the non-motorized waiting area is summarised as five types of reservoirs and three types of right-turn drainage islands; hence, this paper systematically investigates the setup conditions for each kind of non-motorized waiting area. By means of simulation experiments, the efficacy of the suggested configuration approach is confirmed by considering the real intersection in Shijiazhuang City as an example. The results indicate that a reasonably designed waiting area for non-motorized vehicles can effectively reduce delays and increase their throughput.

With the development of emerging technologies such as the Internet of Things and cloud computing, the UAV industry has been iteratively updated, accelerating the integration of UAVs into urban airspace. The air route network is the medium of UAV flight, which can guide high-density UAV flight in an orderly manner. As the key node of the air route, the intersection carries the function of traffic flow convergence and diversion. This paper provides the preliminary concept and operation description of urban air route intersections. Through simulation experiments, the operation characteristics of UAVs are quantitatively analyzed from two levels of safety and efficiency. The results show that when the total number of UAVs is a constant value, the maximum average conflict probability and average passing time of UAVs grow with the increase of the number of connecting routes at the intersection. The concept of intersection operation proposed in this paper can provide guidance for the orderly flight of UAVs, and improve the intersection service ability to a certain extent.

Traffic collision technology is widely used to identify lane change risk, which provides support for improving the safety of automatic driving assistance system. In most existing studies, micro-conflict indicators such as Time-to-Collision (TTC) and Stopping Distance Index (SDI) are used to determine the risk level during the lane change process. Therefore, this study focuses on the safety of the whole lane changing process, regards the lane changing vehicle and its four surrounding vehicles as a system, and proposes a method to quantify the comprehensive risk of lane changing from both the spatial and temporal dimensions. First, lane change samples are extracted from the real trajectory data set. Then, based on TTC and SDI, a macro conflict index, Risk Exposure Level (REL) and Risk Severity Level (RSL), is formulated to quantify the risk between lane changing vehicles and surrounding vehicles in the whole process of lane change. Finally, two macro conflict indicators are integrated to quantify the risk of the whole lane change system via Fault Tree Analysis method. The effectiveness and authenticity of the proposed method are verified by the HighD dataset. Dividing vehicles into different types and analyzing the factors that influence the risk levels of each vehicle type. The results are expected to provide a reference for intelligent vehicle risk warning and trajectory planning, and is valuable in the formulation of highway safety operation management measures.

The commercial deployment of unmanned vehicles remains impeded by safety concerns, with failures in the steering angle sensor (SAS) of the steer-by-wire (SBW) system posing significant risks to operational safety. This paper addresses the gaps in the existing literature regarding the analysis of the hazards associated with SAS failures and the deficiency in research on fault injection strategies during experimental validation. By conducting a thorough examination of the typical faults and their consequences in SAS, we have developed a software-based fault injection strategy for the purposes of testing the efficacy of fault-tolerant control (FTC) systems. Experimental results from a real vehicle platform confirm the validity of the proposed fault injection strategy and the robustness of the FTC system.

As a pivotal component of lock operation supervision and control, the efficacy of the lock monitoring system is contingent upon the security and reliability of lock operations. Given the influence of diverse environmental and equipment variables, the images captured by the digitalized image monitoring system of locks frequently exhibit deficiencies in image quality. This paper presents a summary of the image quality problems encountered in the ship lock monitoring system. The problems are classified and analysed, and the most appropriate functional algorithms are selected. A software architecture model is constructed using the Matlab App Designer development platform. The parameters are then obtained through repeated debugging to design an image processing software that is highly efficient and reliable. This software is designed to optimise and enhance the image quality of the lock monitoring system.

In order to solve the problem of smooth flow of engineering data in the whole life cycle, transportation construction enterprises urgently need to establish a complete set of digital standard system for engineering projects. This paper uses the keyword cluster analysis method to compare and analyze the achievements of BIM standard systems at home and abroad. This paper proposes a set of digital standards for transportation construction enterprises, including four types of standards: data, application, support and management. On the basis of the actual investigation of the project, the enterprise digital standard system fully refers to the framework of the international engineering project digital standard system. It aims to improve the digital management level of projects and enterprises, promote the intelligent process of transportation construction projects, form usable data assets, and promote enterprise standards to go global.

In order to better build a harmonious and low-carbon social environment, the red and green light timing of the intersection is optimized. In this paper, a traffic light control system based on fuzzy control is designed. Specifically, the maximum queue length of the current green light phase of the intersection and the traffic flow of the next green light passing phase are taken as inputs to the fuzzy controller. The fuzzy control rule base is set according to the experience of relevant experts and traffic controllers to output the green light extension schedule for the current green light phase.

With the rapid growth of the economy, container transportation in our country has been flourishing, and the issue of carbon emissions in the collection and distribution process has attracted significant attention. Currently, there are still issues such as unreasonable distribution of cargo flows and uncoordinated proportions of collection and distribution in the development of port collection and distribution in our country. Against this backdrop, an optimization model for collection and distribution is constructed with the goal of minimizing the generalized transportation cost, considering the costs of intransit transportation, transshipment, time, and carbon emissions. The model takes into account constraints on path capacity, transshipment capacity, and transportation demand. A case study is conducted using data from Yangluo Port, and the model is solved using a differential evolution algorithm. The impact of transportation capacity constraints on the optimization scheme is analyzed, leading to the following conclusions: Container port collection and distribution that considers carbon emission costs tends to favor rail transport. Moreover, increasing the capacity of rail and waterway transport in the collection and distribution network can significantly reduce generalized transportation costs and carbon emissions in the process, which is conducive to the development of low-carbon collection and distribution.

The traction drive system is a crucial component of high-speed trains, responsible for transferring and converting energy. It plays a vital role in ensuring the train operates safely and stably. As high-speed electrified railways continue to advance, the traction drive system is becoming increasingly crowded with various electrified equipment. This results in a growing issue of electromagnetic interference, particularly in the form of conducted electromagnetic interference. This interference significantly hampers the safe and efficient functioning of the rail transit system. This paper provides a concise overview of the working principle of the traction drive system. It also summarizes the formation mechanism of electromagnetic interference in the system and the typical coupling paths of the interference. Additionally, it discusses modelling methods and suppression measures for conduction electromagnetic interference, considering factors such as the source of the interference, coupling paths, sensitive equipment, and grounding. An in-depth analysis is conducted on the issue of electromagnetic interference in traction drive systems. This analysis serves as a theoretical foundation and a decision making reference for the design of electromagnetic compatibility in future traction drive systems.

Investigation of various factors’ influence on driver takeover time is crucial under conditional autonomous driving conditions. It can significantly impact the transition of the control process from driving autonomation to manual control and overall driving safety. In this study, utilizing meta-analysis data, 18 influencing factors were identified and investigated to determine their effects on the takeover time. The random parameter accelerated failure time model, known for its high goodness of fit and competence in accounting for the unobserved heterogeneity, was selected for modelling and analyzing the takeover time. The research focused on understanding how different factors influence the takeover time. The findings reveal that activities such as using handheld devices for tasks, higher levels of automation, performing visual tasks as nondriving related tasks (NDRTs), engaging in high cognitive load tasks, responding to visual takeover requests, and encountering interactive factors with other road users all contribute to longer takeover times. Conversely, NDRTs involving visual methods, manual operations for tasks, the existence of takeover requests, tactile takeover requests, high-fidelity simulators, urgent takeover requests, and complex driver reactions were found to shorten takeover time. In conclusion, four suggestions are proposed to optimize the safety design of the autonomous driving system, considering both the takeover time and takeover quality, thereby providing valuable theoretical references for enhancing driving safety.

Unmanned Aerial Vehicles (UAVs) serve as mobile sensors for real-time collection of spatio-temporal passenger flow data within regions, advancing the development of regional intelligent transportation systems. By hovering and capturing video, UAVs continuously monitor new passenger arrivals at points of interest (POIs), transmitting this data to a remote control center. POIs are locations with monitoring demands, referred to as demand points. A cost-effective solution requires the dynamic deployment of limited UAV resources to maximize monitoring performance by collecting the most comprehensive monitoring data possible. Given the dynamic and stochastic nature of passenger flows, a multi-UAV situational awareness and decision-making system for dynamic scheduling is proposed. Situational awareness involves obtaining overall status and using obtained monitoring data for estimation of approximate passenger flow and calculation of real-time monitoring demands. Decision-making entails routing UAVs, modeled as a Dynamic and Stochastic UAV Task Assignment Problem (DSUAVTAP), employing a greedy algorithm for online problem-solving. Using randomly generated datasets, a genetic algorithm is employed to calibrate five unknown parameters in the monitoring demand function. Experiments with 20 different passenger flow configurations compare dynamic UAV scheduling to static monitoring loops. Results show that dynamic scheduling outperforms static scheduling by an average of 9.5% in monitoring performance. Therefore, dynamic UAV scheduling proves superior for monitoring regional passenger flows with the fluctuating and uncertain characteristics. This work lays a foundational framework for future advancements in intelligent UAV scheduling techniques and optimal allocation of UAV resources in regional monitoring.

In order to study the performance of the Navigate on Autopilot System in high-speed ramp diversion scenarios, two scenarios of no interfering vehicles at the off-ramp and stationary vehicles at the off-ramp were selected in this study, and the test data of three representative vehicles were selected among more than 18 test vehicles to be analysed, and suggestions for improvement were made.

As the global transport network becomes increasingly sophisticated, the issue of airport location has become a subject of growing interest. In particular, for hub airports, their location has a significant impact on the economic development of the city in which they are situated. Additionally, it influences the efficiency and cost of air transport in the region where they are located, and even in the country as a whole. This paper presents the construction of a UMp-HMP model for the multiobjective hub airport siting problem without capacity constraints, with the minimum transport cost solved using a Gurobi solver. The model employs the distance cost of the airport as the streaming cost to ascertain the hub airports that should be selected under disparate cost discount factors. It then solves the model using a particular shortest-path optimisation algorithm and constructs a hub route network for 15 cities in China based on the optimal solutions obtained.

The structure of ship systems is complex, and there are many types of subsystems and components. When faults occur, there are many influencing factors, making it difficult to achieve accurate quantitative evaluation. Therefore, the traditional FMECA analysis method is adopted to divide the structure of the electromechanical system and quantitatively evaluate each fault mode; Combined with the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method, the system's fault hazard level was comprehensively quantified, which can provide theoretical and technical support for the analysis of ship fault modes and the determination of fault locations.

Emergency vehicles play a crucial role in saving lives and avoiding property losses. Connected and Automated Vehicles (CAVs) provide a new perspective to further enhance the effectiveness of emergency vehicles priority in the upstream section of the accident on highway segments. Based on the new technology, we propose a lane-changing yielding motion model to control the ahead connected vehicles in the dynamic tracking control area of emergency vehicles. Also, we develop a corresponding execution logic for better implementation of the lane changing model. This proposed model is based on the theory of the artificial potential field to reserve space for the rapid passage of emergency vehicles. Simulation results show that the implementation of lane-changing of ahead connected vehicles to give way can effectively improve the passage efficiency of emergency vehicles, when the traffic flow in the accident section is high. The interference to the overall traffic flow of the road section is small over the entire duration of the accident, which verifies the validity and feasibility of the model. This model provides a new solution for emergency vehicles priority after the occurrence of the traffic accident.

It is important to quickly and accurately determine the evolution of the fire when a fire breaks out on expressway. Current fire simulation can calculate fine-grained fire evolution parameters. However, fire simulation is a systematic work, which should consider the dynamic impact of the environment and rescue measures on the fire, and the dynamic evolution of human exposure to fire. In this work, we integrate environmental models, fire-resistant models, and human damage models into a fire simulation framework, aiming to describe the evolutionary state of multiple elements in a fire. The resulting framework consists of three layers, a parameters initialization layer, a model loading or parameters calculation layer and a real-time output and visualization layer. The simulation results demonstrate the power of the framework to include the coupling evolution of fire and weather, the reverse evolution of fire, and the evolution of damage caused by human exposure to fire. The framework can simulate expressway fires under multi-element coupling and rescue measures, which helps rescuers make correct decisions in fire rescue. The code of this study is available on the website: https://github.com/dtGaoP/fire_evolution.

With the evolution of data-driven autonomous driving technology, transferring driving responsibility from humans to machines is now feasible. Addressing the long-tail distribution problem in autonomous driving data is crucial for enhancing safety. Recent advances in foundational models, including large language models (LLMs) and generative AI models, have significantly improved data synthesis capabilities. This paper reviews the application of foundation models in three key areas: sensor data synthesis, traffic flow synthesis, and world models.

In recent years, digital transformation in the transportation industry has achieved remarkable construction results, The digital transformation concept of "big platform, big data, big system" has penetrated into various fields of intelligent transportation, electromechanical facilities maintenance business is also gradually from relying on manual inspection to platform-based intelligent monitoring, this paper mainly explores the innovative application of data empowerment of the large amount of business data generated by the electromechanical maintenance platform in the multi section management mode, the focus is on the refined management of data-driven business, and typical data is selected for the development and exploration of data empowerment business for electromechanical preventive maintenance, fault reporting, and electromechanical facility equipment procurement, bidding, and other businesses. It promotes the establishment of digital innovation in electromechanical operation and maintenance mode and empowers the development of new quality productivity in electromechanical digital operation and maintenance.

This paper proposes a novel 3D object detection method based on object matching for autonomous driving, which combines point cloud data and image processing. The method utilizes point cloud clustering to determine the location and size of objects, while a neural network classifies objects based on image data. Object detection is then achieved through the matching of these results. Experimental testing of the algorithm demonstrates the effectiveness of this fusion approach.

In the context of increasing urban traffic congestion and the swift development of cutting-edge technologies, notably the latest advancements in information technology, urban aerial transportation is poised to become a pivotal trend in future mobility solutions, offering a viable means to mitigate ground traffic congestion. Air taxis, especially those utilizing Electric Vertical Take-Off and Landing (eVTOL) vehicles, have emerged as a focal point of interest among transportation industry professionals and researchers, owing to their potential to revolutionize urban mobility. This paper explores the optimization of pooled scheduling for eVTOL-based air taxis operating between simulated take-off and landing sites within the Guangdong-Hong Kong-Macao Greater Bay Area. It accounts for various factors, including meteorological conditions such as wind, as well as scenarios involving the replacement of batteries in aerial vehicles. A multi-objective model for the shared dispatch of air taxis is proposed alongside an effective multi-objective optimization algorithm that seeks to enhance the diversity of solutions through the adaptive adjustment of weight vectors. The Guangdong-Hong Kong-Macao Greater Bay Area simulation results substantiate the algorithm's exceptional diversity and convergence capabilities. These outcomes provide novel perspectives on solving urban aerial taxi sharing and dispatch challenges, offering significant theoretical and practical insights for optimizing scheduling in real-world scenarios.

In the context of AI+, a knowledge question and answer system for significant traffic offenses in Beijing was built utilizing deep learning technology, which can rapidly and effectively aid in handling traffic violations. First, the knowledge graph of traffic violation in Beijing was created using technologies such as knowledge acquisition, knowledge extraction, knowledge fusion, and knowledge storage. Second, the BERT-CRF (Bidirectional Encoder Representations from Transformers-Conditional Random Field) model was used to extract and parse natural language questions, while the knowledge graph was used for querying and reasoning to provide replies. To enable the knowledge question and answer system to accurately identify traffic violation incidents, use the BIO annotation method to expand the People’s Daily dataset and the BERT-CRF model was achieved a precision of 88.53%, a recall rate of 92.90%, and an F1 score of 90.66%. By leveraging the BERT-CRF model, the system can quickly and accurately return the queried answers to users. It has been verified that the application effect is good in terms of road traffic safety prompts.

The formation of bus passenger flow corridors is manifested as different directions of bus ODs converging towards the corridor direction due to the travel advantages of the corridor. Therefore, clustering methods are considered to describe the membership of different ODs to the bus passenger flow corridor. Based on the relative positions of OD pairs, this study elaborates on the OD clustering method from the aspects of OD clustering characteristics and similarity measurement. Considering the situation of road public transportation infrastructure, a random user balance model is adopted for the identification of supporting roads in public transportation corridors. According to the allocation results, the parameters are further adjusted for redistribution identification. At the same time, the size of passenger flow in corridors and non corridors is analyzed to test the rationality of the initial clustering center selection, To improve the accuracy of the number of public transportation corridors. For the optimization of public transportation corridors, an evaluation index system for public transportation corridors has been established in three aspects: traffic conditions, facilities, and operational support. The calculation of the values and scores of each corridor indicator can identify weak links in public transportation corridors and help decision-makers improve the operational efficiency of public transportation corridors in a targeted manner. Finally, taking Qingdao City as an example, the algorithm and model proposed in this article were put into practice in terms of identifying and optimizing bus corridors, and optimization suggestions for future bus corridors in Qingdao City were proposed.

The function of non-motorized vehicles in the transportation sector is growingly important as the idea of green travel deepens and urbanisation speeds up. The non-motorized secondary crossing waiting area is quite important for traffic efficiency improvement as a good traffic control tool. Based on the non-motorized vehicle queuing density, pedestrian occupancy space and other parameters, this paper classifies the reservoir-type secondary crossing non-motorized waiting area channelising design method, puts forward the waiting area capacity calculation method, the use of traffic simulation software, verifies the reservoir-type waiting area capacity calculation method. A study was conducted on various indicators of non-motorized vehicle operation after the establishment of non-motorized vehicle waiting areas, and a calculation method for the traffic capacity of non-motorized vehicles passing through intersections under various waiting area conditions was proposed; and the trend of non-motorized vehicle delays after setting up non-motorized vehicle waiting zones of various types is investigated. The computation method of reservoir-type waiting area stock is proposed based on the waiting area capacity, non-motorized vehicle arrivals, straight non-motorized vehicle capacity and other factors; based on the non-motorized vehicle waiting area stock, the non-motorized vehicle throughput calculation method of setting up reservoir-type waiting area is proposed.

With the rapid development of the aviation industry, the demand for air traffic has been growing steadily, and the trend of electric power for aircraft has also taken shape. Due to the relatively low energy density of current batteries, hybrid electric propulsion represents a more feasible option under the present technological conditions. This paper conducts research on fixed-wing vertical take-off and landing (VTOL) air-craft, adopts a series hybrid electric propulsion system(HEPS), finds the efficient working point of the engine based on the universal characteristic curve of the engine, and thus establishes the ideal operating line (IOL) of the internal combustion engine. A state-space model of the hybrid electric propulsion system has been established, and a control law based on Linear Quadratic Regulator (LQR) has been designed. Simulation results indicate that the proposed LQR control law enables the engine to operate along the ideal working line, thereby enhancing the fuel economy of the hybrid electric propulsion system.

In the foreseeable future, connected intelligent vehicles (CAV) will coexist with conventional vehicles (HV), but problems such as unclear traffic rules for mixed traffic flows, unclear safety mechanisms, and optimized formation control schemes always constrain the commercialization of CAV. Optimizing the control of CAV queue and enhancing the continuity and smoothness of the queue is the key to optimize the stability of the mixed traffic flow and the pure intelligent connected vehicle queue, this paper introduces kinetic indexes based on the theory of driving safety field, and combines the road marking and other environmental factors to construct the driving safety field of connected vehicles. Secondly, the CAV queue following model and the mixed traffic flow basic diagram model are derived, and the stability of the designed queue following model is verified by combining with the traffic wave theory. The study shows that the driving safety field designed in this paper can effectively analogize the safety distance constraints, and at the same time, under different CAV vehicle penetration rates, the derived mixed traffic flow queue has stability.

This project develops a driver fatigue warning system based on facial multi-feature fusion. Through real-time monitoring and analysis of the driver's eye movement, mouth expression, head posture and other multi-dimensional facial features, advanced image processing technology and deep learning algorithms are used to fuse and process these data. The system can effectively identify the driver's fatigue state and issue a real-time warning during the driving process, to help the driver take timely measures to avoid traffic accidents caused by fatigue driving. The application of this system, it is expected to improve road traffic safety, reduce the potential risks caused by fatigue driving, and help build a safer driving environment.

The Adaptive Cruise Control ( ACC ) system is an important driving assistance system, which has the functions of constant speed cruise and car-following cruise. ACC belongs to vehicle longitudinal dynamics control. The realization of its function is mainly based on the safe distance between two vehicles to control the driving state of the vehicle. The size of the safe distance has a direct impact on the driver 's driving experience, the safety of the vehicle and the working strength of the vehicle drive / brake system. Therefore, the safe distance model directly affects the advantages and disadvantages of the entire automobile ACC system. Based on this, this chapter designs the overall architecture and working mode of the vehicle adaptive cruise control system. Through the analysis of the fixed headway algorithm ( CTH ) and variable headway algorithm ( VTH ), an improved safe distance algorithm based on CTH algorithm is designed. The algorithm takes into account the driving speed of the vehicle and the relative speed of the front vehicle and the vehicle. The tracking speed and tracking effect of the expected safe distance are better, which provides a guiding route for the design of the automobile ACC system.

Wuhan Tianhe International Airport and Ezhou Huahu Airport are set to become China’s first true dual hubs for both passenger and cargo transport. Achieving rapid and efficient flow of passengers and cargo through these airports, as well as building a highly coordinated transportation network, presents a significant challenge. To date, quantitative research on the coordination between airports and integrated transportation networks has been somewhat limited. This study constructs a coordinated evaluation index system based on the Node-Place model, calculates the index weights using the Entropy Weight Method, and assesses and optimizes the coordination between Tianhe Airport and Huahu Airport with their respective regional integrated transportation networks. The results indicate that Tianhe Airport's node value is close to its place value, positioning it within a subordinate area. This means that both Tianhe Airport and the regional integrated transportation networks are developing in balance, though there remains significant potential for further development of transportation functions. In contrast, Huahu Airport, due to its shorter operational period and incomplete regional transportation network, has lower place and node values. This study helps explore the coordination between airports and regional integrated transportation networks, providing decision support for achieving coordinated development and avoiding resource waste in transportation networks.

Based on the application prospect of carbon capture technology and the requirements of International Shipping Greenhouse Gas emission reduction, the development and application trend of carbon capture technology are summarized in the paper. For the onboard carbon capture system (OCCS) using chemical absorption method, this paper analyzes the main key technologies involved in the application of OCCS, such as absorbent performance, safety design equipment optimization arrangement and liquid CO₂ storage from the perspective of working principle and design arrangement, so as to provide reference for the practical application of onboard carbon capture technology.

Metro is an important part of rail transport, has become an indispensable mode of transport for people's daily travel, with the increasing mileage and capacity, the use of metro vehicles has become a more complex and harsher environment, resulting in the fatigue failure of the metro frame is more and more prominent. This paper takes the fatigue failure of a certain underground as an example, and introduces the modal modification method based on sensitivity analysis when analysing its fatigue life, and carries out vibration and fatigue analysis on the underground frame. The calculation results show that the sensitivity correction method can effectively reduce the error between the simulation model modal and the constructed object, so as to calculate the fatigue life of the frame more accurately.

Optimizing the inbound and outbound traffic is a method to reduce airport flight delay. However, propagation is a characteristic of delay. The delay of departure flight will affect the operation of its destination airport, especially in large hub airports. To consider the contribution of destination airport to the whole airport network while optimizing the airport schedule, has a positive effect on the network delay control. Using the complex network theory, the influential nodes in the airport network is identified. The constraint factor of inbound-and-outbound ratio is considered, and a schedule optimization model is established with the goals of flight delay and operational fairness. The model is validated by taking China's national airport network and Beijing Capital Airport operation as an example. The simulation results show that, the model balances the delay cost of each time period, and the delay of airports in the whole network reduced slightly, and the workload of whole airport network is balanced as well. It is proved that the influence node identification method based on robustness measure is effective.

With the rapid development of the automotive industry and the increasing emphasis on driving safety, the tire pressure monitoring system (TPMS), as an important component of vehicle safety systems, has become a research focus on its accuracy and reliability. This article explores the accuracy evaluation method of a direct tire pressure monitoring system (TPMS) based on artificial intelligence technology through theoretical analysis, simulation research, and real vehicle testing. The research results indicate that the accuracy of the direct tire pressure detection system's detection results is mainly affected by temperature, vibration, and electromagnetic fields. These three indicators can be used to evaluate whether the tire pressure alarm is delayed or advanced. This study provides important references for improving the safety of automobile driving and promoting the development of intelligent safety technology.

Focusing on the testing and inspection business of highway, through the study of real-time data acquisition, database construction, system design, as well as data retrieval and statistics of the laboratory, the digital construction framework of the highway construction site laboratory is constructed, the digital transformation and construction of the highway construction site laboratory is realized, and the problem of difficult preservation and circulation is caused by the paper file information involving a large number of personnel, equipment, materials and other paper file information in the construction process of the project, so as to improve the data utilization. Under the proposed framework, the Majiabu-Jingyang Highway Extension Project in Shaanxi Province is taken as a case study. The application status of the framework is introduced, providing a useful template for the digital construction mechanism of highway laboratories in China.

The construction of flight conflict networks often overlooks the element of flight altitude, which is crucial for a more realistic representation of actual air traffic. In this paper, we propose a three-dimensional flight conflict network model that takes into account the altitude and speed of aircraft. Firstly, we establish an ellipsoidal flight protection zone model centered around the aircraft. Then, we use the aircraft as nodes, and determine potential conflicts between aircraft by whether they enter the flight protection zone. If there is a potential conflict, a connection is formed between the nodes, and the weight of the conflict edge is determined by the proximity effect between the aircraft, thus constructing a three-dimensional flight conflict network. We also analyze the changes in betweenness centrality, degree centrality, and point strength of the nodes in the network, as well as the average point strength and robustness of the network under two different node reduction strategies. The simulation results in MATLAB show that, compared to indiscriminate guidance, degree-priority selective guidance has a more significant effect on reducing the average point strength and robustness of the network, confirming that the degree-priority strategy can effectively reduce the complexity of the flight conflict network. The network model constructed in this study can provide a theoretical basis for air traffic controllers to manage conflicting aircraft.

Accurate subway passenger flow prediction is crucial for intelligent transportation systems to manage traffic, optimize operations, and plan infrastructure. Since passenger flow is affected by factors like station location and date type, further research on short-term high-frequency prediction is needed. This paper proposes a method combining Graph Convolutional Networks (GCN) for spatial dependencies and Informer models for long-term temporal dependencies to capture spatiotemporal relationships. By fusing these features in a fully connected layer, the approach achieves precise passenger flow forecasts. Experiments on Hangzhou Metro Line 1 show that this method is robust and accurate.

To overcome the limitations of existing coupled simulation methods in achieving interaction between external agents and the environment, this study establishes a real-time traffic communication prediction model considering the impact of accidents based on vehicle mounted ad hoc network technology. Implement coupled traffic communication simulation based on existing frameworks, including V2I and V2V simulations, and save simulation data; Mining traffic V2V simulation data, constructing a relationship model between traffic environment and V2V communication based on machine learning methods, and further evaluating the performance of the model and the impact of various indicators. The research content contributes to the construction of an intelligent agent traffic communication coupled simulation platform, which better simulates vehicle road communication, realizes the interaction between external intelligent agents and the environment, and is used for traffic management and control.

To address the confusion in the application of traffic control methods, this paper proposes a comprehensive evaluation model to assess the suitability and effectiveness of various control strategies. This paper reviews existing traffic control and evaluation methods and selects three key indicators: network accessibility, network traffic saturation, and total travel time across the network, to comprehensively evaluate the impact of three control strategies on congestion mitigation. The study concludes that under congested conditions, network control strategies tend to reduce some network accessibility to alleviate congestion. When an incident occurs on a specific road within the urban road network, the variable speed limit method is found to be ineffective, whereas opening the opposite lane is more effective than inducing diversion. This study offers a more accurate assessment of the effectiveness of different control methods, aiding urban road networks in enhancing rapid response and emergency management capabilities during incidents.

In recent years, the rapid development of the economy and the rapid increase in the number of social cars have had a direct impact on the efficiency of the road network, resulting in varying degrees of regional traffic congestion, particularly during the morning and evening rush hours. The real-time data of the network is employed to identify the congestion points at the intersection, analyse the causes of congestion, and select the intersection of Baodi Ring Road and Tangtong Line as a case study to alleviate congestion and optimise the objective of improving road capacity. The accurate quantification of realtime traffic condition data, the reduction of research costs, the formation of a system for the transformation and optimisation of intersections, and the provision of a reference for the optimisation of domestic ordinary trunk highway intersections are all benefits of this approach.

In this paper, the authors take the highway continuous entrance section as the research object and analyse the traffic operation and safety characteristics of this section. Traffic data were collected by UAV, and trajectory data were extracted by Datafromsky to analyse the continuous entrance road section in terms of following, lane changing and traffic safety. The results show that the further to the midstream end, the more obvious the influence of downstream entrances on the front vehicle of the following convoy. The acceleration lane mergeable section in the upstream merge zone has a higher degree of severe conflict with the midstream zone, which is the key section for traffic safety improvement in the continuous entrance section.

The implementation effect of intermittent bus lane (IBL) is closely related to the setting of their clearing distances, and existing studies mainly focus on fixed clearing distances, which are difficult to adapt to real-time road traffic conditions. To address this, considering the characteristics of the vehicular networking traffic environment, a control method for IBL based on dynamic clearance is proposed, and a control model for IBL considering the queuing at downstream intersections is constructed. According to the location of connected vehicles on the IBL and the degree of impact on the operation of buses, different lane-changing rules and decision models are formulated. The SUMO simulation experiment is used for verification, and the results show that the proposed control method model can reduce bus delays, improve bus speed, and to some extent, enhance the traffic capacity of the IBL, without excessively increasing the delay impact on social vehicles. At the same time, this control method is more sensitive to the penetration rate changes under high traffic volume.

This study explores the necessity and importance of improving the accuracy of road traffic accident (RTAS) prediction in terms of human casualties caused by road traffic accidents globally. Taking China as an example, we analysed the increase in RTAS due to population and economic development in the last decade. By comparing the performance of three models, SARIMA, LSTM and SARIMA-LSTM, in RTAS prediction in Jilin Province, we find that the SARIMA-LSTM model significantly outperforms the SARIMA and LSTM models alone. For example, using 2019 data, the RMSE, MAE, and MAPE of the SARIMA-LSTM model improved by 51.79%, 50.84%, and 37.17%, compared with SARIMA, and by 27.51%, 21.85%, and 18.08%, compared with the LSTM model. This indicates that the hybrid model has higher prediction accuracy and adaptability when dealing with complex time series problems. This study provides a new perspective for RTAS prediction and demonstrates that combining traditional time series models with deep learning models can effectively improve the accuracy and robustness of prediction.

To address the issues of excessive delays at urban arterial bottleneck intersections during saturation states, spillover of road section queues, and the need for routine manual intervention in signal control measures, an optimization method for arterial signal control based on historical signal light color data is proposed. This method designs an optimization process for peak hour division, public cycle optimization, phase time allocation, and timing plan verification for single-point simulation of manual control, as well as a calculation method for arterial coordinated phase difference based on queuing equilibrium and flow cutting. The case study focuses on the signal optimization of Beijing East Road in Nanchang during peak hours. Compared with the current control scheme, the signal control plan optimized using the method in this paper significantly reduced the manual control time during peak hours on Beijing East Road, and reduced the average congestion index of the arterial road in both directions by 19%, and the average delay time by 16%. The method, based on practical experience of manual operation and combined with traffic signal control theory, achieves optimization of signal control schemes for saturated arterial roads, effectively solving practical problems such as routine manual control at intersections and queue overflow.