This paper studies the optimization of high-speed railway freight transportation within the context of the passenger and freight co-transportation mode. By analyzing the impact of different freight handling station selections on freight transportation, passenger train schedules, and stop plans, a mixed integer linear programming model is formulated with the purpose of minimizing passenger travel time and penalty cost of unsatisfied freight transportation.The proposed model takes the selection of freight handling station, freight transportation, train schedules, and train stop plans as the main decision variables. When the selection scheme of freight handling station is provided, the proposed model degenerates into a conventional optimization model for freight transportation under the co-transportation mode. By taking the Wuhan—Guangzhou high-speed railway as an example, a numerical experiment is implemented to verify the soundness and rationality of the proposed model, which is solved by the commercial software Cplex coded by C++. The results show that through an integrated optimization encompassing the selection of freight handling station, freight transportation plan, train schedules, and stop plans, the proposed methods can minimize the impact of implementing freight transportation on passengers while efficiently utilizing residual capacity of high-speed railway lines. This approach enables the transportation of nearly 68% of freight demands, with a mere 0.68% increase in passenger travel time and a 1.24% increase in train stops, leading to revenue increase for railway companies.

Demand-Responsive Transit Vehicle Scheduling(DRTVS)is an important part of demand-responsive transit operation planning. A well-designed vehicle scheduling scheme is of great significance in reducing operation costs and improving operational efficiency. Therefore, this study analyzes and summarizes the optimization models and solution algorithms of DRTVS from the past decade, both domestically and internationally. Firstly, a brief introduction is given to the classification and definition of DRTVS models and algorithms. Secondly, the construction and optimization of scheduling models are discussed from four aspects: scheduling methods, stop type, time constraints, and vehicle types. Then, several common algorithm types are summarized, along with their solution effectiveness and applicability conditions. Finally, in consideration of the current research limitations, such as incomplete consideration of modeling factors, overly idealized assumptions, and poor solution accuracy, the future research directions for optimization models and solution algorithms for DRTVS are discussed. The research findings indicate that recent studies primarily focus on dynamic scheduling, variable stops, soft time windows, and multiple vehicle types. In addition, the optimization objectives of DRTVS models commonly encompass factors such as travel time, operating mileage, and service quality. The existing vehicle scheduling models lack broad applicability and operational feasibility. According to research on the solution algorithm, precise algorithms are mainly employed for small-scale scheduling problems, while heuristic algorithms are predominantly used for large-scale scheduling problems.

To address the lack of comparative studies on construction methods for large-scale multimodal transportation networks, this paper investigates the differences in computational efficiency and results among different methods. Firstly, the paper compares the impact of two network representation methods, i.e., route section and hyperpath, on the scale of extended networks across six different-sized bus networks. Secondly, it proposes a method for connecting transit stations with road network, and uses the node contraction method to create joint networks. Finally, in a large-scale multimodal transportation network, three types of shortest paths, i.e., simple path, route, and hyperpath, are computed for 100,000 OD pairs extracted from taxi trips, and their generalized time cost are compared with actual taxi trip costs. The research findings indicate that computation time follows the order of route > hyperpath > simple path, and the average shortest path cost follows the order of simple path > route > hyperpath. Compared with the actual taxi trips, the proportions of OD pairs with lower generalized time costs for simple path, route, and hyperpath are 39.21%, 41.29%, and 42.83%, respectively.

Regarding the problem of predicting passenger flow distribution between Origin-Destination (OD) stations of new urban rail lines, this paper proposes a prediction method for the distribution of OD passenger flows in the urban rail transit network with the operation of new lines. The method takes into account both entry and exit station choices. Firstly, the influence of factors such as station land use, entrance and exit station volume, travel time, and transfer times on the travel choices of urban rail passengers are considered. Two separate choice models, based on discrete choice theory, are developed for entry and exit station choices, respectively. Secondly, a new OD distribution prediction model is established by integrating entry and exit station choices. Finally, the model is validated using the example of the new line 18 integration into the existing Guangzhou Metro network. The results demonstrate show that compared with the traditional models, the proposed method significantly improves the prediction accuracy, the average absolute errors of the passenger flow distribution on the network are reduced by more than 10% and average absolute errors in the OD volumes between new stations reduced by 20%.

To address the problem of track irregularity affecting driving safety and ride comfort, this study investigates the correlation between track irregularity and vehicle vibration response. Based on the spatiotemporal data transmission characteristics between track irregularity and lateral car-body acceleration, a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed, forming a CNN-LSTM combined model. Firstly, the coherence analysis of track irregularity and lateral car-body acceleration is performed, and the influence of train speed on lateral car-body acceleration is explored to determine the input data of the model. Then, the lateral car-body acceleration is estimated using the CNN-LSTM combined model, LSTM model, and multi-body dynamics simulation model, and the results are compared with the measured lateral car-body acceleration. The results show that the CNN-LSTM combined model achieves an average root mean square error of 0.046, which is 0.006 and 0.026 lower than that of the LSTM model and the dynamics simulation model, respectively. This demonstrates the effective estimation capability of the CNN-LSTM combined model for lateral car-body acceleration.

To address the magnetic core saturation caused by unbalanced traction currents in 25 Hz phase-sensitive track circuits, a modeling method for the nonlinear model of a choke transformer is proposed. The approach combines the JA (Jiles-Atherton) hysteresis model and the three-capacitor transformer model. Firstly, a JA hysteresis model parameter identification algorithm based on Particle Swarm Optimization (PSO) is designed. This algorithm, along with the energy calculation method, is utilized to extract the JA hysteresis loop parameters of the iron core and the distribution parameters in the nonlinear model of the choke transformer, respectively. Subsequently, by comparing the calculated distribution parameters with Maxwell's simulation results, it is concluded that the parameters, such as leakage inductance and distributed capacitance of the choke transformer, extracted by our method exhibit good accuracy. Finally, the results of solving the BH curve, excitation current, and output voltage curves of the nonlinear choke transformer model are compared and verified against Pspice simulation results. The research results indicate that the proposed model can be utilized to accurately analyze the saturation problem of magnetic cores in choke transformers. The saturation state of the magnetic core can be determined by analyzing the real-time hysteresis loop shape of the model core or the distortion of the primary current in the choke transformer.

The DC power grid has emerged as a crucial solution for long-distance, high-power energy transmission and the integration of renewable energy sources. As the key equipment enabling flexible interconnection among DC power grids of different voltage levels, DC-DC converters play a vital role in energy conversion and energy distribution. The topology and operational characteristics of these converters profoundly influence the performance of DC power grids. While traditional research on DC-DC converters has primarily focused on low to medium power applications, the rapid advancement of DC power grids necessitates exploring high-power DC-DC converter topologies and their corresponding reviews. This paper first examines the technical requirements of DC-DC converters in typical scenarios such as renewable energy convergence and DC power grid interconnection. It categorizes two-port DC-DC converters based on electrical isolation and thoroughly compares their topology structures and conversion performance. Furthermore, it provides an overview of multi-port DC-DC converters that show promising potential in multi-terminal flexible interconnection scenarios. On this basis, this paper systematically summarizes the technical characteristics and suitable application scenarios of different DC-DC converters. It highlights the advantages and disadvantages of various converter types using a typical ±10 kV/±375 V DC distribution network as an example. Finally, the common issues present in current DC-DC converters are identified based on practical engineering requirements. The paper concludes by envisioning the future research directions of DC-DC converters, considering the trends of lightweight and compact design as well as the need for stable and efficient development. These insights aim to provide valuable guidance for the construction of high-voltage DC power grids.

Addressing the rational allocation of urban traffic resources and the optimal management of transfer passenger flows within comprehensive passenger transport hubs, this study employs prospect theory to enhance the traditional decision-making criteria of travel time and travel cost by introducing a comfort index. The comfort index is transformed into a comfort cost using a novel dimensional conversion coefficient. Considering the uncertain environmental factor of traffic congestion, perceived cost functions are established for each transfer mode under both congested and normal traffic conditions. Combining value and weight functions, two models are formulated: a transfer mode choice model based on comprehensive prospect values under multiple reference points, and a model integrating prospect theory and Logit model under a comprehensive reference point. Furthermore, the evolution processes of passenger transfer mode selection strategy under different initial selection probabilities is analyzed based on the comprehensive prospect value of prospect theory. The validation results show that a comprehensive reference point set based on probability and weighted principles can accommodate passengers' income categories, enabling the uniform conversion of all decision criteria into monetary costs. As a result, this model outperforms others with a 7.53% reduction in error.

This paper proposes a method for identifying road potholes and cracks on road based on the depth high-resolution network HRNet, addressing the issues of low recognition efficiency and poor generalization performance in existing road damage identification methods. The method utilizes an on-site collection of road pothole and crack images using a vehicle-mounted monocular camera, followed by image preprocessing and annotation to generate a road damage dataset. Based on the original HRNet, the four different resolution representations in its network feature extraction layer are respectively fused with the improved convolution attention module to form the E-HRNet network model. To improve the reasoning speed of the E-HRNet model, the number of residual layers of different resolution branches in each step is optimized, and the model is supervised and trained using a joint loss function. Experimental results show that E-HRNet network model achieves an average pixel accuracy and average intersection over union acquired of 94.53% and 88.31%, respectively, for road pothole and crack segmentation. Compared with the original HRNet network model, E-HRNet model shows improvements of 6.53% in average pixel accuracy, 5.38% in average intersection over union, and 1.39% in average class pixel accuracy. Additionally, the E-HRNet model exhibits a 30.3% improvement in model detection frame rate, and a 42.6% reduction in model volume, meeting the requirements of lightweight and real-time detection. Furthermore, compared with similar models such as DDRNet and DeeplabV3+, the E-HRNet network model achieves higher segmentation accuracy for potholes and cracks, effectively addressing issues of missing detection, false detection and blurred boundaries, while demonstrating better performance and generalization.

To address the problem of serious congestion at freeway bottlenecks caused by accidents, this study proposes the use of automated vehicle platoons with speed control to alleviate traffic congestion and improve safety. Firstly, a speed feedback mechanism for connected and automated vehicle platoons is designed using the Basic Safety Message (BSM) data in Vehicle-to-Everything (V2X) technologies. Then, a cellular automata model incorporating the platoon speed feedback mechanism is constructed, and a multi-lane changing model considering the passage utility is introduced to more accurately represent the lane-changing behavior of vehicles in an accident zone. Finally, based on the proposed model, simulation is conducted on the mixed traffic flow consisting of human-driven vehicles and connected and automated vehicles under low, medium, and high traffic demand levels and accident scenarios such as lane blocking and regional speed limits. The results show that the speed feedback mechanism can increase time-to-collision by about 7%~43% and effectively reduce the impact range of traffic accidents, enabling vehicles to pass through bottleneck areas more smoothly and safely.

To address the challenge of diminished accuracy in traffic sign recognition under real-world conditions of poor lighting environment and dealing with spatial changes such as distortion, rotation, and translation, this paper presents an innovative traffic sign recognition approach based on multi-source feature enhancement that integrates spatial positional data with optimized brightness and contrast features. Firstly, aiming to mitigate recognition difficulties in low-light images, a brightness and contrast enhancement module is designed to highlight the feature information of low-light images. Then, a deep image classification network is constructed by combining the spatial transformation unit. Furthermore, a lightweight feature processing network is constructed combining the spatial transformation units. By weakening irrelevant information present in the image and focusing on the region of interest within the data, the background noise is effectively segregated, thereby enhancing the spatial consistency of the input data. Finally, the main classification network performs fine-grained identification of image feature maps and outputs the predicted class labels.Experimental results show that the proposed model achieves an accuracy rate of 99.52% on the public benchmark dataset GTSRB, effectively addressing the problem of low recognition rate for traffic signs in real-world scenarios.

To address the unclear quantitative influence of the accessibility for central cities in the economic growth system of regional non-central cities, this paper takes the twin-city economic circle of Chengdu and Chongqing region as the scope of research. Taking Chengdu and the main urban areas of Chongqing as regional central cities, and using data from the 2010-2019 City Statistical Yearbook, as well as GIS data of the highway and railway networks, a spatial econometric model is employed to evaluate the current state of the driving effects for the central cities. Additionally, a measurement method for the accessibility between non-central cities and central cities is proposed, and a structural equation model of urban economic growth considering the accessibility of central cities is constructed. The research findings indicate that the central cities have not yet exerted a significant positive driving effect on the non-central cities within the Chengdu-Chongqing twin-city economic circle, and exacerbated regional development imbalances. Considering that the influence of the central city accessibility improves the overall fitness of the model, and improves the theoretical framework of urban economic growth. Strengthening the transportation level of cities has a direct impact on promoting capital investment, but it does not directly affect talent and technical factors. Industrial development is the most direct and influential factor affecting growth, and the level of capital is a key mediator of urban economic growth. Enhancing the capital level of cities can stimulate talent inflow and technological progress, thus driving the development of urban industry and economy. Improving the accessibility of central cities is a direct driving force for various economic growth factors, promoting both the development of urban transportation level and attracting key elements for economic development, such as talent, capital, and technology.

The effect of piston wind caused by passing trains directly impacts the fatigue life of various ancillary facilities in subway tunnels. To investigate the surface pressure distribution and the variation of aerodynamic loads on wall-mounted distribution boxes under the influence of piston wind in subway tunnels with varying installation methods, a numerical simulation method is employed. The results indicate that when a train passes by the distribution box, the surface pressure reaches its maximum, which is a negative pressure at the train’s head or tail. Both wall-mounted and suspended distribution boxes experience alternating tensile and compressive aerodynamic forces under the influence of piston wind. The wall-mounted distribution box bears a larger lateral aerodynamic force, while the suspended distribution box bears a larger longitudinal aerodynamic force, both reaching their maximum as tensile forces. When the train speed is lower and the piston wind has not entered the self-mode area, the surface static pressure of the distribution box is linearly related to the train speed and velocity head. The variation in pressure and tension forces on the surface of the distribution box corresponds to the positive and negative pressure variations on the box surface. Under identical conditions, the load of the wall-mounted distribution box is about 5 times that of the suspended distribution box. Although the suspended distribution box installation method is safer, maintenance and reinforcement of the box in the longitudinal direction of the tunnel should be taken into account.

In light of the advantages of High Temperature Superconductivity(HTS) DC induction heating technology, such as good heating quality and high heating efficiency, this paper proposes a dipole-type HTS DC induction heating device based on a cylindrical multi-pole DC motor structure. Firstly, a numerical model of the aluminum billet heating process is established, taking into account the air gap magnetic field of the heating device and the temperature rise characteristics of the aluminum billet, using Maxwell’s equations and Faraday’s law of electromagnetic induction, and the solid heat conduction equation. Secondly, considering the toroidal core of the heating device and the multi-pole structure of the DC motor, this paper constructs electromagnetic and thermal models for both 4-pole and 2-pole configurations. Furthermore, the influence of aluminum billet conductivity, rotational speed, and length on the heating process is investigated by using an electromagnetic-thermal model of the dipole-type heating device, studying the variation process of eddy currents and heating power within aluminum billets. Finally, to address the problem of insufficient magnetic field intensity in the heating region, a polar boot structure is proposed to improve the air gap magnetic field model, based on the influence of the length of the DC motor’s polar boot on the magnitude of the air gap magnetic field. The research results show that under the 2-pole core structure, the maximum magnetic flux density and temperature of the aluminum billet are higher than those under the 4-pole structure by 0.16T and 54K, respectively. There is a positive correlation between the eddy current density and the heating power inside the aluminum billet and its conductivity, length, and rotational speed. The use of a pole boot structure increases the magnetic flux density of aluminum billets by 0.11T and raises the temperature of aluminum billet by \mathrm{4.2} ℃.

This study focuses on the severe side wear of curve rails and wheel wear of heavy haul locomotives on heavy haul lines. From the perspective of curve proportion weight, the UM software is used to establish a dynamic model of heavy haul locomotives and an Archard wear model for wheel wear. The influence of different curve proportion weights on wheel wear is studied, and the changes in dynamic contact points and static equivalent conicity of wheel profile after wear are further analyzed, giving suggestions for setting the curve proportion weight of heavy load lines. The results show that when the curve proportion weight increases to 30%, significant wear occurs on the tread of the first and second wheelsets, with wear rates of 0.827 mm/10,000 km and 0.582 mm/10,000 km, respectively. For curve proportion weights between 20% and 25%, the contact point of the first and second left wheelset exhibits the largest movement in the circular curve section, near the root of the flange. The contact point of the third left wheelset moves the most when the curve proportion weight is between 10% and 15%, approaching the nominal rolling circle position. It is recommended to set the curve proportion weight of heavy haul lines between 10% and 15% for minimal increase in wheel wear and optimal wheel-rail matching relationship.

This study addresses the quantification of the safety level for preventing seismic pounding between adjacent structures. A vulnerability analysis method is proposed to assess the vulnerability of adjacent structures to seismic pounding. The method utilizes peak ground acceleration as the seismic intensity indicator and maximum relative displacement of adjacent structures as the engineering demand parameter. Incremental dynamic analysis is conducted using nonlinear finite element models of the adjacent structures. The parameters of the vulnerability function are determined through maximum likelihood estimation and regression analysis, including linear regression, two-parameter linear regression, and bilinear regression. Then, the proposed method is applied to 6-story and 5-story adjacent reinforced concrete frame structures with equal story heights. Seismic pounding vulnerability curves are compared for different separation distances with different methods. Moreover, the seismic pounding vulnerability of adjacent structures with equal story heights, such as 6-story and 4-story, and 6-story and 3-story, is evaluated. Results show that for higher peak ground motion accelerations, the vulnerability curve obtained through bilinear regression has the best agreement with that obtained through the Monte Carlo method, with the agreement improving as the separation distance increases. The other two regression methods yield higher vulnerability estimates, while the maximum likelihood estimation produces the most conservative results. Moreover, the highest vulnerability estimate is observed for the 6-story and 5-story adjacent structures with unequal story heights, followed by the 6-story and 4-story adjacent structures with equal story heights. These findings suggest that the highest vulnerability estimate occurs when there is a balance between the influence of period ratio and impact location of adjacent structures.

In order to address the issue of inadequate radial suspension force at some positions in the Sharing Suspension Winding Bearingless Switched Reluctance Motor (SSW-BSRM), a two-phase excitation analytical model suitable for SSW-BSRM is established. Firstly, based on the equivalent magnetic circuit diagram of SSW-BSRM with two-phase excitation, the inductance matrix is derived, and then the analytical expressions for radial suspension force and electromagnetic torque are obtained. Secondly, the correctness and excellent characteristics of the derived model are verified by comparing with the finite element calculation results. Finally, the two-phase excitation mode is optimized to mitigate the impact of torque reduction on motor operation. The research results show that SSW-BSRM exhibits good suspension characteristics in the two-phase excitation mode, and the optimized two-phase excitation mode reduces the influence of torque reductio on motor operation.

In this study, strain gauges are installed on the vertical primary dampers, yaw dampers, and lateral secondary dampers on two types of CR400 Fuxing high-speed EMUs. The strain signals from damper loads are analyzed through tests conducted on the Beijing-Shanghai high-speed railway. By conducting data processing and analysis, the temporal profiles, frequency domain amplitudes, and load spectra of the three kinds of dampers in the two types of EMUs are obtained. The time domain and frequency domain characteristics of the corresponding damper loads on the two types of EMUs are analyzed. By employing vehicle system dynamics models and numerical methods, three types of damper loads on the two types of high-speed EMU under random track excitations are determined, thereby revealing the differences in damper loads between the two types of EMUs. The research results demonstrate that along the entire test route, the differences in the vertical primary damper loads between the tested EMUs are negligible. The lateral damper loads of type A EMUs are significantly higher than those of type B EMUs, while the yaw damper loads of type A EMUs are lower than those of type B EMUs. Furthermore, by using appropriate damper and vehicle system dynamics models, the consistency and differences in the damper load test results of the two EMU types on the track can be accurately reproduced.

This study explores the optimization of a multi-modal hybrid operation scheme for trains at loading stations, considering fluctuations in wagon demand. By introducing a multi-modal hybrid operation scheme, the aim is to locally transform the flow of wagons into a train, thereby alleviating the pressure on the technical station in the rail network. Effective processing of fluctuating wagon demand data is employed to minimize the impact of frequent changes in train operation schemes on transportation organization. Firstly, three train operation modes at loading stations are proposed, with the objective of minimizing wagon-hour consumption. Constraints such as a unique operation mode, unique train trip selection scheme, technical station formation capacity, and railway section carrying capacity are considered. Using the average daily wagon demand at loading stations during the statistical period, an optimization model for the train combination operation scheme is constructed. Secondly, a model is developed using dynamic wagon demand data to establish a train combination operation scheme. Then, three-stage mixed heuristic solution process is designed, incorporating deterministic constraints derived from dynamic wagon demand. Finally, experimental scenarios are created to compare the effectiveness of the models derived from the average daily wagon demand and dynamic wagon demand. The research results show that the devised algorithm performs well for both models, with short processing times. The stability of the model with average daily wagon demand is 76%, while the dynamic model’s stability reaches 96%, indicating a 26.3% improvement in stability and greater adaptability.

Railway perimeter protection is crucial for ensuring the safety of railway transportation, and video surveillance stands out as the most widely utilized technology in this domain. Aiming to address the issue of high false positive and false negative rates under challenging lighting conditions, particularly at night, given the ability of infrared imaging to capture thermal radiation from objects and its resilience against variations in lighting and adverse weather, this study investigates the integration of infrared and visible light images to enable all-weather perimeter intrusion detection. Firstly, based on an analysis of the current state of railway perimeter protection technology, the paper discusses the focal points and challenges in research related to object detection through the fusion of infrared and visible light images. Subsequently, it outlines the general methods and procedures for image registration and object detection, while analyzing the progress made in infrared image registration, infrared target detection, and the research advancements in fusion-based object detection using infrared and visible light images in railway scenarios. Finally, this paper provides an outlook on the future trends in multi-source image object detection technology within the context of railway perimeter protection applications. The combination of infrared and visible light video images not only facilitates effective intrusion detection under all⁃weather conditions but also ensures the visual representation of intrusion targets, thereby facilitating subsequent intelligent analysis.

Addressing the optimization of multimodal transportation routes under uncertain transportation time and demand, a scenario-based robust approach is employed to establish an optimized multimodal transportation routes model that minimizes the combined expenses of transportation, transshipment, and warehousing. In this model, constraints such as node operation time windows, fixed departure times for transportation modes, and time windows for receiving at the destination are considered comprehensively. A genetic algorithm is designed and validated for effectiveness. Through case studies, the paper compares transportation plans and costs for multimodal transport under different scenarios, investigating the relationship between the quality of robust optimization solutions, regret coefficients, and the fluctuation ranges of random numbers. Results indicate that the presence and alteration of node time windows lead to the change of transportation costs and transportation schemes. The robust optimization of multimodal transportation paths under mixed uncertainty is impacted by the regret coefficient constraint and the fluctuation range of uncertain factors, resulting in increased transportation costs. Therefore, decision makers in multimodal transportation should anticipate the impact of uncertain factors, choose suitable maximum regret values, and focus on mixed time window constraints at nodes to reduce costs and improve efficiency.

To address the complexity of high-speed rail spatial accessibility’s impact on urban agglomeration economic development, this paper takes the Beijing-Tianjin-Hebei urban agglomeration as the research object. It employs spatial-temporal panel data from 2007 to 2019 related to high-speed rail development and economic growth. Using the Two-Step Floating Catchment Area method (2SFCA) to assess high-speed rail spatial accessibility from both supply and demand angles, the research examines local effects, spillover effects, and spatio-temporal non-stationarity using the Spatial Durbin Model (SDM) with dual fixed elements (time and space) and Geographically and Temporally Weighted Regression (GTWR). The results show that the high-speed rail spatial accessibility has a significantly positive local effect and a negative spillover effect on the overall economic development within urban agglomerations. Furthermore, this impact varies across the three industrial sectors, with the most pronounced effects observed in the secondary industry. High-speed rail spatial accessibility demonstrates spatio-temporal non-stationary concerning the general economic development and the growth of the three industrial sectors in urban agglomerations, with a concentration of influence. The research results reveal that the Beijing-Tianjin-Hebei urban agglomeration primarily experiences siphon-like development. There is an increasing discrepancy in spatial accessibility and economic development between counties that cannot directly benefit from high-speed rail services and other counties. Therefore, it is crucial to continually enhance the high-speed rail network and operational policies, improve integration between high-speed rail and other transportation networks, optimize urban division of labor and industrial layout, and wisely direct the impact of high-speed rail accessibility on urban agglomeration economic development.