Beijing-Shanghai High-speed Railway Research Articles

Research on Computer-aided Adjustment and Optimization of Train Service Plan based on Passenger Flow Assignment

Abstract According to the complex characteristics of the mutual influence of passenger flows between different train adjustment schemes, a computer-aided train service plan adjustment optimization model based on passenger flow assignment is proposed with the goal of minimizing the remaining passenger turnover. The actual operating effects of trains and the impact between different adjustment measures and between trains are also considered. A computer iterative optimization algorithm with 2 stages based on the greedy principle is designed. The computer processing is divided into two stages: train adjustment plan selection and passenger flow information analysis. The selection of adjustment schemes is feedback-corrected based on the results of passenger flow assignment. Finally, taking the train operation plan of the Beijing-Shanghai high-speed railway as the research object, an example is demonstrated, and the results show that the model and algorithm can effectively obtain the optimal train adjustment scheme under the current conditions.

Fatigue test and service performance evaluation of high-speed train hanging equipment bolts

The high-speed train hanging equipment may extend maintenance cycle, prompting an urgent need for bolt service performance evaluation. In this paper, the hanging bolts of the hanging equipment of high-speed trains are taken as the research object, and accelerated fatigue tests of the hanging bolts under five alternating loads are designed and carried out. The SN curves of the bolts under axial loads are obtained with the Goodman modified equation and double logarithmic linear regression analysis, which provides a basis for acquiring the cycle life under different real vehicle loads. A tracking test system for real trains was established, and vibration acceleration data of four types of hanging equipment were obtained when the high-speed train was running on the Beijing-Shanghai high speed railway. An averaging method based on supervised classification was proposed, and the average daily acceleration load spectrum of different hanging equipment with cycle characteristics of 1.2 million km was constructed to provide data support for service mileage evaluation. A service mileage evaluation model of the hanging bolts based on the fatigue life model, Miner's rule and train dispatching rules was proposed. Moreover, the service mileage evaluation of four types of hanging bolt equipment was completed to provide an important reference for the formulation of a bolt repair system for the hanging bolt equipment of high-speed trains.

Safety Control Technology and Monitoring Analysis for Shield-Tunnel-Stacked Underpass High-Speed Rail Bridge Excavation

In order to ensure the safety of the Beijing–Shanghai high-speed railway during the construction and operation of the shield tunnel on Line R1 of the Jinan Metro, a numerical simulation of geological disturbance during the underpass of the small-radius stacked tunnel was carried out. We analyzed the mechanism of geological deformation and the construction factors affecting settlement and found that bridge settlement far exceeded safety standards without taking safety control measures. In this regard, safety control technologies such as setting isolation pile sleeve valve pipes, controlling shield tunneling parameters, grouting control technology, and trolley support technology have been proposed. Monitoring was conducted on the uneven settlement of the bridge surface, the internal and external stresses of the pipe segments, and the soil pressure they were subjected to. The results showed that the surface settlement of the bridge after safety control measures was controlled within the safety standard range, and the stress and soil pressure changes of the pipe reinforcement were within the allowable range, further verifying the effectiveness of the safety control measures.

Deep Reinforcement Learning for Integration of Train Trajectory Optimization and Timetable Rescheduling Under Disturbances.

High-speed trains are susceptible to unexpected events such as strong winds and equipment failures, which can result in deviations from the scheduled timetable. As the density of traffic increases, these delays can quickly spread to other trains, eventually leading to conflicts in the timetable. To ensure the efficiency of high-speed railways, quickly resolving potential conflicts and generating appropriate rescheduling schemes are essential. The existing hierarchical structure of train control and online rescheduling tends to be inefficient in terms of information communication and can even lead to unfeasible rescheduled timetables and trajectories. To address these issues, an integrated structure of timetable rescheduling and train trajectory optimization is proposed by introducing the train minimum running time into the process of timetable rescheduling and using the adjusted running time as the objective of trajectory optimization. The integration model is formulated by considering the constraints of timetable rescheduling such as the maximum number of trains overtaking trains, platforms at stations, and the priority of the train, as well as the constraints of trajectory optimization. A deep reinforcement learning (DRL)-based approach is proposed to solve the problem. Numerical experiments are conducted on a segment of the Beijing-Shanghai high-speed railway line, using adapted data to demonstrate the effectiveness of the proposed method in rescheduling timetables and optimizing train trajectories. The results show that the integrated rescheduled timetable and the optimized train trajectory can be generated simultaneously and the computation time exhibits a linear increase with respect to the size of the problem.

A CALCULATION METHOD FOR HIGH-SPEED RAILWAY CAPACITY BASED ON IMPROVED TRAIN DEDUCTION METHOD

A reasonable calculation of railway capacity is very important for research. With the rapid development of high-speed railways, more and more differences between traditional and modern railways have emerged in the transportation organization and capacity calculation methods. In this article, the calculation methods used under different conditions with different train types are studied based on the train deduction method. Train deduction is a method that calculates the number of trains that cannot pass through the line when other trains change their stop plan or operation speed based on the coefficient of deduction method, which is widely used in China. Then, optimized models for trains with the same-speed and different-speeds are built to determine the maximum number of trains. These models are built based on the constraints of passenger service quality and overtaking times. In addition, the models are solved based on a train operation plan. Hence, the capacity calculated by these methods is more reasonable for the actual condition. Finally, an actual case of Beijing–Shanghai high-speed railway is implemented and tested with the model. The optimal capacity scheme is simulated and analysed, and the result agreed well with the railway transport enterprise.

Research on the Influence of Traction Load on Transient Stability of Power Grid Based on Parameter Identification

The traction load of electrified railways is a special user of the power system, which will have a significant impact on and pose a challenge to the power system after grid connection. Considering the impact and fluctuation of traction load connected to the power grid, this paper proposed a method to study the influence of traction load on the transient stability of the power grid based on parameter identification. Firstly, according to the operation characteristics of traction load, a comprehensive traction load model based on the measurement-based method of induction motor parallel power function model is selected, and the objective function of parameter identification is determined. Then, the specific steps of using the improved gray wolf optimizer (IGWO) algorithm to achieve the parameter identification of the traction comprehensive load model are introduced, including chaotic map, nonlinear convergence factor, and individual position update. Next, with the IEEE39 bus system as the network background, the load parameters identified by the measured data of a line of the Beijing–Shanghai high-speed railway are imported into the Power System Analysis Software Package (PSASP) 7.0 for transient simulation to analyze the impact of traction load on the transient stability of the power grid. Finally, three typical load models are compared with the traction load model to draw relevant conclusions, and it is verified that the traction load characteristics are different from the general load model, which provides reference for the connection of electrified railway traction load to the power grid and the economic and technical construction of the power system.

An Automatic Train Operation Based Real-Time Rescheduling Model for High-Speed Railway

With the continuous development of the Automatic Train Operation (ATO) system in high-speed railways, automatic driving is progressively supplanting manual operations, ushering in a new era of predictability and reliability for high-speed railway transport. Concurrently, the advent of the ATO system provides a notable impact on real-time rescheduling during disruptions, as it equips dispatchers with precise insights into train operation statuses. This paper is dedicated to a thorough analysis of how the transition to automatic driving in train operations influences the real-time rescheduling model. Based on the distinctive impact of the ATO system on real-time rescheduling, we have proposed a mixed-integer linear programming model that combines train re-timing, reordering, and the minimization of passenger delays. To validate the effectiveness of our model, we present several experiments conducted using data from the Beijing–Shanghai high-speed railway line. The results unequivocally demonstrate that our ATO-based model significantly mitigates train delay time, demonstrating its practical value in optimizing high-speed railway operations.

An Integrated Method for Reducing Arrival Interval by Optimizing Train Operation and Route Setting

The arrival interval at high-speed railway stations is one of the key factors that restrict the improvement of the train following intervals. In the process of practical railway operation, sudden conflicts occur sometimes. Especially when the conflict arises at the station, because the home signal cannot be opened in time, the emergency may affect the adjustment of the train operation under the scheduled timetable, resulting in a longer train following interval or even delay. With the development of artificial intelligence and the deep integration of big data, the architecture of train operation control and dispatch integration is gradually improving from the theoretical point. Based on this and inspired by the Green Wave policy, we propose an integrated operation method that reduces the arrival interval by avoiding unnecessary stops in front of the home signal and increasing the running speed of trains through the throat area. It is a two-step optimization method combining both intelligent optimization and mathematical–theoretical analysis algorithms. In the first step, the recommended approaching speed and position are obtained by analytical calculation. In the second step, the speed profile from the current position to the position corresponding to the recommended approaching speed is optimized by intelligent optimization algorithms. Finally, the integrated method is verified through the analysis of two distinct case studies. The first case study utilizes data from the Beijing–Shanghai high-speed railway line, while the second one is based on the field test. The numerical result shows that the proposed method could save the entry running time effectively, compared with the normal strategy given by the train driver. The method can mitigate controllable conflict events occurring at the station and provides theoretical support for practical operation.

Optimal capacity allocation for high-speed railway express delivery

This study investigates the potential of implementing express delivery services within specified time windows on the high-speed railway (HSR) and optimizes the train capacity allocation scheme for HSR express delivery (HSReD). We first propose an integer linear programming (ILP) model for the deterministic demand case to maximize the profit of the HSReD operation (revenue minus transportation cost, loading/unloading costs, and the penalty incurred due to schedule delays). Then, a two-stage stochastic programming model is developed to account for the stochastic demand case, with the objective of maximizing the expected profit. To facilitate the solution process, the two-stage stochastic programming model is transformed into an equivalent nonlinear model, which is further reformulated into an equivalent integer linear programming (EILP) model that can be solved by commercial solvers. Finally, the proposed method is applied on a small toy network, Nanjing-Hangzhou HSR network and Beijing-Shanghai HSR network to illustrate its efficacy.

Distributed asynchronous event-triggered cooperative control for virtually coupled train set subject to gradient terrain and input saturation

The cooperative tracking control problem is investigated for the virtually coupled train set subject to gradient terrain and actuator saturation. This paper proposes an adaptive event-triggered cooperative tracking control scheme, where a distributed asynchronous event-triggered sampling is designed based on the relative states between adjacent trains. In addition, the unknown parameters of high-speed train system are estimated by the adaptive method. By the proposed control scheme, the velocity and position tracking errors between any adjacent trains are ultimately bounded while realizing on-demand interaction between trains. It is proved that the Zeno behavior can be excluded by quantitatively expressing the strictly positive minimum lower bound of the inter-event interval. Finally, a simulation of the Beijing-Shanghai high-speed railway is used to verify the feasibility and effectiveness of the proposed control scheme.

Integrating stop planning, timetabling and rolling stock planning on high-speed railway lines: A multi-objective optimization approach

Designing railway operational planning is a complex and challenging task to cope with fluctuating passenger demand and satisfy actual operational requirements. Focusing on high-speed railway (HSR) lines, this paper proposes an integrated modeling framework for phase-oriented railway operational planning. The framework simultaneously optimizes the stop planning, train timetable problem (TTP) and rolling stock planning (RSP). A multi-objective optimization model, with macro passenger demand, two train speed levels, and overtaking operations taken into account, is formulated to minimize the total train running distance, train dwell time, and rolling stock resources. The constraints involve the train operation zones, train operation process, passenger demand, rolling stock circulation conditions, and actual operational requirements. The proposed model is addressed using a framework based on the ε-constraint method, from which an approximate Pareto-optimal solution set is obtained. A case study on the Beijing-Shanghai HSR corridor demonstrates the effectiveness of the proposed approach and reveals some trade-offs between the proposed objectives.

An Integrated Strategy for Rescheduling High-Speed Train Operation under Single-Direction Disruption

Comparing to other modes of transportation, high-speed railway has the advantages of energy saving, environment friendly, safety and convenience for passengers, and has been more and more popular. However, unforeseen emergencies may disrupt the normal train operation. In this paper, an integrated dispatch strategy (IDS) is proposed to synergistically reschedule the train timetable and rolling stock circulation plan under single-direction disruptions. A two-objective model is formulated, aiming at minimizing both the delay time of passengers and the operation costs of railway companies, to reschedule the train operation efficiently and economically. An algorithm based on Non-dominated Sorting Genetic Algorithms-II (NSGA-II) is designed to solve the model. To accelerate the solving process, we propose a quick method to generate an assignment plan to serve disrupted passengers, and based on the practical experiences, the algorithm acceleration strategy (AAS) is proposed to improve the quality of initial solutions. The model and algorithm are tested on real-world instances of the Beijing-Shanghai high-speed railway line. The results indicate that the average minimized delay time of passengers is 6,012,386 min and the average minimized additional operation costs (operation mileage of standby rolling stocks) are 1623 km, with a decrease of 28.5% and 18.3%, respectively, indicating the model and algorithm are adaptable to handle single-direction disruptions on the railway line, and AAS can further accelerate the computing speed and improve the solutions quality. Finally, the characteristics of disrupted sections of railway lines are well studied and analyzed.

Experimental Research on Diseases of Emulsified Asphalt Mortar Board for Ballastless Tracks

The dynamic performance of high-speed railway systems’ stability, comfort, and safety is an essential topic worldwide. The multiorder elastic modes of components are the key factors. It is the core technology to ensure the quality of the backfill layer of flexible cement-emulsified asphalt mortar (CA mortar) in reducing the elastic vibration structure of the car body. This research results from the manufacturability verification of the Beijing–Shanghai high-speed railway. Four theoretical model methods are used in the research: experimental observation data analysis, simplified orthogonal test, phase contrast and computational analysis, and grey relational analysis to improve the performance indicators of CA mortar. The research conclusion confirms that the best mix ratio, high gas content, and poor fluidity are the core factors affecting the technical indicators of mortar and the focus on quality control. Through this research work, a wealth of theoretical methods and practical experience data to ensure the quality and qualified indicators of mortar have been summarized, and the prevention of acute diseases (bubble and through-holes) has played a guiding role. The research result ensured the optimal quality characteristics and indexes of viscoelastic materials, and the operation of high-speed railways was safe.

Discussing the Negative Pressure Distribution Mode in Vacuum-Preloaded Soft Foundation Drainage Structures: A Numerical Study

The aim of this paper is to clarify the negative pressure distribution in drainage structures of soft foundations reinforced by vacuum preloading. The focus of this study was an actual engineering project, the Beijing–Shanghai high-speed railway; four different soil consolidation models were established using FLAC3D to consider various loading conditions. The consolidation process of the soft foundation was calculated and analyzed in detail. The results show that (1) the settlement developed rapidly within the first 30 days, slowed during the period between 20 and 30 days, and finally stabilized. (2) The settlement curves obtained from the four different models were highly consistent with the site monitoring curve for the first 5 days, after which point significant differences appeared. (3) During the first 20 days, the pore water pressure decreased noticeably within the depth range of 0–18 m. Between days 20 and 30, the rate of pore water pressure decrease slowed down, and after the 30th day, the pore water pressure remained constant at all depths. (4) Vacuum preloading affected the soil to a depth of approximately 16 m. A concave or linear distribution of negative pressure in the drainage structure was found to be a reasonable assumption, providing a reference for the numerical analysis of vacuum preloading.

Fluid–Solid Coupling Numerical Analysis of Pore Water Pressure and Settlement in Vacuum-Preloaded Soft Foundation Based on FLAC3D

There are few calculation methods for the design and construction of vacuum preloading to strengthen soft foundations. Based on the FLAC3D, a calculation model was established for the vacuum preloading project of the Beijing–Shanghai high-speed railway. Through calculation and comparison of measured values, the following results were obtained: (1) The top surface of the reinforcement area and the sand drain can be regarded as the load boundary, which can be realized by assigning the node pore water pressure. (2) After 30 days of vacuum action, the settlement rate at each depth decreased significantly and the deformation gradually became stable. It is reasonable to design the vacuum preloading time as 2–4 months. (3) The negative pore water pressure has different transmission times and uneven distribution, which makes the consolidation time and degree of soil on the same level uneven. After 30 days of vacuum action, this uneven phenomenon will be transformed into a uniform phenomenon. (4) The change time of pore water pressure under vacuum preloading is about 30 days. After that, the pore water pressure at different depths will tend to have different constant values. The influence depth of vacuum preloading can reach 16 m. These works can make up for the deficiency of vacuum preloading calculation methods.

Segmentalized mRMR Features and Cost-Sensitive ELM With Fixed Inputs for Fault Diagnosis of High-Speed Railway Turnouts

Turnouts are crucial to the safety of high-speed railways. Due to the intensive use and complex environment, breakdowns caused by different faults occur frequently in practice. Considering that the operation of turnouts is a multi-stage process during which each stage has its specific health characteristics, this paper proposes segmentalized maximal-relevancy and minimal-redundancy (mRMR) for feature extraction from each stage separately. Based on mathematical analysis of the turnout mechanism, the electric power curve is segmented into four stages, from which time-domain analysis and mRMR are combined to extract valid features corresponding to different movements respectively. Then, a novel classifier named cost-sensitive Extreme Learning Machine with fixed inputs (cf-ELM) is proposed for fault classification. We modify the inputs of ELM and define a new formula to limit the input weights and biases for the sake of stability of the network structure. Besides, a cost-sensitive optimization method is also presented in this classifier to embed the failure degree and data proportion into cost calculation rules to deal with data imbalance. To verify our proposed method, real data collected from a turnout of Beijing-Shanghai high-speed railway is used. It is proven by comparisons that the accuracy of our method has achieved 100% with fast running speed and also outperforms traditional methods in terms of stability and generalization remarkably.

CO2 emissions reduction performance of China's HSR based on substitution effect and demand effect

Abstract As an important transportation infrastructure and transportation backbone in China, high-speed rail (HSR) plays a critical role in promoting the development of green and low-carbon transportation. Calculating the CO2 emissions reduction performance of HSR will be conducive to promote the CO2 emissions reduction work of the railway. Based on the Dalkic HSR CO2 emissions reduction performance model, by adjusting the HSR CO2 emission factor (CEFHSR), the annual times of departures (T) and other parameters, this study develops a Chinese HSR CO2 emissions reduction performance model. Taking the Beijing-Shanghai HSR as the research object, this study conducts a questionnaire survey to explore the substitution effect and demand effect of HSR on different transportation modes; collects data such as passenger volume, average electricity use and annual times of departures of the Beijing-Shanghai HSR in 2019; and calculates the CO2 emissions reduction performance of the Beijing-Shanghai HSR. This study has two main results: (1) It builds a Chinese HSR CO2 emissions reduction performance model based on substitution effect and demand effect. (2) In 2019, the CO2 emissions of the Beijing-Shanghai HSR are 2 898 233.62 t, the CO2 emissions reduction performance of the Beijing-Shanghai HSR is 17 999 482.8 t, the annual CO2 emissions of the Beijing-Shanghai line in ‘No HSR’ case are 7.2 times as in the 'HSR' case and the PKT of the HSR is 10.2 g/pkm. Based on the research results, this study proposes three CO2 emissions reduction policy suggestions. This study would be helpful for further HSR CO2 emissions reduction research and departments related to railway transportation management to make CO2 emissions reduction policies.

Measurement and Prediction of Urban Land Traffic Accessibility and Economic Contact Based on GIS: A Case Study of Land Transportation in Shandong Province, China.

As the basic support of regional economic and social development, land transportation is one of the important engines to promote regional development, and its construction and improvement will have an important impact on the regional economic pattern. Based on the road network of Shandong Province, China, in 2020, according to the Medium and Long-term Development Plan of Comprehensive Transportation Network of Shandong Province (2018-2035), this paper uses the GIS network analysis method, weighted average travel time, modified gravity model and other methods to study the land traffic accessibility and economic relation intensity of prefecture-level cities in Shandong Province, China, in 2020 and 2035. The results show that the distribution of land traffic accessibility in Shandong Province, China, shows a certain regional main road pointing characteristic in 2020, and the urban accessibility level gradually decreases along the Beijing-Shanghai high-speed railway and Jinan-Qingdao high-speed railway to the periphery. In 2035, the land traffic accessibility of Shandong Province, China, will be more spatially distributed as "concentric circles". From 2020 to 2035, the urban land traffic accessibility and the balance of economic contact in Shandong Province, China, will be improved significantly. The research results can provide a theoretical reference for optimizing the traffic network pattern and promoting urban economic contact in Shandong Province, China.

Hierarchical Model Predictive Control for On-Line High-Speed Railway Delay Management and Train Control in a Dynamic Operations Environment

In practice, the operation of high-speed trains is often affected by adverse weather conditions or equipment failures, which result in delays and even cancellations of train services. In this article, a novel two-layer hierarchical model predictive control (MPC) model is proposed for on-line high-speed railway delay management and train control for minimizing train delays and cancellations. The upper layer manages the global objectives of the train operation, that is, minimizing the total train delays and providing guidance for the speed control in the lower layer. The objectives of the lower layer are to satisfy the running time requirements given by the upper layer and to save energy at the same time. The optimization problems in both levels of the hierarchical MPC framework are formulated as small-scale mixed integer linear programming problems, which can be solved efficiently by existing solvers. Particularly, the train control problem is solved in a distributed way for each train. Simulation analysis based on the real-life data of the Beijing–Shanghai high-speed railway shows that the proposed hierarchical MPC framework can meet the real-time requirements and reduce train delays effectively when compared with widely accepted strategies, for example, first-scheduled-first-serve and first-come-first-serve. Moreover, the proposed hierarchical MPC framework also provides good robustness performance for different disturbance scenarios.

An Optimization Method of High-Speed Railway Rescheduling to Meet Unexpected Large Passenger Flow

In the rapidly changing multimodal transportation market, not only the obligation in providing train services but also the responsibility in helping other transportation modes to meet emergencies are instrumental for maximizing the generalized benefit of railway companies, leading to an increased utilization. This paper proposes an optimization framework, which includes multiple dispatching measures of arranging residual seats, modifying stopping plans, and inserting additional trains, to design a rescheduling operation plan on a high-speed rail corridor to meet the passenger demand of unexpected large passenger flow (ULPF), which is generated from the disruption of other transportation modes. Considering the revenues of shifting passengers and the costs of the three dispatching measures as two objectives, we formulate a linear integer programming (LIP) model by employing a time-space network to obtain an optimization rescheduling operation plan. Several experiments based on the Beijing-Shanghai high-speed railway corridor are conducted to evaluate the effectiveness and efficiency of the proposed model. The experimental results demonstrate that the proposed model can be used to obtain a reasonable rescheduling operation plan for serving the passengers from ULPF within an acceptable calculation timeframe.