High-speed Railway Train Research Articles

A hybrid data-mining framework for train rescheduling strategy pattern discovery

Abstract This study presents a hybrid data-mining framework based on feature selection algorithms and clustering methods to perform the pattern discovery of high-speed railway train rescheduling strategies (RSs). The proposed model is composed of two states. In the first state, decision tree, random forest, gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost) models are used to investigate the importance of features. The features that have a high influence on RSs are first selected. In the second state, a K-means clustering method is used to uncover the interdependences between RSs and the influencing features, based on the results in the first state. The proposed method can determine the quantitative relationships between RSs and influencing factors. The results clearly show the influences of the factors on RSs, the possibilities of different train operation RSs under different situations, as well as some key time periods and key trains that the controllers should pay more attention to. The research in this paper can help train traffic controllers better understand the train operation patterns and provides direction for optimizing rail traffic RSs.

PD Characteristics and Parameter Extraction for EPR Cable Terminal in EMUs With Spike Defect at Low Temperature

Ethylene propylene rubber (EPR) cable terminals are widely used in high-speed railway trains. In the alpine environment, partial discharge (PD) and even breakdown failures caused by spike defects frequently occur in EPR cable terminals. To study the discharge development of cable terminals with spike defects in alpine regions, EPR cable terminal test samples with spike defects were made, and a low-temperature PD test system was built to simulate the environmental conditions in alpine regions. The characteristics of the lower PD spectrum were analyzed, and the influence mechanism of low temperature on the discharge activity was analyzed combined with the scanning electron microscope (SEM) scan. The research results show that the discharge development of the terminal spike defect can be divided into three stages: S1~S3. In S1 stage, the degree of PD at –30°C is weaker than that at 20°C dues to the low diffusion ability and weak polarization effect of carriers. In S2 and S3 stages, with the continuous discharge, low temperature makes the deterioration tolerance of ethylene propylene rubber material decrease more seriously, which promotes the development of discharge. The discharge phase interval is larger and the discharge activity is more intense at –30°C than at 20°C, and the breakdown time point is 11.6% earlier. By studying the characteristics of the spectrum, it is found that the deviation of PD φ- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q_ave</i> spectrum at –30°C from positive to negative is an important signal before the critical breakdown of cable insulation, which can provide a reference for judging the severity of terminal spike defects at low temperature.

High-speed railway passenger flow equilibrium among trains of common lines based on travel behavior analysis under dynamic pricing

ABSTRACT This paper aims to make contributions to balancing the passenger flow among high-speed railway trains of common lines as well as raise revenue via a behavioral-based optimal dynamic pricing. Based on an HSR trip choice survey, a set of Nested-logit (NL) models is built to analyze passengers’ preferences for different factors under various scenarios. The upper level belongs to the departure time period while the lower level belongs to specific trains with different levels of service. Given the conditions of fixed transportation capability, an optimization model is built to optimize the pricing among trains of common lines. The NL model results show that the time values of round trips are greater than that of one-way trips, and the time values of business trips are greater than that of leisure trips. Besides, the optimization model is verified to be capable of raising revenue, improving passenger flow equilibrium, and inducing extra passenger demand.

Handover Optimization Algorithm Based on T2RFS-FNN.

As a key technology for highly reliable communication in the fifth generation mobile communication for railway (5G-R) high-speed railway wireless communication system, once the handover fails, it will pose a serious risk to the safe operation of high-speed railway. As the speed of high-speed trains continues to increase, the handover will become more frequent, and how to improve the success rate of the handover is a key problem that needs to be solved. In this paper, we proposed an optimization algorithm based on the interval type 2 feature selection recurrent fuzzy neural network (T2RFS-FNN), which is a recurrent fuzzy neural network with interval type 2 feature selection, to address the problem of fixed hysteresis threshold and single consideration for the handover algorithm between the control plane and the user plane of the high-speed railway under 5G-R. The algorithm integrates reference signal receiving power (RSRP). Reference signal receiving quality (RSRQ) and throughput to optimise the hysteresis threshold. First, a feedforward neural network structure is designed to implement fuzzy logic inference, and an interval type-two Gaussian subordination function is used to improve the nonlinear expressiveness of the model. Then, a feature selection layer is added to determine the output of the affiliation function, which completes the optimization of the hysteresis threshold and overcomes the drawback of the fixed hysteresis threshold of the handover algorithm. Finally, simulation analysis of the control-plane and user-plane handover algorithms is carried out separately. The results show that the proposed method can effectively improve the success rate and reduce the ping-pong handover rate compared to the comparison algorithms. The results provide a theoretical reference for the speedup of high-speed railway trains and the evolution of the global system for mobile communications for railway (GSM-R) to 5G-R.

Fault Early Warning Model for High-Speed Railway Train Based on Feature Contribution and Causal Inference

The demands for model accuracy and computing efficiency in fault warning scenarios are increasing as high-speed railway train technology continues to advance. The black box model is difficult to interpret, making it impossible for this technology to be widely adopted in the railway industry, which has strict safety regulations. This paper proposes a fault early warning machine learning model based on feature contribution and causal inference. First, the contributions of the features are calculated through the Shapley additive explanations model. Then, causal relationships are discovered through causal inference models. Finally, data from causal and high-contribution time series are applied to the model. Ablation tests are conducted with the Naïve Bayes, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and other models in order to confirm the efficiency of the method based on early warning data regarding the on-site high-speed train traction equipment circuit board failure. The findings indicate that the strategy improves the evaluation markers, including the early warning accuracy, precision, recall, and F1 score, by an average of more than 10%. There is a 35% improvement in the computing efficiency, and the model can provide feature causal graph verification for expert product decision-making.

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.

Effects of Ti microalloying on the microstructure and mechanical properties of acicular ferrite casting steel for high-speed railway brake discs

Owing to the continuous interest on high-speed railway trains, research on the brake disc made of ferritic cast steel with high strength and toughness, and the manufacturability of complicated cooling fins has been conducted. In acicular ferrite, Ti is a major element; however, studies on the effect of Ti composition on the mechanical and impact properties are limited to a Ti content of up to 0.05 wt%. In this study, the microstructure and mechanical properties of high-speed railway brake disc steel material with 0–0.12 wt% Ti were investigated. The specimens were subjected to the same quenching–tempering heat treatment, in which acicular ferrite formation was observed by optical microscopy, scanning electron microscopy, electron backscatter diffraction (EBSD), electron probe microanalyzer, and transmission electron microscopy. Ferrite blocks, such as acicular ferrite, granular bainite, bainitic ferrite, and polygonal ferrite, were characterized in detail and quantitatively analyzed by EBSD. For different Ti contents, ferrites of various morphologies were noted. Moreover, the types of precipitates were determined to be cementite, Cr-rich carbide, V-rich carbide, and Ti-rich carbide. The highest hardness, impact toughness, and strength were measured with the addition of 0.06 wt% Ti. The crack propagation mode in the impact specimen was observed using fracture surface analysis. In addition, the relationship between the microstructure and mechanical properties, such as strength, toughness, and hardness, was discussed based on the alloying Ti content. In this study, the effect of acicular ferrite and precipitates on toughness in ferrite casting steel was confirmed, and the optimal Ti content was confirmed.

Study on the Evolution of Wheel Wear and Its Impact on Vehicle Dynamics of High-Speed Trains

Wheel wear is one of the most critical factors affecting the vehicle performances and maintenance costs of railway vehicles. However, previous research has to ignore the initial wheel-rail profiles for the evolution of wheel wear. Therefore, this work investigates the relationship between the evolution of wheel wear corresponding to different initial wheel-rail profiles and vehicle dynamics, wheel-rail deterioration. Firstly, the evolution of wheel wear during a long service period is measured from two high-speed railway trains running on two different lines. Contact geometry, e.g., equivalent conicity and contact pair distribution, are extracted. After that, the influence of wheel wear on the vehicle dynamic performance is studied using a multi-body dynamic software. The calculated contact parameters, e.g., pressure, shear traction, and creepage, are used to analyze the distribution of rolling contact fatigue. Based on the experimental and simulation results, the initial wheel and rail profiles significantly affects the wheel wear pattern, the thin rim wheel has uniform wear, and other wheels occurs hollow wear. The hollow wear can lead to gradual deterioration of vehicle dynamics, which conversely aggravates the wheel reprofiling.

Field test and numerical reconstitution of natural winds at the tunnel entrance section of high-speed railway

PurposeThe purpose of this paper is to understand the natural wind field characteristics of the tunnel entrance section and analyzing the aerodynamic performance of high-speed railway trains (HSRTs) under natural winds.Design/methodology/approachThree typical tunnel entrance section sites, namely, tunnel–bridge in a dry canyon (TBDC), tunnel–bridge in a river canyon (TBRC) and tunnel–flat ground (TF), are selected to conduct a continuous wind field measurement. Based on the measured wind characteristics, the natural winds of the TBDC and TF sites are reconstituted and imported into the two corresponding full-scale computational fluid dynamics models. The aerodynamic loads of the HSRT running on TBDC and TF with reconstituted winds are simply analyzed.FindingsThe von Kármán spectrum can be used to describe the wind field at the tunnel entrance section. In the reconstituted natural wind condition, a time-varying feature of wind speed distribution and leeward side vortex around the HSRT caused by the wind speed fluctuation is found. The fluctuating amplitude of aerodynamic loads at the TBDC infrastructure is up to 97.9% larger than that at the TF infrastructure.Originality/valueThe natural wind characteristics at tunnel entrance sections on the high-speed railway are first measured and analyzed. A numerical reconstitution scheme considering the temporal and spatial variation of natural wind speed is proposed and verified based on field measurement results. The aerodynamic performance of an HSRT under reconstituted natural winds is first investigated.

Quantitative Distribution Characterization and Correlation Study of Composition, Structure and Hardness of Rim Region in Railway Wheel.

The railway wheel is the key component of high-speed railway train. To assure the safety in service, higher requirements are put forward in this study for the composition, microstructure uniformity, and comprehensive properties of wheel materials. In this paper, the high throughput quantitative distribution characterization methods of composition, microstructure, inclusions and Vickers hardness of high-speed railway wheel materials based on the spark source original position analysis technique, high throughput scanning electron microscope (SEM) combined with image batch processing technology, and automatic two-dimensional quantitative distribution analysis technique of inclusions and micro hardness have been studied. The distribution trend of the content of nine elements, size and quantity of sulfides and oxides, ferrite area fraction, and Vickers hardness from the wheel tread surface to the radial depth of about 50 mm below the surface has been discussed. The influence of inclusions distribution on the element segregation and the effect of rim-chilling process with different water spraying angle on the distribution of microstructure and micro hardness have been investigated. It was found that unsynchronized cooling on both sides of the rim altered the phase behavior of ferrite and pearlite and obvious inhomogeneity distribution of ferrite appeared, which led to the asymmetrical Vickers hardness in areas near or away from the flange. Based on the quantitative characterization of area fraction and micro hardness on the same location of wheel rim, a statistical mapping relationship between ferrite area fraction and Vickers hardness was established.

A discussion on design fires for high-speed railway train cars

High-speed rail systems are expanding everywhere for carrying thousands of passen-gers every day. Train cars in underground spaces travel under dense urban areas, tun-nels in mountainous places and bridges across rivers. Design fire in burning a train car should be studied carefully for fire hazard assessment in those long underground or en-closed spaces. There are full-scale burning testing results on the main combustibles in-cluding seat, curtain and passenger luggage in some train cars. The heat release rate of a high-speed train car model CRH1 will be studied using such data together with a two-layer fire zone model, the Consolidated Model of Fire and Smoke Transport (CFAST). Simulation will be performed on the full-sized train car with the heat release rate of the seat measured by experiment. Two fire scenarios with ignition points located in the front and rear areas of the train compartment are considered. The results of the fire simulations show that the heat release rate can reach 43 MW. The value is similar to the full-scale burning experiments on train cars in Sweden and South Korea. Active fire protection systems have to be seriously considered in those underground or enclosed areas.

An emerging unrated mobile reservoir for antibiotic resistant genes: Does transportation matter to the spread

The regional distribution of antibiotic resistance genes has been caused by the use and preference of antibiotics. Not only environmental factors, but also the population movement associated with transportation development might have had a great impact, but yet less is known regarding this issue. This research study has investigated and reported that the high-speed railway train was a possible mobile reservoir of bacteria with antibiotic resistance, based on the occurrence, diversity, and abundance of antibiotic resistant bacteria (ARB), antibiotic resistance genes (ARGs), and mobile gene elements (MGEs) in untreated train wastewater. High-throughput 16S rRNA sequencing analyses have indicated that opportunistic pathogens like Pseudomonas and Enterococcuss were the predominant bacteria in all samples, especially in cultivable multi-antibiotic resistant bacteria. The further isolated Enterococcus faecalis and Enterococcus faecium exhibited multi-antibiotic resistance ability, potentially being an indicator for disinfection proficiency. Positive correlations amongst ARGs and MGEs were observed, such as between intI1 and tetW, tetA, blaTEM, among Tn916/154 and mefA/F, qnrS, implying a broad dissemination of multi-ARGs during transportation. The study findings suggested that the high-speed railway train wastewater encompassed highly abundant antibiotic-resistant pathogens, and the wastewater discharge without effective treatment may pose severe hazards to human health and ecosystem safety.

Ticket Allocation Optimization of Fuxing Train Based on Overcrowding Control: An Empirical Study from China

At the peak of passenger flow, some passengers extend travel sections, which will be likely to lead to overcrowding of high-speed railway (HSR) trains. Therefore, the problem of train overcrowding control needs to be considered in ticket allocation. Firstly, by simulating the passenger demand function and utility function, an optimization model of ticket allocation for multiple trains and multiple stops with the goal of maximizing revenue is constructed. Secondly, the concepts of the travel extension coefficient and risk coefficient are introduced, the number of passengers is estimated under the risk coefficient as the probability, and the total number of passengers on the train arriving at any station is obtained. Thus, preventing the number of passengers on the train from exceeding the train capacity is introduced to the ticket allocation optimization model of multiple trains and multiple stops as a constraint. Finally, this model is solved by the particle swarm optimization algorithm (PSO). The research results show that the idea of controlling passenger numbers so as not to exceed train capacity based on ticket allocation proposed in this paper has strong practical feasibility. By reasonably and accurately allocating the tickets to the departure terminal section and long-distance terminal sections, it can ensure that, even if there are some passengers extending their travel section, the train will not be overcrowded under a certain probability, improving the train safety and passenger travel experiences.

Comparative study on the wind characteristics of tunnel–bridge and tunnel–flat ground infrastructures on high-speed railway

The difference in natural wind characteristics of the tunnel–bridge (TB) and tunnel–flat ground (TF) infrastructure may result in varied aerodynamic behaviours of the high-speed railway train (HSRT). Hence, continuous natural wind sampling is conducted on typical TB and TF sites by using three ultrasonic and one three-cup anemometers. The mean and fluctuating wind characteristics of the two sites are compared and discussed in detail based on field measurement data. The main results can be concluded as follows: Firstly, the prevailing wind direction on the TB site is perpendicular to the running direction of the HSRT due to the occlusion effect, whereas the mean direction on the TF site is easily deflected by the near-ground vegetations and other obstacles. Secondly, the turbulence intensities of the TF site are larger than those of the TB site, and the lognormal distribution is applicable for describing the probability distribution of measured turbulence intensity on the two sites. Thirdly, the von Kármán spectrum accurately depicts the wind energy distribution of the TB and TF sites in the frequency domain, whereas the empirical spectrum adopted by the specification show inferior accuracy. Lastly, the coherence distance of the TB site is considerably longer than that of the TF site. The coherence distance of u and v components of the TB site is seven times longer than a train body (approximately 25 m), and that of u component of the TF site is shorter than two times of a train body.

A Memetic Algorithm for High-Speed Railway Train Timetable Rescheduling

This study addresses a high-speed railway train timetable rescheduling (TTR) problem with a complete blockage at the station and train operation constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that minimizes the weighted sum of the total delay time of trains. A memetic algorithm (MA) is proposed, and the individual of MA is represented as a permutation of trains’ departure order at the disrupted station. The individual is decoded to a feasible schedule of the trains using a rule-based method to allocate the running time in sections and dwell time at stations. Consequently, the original problem is reformulated as an unconstrained problem. Several permutation-based operators are involved, including crossover, mutation, and local search. A restart strategy was employed to maintain the the population diversity. The proposed MA was compared with the first-scheduled-first-served (FSFS) algorithm and other state-of-the-art evolutionary algorithms. The experimental results demonstrate the superiority of MA in solving the TTR through permutation-based optimization in terms of constraint handling, solution quality, and computation time.

Rescheduling Urban Rail Transit Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains

This paper develops a multi-objective mixed-integer linear programming model for the problem of robust rescheduling for capacitated urban rail transit (URT) trains to serve passengers from delayed high-speed railway (HSR) trains. The capacity of each extra train is not assumed to be unlimited in this paper. Robust passenger assignment constraints are developed to ensure that delayed passengers can board the URT trains under different random delay scenarios of HSR operations. Robust dispatching constraints of URT trains are designed for a stable disrupting number of URT trains across different scenarios. The multi-objective model is used to maximize the number of expected transported passengers and minimize the number of extra trains and operation-ending time of all extra trains. An iterative solution approach based on a revised version of the epsilon-constraint method combined with the weighted-sum method is designed for the computation of the multi-objective model. Computational experiments are performed on the Beijing URT lines and the Beijing-Shanghai HSR line. We evaluate the impact of the robustness constraints of passenger assignment and the number of extra trains to ensure that the number of trains are maintained and the passengers can successfully take the trains during different delayed scenarios.

Wheel Tread Wear Prediction of High-Speed Railway Train

Wheel Tread Wear Prediction of High-Speed Railway Train

Study on Comprehensive Optimization of Electric Multiple Unit Trains Passing Station Route and Rolling Stock Scheduling

In order to overcome the contradiction between the rolling stock scheduling problem of EMU at a macro level and the problem of EMU train passing station route at a micro level in some special cases, this paper comprehensively optimizes the electric multiple unit train passing station route and rolling stock scheduling. By taking the operation of the Chinese high-speed railway EMU trains as the actual background and using the actual situation that the first-level inspection and repair cycle time is twice the cycle of a high-speed railway train timetable, based on the establishment of a 48-hour continuous network, a 0-1 type integer linear programming model is constructed. While obtaining the rolling stock scheduling of EMU trains, the EMU train passing station route scheme is obtained. The experimental results show that, compared with solving the problem in two stages, the comprehensive optimization according to the method in this paper can better ensure the feasibility of the passing station route scheme and provide decision support for the actual operation.

Rescheduling of high-speed trains: a reinforcement learning approach

With the construction of high-speed railway networks in China, the traffic density is constantly increasing, and the complexity and difficulty of train rescheduling sharply increase when trains are delayed due to emergencies. How to dynamically adjust the train group operation to reduce the delay and improve the punctuality rate is the core of operation adjustments. In this paper, a model-free reinforcement learning method is proposed for the operation adjustment of high-speed trains in emergency situations. Firstly, the operation adjustment of multiple trains in multiple stations and blocks is modeled as a sequential multi-stage decision-making process of constrained resource occupation and allocation, and a dynamic spatio-temporal topological matrix is proposed to model the stations and blocks. In view of the strong spatio-temporal correlation of high-speed railway trains, a reinforcement learning state space, action space and reward function containing spatio-temporal distribution information such as vehicle positions and network resources are proposed for the first time, and an effective reward feedback mechanism is constructed. Then, aiming at the difficulty of huge search space in high-speed railway operation systems, this paper proposes an adaptive generation method of heuristic action subspace. This method uses some explicit static constraints to construct heuristic rules to reduce the search space, which can effectively reduce the trial-and-error times of model-free reinforcement learning, improve the efficiency of solution, and retain the advantages of model-free generality. Finally, the simulation analysis of the case based on the actual data of Beijing-Guangzhou high-speed railway shows that the proposed algorithm can converge well and significantly reduce the delay propagation within the train group under multiple delays caused by the high wind speed limit in different space and time ranges. Compared with MILP, ACO, and FCFS algorithms, the proposed method can reduce the average delay time of the train group by 2%–20%.

Robust Optimization of High-Speed Railway Train Plan Based on Multiple Demand Scenarios

The optimization of train plans is highly dependent on the space–time distribution of passenger demand in high-speed railway systems. A train plan usually needs to be implemented on multiple operation days, and obviously the amount and space–time distribution of demand over these days has noteworthy differences. To ensure the same train plan is able to be implemented on multiple operation days while effectively satisfying the different levels of demand on those days, a novel robust optimization of a high-speed railway train plan based on multiple demand scenarios is performed in this research. Firstly, the passenger demand of each operation day is described as a demand scenario, and a candidate train set is generated that is able to satisfy the multiple demand scenarios. Then, a regret value corresponding to the total cost, including the train operation cost and passenger travel expense, is proposed to measure the deviation in the costs generated between the robust and the optimal train plan under each demand scenario. Then a robust optimization model for a high-speed railway train plan is constructed to minimize the maximum regret value. Moreover, a simulated annealing algorithm for solving the model is designed by constructing some neighborhood solution search strategies for multiple demand scenarios. Finally, the validity and feasibility of the proposed robust optimization method for train planning are verified on the Shijiazhuang–Jinandong high-speed railway line in China.