Wuhan-Guangzhou High-speed Railway Research Articles

Study on dynamic response of high speed train window glass under tunnel aerodynamic effects

PurposeThis paper aims to analyze the bearing characteristics of the high speed train window glass under aerodynamic load effects.Design/methodology/approachIn order to obtain the dynamic strain response of passenger compartment window glass during high-speed train crossing the tunnel, taking the passenger compartment window glass of the CRH3 high speed train on Wuhan–Guangzhou High Speed Railway as the research object, this study tests the strain dynamic response and maximum principal stress of the high speed train passing through the tunnel entrance and exit, the tunnel and tunnel groups as well as trains meeting in the tunnel at an average speed of 300 km·h-1.FindingsThe results show that while crossing the tunnel, the passenger compartment window glass of high speed train is subjected to the alternating action of positive and negative air pressures, which shows the typical mechanic characteristics of the alternating fatigue stress of positive-negative transient strain. The maximum principal stress of passenger compartment window glass for high speed train caused by tunnel aerodynamic effects does not exceed 5 MPa, and the maximum value occurs at the corresponding time of crossing the tunnel groups. The high speed train window glass bears medium and low strain rates under the action of tunnel aerodynamic effects, while the maximum strain rate occurs at the meeting moment when the window glass meets the train head approaching from the opposite side in the tunnel. The shear modulus of laminated glass PVB film that makes up high speed train window glass is sensitive to the temperature and action time. The dynamically equivalent thickness and stiffness of the laminated glass and the dynamic bearing capacity of the window glass decrease with the increase of the action time under tunnel aerodynamic pressure. Thus, the influence of the loading action time and fatigue under tunnel aerodynamic effects on the glass strength should be considered in the design for the bearing performance of high speed train window glass.Originality/valueThe research results provide data support for the analysis of mechanical characteristics, damage mechanism, strength design and structural optimization of high speed train glass.

Statistical Analysis of Train Operation and Passenger Distribution Based on Real Records: A Case Study of Wuhan-Guangzhou HSR

This paper summarizes the results of an effort aimed at improving train operation schedules on Wuhan-Guangzhou high-speed railway (WG-HSR). The real-record train operation and passenger tickets-booking records of WG-HSR are used for statistical analysis on the train service quality and passenger distribution. More specifically, the train service frequency and interval time at each station are analyzed. Based on this, the temporal and spatial distribution of capacity utilization in each section are investigated. In order to get a holistic view of passenger flow characteristics, the passenger volume during different time periods and between several origin and destination (OD) pairs are investigated to characterize travellers’ spatial-temporal preferences. The passenger distributions on some long-distance trains are shown to get the number and proportion of cross-line passengers travelling on the WG-HSR. Moreover, for a better understanding of the seat capacity utilization of trains, the load rates of trains in various sections and time periods are investigated. Specifically, the relationship between the average load rate of trains and trains’ running distance is explored, finding that the longer the non-cross-line train travels is, the higher the average load rate is. This study provides insightful findings that help understanding HSR operation and conducting further research.

Effect of high-speed trains passing each other on wheel wear under different track excitations

Considering the aerodynamic loads arising during the process of two high-speed trains passing each other (hereinafter referred to as TPEO) as well as different track excitations on different railway lines, this study examined the combined effect of TPEO and different track excitations on wheel wear. A vehicle system dynamics model was built using the actual parameters of a high-speed train. The wheel/rail contact problems were analyzed using a combination of semi-Hertzian contact theory and Kalker’s simplified theory. Elkins wear index was used as a criteria for evaluation of wheel wear. The additional excitation arising from the pressure wave during TPEO at a constant speed of 300 km/h was applied to the model. Then the effect of the aerodynamic excitation due to TPEO on wheel wear under four track excitation conditions was investigated for the whole train, different cars, and different axles. The results show that under different track excitations, the train’s wheel wear increased in the case where one TPEO event occurred compared to that in the case of no TPEO event. Under the four track conditions (no track excitation, the track excitations on the Wuhan–Guangzhou high-speed railway, German track excitation spectra, and track excitation on the Beijing–Tianjin intercity railway), the total wheel wear increased by 17.36%, 0.69%, 0.82%, and 1% respectively over the entire trip, and increased by 1600%, 65.77%, 78.62%, and 79.73% at the moment of TPEO. A comparison between different cars shows that under different track excitation conditions, the tail car’s wheel wear was the most affected by TPEO, while the head car’s wheel wear was the least affected. A similar pattern is observed in the variation in transverse displacement of wheels under TPEO between different cars. For each bogie, TPEO under different track excitations had a greater effect on the front-axle wheel wear than on the rear-axle wheel wear.

Prediction of high-speed train delay propagation based on causal text information

The delay-causing text data contain valuable information such as the specific reasons for the delay, location and time of the disturbance, which can provide an efficient support for the prediction of train delays and improve the guidance of train control efficiency. Based on the train operation data and delay-causing data of the Wuhan–Guangzhou high-speed railway, the relevant algorithms in the natural language processing field are used to process the delay-causing text data. It also integrates the  train operating-environment information and delay-causing text information so as to develop a cause-based train delay propagation prediction model. The Word2vec model is first used to vectorize the delay-causing text description after word segmentation. The mean model or the term frequency-inverse document frequency-weighted model is then used to generate the delay-causing sentence vector based on the original word vector. Afterward, the  train operating-environment features and delay-causing sentence vector are input into the extreme gradient boosting (XGBoost) regression algorithm to develop a delay propagation prediction model. In this work, 4 text feature processing methods and 8 regression algorithms are considered. The results demonstrate that the XGBoost regression algorithm has the highest prediction accuracy using the test features processed by the continuous bag of words and the mean models. Compared with the prediction model that only considers the train-operating-environment features, the results show that the prediction accuracy of the model is significantly improved with multiple regression algorithms after integrating the delay-causing feature.

The Impact of High-Speed Railway Opening on Regional Economic Growth: The Case of the Wuhan–Guangzhou High-Speed Railway Line

With the advent of the “ear of high-speed railways”, the space–time distance between cities has drastically decreased. The opening of high-speed railways has not only increased the factor-flow speed across regions, but has also reduced the cost of factor movement. However, there is some controversy about whether high-speed railways can be used to promote regional economic growth. The Wuhan–Guangzhou high-speed railway is the earliest section of the Beijing–Guangzhou high-speed railway, and it was the first high-speed railway in China with a real speed of 350 km per hour. The Wuhan–Guangzhou high-speed railway opening spawned a three-hour economic circle in the south of China, injected new vitality into the economic and social development along the line, and had an important impact on accelerating regional economic interoperability and integration. Taking this into account, this paper took the Wuhan–Guangzhou high-speed railway as the case study. Firstly, this paper used the theory of system dynamics to analyze the mechanism of the impact of high-speed railway opening on regional economic growth. Secondly, the accessibility model was used to analyze the impact of the high-speed railway opening on the accessibility of cities along the line, and a difference-in-differences model (DID) was used to explore the impact of the high-speed railway opening on regional economic growth. Finally, on the basis of the analysis of the previous mechanism, the mechanism of the impact of the high-speed railway on regional economic growth is discussed. The results of this study show the following: (1) Since the opening of the high-speed railway, the accessibility of cities along the line has grown by more than 60%. (2) The opening of the high-speed railway has promoted regional economic growth, mainly through the influence mechanism of “increasing the total cargo volume”. This study not only scientifically and quantitatively analyzed the impact of the opening of high-speed rail on regional economic growth, taking the Wuhan–Guangzhou high-speed railway as an example for empirical analysis, but also further summarized the high-speed railway construction achievements in China and provides reference experience for planning the opening of high-speed railways.

Evaluating operational risk for train control system using a revised risk matrix and FD-FAHP-Cloud model: A case in China

An accident caused by train control system may not only bring expensive loss, but also result in severe destruction to surrounding environment. The purpose of this study is to study operational risk of Chinese Train Control System- Level 3 (CTCS-3). An integrated risk assessment method is developed by combining fishbone diagram (FD), Fuzzy Analytic Hierarchy Process (FAHP) with cloud model. Based on fishbone diagram, risk factors (RFs) of train control system are identified from two perspectives of hazard event, namely occurrence frequency and sequence severity. To further classify RFs quantitatively, a revised risk matrix based on FAHP is introduced to calculate the consequence and likelihood weights for each risk factor (RF), and then Risk Index (RI) is getting by making product of consequence and likelihood weights, which reflects relative influence degree on operational risk. Finally, in order to assess the comprehensive operational risk of CTCS, cloud model is used to logically calculate quantitative results, and the CTCS-3 in Wuhan-Guangzhou high-speed railway is empirically investigated to analyze its overall operational risk level. The result shows that its risk level is between medium and low, and more inclined to low, which means being acceptable but not ideal. Meanwhile, some specific suggestions and measures are proposed to eliminate or reduce RFs with higher frequency and worse consequence.

Optimization Problem of Pricing and Seat Allocation Based on Bilevel Multifollower Programming in High-Speed Railway

This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China’s Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method.

Analysis of the Impact of High-Speed Railway on County Economic Development Based on the Synthetic Control Method: The Hubei Province in China

Studying the impact of the high-speed railway (HSR) on economic growth has important practical significance and can provide a theoretical basis for government investment in infrastructure. In this study, the adoption of HSR was used for natural and rigorous exploration. Based on the panel data on counties in Hubei Province of China from 2001 to 2017, a synthetic control method was used to analyse the impact of HSR on the economic growth of county-level areas with different economic bases. The results showed that HSR had different influences on the economic development of county-level cities with different economic bases in the short term. The Wuhan-Guangzhou HSR had significantly promoted the gross domestic product (GDP) growth of Chibi City with a good economic foundation. The robustness test results revealed that the promoting effect was significant at a level of 3.7%. The Hefei-Wuhan section of the Shanghai-Wuhan-Chengdu HSR had significantly inhibited the GDP growth of Hong’an County by developing on a weak economic foundation. The robustness test results revealed that the inhibitory effect was significant at the level of 2.8%. However, the Hefei-Wuhan section had a stable and promoting effect on Macheng City. Based on the abovementioned conclusions, each county-level city should focus on long-term economic development, invest in HSR construction based on local conditions instead of blind expansion, make use of HSR to vigorously develop characteristic industries, and implement talent retention and introduction policies.

Integrated optimization of line planning and train timetabling in railway corridors with passengers’ expected departure time interval

Line planning and train timetabling are two basic problems in railway operations. A commonly used way is to first obtain the line plan and then design the train timetable based on the pre-generated line plan. This paper expects to find an effective way to combine these two problems into an integrated optimization model, in which the line plan and timetable are optimized simultaneously. Specifically, with the help of some linearization techniques, we formulate a mixed integer linear programming (MILP) model to find the optimal number of operated trains, as well as the origin and destination stations, stop plan, arrival/departure times of each train at each station and assignment of passenger demands on each train, by employing the railway corridor information and time-related passenger demands as inputs. We aim to simultaneously minimize the operation cost and total travel time of trains, and the bi-objective problem is effectively transformed into a single-objective problem by linear weighted methods. Finally, a series of numerical experiments based on Wuhan-Guangzhou high-speed railway corridor in China verify the effectiveness of the integrated optimization methods.

An Integer Linear Programming model for integrated train stop planning and timetabling with time-dependent passenger demand

We consider the problem of jointly optimizing train stop plan and timetable for a set of trains on a railway corridor, with focus on time-dependent passenger demand: these are very relevant elements in railway operations, and closely related to the service quality provided to the passengers. The train stop planning problem requires to determine the stopping pattern for each train, while the train timetabling problem calls for finding departure and arrival times of each train at each visited station. The solution of both problems determines the train services available to the passengers, with the corresponding stops, the departure and arrival times, and the total travel time. To define train services that are convenient for the passengers, it is important to satisfy the passenger demand: in particular, we take into consideration the desired departure time intervals of passengers from their origin station towards their destination station. Our goal is to determine a solution to the integrated problem that includes these three aspects (stop plan, timetable, time-dependent passenger demand). The integrated problem is solved at the tactical level, and includes constraints imposing that the demand is satisfied in the desired time intervals, and the train capacity is respected. The solution of the problem defines the train services, by determining the passenger flow on trains and the time intervals in which passengers have to be served.We formulate an Integer Linear Programming (ILP) model for this integrated problem, and solve it with the commercial optimization solver CPLEX on a real-world instance of the Wuhan–Guangzhou high-speed railway line in China, by considering different time interval lengths in which the demand must be satisfied. In addition, we compare the obtained results with those determined by solving a model that neglects the time-dependency of the passenger demand. The computational results show that the train stop plan and timetable are deeply influenced by the desired departure time intervals of the passengers, and that neglecting time-dependency results in more than 30% of the passengers departing in a different time interval than the desired one.

Two-stage robust railway line-planning approach with passenger demand uncertainty

A railway line-planning problem is one of the fundamental problems in the strategic planning of railway operations. Operation zone, frequency, stop schedule, and passenger distribution for each train based on passenger demand are determined to minimize both the operation cost of the railway enterprise and the total travel cost to the passengers. However, passenger demand uncertainty makes balancing transport capacity and fluctuating demand challenging. Therefore, this paper proposes a two-stage robust optimization model wherein the nominal line plan is determined in the first stage (i.e., the case in which passenger demand has no fluctuation) and the rescheduled line plan based on the nominal line plan is determined according to the demand realization in the second stage. A Lagrangian relaxation algorithm and some strengthening techniques are designed. The model and algorithm are tested on real-world instances of the Wuhan–Guangzhou high-speed railway line under uncertain passenger demands. Computational results indicate that the proposed solution algorithm can yield a high-quality solution in 3600 s. In addition, the proposed two-stage robust solution can help minimize both operation and travel costs on average when compared with the nominal solution under uncertainty realization.

High-Speed Train Platoon Dynamic Interval Optimization Based on Resilience Adjustment Strategy

Resilience adjustment refers to the generation of a control strategy by evaluating the interaction between related factors. Tracking intervals of the high-speed train platoon change dynamically, which directly influences the operation safety and efficiency, and constrains the train operation trajectories. In China, the tracking interval is getting shorter. To ensure safety and improve efficiency, we research a dynamic interval resilience adjustment strategy based on the moving block system. Firstly, the optimal offline operation strategy is obtained by solving the multi-objective optimization model with the improved gravitational search algorithm (I-GSA). The resilience adjustment mechanism is developed to evaluate the tracking interval and choose the appropriate driving strategy to adjust operation states based on the resilience tracking interval model. Then, we study the relation between operation strategy and departure interval, and a seeker optimization algorithm (SOA) is used to obtain the optimal departure intervals and driving strategies. Simulations are conducted based on the sections between Chibi North station and Changsha South station in Wuhan-Guangzhou high-speed railway. The results indicate that the total operation time decreased by 191s and the operation safety can be ensured at any time.

Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty

In this work, we consider the problem of scheduling a set of trains (i.e., determining their departure and arrival times at the visited stations) and simultaneously deciding their stopping patterns (i.e., determining at which stations the trains should stop) with constraints on passenger demand, given as the number of passengers that travel between an origin station and a destination station. In particular, we face the setting in which demand can be uncertain, and propose Mixed Integer Linear Programming (MILP) models to derive robust solutions in planning, i.e., several months before operations. These models are based on the technique of Light Robustness, in which uncertainty is handled by inserting a desired protection level, and solution efficiency is guaranteed by limiting the worsening of the nominal objective value (i.e., the objective value of the problem in which uncertainty is neglected). In our case, the protection is against a potential increased passenger demand, and the solution efficiency is obtained by limiting the train travel time and the number of train stops. The goal is to determine robust solutions in planning so as to reduce the passenger inconvenience that may occur in real-time due to additional passenger demand. The proposed models differ in the way of inserting the protection, and show different levels of detail on the required information about passenger demand. They are tested on real-life data of the Wuhan–Guangzhou high-speed railway line under different demand scenarios, and the obtained results are compared with those found by solving the nominal problem. The comparison shows that robust solutions can handle uncertain passenger demand in a considerably more effective way.

Cause-specific investigation of primary delays of Wuhan–Guangzhou HSR

ABSTRACT This paper presents the results of acase study on the causes and effects of typical service disruptions in aHigh-speed rail (HSR) system in China–Wuhan–Guangzhou High-speed railway (WH-GZ HSR). With acause-specific approach, seven delay causalities leading to primary delays (PDs) are identified, and the properties and consequences of each primary delay (PD) factor is derived. The comparison of candidate distributional forms shows that the Log-normal distribution model can approximate better the length of all identified PDs. For each PD cause, the distribution of delay duration is estimated and tuned. Next, cause-specific distributional models for PDs severity are discussed. The models for the number of affected trains are presented in the form of inverse regression models with specific domains. Then, comparing five different kinds of candidate models, the results show that the Cubic is the best to approximate the distributions of total-affected time.

Forecasting primary delay recovery of high-speed railway using multiple linear regression, supporting vector machine, artificial neural network, and random forest regression

Accurate prediction of recoverable train delay can support the train dispatchers’ decision-making with timetable rescheduling and improving service reliability. In this paper, we present the results of an effort aimed to develop primary delay recovery (PDR) predictor model using train operation records from Wuhan-Guangzhou (W-G) high-speed railway. To this end, we first identified the main variables that contribute to delay, including dwell buffer time, running buffer time, magnitude of primary delay time, and individual sections’ influence. Different models are applied and calibrated to predict the PDR. The validation results on test datasets indicate that the random forest regression (RFR) model outperforms the other three alternative models, namely, multiple linear regression (MLR), support vector machine (SVM), and artificial neural networks (ANN) regarding prediction accuracy measure. Specifically, the evaluation results show that when the prediction tolerance is less than 1 min, the RFR model can achieve up to 80.4% of prediction accuracy, while the accuracy level is 44.4%, 78.5%, and 78.5% for MLR, SVM, and ANN models, respectively.

A maximum noise-level prediction method for high-speed railways in China: A case study using the Baiyun campus of Guangdong polytechnic normal university

China's high-speed railway system has made rapid progress in the past 10 years, but at the same time, it has brought noise pollution problems to the surrounding areas. The Baiyun Campus of Guangdong Polytechnic Normal University is located near the Wuhan-Guangzhou high-speed railway. To evaluate the impact of high-speed railway noise on student learning, the sound pressure levels, spectrum of frequencies, window attenuations and other parameters of the noise from the Wuhan-Guangzhou high-speed railway are measured. A maximum noise-level prediction model for high-speed railway noise is established based on the experimental data. The distribution of the maximum noise level in the school district is calculated used the established model and the effect of noise on student learning was discussed. The results show that the model prediction results are consistent with the experimental results, and the average absolute error is less than 2 dBA. Although the hourly equivalent noise level is in accordance with the environment quality standard of noise, the maximum noise level when the train is passing is still high, exceeding 65 dBA inside some classrooms and even exceeding 70 dBA in open space, which has a significant impact on student learning. The results of this study have important application value for assessing the impact of high-speed railway noise in China.

A data-driven time supplements allocation model for train operations on high-speed railways

ABSTRACTThis paper presents a time supplements allocation (TSA) method that incorporates historical train operation data to optimize buffer-time distribution in the sections and stations of a published timetable. First, delay recovery behavior is investigated and key influential factors are identified using real-world train movement records from the Wuhan–Guangzhou High-speed Railway (WH-GZ HSR) in China. Then, a ridge regression model is proposed that explains delay recovery time (RT) regarding buffer times at station (BTA), buffer times in section (BTE), and the severity of the primary delay (PD). Next, a TSA model is presented that takes the quantitative effects of identified factors as input to optimize time supplements locally. The presented model is applied to a case study comparing the existing and optimized timetables of 24 trains operating during peak morning hours. Results indicate an average 12.9% improvement in delay recovery measures of these trains.

Robust Train Timetabling and Stop Planning with Uncertain Passenger Demand

The integrated Train Timetabling and Stop Planning (TTSP) problem calls for determining the optimal timetables for a given set of trains, while choosing, for each train, the subset of stations where it will stop. Both the timetable and the stop plan are determined based on the passenger demand, i.e. on the number of passengers travelling between an origin and a destination stations. In this work, we study the Robust TTSP (RTTSP), where passenger demand is considered to be uncertain, as it is often the case in real practice. We propose an Integer Linear Programming (ILP) model for RTTSP based on Light Robustness, an effective technique introduced in [Fischetti, M., and M. Monaci, Light robustness In: Ahuja RK, Möhring RH, Zaroliagis CD (eds) Robust and online large-scale optimization. Lecture Notes in Computer Science 5868 (2009), 61–84. Springer, Berlin Heidelberg]. We test the proposed ILP model on real-world data of the Wuhan-Guangzhou high-speed railway corridor under different demand scenarios.

An Optimization Method for Train Seat Inventory Control

Railway passenger transportation plays a fundamental role in China, reasonable revenue is the guarantee of railway's regularly development such as equipment replacement, technology enhancement, etc. Although many studies on the railway revenue models have been conducted, there is a lack of effective modeling which considers the multiple trains and the multiple levels of seats in real operation. Aiming to improve the revenue of railway transportation industry, this paper proposes a new optimization method for the train seat inventory control problem with the consideration of the multiple trains and the multiple levels of seats. As the in-depth research of this problem, an integer linear programming model is formulated which aims to maximize the total revenue of rail industry. The commercial software MATLAB with CPLEX solver is employed to obtain the approximate optimal solutions. The effectiveness and performance of the proposed approaches are testified by two examples implemented on a simple railway corridor and Wuhan–Guangzhou high-speed railway corridor. Moreover, sensitivity analysis experiments are given to explore the impact on the revenue if the model parameters are changed.

Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners’ defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan–Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments.