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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. [ABSTRACT FROM AUTHOR]

As high-speed rail (HSR) and air transportation developed rapidly in the last decade, their coopetition and interactional relationship has reshaped China's urban networks. Based on HSR and flight schedule data from 2009 to 2019, this paper constructs a weighted network to compare China's urban networks and their evolution, and employs machine learning to investigate the potential determinants. The results indicated that both networks tend toward polarization in the overall distribution and that the structure of urban networks under HSR networks is more hierarchical. That of HSR networks gradually forms a corridor structure along the trunk lines, while that of airline networks mainly shows a diamond spatial structure with Beijing, Shanghai, Guangzhou, and Chengdu as the cores. As for the evolution of urban networks, geographical factors, per capita GDP, and the tourism function of cities have more important impacts on that of HSR networks, while the network's topological structure and education resources have a greater impact on that of airline networks. Some socioeconomic attributes, such as urban administrative level, population, and the proportion of tertiary industry, have similar and limited influences on the two networks. • The paper constructed a comparative analysis on the evolution of China's urban networks during the last decade. • Employing machine learning to investigate the potential determinants. • The study examined the distinct attributes of the evolution and structure of high-speed rail and airline networks. • The study contrasts the determinants influencing the evolution of high-speed rail and airline networks at the city level. [ABSTRACT FROM AUTHOR]

The accurate prediction of passenger flow is crucial in improving the quality of the service of intercity high-speed railways. At present, there are a few studies on such predictions for railway origin–destination (O-D) pairs, and usually only a single factor is considered, yielding a low prediction accuracy. In this paper, we propose a neural network model based on multi-source data (NN-MSD) to predict the O-D passenger flow of intercity high-speed railways at different times in one day in the short term, considering the factors of time, space, and weather. Firstly, the factors that influence time-varying passenger flow are analyzed based on multi-source data. The cyclical characteristics, spatial and temporal fusion characteristics, and weather characteristics are extracted. Secondly, a neural network model including three modules is designed based on the characteristics. A fully connected network (FCN) model is used in the first module to process the classification data. A bi-directional Long Short-Term Memory (Bi-LSTM) model is used in the second module to process the time series data. The results of the first module and the second module are spliced and fused in the third module using an FCN model. Finally, an experimental analysis is performed for the Guangzhou–Zhuhai intercity high-speed railway in China, in which three groups of comparison experiments are designed. The results show that the proposed NN-MSD model can predict many O-D pairs with a high and stable accuracy, which outperforms the baseline models, and multi-source data are very helpful in improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

Time-varying passenger flow is the input data in the optimization design of intercity high-speed railway transportation products, and it plays an important role. Therefore, it is necessary to predict the origin-destination (O-D) passenger flow at different times of the day in combination with the stable time-varying characteristics. In this paper, three neural network-based hybrid forecasting models are designed and compared, named Variational Mode Decomposition-Multilayer Perceptron (VMD-MLP), Variational Mode Decomposition-Gated Recurrent Unit Neural Network (VMD-GRU), and Variational Mode Decomposition-Bidirectional Long Short-Term Memory Neural Network (VMD-Bi-LSTM). First, the time-varying characteristics of passenger travel demand under different time granularities are analyzed and extracted by the VMD method. Second, three neural network prediction models are constructed to predict the passenger flow sequence after VMD decomposition and reconstruction. Experimental analysis is performed on the Guangzhou Zhuhai intercity high-speed railway in China, and the passenger flow at different time periods of the day under different time granularities is predicted. The following results were found: (i) The number of hidden neurons and the number of iterations of the hybrid forecasting model have a great impact on the prediction accuracy. The error of the VMD-MLP model fluctuates less and it performs more smoothly than both the VMD-GRU model and the VMD-Bi-LSTM model. (ii) The VMD-MLP, VMD-GRU, and VMD-Bi-LSTM models can basically reduce the MAPE error to less than 10%. With the increase of time granularity, RMSE and MAE errors tend to gradually increase, while the MAPE error tends to gradually decrease. (iii) For passenger flow under a smaller time granularity, the prediction accuracy of the VMD-MLP model is higher, while for passenger flow under a larger time granularity, the prediction accuracy of the VMD-GRU and VMD-Bi-LSTM models is higher. (iv) The proposed neural network-based hybrid models outperform the existing models and the hybrid models perform better than the single models. [ABSTRACT FROM AUTHOR]

Primary delays are the driving force behind delay propagation, and predicting the number of affected trains (NAT) and the total time of affected trains (TTAT) due to primary delay (PD) can provide reliable decision support for real‐time train dispatching. In this paper, based on real operation data from 2015 to 2016 at several stations along the Wuhan–Guangzhou high‐speed railway, NAT and TTAT influencing factors were determined after analyzing the PD propagation mechanism. The eXtreme Gradient BOOSTing (XGBOOST) algorithm was used to establish a NAT predictive model, and several machine learning methods were compared. The importance of different delayinfluencing factors was investigated. Then, the TTAT predictive model (using support vector regression (SVR) algorithms) was established based on the NAT predictive model. Results indicated that the XGBOOST algorithm performed well with the NAT predictive model, and SVR was the optimal model for TTAT prediction under the verification index (i.e., the ratio of the difference between the actual and predicted value was less than 1/2/3/4/5 min). Real operational data in 2018 were used to test the applicability of the NAT and TTAT models over time, and findings suggest that these models exhibit sound applicability over time based on XGBOOST and SVR, respectively. [ABSTRACT FROM AUTHOR]

High-speed rail (HSR) is one of the essential innovations in the field of transportation in the latter half of the twentieth century. In China, the rapid development of HSR has received increasing attention and resulted in a boost of tourism, with significant impact on the development of cities that operates HSR. To accurately comprehend how will the operation of HSR influence the regional CO2 emissions, this paper applies the modified STIRPAT model, combining with real data on high-speed rail passenger flow volume of the Beijing-Guangzhou high-speed rail Hunan section. The results show that (1) the high-speed rail operation is also a significant impact factor for regional CO2 emissions. (2) Considering the operation of HSR, the ranking of contribution rate of driving factors for regional CO2 emissions is as follows: GDP per capita, energy consumption per unit of GDP, arrival volume of high-speed rail, originated volume of high-speed rail, the proportion of coal in the energy mix, proportion of the tertiary industry, and population. (3) Surprisingly, the numerical research result shows that the operation of HSR for the cities may promote regional CO2 emissions, while the increase in urban population and the optimization of energy structure have a reducing effect on regional carbon emissions. There is a transfer effect of the operation of HSR and region development, which results in the rising of regional CO2 emissions. Thus, it is urgent to research on the decoupling of economic growth from CO2 emissions. The findings could be conducive for the government and railway company to evaluate and administrate the operation of high-speed rail and adequately deal with the relationships between the high-speed railway and regional development. [ABSTRACT FROM AUTHOR]

With the development of urban rail transit, taking the metro train has become one of the main modes of transportation, and passengers have an increasing demand for the comfort of taking the metro trains. This paper mainly discusses the impact of noise and vibration caused by metro train on passengers. All 13 metro lines in Guangzhou, China, were selected to conduct the questionnaire survey on the subjective perception of 601 respondents. At the same time, noise and vibration measurements were carried out in the train. The results show that the distribution of noise and vibrations along the metro lines is not uniform, and 50.6% of the interviewees are disturbed by noise. Wheel-rail squeal was found to be the most annoying and disturbing noise source. Three dose-response relationships for noise, vertical vibration, and horizontal vibration are proposed, respectively. The proposed dose-response relationship can be applied to the evaluation of noise annoyance or vibration perception in an environment similar to metro lines. Once the noise or vibration level of a metro line is obtained, the noise disturbance or vibration perception can be estimated. As for the dose-response relationship of vibration perception, people's sensitivity to vibration is much lower than that to noise. Horizontal vibrations are more acceptable to passengers, while vertical vibrations are more disturbing to passengers. The results are helpful to predict the noise annoyance and vibration perception of train passengers between metro stations, and to achieve the purpose of designing effective noise and vibration reduction measures. [ABSTRACT FROM AUTHOR]

In the peak period of a railway system, operators typically add additional trains to provide increased capacity to satisfy the increasing passenger demand. The paper proposes a new optimization framework for designing the operation plan, which includes the number of additional trains, train type, stop plan, and timetable, for additional trains in a peak period. A space-time network representation is used to obtain a feasible primary operation plan by finding a set of feasible space-time paths in the space-time network. Considering simultaneously the passenger demand and the trains' total travel times, we formulate a biobjective integer programming model for generating a cost and passenger responsible primary operation plan. A set of loading capacity constraints are formulated in the model to guarantee a suitable loading capacity for each station's passenger demand and better service for passengers. The CPLEX solver is used to solve the proposed model and to generate the optimal operation plan. Two sets of numerical experiments are conducted on a small-scale rail corridor and on the Wuhan-Guangzhou rail corridor to evaluate the performance of the proposed method. The results of the experiments show that the primary operation plan can be obtained within an acceptable computation time. [ABSTRACT FROM AUTHOR]

The condition-based maintenance of high-speed railway catenary is an important task to ensure the continuous availability of train power supply. To improve the condition monitoring of catenary, this paper presents a novel scheme to detect catenary local irregularities using pantograph head acceleration measurements. First, a series of experimental inspections is carried out in a section of the Beijing–Guangzhou high-speed line in China. The time intervals between the inspections are shortened from the traditional six months to about 40 days, which enables monitoring the short-term degradation of local irregularities. Then, based on the wavelet packet entropy, an approach is proposed to detect local irregularities with different scales in length. Criteria for identifying and verifying the local irregularities are established based on the gradient and repeatability of entropy from multiple measurements. Results from the experimental inspections show that different scales of local irregularities can be detected by the proposed scheme. By using frequent inspections, local irregularities can be effectively verified after about seven inspections. The spatial distribution of local irregularities is found to be closely related to the catenary structure. These findings provide valuable information to deploy the scheme for a railway network. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• A novel method to examine competition of High-Speed Trains and flights is proposed. • We propose a time window method to examine the competition at a city-pair scale. • We find that HTSs and flights are scheduled differently within one-day circle. • Four markets are identified based on HSTs' and flights' spatiotemporal differences. Scheduling strategies are important to improve network efficiency and optimize time slots for both high-speed train (HST) and air travel. Although many scholars have studied HST and air travel competition from the perspectives of the spatial patterns and travel costs of the two transportation modes, few studies have investigated their potential temporal competition. To fill this research gap, this paper uses a time window method to examine the relationship between HSTs and flights operating between city-pairs. From the perspective of departure time, an analysis was conducted in the overlapping network of HSTs and flights at the city-pair level in China, based on one-day schedules. The results show that (1) HSTs and flights were temporally scheduled differently according to their within-day dynamics, and (2) HSTs and flights experienced fierce competition from 7 am to 9 am on average. Furthermore, the proposed classification method identifies city-pairs in a single-mode dominant market, full competition market, partial competition market, and complementary coordination market. Spatially, 24 city-pairs operating 343 flights with 533 train competitors in one day formed the full competition market with high-frequency schedules, including such city-pairs as Beijing-Shanghai, Guangzhou-Shanghai, and Shanghai-Xi'an. The method to classify city-pairs according to the similarity and diversity of HST and flight schedules may be generalized for allocating time slots for two modes. This work may help to schedule HSTs and flights in future transportation management. [ABSTRACT FROM AUTHOR]

The development of the HSR station area is the result of the combined effect of the three elements of transport, place, and industry. This study introduces the industrial dimension and constructs the node-place-industry model to empirically analyze the development potential of station areas along the Hunan section of the Beijing–Guangzhou and the Shanghai–Kunming high-speed railway lines. The results show that (1) the development of the three spatial elements of the station area is mostly out of sync, and the node value has the highest fit with the integrated potential value of the station area; (2) there is a significant correlation between the magnitude of the combined potential of the station area and the site location, station class and time of development; (3) according to the results of the cluster analysis, it was found that most of the stations were in a state of disequilibrium, and the main reason was that the functional value of the place did not match with the value of industrial aggregation. In particular, the introduction of the industry dimension extends the NP model and establishes a tessellated analytical framework for station type classification, providing an interesting assessment tool for the sustainable development of transport hub areas. [ABSTRACT FROM AUTHOR]

We developed a train operation simulation tool to inspect a train operation plan. In applying an improved Petri Net, the train was regarded as a token, and the line and station were regarded as places, respectively, in accordance with the high-speed train operation characteristics and network function. Location change and running information transfer of the high-speed train were realized by customizing a variety of transitions. The model was built based on the concept of component combination, considering the random disturbance in the process of train running. The simulation framework can be generated quickly and the system operation can be completed according to the different test requirements and the required network data. We tested the simulation tool when used for the real-world Wuhan to Guangzhou high-speed line. The results showed that the proposed model can be developed, the simulation results basically coincide with the objective reality, and it can not only test the feasibility of the high-speed train operation plan, but also be used as a support model to develop the simulation platform with more capabilities. [ABSTRACT FROM AUTHOR]

This paper investigates the mode choice behavior of inter-city passengers among air transport, high-speed rail (HSR), and air and high-speed rail (AH) integration services. Stated preference survey has been conducted for four typical city pairs that are located in the Beijing-Guangzhou corridor, China. Modal split models are proposed and calibrated based on the collected survey data. The proposed models are used to identify the key factors affecting passengers’ mode choices and to estimate the modal split of passenger travel demand for some inter-city transportation markets of China. Sensitivity analyses are also performed to reveal the market potential of the AH integration service in China. It has been found: (i) when the inter-city travel distance exceeds a threshold, passengers become less sensitive to the connection time of the AH service, (ii) the most competitive haul distance for the AH service is between 1200 km and 1600 km, and (iii) the en route travel time is the most important factor affecting the market share of the AH service. [ABSTRACT FROM AUTHOR]

As one of the largest civil engineering systems in China, the network of high-speed railway expanded fast in the last decade. The safety of railway operation is of great concern for the whole country. Railway track irregularity is a potential threat to safety of operation and comfort of passengers, and remains a challenging issue for researchers and engineers. Currently, track irregularity data are recorded by various sensors in the comprehensive inspection cars in China. To reveal relations between the operation of high-speed trains and the track geometry, the data mining and random modeling of track irregularity are needed. In this paper, different methods to evaluate the track irregularities are presented at first. A case study of a section in Beijing-Guangzhou high-speed railway is studied using the Karhunen-Loève expansion. Track geometries that are a representation of this railway network are generated along with statistical and frequency validations. As an application based on generated random track geometries, accelerations of train body under different traveling velocities are calculated and analyzed using a simulation model. [ABSTRACT FROM AUTHOR]