High-speed Rail Passengers Research Articles

Service Quality in Rail Systems: Listen to the Voice of Social Media

Service quality is essential to increase and maintain user loyalty to the railway system. In the literature, surveys have been used to measure user satisfaction, and mathematical methods have been applied to quantify the survey results. In recent years, user-generated content, including comments and complaints shared via social media, has been used to measure the quality of rail services. This content may provide important insights into the quality of the service provided with its dynamic structure. In this study, a SERVQUAL-based social-media analytics approach is used to measure railway service quality, placing special emphasis on the temporal variations in a national rail system. Topic modeling was used to assign each content item to the relevant service dimension and sentiment analysis was applied to measure the level of satisfaction. Importance–performance analysis was employed at the final stage to generate policy suggestions. Gathering more than 2.3 million social-media messages posted from 2011 to 2021, we examined the temporal evolution of service quality of the Turkish rail system. The results reveal the most and least important services and the satisfaction level of each dimension. The differences between the priorities of conventional and high-speed rail passengers are defined, and policy recommendations are presented.

Coupling development of high-speed rail network and innovative cooperation based on travel behavior of depressed passengers

Background Depression is a common mental illness. Patients with depression show somatization symptoms such as loss of appetite, insomnia and low mood. The etiology and pathogenesis of depression are still unclear, and the pathogenesis is related to biochemical, neuroendocrine, sleep and brain electrophysiological abnormalities. This study takes depressed passengers as the research object to explore the intervention and influence of innovative high-speed rail services on patients with depressive episodes when they travel, so as to provide ideas for the coupling development of high-speed rail network and innovative cooperation.Subjects and Methods38 cases of high-speed rail passengers with depression were randomly divided into the experimental group and the control group. The experimental group was treated with innovative psychological services of high-speed rail. Hamilton Depression Scale (HAMD) was used to evaluate the degree of depression before and after treatment, and Hamilton Anxiety Scale (HAMA) was used to evaluate the degree of anxiety.ResultsThe scores of HAMD and HAMA in the experimental group after innovative psychological intervention services were lower than those before participation (P<0.05), and the difference was statistically significant.ConclusionsThe innovative high-speed rail depression psychological intervention service has a positive impact on depressed passengers. After the intervention, depressed passengers’ depressed mood and pessimistic thoughts are improved, they no longer resist others, and their discomfort symptoms are significantly reduced. At the same time, this intervention mode opens new ideas for the coupling development of high-speed rail network and innovative cooperationAcknowledgementFoundation: National Natural Science Foundation of China (No. 41301104); Foundation: National Natural Science Foundation of China (No. 41871108).

Investigation of the Policy Effect of High-Speed Metropolitan Rail Service in Seoul Through Latent Class Modeling Analysis of Stated Preference Survey

The sprawl of megacities is increasing the need to improve public transit services to cover longer distances in shorter times than current services. High-speed metropolitan rail services have been introduced to satisfy the needs of passengers and are expected to alleviate the transportation problems caused by urban sprawl. However, the effectiveness of public transit demand inducement policies, which have been employed in various ways in South Korea, has been questioned. Thus, agencies responsible for high-speed metropolitan rail services should also evaluate whether such policies will work properly. The achievement of the policy can be evaluated as sufficiently inducing the demand of the target group, and investigated through the residence information and the current travel behavior. This study investigates the current travel behavior of users to be replaced when a new high-speed metropolitan rail service is introduced and directly analyzes the travel mode shifting effect. While previous studies focused on increasing the applicability to general services by classifying groups based on personal attributes, this study focuses on evaluating detailed policy achievements for specific services. We apply a latent class modeling approach using stated preference survey data for group classification. In particular, by employing the user’s current travel behavior as a class membership attribute, the characteristics of potential passengers of the high-speed metropolitan rail service are analyzed. The estimation results suggest that current travel behavior significantly classifies high-speed rail passengers. In addition, the policy achievement was investigated in detail by region and operation time, and several additional policy directions are presented to satisfy the policy goals.

Sitting comfort analysis and prediction for high-speed rail passengers based on statistical analysis and machine learning

Various comfort evaluation models have been extensively studied for improving driver seat and office seat designs for the purpose of improving seat comfort. High-speed rail (HSR) seat comfort is influenced by specific environments, activities, and postures, and targeted in-depth research on this topic is lacking. Existing evaluation cannot ensure the validity of data at the early stages of model training because of their direct reliance on subjective evaluation data, thereby limiting the improvement in the accuracy of prediction. In this study, we designed a sitting observation experiment and static sitting comfort experiment for the typical activities of HSR passengers. The sitting posture of HSR passengers under typical activities was classified for different comfort levels based on the factors influencing the sitting posture in terms of bone and muscle biomechanics. The correlations between the subjective discomfort in different sitting positions, objective change patterns, and body pressure distribution parameters were statistically analysed. The possibility of using sitting duration and sitting frequency as objective behavioural indicators of discomfort evaluation was indicated. Based on the verification of the reliability of the evaluation data, the classification performance of various machine learning algorithms was compared and analysed, and a sitting comfort prediction model was established based on the gradient boosting machine algorithm, with an accuracy of 89.5%. This study serves as an effective reference for improvement strategies for HSR passenger comfort and will inspire new ideas for exploring objective indicators for sitting comfort evaluation.

Evolutionary ensemble generative adversarial learning for identifying terrorists among high-speed rail passengers

As one of the most salient features of China’s economic development, high-speed rail (HSR) is considered to be an attractive target and travel mode for terrorists. Distinguishing potential terrorists from normal passengers is of critical importance to public security, but very challenging because terrorists constitute only a very small fraction of HSR passengers, especially when they can disguise their attributes and behaviors to deceive the classifiers. For this extremely imbalanced classification problem, we propose a novel evolutionary generative adversarial network (GAN) ensemble method, where each GAN in the ensemble simultaneously trains a discriminator to identify abnormal samples from a large number of passenger profiles and trains a generator to produce abnormal samples that are disguised as normal ones in a subspace of the sample space, and the final classifier combines these GANs using an evolutionary fusion method. Experiments on benchmark problems demonstrate that the proposed method has very competitive performance compared to popular imbalanced classifiers. The successful applications in terrorist identification for China Railway also demonstrate the feasibility and effectiveness of our approach.

Evolutionary Ensemble Adversarial Learning for Identifying Terrorists Among High-Speed Rail Passengers

Evolutionary Ensemble Adversarial Learning for Identifying Terrorists Among High-Speed Rail Passengers

How Does the Spatial Structure of High-Speed Rail Station Areas Evolve? A Case Study of Zhengzhou East Railway Station, China

The rapid development of high-speed rail (HSR) and station areas has shortened the spatial and temporal distances among cities, improved the accessibility of cities, and affected the spatial agglomeration and diffusion of populations and of social and economic activities. This has led to spatial reconfiguration of production factors within cities, which has the potential to drive the reconstruction of urban spatial structures. Based on POI and land-use data, this paper defines the spatial scope of the HSR station area and explores the characteristics and influencing factors of its spatial structure from the perspective of industry. The study area i is set at 2000 m. Since the opening of the HSR, the industrial distribution has exhibited a significant circular, multi-core, and axial belt spatial structure. The spatial structure of each sub-industry is different. On the whole, internal and external transport and agglomeration economies have significant impacts on the industrial spatial distribution, and land rent has gradually decreased in importance with the development of HSR station areas. The intensity of the effects of different factors varies among different industries. The mechanisms by which the spatial structure of the station area is formed are discussed and include location accessibility, micro-market factors, node station attributes, the availability of a sufficient amount of undeveloped land, the characteristics and needs of HSR passengers, and policies and systems.

Is high-speed rail socially exclusive? An evidence-based worldwide analysis

The extent to which high-speed rail (HSR) could be socially egalitarian has been disregarded, despite evidence that upper social-occupational groups are the primary users of high-speed trains (HSTs). In this context, this review aims to fill the gap by investigating the social attributes of HSR passengers based on worldwide, available ex post data derived from surveys at both national and corridor levels. The study’s results converge to conclude that HSR passengers are predominantly male, higher income, highly educated and belonging to higher social-occupational groups. Key indicators of social inequalities (i.e. income, occupational group and education) show a much sharper differentiation in HSR use than gender and age. Social inequalities arguably shape the uneven use of HSR through available income compared to HSR fares; through income compared to total expenditure on non-business travel; and through travel purposes, considering the high share of business travel aboard HSTs. Our findings highlight “greener” transport means and policies may not be socially inclusive. They also raise the issue of facilities (co-)funded by taxpayers that do not benefit the masses.

A Qualitative Study on Providing Alternative Solutions for Handling the HSR Passenger\u2019s Luggage

The objective of this paper is to look at the problem of high-speed rail (HSR) passengers’ luggage and provide alternative solutions to improve luggage handling. A qualitative study is carried out to assess the current state of HSR practices, analyse existing issues with HSR passenger luggage and provide alternative solutions, including modification of the passenger unit, double-deck rolling stock, an additional train for transporting luggage, and repositioning of passenger seats. Four solutions are discussed that could solve the problem of HSR passenger luggage handling and evaluated from four aspects namely passengers, railway operator, cost and passenger comfort level. The solution of additional trains for transporting luggage may be the preferred solution when compared with other options, without compromising the comfort of the passengers or the profit of the railway company.

Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China

High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the travel experience of HSR passengers, this study aims to determine passenger demands (PDs) and to evaluate passenger satisfaction by using a combination of online review analysis and large-scale group decision-making (LSGDM). By using web crawler technology, online reviews related to HSR were harvested from a microblogging platform to extract PD data and information. The six PDs that reflect the most frequent concerns of passengers were identified by analyzing the online reviews. The level of satisfaction of passengers with respect to these PDs was analyzed based on the online responses from 100 HSR passengers and by adopting the interval-valued two-tuple linguistic representation model. The final degrees of satisfaction and rankings of the PDs were then determined by using the LSGDM approach with the k-means clustering method and a consensus-reaching process. This research thus constructs an index system of HSR passenger satisfaction evaluation based on online-review analysis and evaluates the process by using LSGDM approaches. The conclusions provide insights into the improvements desired by HSR passengers for in-cabin services and to improve passenger satisfaction.

Identifying and prioritizing factors affecting in-cabin passenger comfort on high-speed rail in China: A fuzzy-based linguistic approach

Factors affecting customer comfort are crucial for the success of many services such as public transportation, health facilities, and so on. Therefore, the identification and prioritization of such factors is an important demand from stakeholders. This research aims to identify and prioritize the factors that affect in-cabin passenger comfort on high-speed rail (HSR) based on empirical evidence collected from China. For the identification process, the quality-management tool known as quality function deployment (QFD) to capture the voice of the customer is used to discover the most important demands as the driving influencing factors of in-cabin passenger comfort, by utilizing passengers’ feedback regarding their experiences with HSR posted in social media. Such factors will be prioritized by a fuzzy linguistic group decision-making approach based on the adoption of generalized comparative linguistic expressions obtained from a questionnaire investigation on randomly selected HSR passengers, and accomplishing a decision-solving procedure that includes consistency-checking and consensus-reaching processes to achieve an effective, reliable, and agreed prioritization of the factors. The research outputs will suggest the factors of greatest concern among all HSR passenger demands. This study gives HSR operators and designers not only insights and tools for managing HSR passenger demands but also advice for refining the design and service quality of HSR in China.

High-speed rail, tourists’ destination choice and length of stay: A survival model analysis

We analysed the determinants of the length of stay for tourists arriving in a Mediterranean coastal destination by means of high-speed rail (HSR) service. This study is based on data obtained from a survey completed by HSR passengers returning from holiday in Costa Daurada (Catalonia). The empirical analysis is based on estimations made using a survival model. The influence of the availability of HSR service on tourists’ destination choices together with the tourists’ profiles, party structure or accommodation characteristics was used as explanatory variables. Results revealed that the existence of HSR services played a minor role in tourists’ decision of whether to visit the Costa Daurada. Also, evidence suggests that the existence of the HSR station would only affect the length of stay of those tourists who stay overnight in second residences.

Understanding High-Speed Rail Passengers in China: A Segmentation Approach

China has established the world’s largest high-speed rail (HSR) system, which has fundamentally changed the way people travel in the domestic market. As China aims to double its HSR capacity in the next few years, the HSR population will continue to grow, which calls for an in-depth understanding of HSR passengers. While HSR has been of academic interest for many years, existing research has not provided meaningful demographic segmentation in the HSR context. This paper collected empirical data from HSR passengers in Beijing and Shanghai, the largest HSR markets in China, and performed a cluster analysis based collectively on three demographic variables—age, income, and education, which led to the formation of four segments—High-Ed Youths, Mature Travelers, New Starters, and Elite Travelers. Significant differences were found in terms of the passenger demographics and travel experiences across the four segments, to support the validity of the clustering solutions. The multivariate analysis of variance (MANOVA) test further revealed cross-segment differences in terms of passenger evaluation of five HSR variables—reasonableness of price, reliability, food choices, employee service, and likelihood for recommendation, suggesting the possibility of predicting passenger perceptions and behaviors based on their cluster membership. The findings demonstrate that passenger segmentation based on multiple demographic variables can provide deeper insights into the HSR population. For HSR providers in China, an understanding of the characteristics of the four passenger segments can assist them in developing service and communication strategies to cater to the different passenger needs.

Development of High-Speed Rail in the People's Republic of China

High-speed rail (HSR) construction is continuing at a rapid pace in the People’s Republic of China (PRC) to improve rail’s competitiveness in the passenger market and facilitate inter-city accessibility. To take advantage of spillover effects, bring economic cohesion at the local level, and recover the local matching investment in infrastructure, all cities have planned new towns around HSR stations. Speeding up HSR construction has required the development of many standardized technologies and processes. Plans for HSR stations usually situate them in suburbs, far from city centers, to reduce the cost of property right of way relating to the removal of housing or industry as well as to lower the complexities in negotiations. However, city centers remain the main starting and destination terminals for most HSR passengers, especially businessmen who use HSR frequently. Besides, large railway stations in suburbs, providing a comfortable waiting space for passengers, have prolongate the travel time for HSR users. A reliable and high-quality public transit service, connecting an HSR station and the city center at the launch of HSR operations, is essential to curb the increase in car/taxi use. Studies have also suggested that, instead of building one big HSR station in the suburb of a metropolis, constructing multiple stations in the vicinity of city centers will greatly reduce the access/egress time, thereby enhancing travel efficiency.

Analyzing high speed rail passengers’ train choices based on new online booking data in China

This study explores two nonparametric machine learning methods, namely support vector regression (SVR) and artificial neural networks (ANN), for understanding and predicting high-speed rail (HSR) travelers’ choices of ticket purchase timings, train types, and travel classes, using ticket sales data. In the train choice literature, discrete choice analysis is the predominant approach and many variants of logit models have been developed. Alternatively, emerging travel choice studies adopt non-utility-based methods, especially nonparametric machine learning methods including SVR and ANN, because (1) those methods do not rely on assumptions on the relations between choices and explanatory variables or any prior knowledge of the underlying relations; (2) they have superb capabilities of iteratively identifying patterns and extracting rules from data. This paper thus contributes to the HSR train choice literature by applying and comparing SVR and ANN with a real-world case study of the Shanghai-Beijing HSR market in China. A new normalized metric capturing both the load factor and the booking lead time is proposed as the target variable and several train service attributes, such as day of week, departure time, travel time, fare, are identified as input variables. Computational results demonstrate that both SVR and ANN can predict the train choice behavior with high accuracy, outperforming the linear regression approach. Potential applications of this study, such as rail pricing reform, have also been identified.

Managing reliever gateway airports with high-speed rail network

On the basis of model analysis, this study proposes an effective method for managing reliever gateway airports when the main gateway airport is completely dysfunctional owing to a catastrophe. In particular, we deal with the case where the main and reliever airports are connected by high-speed rail (HSR), and we discuss a desirable and effective support policy for regaining passenger flow from/to the affected area. First, we analyze the market behavior under the usual condition as the base case by adopting the bi-level model, which is a supply-demand interaction model. Second, under the supposition of a catastrophe, we set up some scenarios of management policies, i.e. (i) no special policy and (ii) providing support to HSR passengers to induce them to the reliever gateways. Through such scenario analyses, we show that (i) HSR fare restriction is required to regain sufficient passenger flow and (ii) providing fare support to HSR passengers is an effective way to regain passenger flow using reliever gateways, which can contribute toward building a robust air transport network.

How do passengers use travel time? A case study of Shanghai–Nanjing high speed rail

Traditional travel behavior theory regards travel time as a waste. Recent studies suggest that it carries a positive utility, among other reasons for the benefit of the activities conducted while traveling. However, most studies of travel time use have focused on conventional trains in developed countries. Few have systematically examined the permeation of information and communication technology (ICT) into travel time use and the correlates of activity participation in developing countries, particularly on high speed rail (HSR). Using a survey conducted on the Shanghai–Nanjing corridor (N = 901), this study examines how HSR passengers use their travel time and explores the correlates of the different types of activities of business and non-business travelers, respectively, through multivariate probit models. We found that 96% of the respondents use ICT during their HSR journey and that most passengers spend some of their travel time on work-related activities. Moreover, items carried and advance planning as well as work-related travel attributes contribute significantly to activity participation. However, the factors affecting time use of business and non-business travelers differ. HSR service design should facilitate passenger engagement in various activities and improvement of their travel experience. A stable internet connection, adequate power sockets, and a noise-free environment will promote both work and leisure activities on the HSR.

Changing travel behaviour of high-speed rail passengers in China

ABSTRACTThis paper studies the impact of high-speed rail on travellers’ behaviour in China through the study of travellers’ characteristics and their satisfaction with the railway. A sample of 328 passengers from the Shenzhen-Xiamen Railway (XSR) is surveyed by questionnaires. The results confirm that the XSR development has partly changed the respondents’ decision to take the railway or choose its tourism-related products. Socio-demographic characteristics including age, occupation, monthly personal income, and annual travel expenditure show a significant influence on the changed travel behaviour though the tendency of such influence is not very apparent. Convenience and comfort lead to travellers’ satisfaction and these two aspects shorten the duration of stay of travellers and provide more short-haul rail-connected destination options. However, railway satisfaction is not highly associated with XSR-related product and service provision, but is related to the condition and the quality of the railway itself.

High speed rail and coastal tourism: Identifying passenger profiles and travel behaviour.

In this paper, we characterise tourists most likely to visit a coastal destination by high-speed rail (HSR). Our data came from a survey conducted among HSR passengers during 2014’s high season (July and August) at Spain’s Camp de Tarragona and Alicante Stations, each of which is near a mass tourism destination on the Mediterranean coast: the Costa Daurada and the Costa Blanca, respectively. We used responses to the survey, which presented binary discrete-choice situations, to construct a database necessary for a logistic regression model that allowed us to examine how passenger profile, trip characteristics, and stay conditions influenced the use of HSR services on visits to each coastal destination. Results highlighted significant differences in the profiles of tourists who arrived at each destination by HSR and, in turn, that no specific tourist profile is associated with HSR, even for two stations that serve sunny beach destinations. Among its implications, to analyse travellers that HSR can attract, it is vital to consider the specific characteristics of each destination and its current market.

The Impact of High-Speed Rail on Residents’ Travel Behavior and Household Mobility: A Case Study of the Beijing-Shanghai Line, China

With the improvement of China’s high-speed rail network, there have been many economic and social benefits for local residents. Based on a questionnaire conducted in stations on the Beijing-Shanghai line, and through an analysis of high-speed rail passenger travel behavior and family relocation, we explored the social effects of high-speed rail. The study found that high-speed rail passengers are mainly young, highly educated, and have a middle to high income. However, with the popularization of high-speed rail, such differences in the social stratum of high-speed rail passengers are expected to disappear. Through an analysis of passenger travel status, we found that the areas surrounding high-speed rail stations are very accessible to the main cities, and are well connected by other public transport. With the emergence of the “high mobility era” based on the high-speed rail network, the separation of workplace and residence and the number of “double city” households are increasing, primarily in the Beijing-Tianjin and Shanghai-Nanjing (especially in Suzhou-Kunshan-Shanghai) regions. In addition, high-speed rail introduces the possibility of household mobility, with 22.7% of the respondents in this study having relocated since the Beijing-Shanghai line opened. Household mobility is apparent primarily among big cities, with movement toward nearby cities. We also found that occupation, income, residence time, and schooling of children have a significant impact on households. With the improvement of high-speed rail networks, household mobility will become a common phenomenon and research on domestic mobility will continue to increase.