High-speed Railway Station Research Articles

Research and design of a deformation monitoring system for the platform and canopy of a railway station

In this paper, the deformation of the platform and canopy of a high-speed railway station is studied, and an online monitoring system for platform canopy deformation is designed to solve the problem of online monitoring of platform canopy deformation. Due to the conventional one-dimensional measurement has been difficult to meet the needs of high-speed railway industrial production and daily life. Nowadays, laser radar and binocular cameras for three-dimensional data measurement have gradually emerged. At present, there is almost no three-dimensional data acquisition equipment with high measurement accuracy, low price, and a high degree of automation in the market. Given the lack of this equipment in the market, this paper presents a three-dimensional data acquisition system based on a laser rangefinder. By collecting, processing, and analyzing the data of the platform and canopy, the purpose of prediction can be achieved, and then the abnormal response, damage, and degradation of the structure can be found in time. The study shows that the monitoring system is helpful to detect deformation and give early warning, and effectively prevent the occurrence of hazards.

Designing Flexible-Bus System with Ad-Hoc Service Using Travel-Demand Clustering

Providing direct and affordable transit services for travelers is the goal of the evolving flexible-bus (FB) system. In this study, we design an FB system with an ad-hoc service, to supplement traditional public transit and provide a better FB service. We first build up a mathematical model to optimize bus-stop sites, routes, and schedules, where the unmet travel demand is served by an ad-hoc service with relatively high cost. Then, we cluster travel demand spatially and temporarily, using the ST-DBSCAN algorithm. We use the simulated-annealing algorithm, which has better convergence and diversity than other heuristic algorithms, to solve the suggested model in large-scale networks. To demonstrate the effectiveness of the proposed model, we run experiments on a small network and a large real-world network of Shenzhen airport, which shows that the FB system with ad-hoc service can reduce overall cost and improve social welfare, compared to taxies and FB only. In addition, it provides affordable transit services with shorter walking distances and lower waiting times, which can be deployed in airports or high-speed railway stations with massive, irregular travel demands.

Double-edged trains: Economic outcomes and regional disparity of high-speed railways

We illuminate the causal relationship between high-speed railway (HSR) expansions and economic development, focusing on HSR in Japan–the Shinkansen–from 1983 to 2020. To address endogeneity concerns about HSR station construction, we employ a market access approach that captures both the direct and indirect impacts of HSR expansion. The results show that a 1% increase in HSR market access increases the land price by 0.176%, total income (hereafter, income) by 0.425%, and income per capita by 0.023% of Japan. However, most of the benefits are focused in Tokyo and other developed areas, while the economic growth due to HSR expansion of cities outside these areas is negative or statistically insignificant. We confirm the robustness of the results through the instrumental variable (IV) approach and a series of robustness checks. Next, we conduct counterfactual analyses using regression results to evaluate future Japanese HSR plans: the Linear Shinkansen, regional expansion, and a policy that would implement both. Simulation results reconfirm that future HSR plans will induce economic growth but, at the same time, aggravate regional disparity; thus, the expected economic outcomes may be double-edged.

LSTM-Based Transformer for Transfer Passenger Flow Forecasting between Transportation Integrated Hubs in Urban Agglomeration

A crucial component of multimodal transportation networks and long-distance travel chains is the forecasting of transfer passenger flow between integrated hubs in urban agglomerations, particularly during periods of high passenger flow or unusual weather. Deep learning is better suited to managing massive amounts of traffic data and predicting extended time series. In order to solve the problem of gradient explosion or gradient disappearance that recurrent neural networks are prone to when dealing with long time sequences, this study used a transformer prediction model to estimate short-term transfer passenger flow between two integrated hubs in an urban agglomeration and a long short-term memory network to incorporate previous historical data. The experimental analysis uses two sets of transfer passenger data from the Beijing-Tianjin-Hebei urban agglomeration, collected every 30 min in May 2021 on the transfer corridors between an airport and a high-speed railway station. The findings demonstrate the high adaptability and good performance of the suggested model in passenger flow forecasting. The suggested model and forecasting outcomes assist management in making capacity adjustments in time to correspond with changes, enhance the effectiveness of multimodal transportation systems in urban agglomerations and significantly enhance the service of long-distance multimodal passenger travel.

RIS‐Assisted Coverage Optimization for 5G‐R Channel in Station Scenario Based on ML and RT

As one of the most important radio communication scenarios in vertical industry, the high‐speed railway (HSR) station is facing the challenge of coverage optimization due to its complex structure. Regarding the wireless network planning and optimization of HSR stations as a part of the customized network, this paper makes an analysis on the 5G‐R channel in the HSR station scenario. Channel characteristics, including path loss, power ratio (PR), and angular spread (AS), are extracted on the basis of ray tracing (RT). Multipath components can also be distinguished based on RT. In order to achieve a better performance, the reconfigurable intelligent surface (RIS) technology is adopted to the network deployment. Moreover, machine learning (ML) is used to locate the best direction of the beam. The analysis results show that the received power in RIS‐assisted channels is significantly promoted. Our research can provide a deep understanding to the 5G‐R channel in station scenario and a well reference for the design and optimization of the customized network.

Scatter-GNN: A Scatter Graph Neural Network for Prediction of High-Speed Railway Station—A Case Study of Yinchuan–Chongqing HSR

The Yinchuan–Chongqing high-speed railway (HSR) is one of the “ten vertical and ten horizontal” comprehensive transportation channels in the National 13th Five-Year Plan for Mid- and Long-Term Railway Network. However, the choice of node stations on this line is controversial. In this paper, the problem of high-speed railway station selection is transformed into a classification problem under the edge graph structure in complex networks, and a Scatter-GNN model is proposed to predict stations. The article first uses the Node2vec algorithm to perform a biased random walk on the railway network to generate the vector representation of each station. Secondly, an adaptive method is proposed, which derives the critical value of edge stations through the pinching rule, and then effectively identifies the edge stations in the high-speed railway network. Next, the calculation method of Hadamard product is used to represent the potential neighbors of edge sites, and then the attention mechanism is used to predict the link between all potential neighbors and their corresponding edge sites. After the link prediction, the final high-speed railway network is obtained, and it is input into the GNN classifier together with the line label to complete the station prediction. Experiments show that: Baoji and Hanzhong are more likely to become node stations in this north–south railway trunk line. The Scatter-GNN classifier optimizes the site selection strategy by calculating the connection probabilities between two or more candidate routes and comparing their results. This may reduce manual selection costs and ease geographic evaluation burdens. The model proposed in this paper can be used as an auxiliary strategy for the traditional route planning scheme, which may become a new way of thinking to study such problems in the future.

Flexible Optimal Model and Algorithm for Track Utilization in High-speed Railway Stations

Abstract Track utilization is the most important technical operation in high-speed railway stations. It is an effective way to take flexible management based on dispatchers’ decision preferences into consideration for making track utilization plans to relieve the influence caused by unmeasurable unstructured factors. Thus, based on the flexible management concept and taking the flexible optimal for track utilization in high-speed railway stations as the object, time and space occupation safety trajectories of arrival routes, departure routes and tracks are all analysed. Then, taking the following constraints into consideration—minimum safety time intervals for various routes and tracks occupation, space-time arc occupation and decision-makers’ preferences—a flexible optimal model for track utilization in high-speed railway stations is established to maximize its balance and robustness and to minimize its volatility at the same time. Further, a flexible optimal solution based on a simulated annealing algorithm is designed to make a safety track utilization plan in high-speed railway stations integrating the dispatchers’ decision preference. The results from the experiments show that the proposed methodology can effectively make satisfied safety track utilization plans based on decision-makers’ preferences, which can improve its balance and robustness level significantly. Meanwhile, its volatility can be reduced as much as possible caused by flexible management based on artificial intervention to ensure the relative stability of the plan.

Numerical modelling and field monitoring study on large-span tunnelling using pretensioned bolt–cable combined support system

This study focused on a new support method, a pretensioned bolt–cable combined support system, for large-span tunnelling. The Badaling tunnel is a high-speed railway station tunnel that has a span of 32.7 m, a cross-sectional area of 494 m2, and a maximum depth of 102 m. The super large cross-section and unfavorable geological conditions around the tunnel resulted in considerable difficulties for tunnel construction. Because this tunnel was successfully excavated using the pretensioned bolt–cable support, the mechanical characteristics of this new support system are analyzed in this study. First, the necessity of a bolt–cable support for large-span tunnelling was analyzed using a numerical model. The deformation characteristics of rock mass in the large-span tunnel are discussed based on the modelling results of the tunnelling process. Second, based on field monitoring results, the mechanical behaviors of pretensioned rock bolts and anchor cables were analyzed. The anchoring forces of the rock bolts and anchor cables were determined by the pretension forces, loss of pretension forces, and passive support effects. The development of the anchoring force can be divided into three phases: rapid loss of the pretension force, a fluctuation phase, and a stable phase. Finally, The loading effect and mechanical responses of the primary lining were analyzed according to the monitoring results of the primary lining, which indicated that the rock mass was effectively reinforced by the pretensioned bolt–cable system and that the primary lining was safe.

Assessment and prediction of railway station equipment health status based on graph neural network

The equipment in railway station is complicated and diverse, and the health status assessment and prediction of equipment is crucial to the safe and stable operation of stations. Graph Neural Networks (GNNs) effectively combine graph data with deep learning technology, which has stronger data and knowledge representation capability and can efficiently handle some non-Euclidean spatial data problems with irregular station equipment associated network structure. Based on this, this paper takes the automatic gate machine and X-ray security checker as an example and proposes a health status assessment and prediction scheme for railway passenger station equipment based on Graph Long Short-Term Memory (G-LSTM) neural network. This paper first analyzes the main factors affecting the health status of passenger station equipment, as well as the correlation between the equipment. Then, the initial graph network structure of the passenger station equipment is constructed, and the G-LSTM model is used to evaluate and predict the health status of the passenger station equipment. Finally, this paper takes the automatic gate machine and X-ray security checker of a high-speed railway station in Beijing as an example to verify the proposed method. The experimental results show that all evaluation metrics perform well, indicating that the G-LSTM model has high accuracy in assessing and predicting the health status of automatic gate machine and X-ray security checker. This paper realizes the health status assessment and prediction of railway passenger station equipment, which can provide some reference for the Prognostics and Health Management (PHM) of equipment in railway stations.

A belief rule-base approach to the assessment and improvement of seismic resilience of high-speed railway station buildings

As an important part of the railway system and the key node in the railway transportation network, the railway station undertakes the critical task of realizing convenient passenger transfer and efficient mass transportation. Resilience assessment and improvement of complex communal buildings have recently received significant attention, however, the complexity of including components, spatial structure, and engineering facilities made it challenging to find an accepted framework to assess the resilience of high-speed railway (HSR) stations. To overcome this challenge, the included subsystems for various operations of HSR stations are sorted out in detail, and the hierarchical belief rule-base (BRB) is adopted to establish the quantitative relationship between station function and each subsystem at different states after an earthquake through statistical analysis, expert knowledge, and logic analysis. Subsequently, optimization of the initial parameters in BRB is performed as a demonstration in case users need a more accurate BRB based on their experience and statistics. Finally, three subsystems repair sequential solving methods to improve the seismic resilience of HSR stations are proposed and discussed: the traversal algorithm, sensitivity-based method, and simulated annealing (SA) algorithm. The method proposed in this study paves the way for research on the resilience assessment of HSR station buildings.

Comparative analysis and parametric study of an innovative artificial air column ventilation mode in a high-speed railway station

With the rapid development of high-speed railways, the air distribution and thermal comfort in the waiting halls of high-speed railways have attracted more attention. In this study, three ventilation schemes (artificial column attached ventilation (ACAV), nozzle outlet air supply (NOAS), and artificial column attached ventilation with nozzle outlet air supply (AVNOAS)) were compared by applying numerical methods. Five evaluation indices which can objectively assess indoor airflow organization and thermal environment were used in this study. Under identical air volumes, the average air temperature of AVNOAS was 26.80 °C. Compared with ACAV and NOAS, the ADPI of AVNOAS was raised by 5.35% and 26.42% respectively, and the RWI of AVNOAS was smaller, indicating that AVNOAS should be recommended for application in the waiting halls of high-speed railways. To optimize the ventilation performance of AVNOAS, further studies were discussed in terms of the air-supply angle and the proportion of air volume distribution (PAVD). The thermal environment of Case 1–3 (θ = −15°) is better than those under other air-supply angles. When the PAVD of AVNOAS was 2:1, a better thermal environment was created in high-speed railway stations. The results of the present study intended to support the application of this innovative attached ventilation in high-speed railway stations.

Shaking table test on seismic performance of integrated station-bridge high-speed railway station

The structure of integrated station-bridge is commonly used in the construction of high-speed railway station, which mainly consists of the frame part and the grid part. The complicated structure is characterized by the discontinuous vertical stiffness and the large longitudinal span, which results in an undefined mechanism of structural dynamic catastrophe. In this paper, the shaking table test was carried out with the model scale ratio of 1/25 to study the seismic performance, including the failure mode, acceleration, displacement and internal force responses, etc. The results show that the buckling of the frame occurs under the PGA of 0.82 g, which mainly concentrates on the third floor. The beam end of the top joint buckles obviously under the PGA of 1.27 g. The members of grid begin to yield under the PGA of 0.40 g, in which the order of yield member was web and chord in the truss. Based on the results of experiments, a seismic performance evaluation method of the composite structure is proposed for the damage of the grid frame and frame under different seismic intensities.

Optimal zoning for building zonal model of large-scale indoor space

Zonal model is a powerful numerical modeling approach that can quickly simulate thermal parameter distribution by dividing the indoor space into zones (zoning). However, most previous zoning methods are based on modelers’ experiences or empirical knowledge. Flow field details will be lost if the zoning is too coarse; while the computational load will be high if the zoning is too fine, especially for large-scale spaces. Therefore, this paper proposes an optimal zoning method for large-scale spaces to compromise the model accuracy and computational efficiency. In this method, air velocity and temperature differences before and after the zoning were used as indicators for accuracy. An improved particle swarm optimization method was adopted to obtain optimal Pareto solutions; and the technique for order preference by similarity to an ideal solution (TOPSIS) method was implemented to minimize the number of zones for enhancing computational efficiency. A large high-speed railway station was used as an example to verify the proposed optimal zoning method. The accuracy and computational efficiency of the proposed non-uniform zoning method were compared with a conventional zoning method and a uniform zoning method. Compared with the conventional zoning method, the proposed method reduced the number of zones by 96.2%; while critical temperature distributions and airflow patterns were well preserved. Compared with the uniform zoning method with the same number of zones, the proposed method improved the accuracy by 45.2% at most and 18.5% on average. Applicability analysis was also carried out to show that the proposed zoning method was reliable.

Numerical study on existing RC circular section members under unequal impact collision

Traffic accidents and derailed train-related incidents have occurred more often than ever in recent years, resulting in some economic damage and casualties. Reinforced concrete (RC) constructions often involve derailed train and vehicle accidents. Rarely are such side collisions studied in previous studies. To do this, high-fidelity simulation-based finite-element (FE) models are created in this paper to accurately simulate the collision of circular RC members with a derailed train. The reinforced concrete member structure is common in high-speed railway stations. The impact energy of the impact body is significant, causing structural member failure. It analyses the dynamic behavior of reinforced concrete members under unequal span impact loads. Numerical implementations of impact issues are discussed from the perspective of geometric, contact, and material properties. The reliability and precision of the ABAQUS code to solve impact issues are verified by comparing failure modes, impact, and deflection time history experimental outputs. By analysing the impact response characteristics, used the control variables to study the failure process and mode (including the characteristics of impact and reaction forces, deflection time history curve, impact force–deflection curve, and bearing reaction force–deflection curve). The reinforcement ratio, impact velocity, concrete strength, and slenderness ratio significantly affect shear crack pattern and development. Changes in impact velocity and slenderness ratio also affect member failure modes.

Experimental Study of Phase Change Microcapsule Suspensions Applied in BIPV Construction

In this paper, a phase change microcapsule suspension MPCMS25 with a mass fraction of 10% was prepared with TH-ME25 as the phase change microcapsule particles and deionized water as the base fluid. The experimental benches of the Building Integrated Photovoltaic (BIPV) system and BIPV-MPCMS system were set up, and the comparative tests were carried out in Nanjing to study the optimization effect of phase change microcapsule suspension on the thermal and electrical properties of the BIPV system. The results show that MPCMS25 reduces the component temperature of the system by 8.8 °C and the backplane temperature by 11.1 °C. The optimization time of the component operating temperature and the backplane temperature is 9.5 h and 9.75 h, respectively. Delay appearance of peak module operating temperature by 114 min and peak backplane temperature by 125 min. In addition, the suspension can also improve the power conversion efficiency (PCE) of photovoltaic modules by 0~5%. After a simulation study on the energy consumption of a high-speed railway station, it is found that using the BIPV-MPCMS system as the building envelope can achieve an energy saving rate of about 8.5%.

Establishment and Saliency Verification of a Visual Translation Method for Cultural Elements of High-Speed Railways: A Case Study of the BZ Railway Line

The high-speed railway station symbol system, generated from regional culture translations, not only improves transfer efficiency but also reveals the area’s unique urban cultural context. In this study, we used an eye-tracking technique and virtual reality technology to examine the visual cognitive preferences of the existing cultural translation method used by the Beijing–Zhangjiakou high-speed railway. Then, considering the design and layout of the existing station symbol system, we analyzed the visual saliency of different elements such as images, words, and symbols in three types of spaces in the Taizicheng high-speed railway station. The experiment site was located in the physical laboratory of the School of Architecture and Design at Beijing Jiaotong University. A total of 94 students from different majors were selected to participate in the experiment, with 92 datapoints eventually being deemed valid. The experiment data showed the following. First, the overall significance ranking of three scenes in the Taizicheng station was: S1 (81.10%) > S2 (64.57%) > S3 (49.57%). The cognitive correctness rankings of the number positions of the three scenes were: S1: 5 > 2 > 3 > 1 = 4; S2: 4 > 2 > 3 > 1 > 5; S3: 1 > 3 > 2 > 5 > 4. Second, the significance ranking of the transliteration element in S1 was: Images > Words > Sculptures > Patterns > Colors; S2 was: Patterns > Colors > Words > Images > Sculptures; and S3 was: Colors > Images > Words > Patterns > Sculptures. The results underscore the validity of the Beijing–Zhangjiakou cultural translation and offer a reference for station layout and spatial optimization. Finally, they provide new ideas for the design and layout of station symbol systems.

Experimental Study on Existing RC Circular Members Under Unequal Lateral Impact Train Collision

With the fast growth of high-speed rail in recent years, derailment has become the first hidden danger of high-speed rail transportation. The high-speed train passes near the station building. So the train may derail and hit the station building. Building a high-speed railway station usually uses a reinforced concrete structure. As a result of high impact energy on the impact body, the reinforced concrete (RC) member may fail; the impact point is near the member's foot; the structural member's constraint can be considered fixed support. This paper investigates the dynamic behavior of four types of circular reinforced concrete members under unequal lateral impact loads. The RC member's failure mechanism and dynamic response addressed the significance of unequal lateral impact load. The usual circular reinforced concrete members are used as the model to perform the drop-weight impact test. The specimens' crack pattern, failure mechanism, impact, deflection, and strain time–history curves are obtained. Findings show that between the impact point and the adjacent support, shear fractures occur that fail in shear mode. Shear cracks are based on impact velocity, longitudinal reinforcement ratio, and stirrup ratio. One type is more destructive to members and nodes. A shear fracture occurs when a longitudinal reinforcement fractures towards the closer support. The effects of impact velocity, longitudinal reinforcement ratio, and stirrup ratio on the dynamic impact response are studied. The experimental results may help improve structural member impact resistance. The critical section (right side) computed the static shear resistance using shear force, whereas the maximum external load resistance determines static bending moment resistance. Understanding how circular members fail to be subjected to unequal lateral impact loads provides insight into circular RC members' impact design and damage evaluation.

Italian High-Speed Railway Stations and the Attractivity Index: the Downscaling Potential to Implement Coworking as Service in Station

This article introduces a methodology to evidence the current attractiveness level of Italian high-speed railway stations in a GIS environment, involving station services and flow parameters. The model has been relevant to detect stations with lower attractive capacity, and afterward, to implement the station attractivity, the work proposed employing a coworking spaces strategy as a service in station. Coworking spaces produce benefits both for the traveler and the transport company. These places became part of the services offered within railway stations since they are flow providers able to change appearance and idea of experience at station. In France, a coworking strategy has been created from the collaboration of Regus, leader company in coworking spaces supply, and the French railway group (SNCF). The Italian railway company (Ferrovie dello Stato) does not consider the attractiveness potential of coworking in the management of station resources; coworking spaces in Italy are placed outside stations. Accordingly, Torino Porta Susa station has been identified as one of the stations with low attractivity capacity from the methodology implemented, and it has been chosen as the case study to implement the coworking strategy. The choice of Torino Porta Susa is accurate also for showing the value of associating coworking as urban policies support. The coworking strategy can implement attractiveness levels and, in a long-term future perspective, encourage sustainable mobility targets.

Coupling Mechanisms and Development Patterns of Revitalizing Intangible Cultural Heritage by Integrating Cultural Tourism: The Case of Hunan Province, China

The integration of the cultural/creative and tourism industries is one way to present different types of regional cultural heritage to the world. This study examined the coupling of intangible cultural heritage and cultural tourism development in Hunan, seeking a pattern for the development of the former under the latter. The study included analyzing the coupling between tourism resources and ICH and exploring modes of revitalizing or developing ICH in the context of cultural tourism. We used methods of GIS spatial technology, including nearest neighbor indexing, kernel density estimation, and queuing analysis, to estimate the kernel density of a buffer of points (high-speed railway stations, and 5A and 4A scenic spots) and lines (high-speed railways and highways). The results show (1) the overall spatial aggregation of Hunan’s ICH resources; (2) how tourist attractions drive ICH resources, as illustrated by the coupling between the spatial distribution of ≥4A attractions and ICH; and (3) the pattern of high-speed road and railroad network development, featuring region-wide guidance for the development of Hunan’s ICH resources. This research not only provides development patterns and concepts for the study of intangible cultural heritage and tourism development planning in Hunan, but also provides useful references for the combination of the two in other regions.

Spherical Linguistic Petri Nets for Knowledge Representation and Reasoning Under Large Group Environment

Fuzzy Petri nets (FPNs) are a promising modeling tool for knowledge representation and reasoning of rule-based expert systems. However, there exist limitations in representing ambiguous knowledge and performing approximate inference in traditional FPNs. Additionally, knowledge parameters are usually provided by some experts in existing FPN methods. In response to these issues, a new version of FPNs, named spherical linguistic Petri nets (SLPNs), is introduced in this article for knowledge representation and reasoning in the large group context. To this end, spherical linguistic sets are applied to capture imprecise knowledge and represent the uncertainty of experts’ judgements. Furthermore, a large group knowledge acquisition approach is developed to determine knowledge parameters. A bidirectional inference algorithm is developed for implementing the reasoning process and identifying the root causes of an appointed event. Finally, the efficacy and superiority of our developed SLPNs are illustrated by a realistic example regarding stampede risk level assessment in a high-speed railway station.