Beijing Capital Research Articles

Airport Express Passenger Flow Prediction Based on “Time-Feature” Cooperative Attention

Accurate prediction of airport express passenger flow is the basis for realizing intelligent, refined and efficient management and control of the airport rail transit system, and is of great significance to improving airport service levels and operational efficiency. Due to the numerous influencing factors that overlap with each other, and the complex mechanism of factors affecting passenger flow timing, accurate prediction of airport express passenger flow is extremely challenging. This paper proposes an airport express rail passenger flow prediction model based on the "time-feature" collaborative attention mechanism, which achieves accurate capture of the impact of multi-dimensional factors on airport express rail passenger flow in different time series. Experiments were conducted based on actual passenger flow data of the Beijing Capital International Airport Express Rail Link, and the results showed the effectiveness of the proposed method.

Enhancing airline connectivity: An optimisation approach for flight scheduling in multi-hub networks with bank structures

While one salient characteristic of hub airports lies in connecting passengers, the full-service airlines in North America concentrate their networks spatially over a number of hubs. Having witnessed the emerging multi-hub network, this paper investigated the flight scheduling problem under a multi-hub configuration, taking a well-defined bank structure and airport operational restrictions into account. An integrated non-convex Mixed-Integer Nonlinear Programming (MINLP) approach was proposed to enhance airline connectivity, considering different combinations of traffic flow direction and connecting times. To verify the scalability and effectiveness of the proposed model, a comprehensive case study has been undertaken with real-world scheduling data from Air China, which was solved by a novel problem-specific Selective Simulated Annealing (SSA) algorithm. Substantial improvements were achieved without sacrificing the scheduling efficiency. Precisely, the programme adjusted the flights during a typical operational day in a timely manner. The post-optimisation outcomes have witnessed its effectiveness with a 17.97%, 17.06%, 22.41% and 53.86% increase in airline connectivity at its four major hub airports (Chengdu Shangliu, Beijing Capital, Shanghai Pudong and Hongqiao) in China, respectively. A clear pattern of the bank structure also confirms its positive impact on airline connectivity under the multi-hub network configuration. Lastly, a comparative analysis for the distribution of all feasible connections further highlights the critical challenge concerning the role of the hubs in a multi-hub network. More specifically, Air China’s multi-hub network systematically performs better on Domestic-International routes, due to flight schedule, frequencies, geographical placements and detours. Among the four hub airports, Beijing Capital International Airport stands out as a dominant one, which implies its potential to serve as a robust international hub airport.

Location dynamics of coworking spaces in China: insights from Beijing metropolitan

ABSTRACT This paper explores the location determinants of coworking spaces (CSs) in the Chinese capital of Beijing, thus filling the gap in the literature about CS location in urban China. The study applies a count data model (CDM) to the data collected in 2022 to answer three research questions: (1) Where are CSs located and what are the location determinants? (2) How does Beijing’s urban structure affect CS location determinants? (3) What role do creative industry specialisation, variety, government facilities and incentives play in CS location? The findings reveal that CSs in Beijing – most of them promoted by public authorities – tend to cluster in the central city where there is a high diversity of knowledge-intensive and creative industries. The semi-polycentric urban structure of Beijing affects the location of CSs by creating localised activity hubs. Furthermore, the study finds evidence that creative industry specialisation and variety are crucial factors in CS location. In addition, government facilities and incentives play a key role in driving their locations.

A Comparative Study of The Palace of Versailles and Imperial Garden Yuanmingyuan

The Palace of Versailles was constructed in the enchanting city of Paris, while the majestic Yuanmingyuan was erected in the imperial capital of Beijing. Built during the same era, they serve as splendid exemplars of Western and Eastern royal gardens and architectural marvels respectively. The purpose of this paper is to compare the Palace of Versailles and the Imperial Garden Yuanmingyuan, two historic royal palaces, and discuss the differences in their construction background, planning layout, architectural style and garden features. Because of the disparity in the personalities of the two emperors, the palace possesses its own distinctive attributes. The Palace of Versailles is famous for its magnificent architecture and French gardens, which demonstrated the power and luxury of Louis XIV; the Yuanmingyuan, on the other hand, is famous for its unique Chinese garden style and exquisite architectural complex, which reflects Emperor Kangxis love and pursuit of traditional Chinese culture. By comparing the two palaces, we can better understand the differences in architecture and garden design across diverse cultural backgrounds, as well as their profound impact on architecture styles, landscaping techniques and cultural heritage.

CR-TransR: A Knowledge Graph Embedding Model for Cultural Domain

As a combination of information computing technology and the cultural field, cultural computing is gaining more attention. The knowledge graph is also gradually applied as a particular data structure in the cultural area. Based on the domain knowledge graph data of the Beijing Municipal Social Science Project “Mining and Utilization of Cultural Resources in the Ancient Capital of Beijing,” this article proposes a graph representation learning model CR-TransR that integrates cultural attributes. Through the analysis of the data in the cultural field of the ancient capital of Beijing, a cultural feature dictionary is constructed, and a domain-specific feature matrix is constructed in the form of word vector splicing. The feature matrix is used to constrain the embedding graph model TransR, and then the feature matrix and the TransR model are jointly trained to complete the embedded expression of the knowledge graph. Finally, a comparative experiment is carried out on the Beijing ancient capital cultural knowledge graph dataset and the effects of the classic graph embedding algorithms TransE, TransH, and TransR. At the same time, we try to reproduce the embedding method with the core idea of neighbor node information aggregation as the core idea, and CRTransR are compared. The experimental tasks include link prediction and triplet classification, and the experimental results show that the CRTransR model performs better.

Long-Term SAR Data Analysis for Subsidence Monitoring and Correlation Study at Beijing Capital Airport

Land subsidence, resulting from natural or human activities, is a global environmental geological disaster. The Interferometric Synthetic Aperture Radar (InSAR) time-series analysis technique offers high spatial and continuous temporal resolution, providing data and a foundation for investigating regional land subsidence and its evolution mechanism. Beijing Capital International Airport (BCIA) has experienced uneven land subsidence since 1935, together with severe fissures significantly affecting its normal operations. In this study, the time-series InSAR method was successfully applied to monitor the gradual increase in uneven local subsidence and ground fissures activity at BCIA from June 2003 to March 2023. Initially, ENVISAT-ASAR, Cosmo-SkyMed, and Sentinel-1 data were processed by time-series InSAR techniques to generate deformation rate maps and time series for the airport area. Subsequently, a comparison was made between the displacement time series from InSAR and ground leveling measurements to assess the accuracy of InSAR-derived measurements. Through a comprehensive analysis of the distribution characteristics of land subsidence at the airport, a long-standing ground fault was located within the airport was identified. A preliminary discussion on the development status of this ground fissure was carried out based on the visual interpretation of optical images. Lastly, the inducing factors and evolutionary conditions of land subsidence were discussed. This case demonstrates the applicability of InSAR technology in identifying and monitoring geological processes such as land subsidence and ground fissure activities. It provides a scientific approach to exploring and studying the causes and formation mechanisms of land subsidence and ground fissures in the Beijing Capital Airport area.

Field‐tested signal controller to mitigate spillover using trajectory data

AbstractTrajectory data from connected vehicles (CVs) provide a continuous and reliable means of obtaining information that can be leveraged to optimize traffic signals. This paper proposes a real‐time traffic signal control method using CV trajectory data as the sole input. The primary goal of the proposed signal control method is to prevent queue spillover, which may significantly decrease the traffic efficiency on urban networks and induce high delays to the travelers. The proposed method formulates the signal control problem via a linear quadratic optimization model, considering the constraints related to the duration and variability of green lights in practical traffic signal control systems. Compared to conventional max‐pressure‐based methods, the optimization model offers enhanced efficiency in handling these constraints, making it highly suitable for real‐life implementation. The proposed method has undergone testing in both simulated environments and real‐world applications. In the simulation experiments, the proposed method has been demonstrated to effectively reduce spillover risks and outperform a conventional max‐pressure‐based approach even when the CV penetration rate is as low as 5%. In the real‐world experiment, the proposed method had been tested in a traffic network around the Beijing Capital International Airport for several months. The severity of spillovers, which was represented by two performance indicators, had been significantly reduced after implementing the proposed method.

Medical figures in Wang Ao Ji

Wang Ao was a famous politician and litterateur in the middle of Ming Dynasty. Wang Ao Ji comprehensively sorts out Wang's works for the first time, and collects all the poems and notes written by himself. A total of 38 physicians were recorded in Wang Ao Ji, among them, Wang Ao had close contacts with many physicians such as Zhou Geng, Zhou Xu'an, Sheng Rubi, Zhang Yangzheng and Chen Chong. Wang Ao and Zhou Geng are both core members of the literary society of Wuzhong, in the capital of Beijing. Wang Ao once wrote an epitaph for Zhou Xu'an, a tablet inscription for Sheng Rubi and a biography for Zhang Yangzheng. And Chen Chong once cured Wang's nephew's diarrhea caused by acne rash.The biographical records of physicians in Wang Ao Ji are highly reliable, and some medical information is the main source of official history and medical history works, which contains important historical value. Relevant contents about medical figures recorded in class of works, local chronicles, genealogy, anthology, notes and other ancient books, to a great extent, enrich biographical data of physicians, which is worth further research.

Analysis of the characteristics of viral infections in children with diarrhea in Beijing from 2018 to 2022

Objective: To explore the characteristics of viral infections in children with diarrhea in Beijing from 2018 to 2022. Methods: Real-time PCR and enzyme-linked immunosorbent assay were used to detect viral nucleic acid of Norovirus (NoV), Sappovirus (SaV), Astrovirus (AstV), Enteric Adenovirus (AdV) or antigen of Rotavirus (RV) in 748 stool samples collected from Beijing Capital Institute of Pediatrics from January 2018 to December 2021. Subsequently, the reverse transcription PCR or PCR method was used to amplify the target gene of the positive samples after the initial screening, followed by sequencing, genotyping and evolution analysis, so as to obtain the characteristics of these viruses. Phylogenetic analysis was performed using Mega 6.0. Results: From 2018 to 2021, the overall detection rate of the above five common viruses was 37.6%(281/748)in children under 5 years old in Beijing. NoV, Enteric AdV and RV were still the top three diarrhea-related viruses, followed by AstV and SaV, accounting for 41.6%, 29.2%, 27.8%, 8.9% and 7.5%, respectively. The detection rate of co-infections with two or three diarrhea-related viruses was 4.7% (35/748). From the perspective of annual distribution, the detection rate of Enteric AdV was the highest in 2021, while NoV was predominant in the other 4 years. From the perspective of genetic characteristics, NoV was predominant by GⅡ.4, and after the first detection of GⅡ.4[P16] in 2020, it occupied the first two gene groups together with GⅡ.4[P31]. Although the predominant RV was G9P[8], the rare epidemic strain G8P[8] was first detected in 2021. The predominant genotypes of Enteric AdV and AstV were Ad41 and HAstV-1. SaV was sporadic spread with a low detection rate. Conclusion: Among the diarrhea-related viruses infected children under 5 years of age in Beijing, the predominant strains of NoV and RV have changed and new sub-genotypes have been detected for the first time, while the predominant strains of AstV and Enteric AdV are relatively stable.

The application of knowledge graphs in the Chinese cultural field: the ancient capital culture of Beijing

A methodology is proposed to introduce knowledge graphs into the study of the Chinese cultural field for use in a newly designed, complete application. At present, the combination of culture and information technology has become a trend. Among various technologies, knowledge graphs are a very promising option. The contributions of this paper are as follows: it supplies for the first time a knowledge graph in the cultural field of the ancient capital of Beijing, establishes a domain knowledge base, and develops a platform for visual analysis and interactive question and answer. In this process, a framework for applying knowledge graphs to research in the cultural field is summarized, providing ideas for research in the cultural field.

Measuring changes of Major Asian airportsʼ air connectivity and suggestions for improvement post COVID-19

This study measures the changes of the transit connectivity of ten major East Asian hub airports for the routes between Southeast Asia and North America from 2004 to 2023. According to the research results, Taiwan Taoyuan Airport has the best flight connection quality on transit routes between Southeast Asia and North America, while Tokyo Narita Airport has a higher number of feasible connecting flights than other airports. The COVID-19 pandemic has led to a huge decline in the flight supply at Hong Kong and Chinese airports in North America and Southeast Asia routes. The traffic volume at Beijing Capital and Shanghai Pudong Airport dropped by 50%-80%, and Guangzhou Baiyun Airport has no direct flights to North America after 2022. Due to the impact of the Anti-Extradition Law Bill Movement, flights at Hong Kong Airport have dropped by more than 60%. In 2023, the number of feasible connecting flights at Taoyuan Airport has surpassed that of Hong Kong Airport and is not far from Incheon and Narita Airport. With the recovery of the aviation market post-pandemic, airports with fewer feasible connecting flights can actively resume or develop new routes, and airports with low transfer quality can further shorten the connecting time for transfer flights.

Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach

Accurate airport capacity estimation is crucial for the secure and orderly operation of the aviation system. However, such estimation is a non-trivial task as capacity depends on various meteorological and operational features. The complex coupling characteristics among these multi-source features have proved to be challenging for most of the traditional regression models. Recently, enhanced by its excellent ability to mine nonlinear relationships, the machine learning methods trigger widely applications. However, due to the imbalance of features scatter and the neglect of temporal dependences in aviation systems, existing machine learning methods for airport capacity prediction still have room for improvement. In light of these, this paper presents a novel airport capacity prediction method based on the multi-channel fusion Transformer model (MF-Transformer). Besides the commonly used aviation features, we unprecedentedly harness the power of the high-dimensional meteorological feature for accurate prediction. As to the model, we construct a multi-channel feature fusion structure, which includes a three-channel network for multi-source features extraction and an attention-based feature fusion module between channels. In each channel, the Transformer-based model is utilized to capture the temporal dependences of features. We conduct experiments on the capacity prediction tasks of the Beijing Capital International Airport which is the largest airport in China and verify that the proposed MF-Transformer outperforms benchmarks under different prediction horizons.

Prediction of Flight Delays at Beijing Capital International Airport Based on Ensemble Methods

Predicting flight delays plays a critical role in reducing financial losses and increasing passenger satisfaction. Due to their ability to combine multiple algorithms, ensemble methods have demonstrated strong predictive performance in many research fields. In this paper, ensemble methods are adopted to predict flight delays. First, based on the current studies, two novel explanatory variables, named arrival/departure pressure and cruise pressure, are proposed as factors affecting flight delays. Second, we introduce the ensemble methods and select representative algorithms for the prediction problem. In addition to the ensemble methods, classical algorithms are also used to predict flight delays. Finally, the actual operational data of Beijing Capital International Airport were utilized to conduct a case study. The results show that the stacking method has better prediction performance than other baseline methods. The mean absolute error (MAE) of the stacking method was about 12.58 min on the test dataset. Furthermore, we tested the effect of the two explanatory variables proposed in this paper, and the results show that the MAE was reduced by about 20% by using the stacking method.

Effects of buildings on wind shear at the airport: Field measurement by coherent Doppler lidar

Wind shear has a negative impact on aircraft safety, particularly during takeoff and landing processes. The buildings inside or around the airport can induce wind shear by disturbing the airflow along the glide path. This paper investigates the building-induced wind shear along the glide path of aircraft by utilizing two coherent Doppler lidar systems deployed at Beijing Capital International Airport. A well-designed coordinated scanning strategy of lidar was used to measure the building-induced wind shear along the glide path and the wind field influenced by buildings. With a correlation coefficient of 0.97 for wind speed and 0.99 for wind direction, the wind field estimated by lidar was well validated by those from sonic anemometers. In addition, the lidar-observed wind shear events are verified by pilot reports. The results show that building wakes bring the velocity deficit and turbulence increase along the glide paths. It is susceptible to wind shear due to the rapidly changing wind speeds when the aircraft flies through these wake regions. The relative frequencies of wind shear events reached above 70 and 50 at 36L and 01, respectively, in the wind direction range of 270°∼300° with a wind speed greater than 12 m/s. The statistical analysis of lidar-observed wind shear reveals that the wind shear frequently occurs within the height of 40 ∼ 80 m and the distance of 763 ∼ 1526 m at both 36L and 01.

Formation mechanism of ground fissure at Beijing Capital International Airport revealed by high-resolution InSAR and numerical modelling

A NE-SW trending ground fissure that extends for 13 km was first discovered at Beijing Capital International Airport (BCIA) in 1985. There are many factors that influenced its formation mechanism, including the activity of the Shunyi-Liangxiang (SL) fault, land subsidence and human engineering activities. Fine-scale surface deformation at BCIA was obtained by interferometric synthetic aperture radar (InSAR), using 27 TerraSAR-X data with a 3 m resolution, to better understand the failure phenomenon and formation mechanism of the ground fissure. Spatially, the SL fault intersects runway 36R obliquely at the airport, and the fissure forms along the fault. A zone of subsidence was observed on the footwall (northwest side) of the SL fault; however, the ground fissure along the SL fault dips towards the southeast. The InSAR subsidence rate profiles show a rate difference of 9–17 mm/yr across the ground fissure, consistent with the deformation characteristics of concrete surface fractures at BCIA. Constrained by the subsidence rate obtained by InSAR, the discrete element method (DEM) was applied to simulate the failure process of runway 36R. The formation mechanism of the ground fissure was revealed: the SE-dipping SL normal fault is the most important geological factor controlling the boundary of subsidence and extension of the ground fissure; the regional subsidence on the footwall of the SL fault is the main triggering factor in the development of the ground fissure; the damage of the concrete surface induced localized uplift; and the decrease in soil strength in the fault zone has led to localized subsidence along the ground fissure.

Quantifying the Resilience Performance of Airport Flight Operation to Severe Weather

The increased number of severe weather events caused by global warming in recent years is a major turbulence factor for airport operation and results in more irregular flights. Quantifying the system response status towards turbulence is critical, in order for airports to deal with severe weather. For this reason, we propose a resilience framework that is in compliance with resilience theory to evaluate airport flight operations. In this framework, the departure rate (DPR), normal weather baseline (NWB), and nonnegative general resilience (NGR) were defined and used. Meanwhile, the whole process is divided into five phases before and after disturbance, and the system capacities of susceptibility, absorption, adaptation, and recovery are assessed. In order to clarify the performance of the framework towards various severe weather conditions, an analysis was conducted at Beijing Capital Airport in China based on a dataset that includes both the meteorological terminal aviation weather report (METAR) and flight operations from January to July 2021. The results show that the newly proposed resilience framework can commendably reflect airport flight operation performance. The airport flight operation resilience characteristic is different with severe weather. Compared to sandstorms and snow, airport flight operation with stronger robustness was observed during thunderstorm events. The study also confirms that, as the weather warning level increases, the disruption time increases and response time decreases accordingly. The above results could assist researchers and policy makers in clearly understanding the real-world resilience of airport flight operation, in both theory and practice, and responding to emergent disruptive events effectively.

Multi-Objective Decision Method for Airport Landside Rapid Transit Network Design.

To better deploy the landside rapid transit network for large airports, this study proposes a multi-objective transit network design model to maximize passenger demand coverage, reduce passenger travel time and minimize operational cost simultaneously. This model is formulated as an equivalent integer programming problem by predefining the transportation corridors and passengers’ OD pairs. A branch-and-cut algorithm is proposed to find a non-inferior solution set. We also conduct trade-off analysis between efficiency, effectiveness and equity under each deployment strategy using the modified Gini coefficient method. The effectiveness of the proposed model and solution algorithm are tested with rapid transit network of the Beijing Capital International Airport. Results show that among the three common network topologies, including star, tree and finger, the passenger demand coverage and travel time reduction per unit cost under star topology outperform the other two topologies. As for finger topology, the performances of the passenger demand coverage and travel time reduction are the best among the three, but the cost is the poorest. In addition, the trade-off analysis shows that the solution whose objective is to maximize passenger demand coverage has a higher efficiency and a lower unit cost than the solution whose objective is to reduce travel time. However, the latter has a higher level of equity, especially for the medium and low-cost solutions. The proposed method in this study can help the decision makers to design effective landside rapid transit networks for large airports to improve the service level.

Do Internet Search Data Help Forecast Air Passenger Demand? Evidence From China's Airports.

Before making travel plans, people often use the Internet to collect relevant information to help themselves make better decisions. Among the numerous information search channels, Internet search engine is used by the vast number of travelers because of its low cost and high efficiency. To a large extent, Internet search behavior is the external manifestation of users’ psychological activities, reflecting their concerns, needs and preferences. Therefore, Internet search data can reflect the air passenger demand information to a certain extent. In this manuscript, a novel decomposition ensemble model is proposed to discuss the role of Internet search data in air passenger demand forecasting. In the empirical study, the relevant data of Shanghai Pudong International Airport and Beijing Capital International Airport are taken as samples. The results show that the proposed forecasting model can integrate the advantages of decomposition-ensemble strategy and deep learning algorithm, and achieve more accurate and reliable prediction results than all benchmark models. This further indicates that adding Internet search data into the forecasting model can effectively improve the prediction performance of air passenger demand, and can provide scientific and reliable decision support for air transport management.

Energy-Saving Applications Based on Train Mass Online Learning Using Time-Varying Train Model

The current train model of the train control system is unable to accurately reflect the influence of nonlinear running resistance, line conditions, the mutative train mass value, and external environment changes on the model in train dynamics, resulting in a defect of poor train model performance. The train basic model and additional resistances are discussed in this paper, a novel neural network online learning method of the time-varying dynamic train model is proposed, combined with the characteristics of rail transit lines, and a neural network learning algorithm is designed by categories and steps. This method can identify the train mass value that changes continuously with passengers during running. The energy savings resulting from using the actual varying train mass in the train control system are calculated. The results show that, when compared to the traditional model’s invariant approximate empirical parameters, the time-varying parameter model can follow changes in the train and line environment and obtain quantitative expressions of curve resistance and tunnel resistance with speed. The time-varying train model was used to conduct engineering tests on the Beijing Capital Airport Line; the online learning deviation of train mass was controlled within a margin of 3.08%, and at the same time, energy consumption decreased by 6.13%.

Value Assessment of Airport Billboards Based on Passenger Big Data.

As an important element of the airport ecosystem, airport billboards are playing a crucial role in publicizing the city image and facilitating humanistic airport construction. At the same time, airport billboards have great commercial value and is a popular channel for enterprises to prompt their products and to build their brand image. Currently, most airports in China adopt a simple fixed pricing mechanism for airport billboards. Specifically, for any type of billboard, the advertising price is mainly determined by considering historical prices and the total passenger flow of the entire airport during a whole year. However, this seemingly crude pricing mechanism only considers macro-level data of passenger flow and fails in reflecting the real value of billboards in different locations effectively, since the value of a particular billboard depends not only on its media form, but also on the number of passengers flowing through and whether these passengers are the target customers of the advertising content. Based on big data on airport layout, flight information, and passenger attributes, this paper proposes a time- and location-based value assessment model for airport billboards. Using sample data collected from the Beijing Capital International Airport, the assessment model is adopted to evaluate the value of two real billboards in Terminal T3. Application of this model can reflect the difference in the value of airport billboards located in different spots during various periods. Furthermore, this model provides a solid foundation for airport executives to develop differentiated/dynamic pricing and flexible advertisement scheduling strategies, thereby improving the overall efficiency.