Shanghai Pudong International Airport Research Articles

Short-Term Air Traffic Flow Prediction Based on CEEMD-LSTM of Bayesian Optimization and Differential Processing

With the rapid development of China’s civil aviation, the flow of air traffic in terminal areas is also increasing. Short-term air traffic flow prediction is of great significance for the accurate implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow prediction, this paper proposes a short-term air traffic flow prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) of the Bayesian optimization algorithm and data differential processing. Initially, the model performs CEEMD on the short-term air traffic flow series. Subsequently, to improve prediction accuracy, the data differencing is employed to stabilize the time series. Finally, the smoothed sequences are, respectively, input into the LSTM network model optimized by the Bayesian optimization algorithm for prediction. After data reconstruction, the final short-term flow prediction result is obtained. The model proposed in this paper is verified by using the data from Shanghai Pudong International Airport. The results show that the evaluation indexes of the prediction accuracy and fitting degree of the model, RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R2 (Coefficient of Determination), are 0.336, 0.239, and 97.535%, respectively. Compared to other classical time-series prediction models, the prediction accuracy is greatly improved, which can provide a useful reference for short-term air traffic flow prediction.

Generating trajectory data without behavior modeling: An online virtual reality method in wayfinding performance evaluation for buildings

Wayfinding performance is one of the key issues in pre- or post-occupancy evaluation for large-scale public buildings and such evaluation typically revolves around participants’ wayfinding trajectories. Traditional evaluation methods, including laboratory virtual reality (VR) experiments and computer simulations, have their respective advantages in accuracy or cost-efficiency. However, they either suffer from high costs and low efficiency or sacrifice the diversity of behavioral data for higher efficiency and lower costs. Therefore, this paper proposes a novel method named “sampling as simulation” (SaS) via an online VR wayfinding experiment. This method allows for the rapid collection of behavioral data at a lower cost to generate highly accurate wayfinding trajectory data for evaluation purposes. It utilizes a model-image integrated approach to create realistic online virtual environments and employs a spatiotemporal replay algorithm to generate a crowd of true-trajectory-driven avatars, significantly enhancing the immersion and realism of the online virtual scenes. This method was demonstrated and validated with a wayfinding performance post-occupancy evaluation (POE) project at the International Departure of Satellite Terminal One (S1) in Shanghai Pudong International Airport (PVG). In this example, a total of 13,712 original trajectories were collected within a 72-h time window, serving as the original dataset for evaluation. Based on this dataset, the study effectively simulated wayfinding behavior in three different scenarios and evaluated wayfinding performance. Compared to existing methods, the SaS method not only improves data collection efficiency and reduces costs but also maintains the diversity of behavior by generating transformed trajectories directly from human data without behavior modeling.

Fog climatology at Shanghai Pudong International Airport

AbstractUsing 22‐year ground observations on meteorological variables, we conducted a statistical analysis to reveal fog characteristics at Shanghai Pudong International Airport (SPIA). Fog events were classified by fog types using an objective method. Two types of advection fogs are dominant in fog events at SPIA, followed by radiation fogs. Different fogs have evident annual and monthly variations in frequency. Advection fog mostly appear from February to April, while radiation fog is mainly between November and February. Six synoptic patterns associated with fogs were determine by a self‐organizing maps cluster method, two of which, namely ‘west of marine high pressure’ and ‘inverted trough’, are most favourable for the formation of advection fogs, whereas ‘bottom of weak cold high‐pressure’ is favourable for radiation fogs. The frequency of advection fog occurrence exhibits temporal fluctuations with distinct peaks occurring 2–3 h after sunset, around midnight, and 1–2 h before sunrise, while almost all radiation fog occur in the second half of the night, and favourable conditions for fog onset become more effective with the length of the night. Radiation fog exhibits a longer duration than advection fog and tends to result in lower visibility. The prevalent wind direction for the formation of advection fog is generally from the east to the southeast, whereas radiation fog is typically associated with westerly winds. The temperature and air pressure during the formation of both advection and radiation fogs follow a normal distribution in most seasons. More fog characteristics were categorized by fog types and seasons.

An agent-based simulator for airport ground handling services

Airport ground handling plays an essential role in shortening the turnaround time for better utilization of aircraft that can reduce the cost per flight. One of the key challenges for improving the efficiency of airport ground operations is to comprehensively simulate the ground handling processes to optimize and evaluate various operation strategies. In this work, we develop an agent-based simulator that specifically focuses on the whole process of airport ground handling from landing to takeoff. It allows different types of agents, i.e. aircraft, ground vehicles, operators, and dispatchers, to execute their own behaviors according to the ground operation characteristics. The interactions among different agents are also modeled according to the specific service agreements. We use the Shanghai Pudong International Airport (PVG) for the case study to demonstrate the interfaces and outputs of the simulator including the simulated flight information, the progress of ground handling services, airfield status, etc. With the developed simulator, we could not only reproduce the real-world operations of different types of services but also have enough potential to optimize and evaluate various airport ground operation strategies under a series of complex scenarios.

Field assessment of rigid pavement stabilisation using cementitious grout: case study

On the 2nd runway of Shanghai Pudong International Airport, the field assessment was conducted for rigid pavement stabilization using cementitious grout. The support performance of underlying layers and voids beneath slabs were considered, and the deflection testing was performed before and after grouting. The performance of pre-treatment and post-treatment pavements, and effects of injecting depth and curing time on grouting treatment were statistically analyzed. Results indicated that the support performance of underlying layers along the wheelpath was gradually getting deteriorated and non-uniform during the service period, and the number of voids beneath slabs would also increase annually. The injecting depth at 10 cm under subbase layer was proposed for rigid pavements with the crushed stone subbase layer. Besides, the performance evaluation of cementitious grouting stabilization for rigid pavement was recommended after 60-day curing. Finally, results demonstrated that the objective of pavement stabilization for the 2nd runway was successfully achieved.

A data-driven approach to the strength evaluation of airfield rigid pavement using in situ instrumentation data

The pavement strength evaluation plays a vital role in the airfield operation and management. Traditionally, the evaluation has relied on the Heavy Weight Deflectometer (HWD) test. This method encounters challenges, including interruptions in airfield operations, limited coverage of inspection locations, extensive time required for data collection and analysis. This paper introduces a methodology for evaluating the structural strength of airfield rigid pavement using instrumentation data, aiming to enhance both the efficiency and accuracy of such evaluations. Strain responses, collected using the instrumentation system at Chengdu Tianfu International Airport under HWD loading, are extensively analyzed. A three-dimensional numerical simulation model encompassing nine slabs was formulated, leveraging finite element analysis to replicate pavement working conditions that were not covered by the instrumentation samples. The merging of the instrumentation and simulation samples resulted in a comprehensive database for diverse pavement working conditions. This facilitated the establishment of a data-driven framework for the fusion of mechanical models. From this database, a neural network mapping model was developed to correlate the instrumentation data with the pavement slab modulus and subgrade reaction modulus. These correlations were subsequently employed for the calculation of the pavement classification number (PCN). The primary advantages of the proposed method include real-time monitoring and evaluation of airfield rigid pavement strength without interference to airfield operations. The developed method has been deployed at seven hub airports in China, encompassing Shanghai Pudong International Airport (PVG), Beijing Capital International Airport (PEK), and Chengdu Tianfu International Airport (TFU), etc.

Emerging and legacy perfluoroalkyl and polyfluoroalkyl substances (PFAS) in surface water around three international airports in China

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a large category of crucial environmental contaminants of global concerns. There are limited data on PFAS in surface water around international airports in China. The present study investigated the concentrations, distributions, and sources of emerging and legacy PFAS in surface waters around Beijing Capital International Airport (BC), Shanghai Pudong International Airport (SP), and Guangzhou Baiyun International Airport (GB) in China. Twenty-seven target compounds were quantified. The Σ27PFAS concentrations ranged from 19.0 to 62.8 ng/L (mean 36.1 ng/L) in BC, 25.6–342 ng/L (mean 76.0 ng/L) in SP, 7.35–72.7 ng/L (mean 21.6 ng/L) in GB. The dominant compound was perfluorooctanoic acid (PFOA), which accounted for an average of 27% (5%–65%) of the Σ27PFAS concentrations. The alternatives with –C6F12- group had detection frequencies ranging from 72% to 100%. The partition coefficient results indicate that the longer chain PFAS (C > 8) tend to be more distributed in the particle phase. Fifty suspect and nontarget PFAS were identified. In GB, 44 PFAS were identified, more than SP of 39 and BC of 38. An ultra short-chain (C = 2) precursor, N-methylperfluoroethanesulfonamido acetic acid (MeFEtSAA), was identified and semi-quantified. Domestic wastewater discharges might be the main sources around BC, while industrial and aviation activities might be the main sources around SP and GB.

Station Layout Optimization and Route Selection of Urban Rail Transit Planning: A Case Study of Shanghai Pudong International Airport

In this paper, a cost-oriented optimization model of station spacing is presented to analyze the influencing factors of station spacing and layout near Shanghai Pudong International Airport. The Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm is used to cluster and analyze the high population density, and optimize the station layout in the southwest of Pudong International Airport. A spatial analysis of the land use and geological conditions in Pudong New Area is given. Combining the optimal station spacing, ideal location and spatial analysis, five routing schemes to Pudong International Airport are proposed. The DBSCAN and K-means algorithms are used to analyze the “PDIA-SL” dataset. The results show that the space complexity of the HDBSCAN is O(825), and the silhouette coefficient is 0.6043, which has obvious advantages over the results of DBSCAN and K-means. This paper combines urban rail transit planning with the HDBSCAN algorithm to present some suggestions and specific route plans for local governments to scientifically plan rail transit lines. Meanwhile, the research method of station layout, which integrates station spacing, ideal location and spatial analysis optimization, is pioneering and can provide a reference for developing rail transit in metropolises.

Ground infrastructure monitoring in coastal areas using time-series inSAR technology: the case study of Pudong International Airport, Shanghai

ABSTRACT Shanghai Pudong International Airport (PDIA), with its east side built along the coast with weak geological conditions, is prone to uneven foundation settlement due to the consolidation and compression of soil and erosion of coastal tides, affecting the safe operation of the airport. Therefore, it is crucial to conduct dynamic subsidence monitoring within the airport, especially in the runway area. 29 scenes of ascending track Sentinel-1A radar images from August 2016 to June 2018 are selected to perform surface deformation inversion based on PS-InSAR and improved SBAS-InSAR for PDIA and its around coastal area. Through cross-validation, the reliability of the time-series InSAR technique for dynamic monitoring of surface deformation of coastal zone infrastructures is confirmed. The results show severely uneven settlement. By combining the monitoring results with the local geological and hydrological dataset, the driving factors of differential deformation of the infrastructures are analyzed, including stratigraphic geological conditions, ground loadings, foundation treatment methods, water erosion, and groundwater level changes. Finally, the time-series deformation characteristics and the causes of PDIA's runway are emphasized based on the PS deformation results. This case provides a reference for the safety management of critical infrastructure in coastal areas using advanced InSAR technique.

Finding Similar Historical Scenarios for Better Understanding Aircraft Taxi Time: A Deep Metric Learning Approach

Accurate prediction of aircraft taxi time is crucial to optimizing route scheduling and improving airport efficiency. However, studies usually give only a single deterministic taxi time predicted by models, which lacks interpretability and affects controllers’ trust. To solve this problem, using the idea of auxiliary decision-making based on similar scenarios, a deep metric learning model is proposed to learn the similarity among historical situations, consisting of flight properties, surface traffic, and meteorological conditions. Combining the model with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest-neighbor regression algorithm, a set of historical circumstances similar to a reference scenario can be found and used to predict the taxi time. Experimental verification using Shanghai Pudong International Airport data shows that the model achieves better performance than the state-of-the-art variants for taxi-out time prediction accuracy. More importantly, similar historical scenarios can provide insights into the uncertainty quantification of aircraft taxi time and serve as a basis for controllers to make decisions. The methodology enriches decision support tools and enhances airports’ refined management capabilities.

Air travel demand forecasting based on big data: A struggle against public anxiety.

It is of great significance to accurately grasp the demand for air travel to promote the revival of long-distance travel and alleviate public anxiety. The main purpose of this study is to build a high-precision air travel demand forecasting framework by introducing effective Internet data. In the age of big data, passengers before traveling often look for reference groups in search engines and make travel decisions under their informational influence. The big data generated based on these behaviors can reflect the overall passenger psychology and travel demand. Therefore, based on big data mining technology, this study designed a strict dual data preprocessing method and an ensemble forecasting framework, introduced search engine data into the air travel demand forecasting process, and conducted empirical research based on the dataset composed of air travel volume of Shanghai Pudong International Airport. The results show that effective search engine data is helpful to air travel demand forecasting. This research provides a theoretical basis for the application of big data mining technology and data spatial information in air travel demand forecasting and tourism management, and provides a new idea for alleviating public anxiety.

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.

The Impact of Airport Facility Service Quality on Brand Experience and Passenger Satisfaction: Considering the Mediating Role of Brand Engagement

In the past decade, as more and more passengers choose to fly on trips, China’s airport infrastructure construction has achieved world-renowned achievements. Despite the growing opportunities and demands for using brand research to assist airport industry services in improving, few studies have investigated the impact of service quality in terminal facilities on brand due to the diversity of service. This study uses structural equation models based on empirical research to explore the impact of facility service quality, including processing facility and non-processing facility, on airport brand experience and passenger satisfaction. This study also aims to assess the mediating effect of brand engagement on the relationship between facility service quality, brand experience, and passenger satisfaction. At the same time, this study also uses importance–performance map analysis (IPMA) to find specific items influencing brand engagement. The sampling method used a random sampling approach, with a total of 186 questionnaires distributed at Shanghai Pudong International Airport for data analysis. The results show that airport facility service quality is significant for brand engagement and experience, as well as for satisfaction, especially for processing facilities. In addition, the IPMA results show that facility services involved in the check-in process is of high importance, which requires more attention from managers. Overall, the findings of this study extend the understanding of service quality, brand engagement, brand experience, and passenger satisfaction in the context of an international airport, and they offer implications for Shanghai Pudong International Airport regarding the improvement of its facilities and brand.

The Relationship between Urban Public Art and Regional Environment Based on Wireless Network Technology.

The relationship between urban public art and regional environment based on wireless network technology is studied. Combining domestic specific cases and referring to sociology, art, and design thinking, combing about the relationship between the city's public art and the regional environment, and further integration of its relationship components, at the same time, the planning study of Changning district, Shanghai, and the actual operation of the Shanghai Pudong International Airport, which incorporates the regional mesolevel background, are examples for case analysis. In reference cases and practical cases, they are deduced in sequence, starting from the environmental context of urban areas to the evolution model of public art. Moreover, the specific event background also constitutes a special opportunity period for the sudden growth of public art.

Managing the Gate Assignment Problem in the Hub Airport with Satellite Halls: A Transfer Demand-Oriented Approach.

This study focuses on managing the gate assignment in the hub airport with both main terminal and satellite halls. We first formulate the gate assignment problem (GAP) as a binary linear programming model with multi-objective functions, where the practical constraints, e.g., gate time conflict and gate compatibility, are considered. Then, we incorporate the impact of gate assignment on transfer passengers and formulate the transfer demand-oriented gate assignment problem (TGAP) as a nonlinear model. A linearization approach and a heuristic approach are designed to solve the TGAP model. A case study is conducted based on the practical data of the Shanghai Pudong International Airport, where a comparison between the results of GAP and TGAP by the two proposed approaches is demonstrated. It shows that the proposed TGAP model and solution approaches can not only enhance the service for transfer passengers but also improve the gate utilization efficiency in the hub airport.

Ground Deformation Pattern Analysis and Evolution Prediction of Shanghai Pudong International Airport Based on PSI Long Time Series Observations

Being built on the reclamation area, Shanghai Pudong International Airport (SPIA) has been undergoing uneven subsidence since the beginning of its operation in 1999. In order to explore the evolution characteristics of ground deformation in the SPIA reclamation area and further provide assurance for the airport’s safe operation, 141 Sentinel-1A images from October 2016 to September 2021 were selected to acquire time-series ground deformation observations by the StaMPS PSI processing procedure. We subsequently built a ground deformation prediction model using the Long Short Term Memory (LSTM) neural network for the short-term prediction of the SPIA deformation severity area. On this basis, the spatial-temporal evolution trends of SPIA ground deformation in the reclamation area were revealed concerning the influence and mode of action of geological conditions and environmental factors. Finally, we proposed targeted recommendations and strategies for the comprehensive ground deformation prevention and control needs of SPIA. The results indicated that the SPIA exhibits overall subsidence in the eastern part, with the maximum deformation rate reaching −57.29 mm/a. Meanwhile, the central and western part has a local uplift with the maximum deformation rate reaching 32.76 mm/a. The proposed LSTM ground deformation prediction model demonstrated excellent robustness in the region of uneven deformation, and the prediction results were in high agreement with the StaMPS PSI monitoring results. The time-series observations and prediction results are expected to provide references for the expansion project of SPIA and help the research of ground deformation and prevention in related fields.

Evaluation of the Elements of Ageing-Friendly Service Level for the Smart Construction of Major Hub Airports in China

The smart construction of hub airports is generally mature, but there are differences in ageing-friendly services. Based on the DEMATEL-AHP method, this paper analyzes the influencing factors and differences in the development level of ageing-friendly services in four major hub airports in China (Beijing Daxing International Airport, Shanghai Pudong International Airport, Guangzhou Baiyun International Airport, and Shenzhen Baoan International Airport). The paper finds that: the indicators of the degree of full passenger service in the four major hub airports have the highest influence value as active elements within the ageing-friendly service level indicator system, while full escort service and special passenger or caring service counters are most likely to be influenced by other indicator elements; the degree of full passenger self-service, priority travel service access, and baggage check-in security system or manual check-in service are the core elements that influence the ageing-friendly level the core elements that influence the improvement of the ageing-friendly service level at hub airports determine the trend direction of the overall ageing-friendly service level; immigration processing services and hand luggage security check information management system service are mostly shown as the cause elements; the service scope covered by the ageing-friendly service content at hub airports has little difference, but the emphasis of the ageing-friendly service is different.

A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. And then, based on mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by air passenger throughput data of the Shanghai Pudong International Airport (PVG), China. The experimental results prove MI-SVR model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on MI. The main findings are considered instrumental to the airport operation and air traffic optimization.

Research on Airport Scheduling of FGAP Multi-Objective Programming Model Based on Uncertainty Theory

Based on the current background of airport management and flight-gate scheduling in China, this paper takes Shanghai Pudong International Airport’s flight number of the rising and landing aircraft in a certain day as the research object, and it establishes an uncertain FGAP (Flight-Gate Assignment Problem) multi-objective programming model under the framework of uncertainty theory. Using genetic algorithm to solve the model, the specific flight-gate assignment scheduling plan is given. The research results show that the model in this paper can effectively alleviate the problems, such as unbalanced flight-gate allocation and excessive operating pressure of a single gate, in the conventional model, and make the allocation and scheduling more reasonable and efficient. Finally, we give the future application of uncertainty theory in finance and management, as well as the prospect of combining it with symmetry in physics.

Trajectory clustering for arrival aircraft via new trajectory representation

Trajectory clustering can identify the flight patterns of the air traffic, which in turn contributes to the airspace planning, air traffic flow management, and flight time estimation. This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation. The proposed method consists of four significant steps: representing the trajectories, grouping the trajectories based on the new representation, measuring the similarities between different trajectories through dynamic time warping (DTW) in each group, and clustering the trajectories based on k-means and density-based spatial clustering of applications with noise (DBSCAN). We take the inbound trajectories toward Shanghai Pudong International Airport (ZSPD) to carry out the case studies. The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns, but also improve the performance of flight time estimation.