Flight Schedule Research Articles

Development of a mathematical model for managing schedule delays in air traffic operations

The object of this research is delay in air traffic operations. The problem in this research that must be solved is how to reduce the impact of frequent delays which cause time efficiency but cause increased operational costs and make customers dissatisfied with air traffic services and then there is time complexity which is difficult to overcome. The interpretation of this research is to analyze existing problems and then apply mathematical methods so that it is possible to develop a model that is able to dynamically optimize flight rescheduling which can be beneficial for customers in reducing waiting times. This model will consider many important variables in managing delay schedules including real-time weather conditions, aircraft availability, airport capacity so that the results of this model show the ability to reduce the frequency and duration of delays which can increase customer satisfaction. This application shows that the model developed has main characteristics such as flexibility in adjusting schedules in terms of delays and accuracy in predicting potential delays so that the problems analyzed and researched can be resolved effectively and efficiently. This model can predict schedule delays with an accuracy level of 90 % according to predetermined input variables. Then there are quantitative benefits in the form of reducing operational costs for delays, increasing prediction accuracy and optimizing flight schedules. Qualitatively there are benefits in customer satisfaction and faster and more effective decision making. The scope of this research includes managing flight schedules at airports and international hubs. Implementation of this model is important to ensure high operational efficiency and minimize the impact of delays in various operational conditions

Analysis of Airport Operation Control Center (AOCC) Unit Services at Adi Soemarmo Boyolali International Airport

Airport Operation Control Center (AOCC) is the front line in supervising and controlling all problems and is the control center of Adi Soemarmo Solo International Airport operations in serving Airport Service Users. AOCC involves all stakeholders at the Airport by integrating the systems owned by each stakeholder so that they can operate effectively and efficiently. This study aims to determine how the AOCC unit service system is at Adi Soemarmo Boyolali International Airport and what factors can cause problems and solutions to AOCC unit service problems. This study uses a descriptive qualitative approach. The data used are primary and secondary data. Primary data is obtained from observation and interviews, while secondary data comes from literature studies and photographs related to the problems studied. The data analysis techniques used are data reduction, data presentation and drawing conclusions. To test the validity of the data, triangulation techniques are used. The research time is in August - September 2023. The results of this study indicate that a good service system was found by the Airport Operation Control Center (AOCC) Unit to service users starting from coordination with all parties, namely Airlines and Ground Handling, Air Traffic Control (ATC), Apron Movement Control (AMC), Information Unit, Aviation Security, Airport Operation Landside And Terminal (AOLT), Traffic Administration Office and Central Flight Schedule Office (CFSO) and the Airport Mechanical Unit. The service system uses the ACDM (Airport Collaborative Decision Making) system, namely with collaboration between stakeholders to share information between related parties in real time and make joint decisions in a win-solution manner. Factors that can cause problems are related to coordination between one and another so that it can harm several parties. The solution to various problems is to implement ACDM (Airport Collaborative Decision Making), where related units in airport operations synergize with each other, receive and share information from each unit, make proactive and appropriate joint decisions, anticipate unexpected events and provide the best quality of service, cohesiveness and unity of vision and mission.

The study on the Internet of Things implication in improving the punctuality of air transportation in bad weather

Air travel is a popular choice for people who seek to travel outside in the present. It greatly promotes communication and economic development. However, flight delays can have seriously disappointing effects on various aspects. For passengers, they not only need a long time to wait and additional expenses, but they also experience more psychological stress. And airlines face economic losses, reduced customer satisfaction, and disruptions in flight scheduling, which further affect operational efficiency. Therefore, this paper aims to explore potential methods that are combined with the Internet of Things to improve the punctuality of air transportation in order to ease the hard situation of flight delays caused by bad weather. And based on the previous related papers, the author of this paper thinks a kind of meteorological monitoring and early warning system can be built based on the Internet of Things to improve real-time weather monitoring and flight scheduling, as well as ground service optimization. Hence, people can improve the punctuality of air transportation by optimizing resource utilization and cost management so that the whole industry can bring significant economic benefits to airline companies and improved travel experiences to passengers.

Optimization of multi-path approach flight sequencing based on rolling horizon control

Arrival flight sequencing optimization is an effective method to improve the landing efficiency of incoming flights and reduce flight delays. Based on this, with the goal of maximizing landing efficiency, combined with multi-runway and multi-route selection, and considering the actual waypoint restrictions, a mixed integer programming model for multi-path and multi-runway integrated arrival flight sequencing optimization is proposed. In order to solve the real-time problem of large-scale flight sequencing calculation, a multi-waypoint rolling horizon control algorithm is proposed. The terminal area of Guangzhou Baiyun International Airport is used as an example for verification, and the actual arrival flight data is used to carry out calculation experiments. In terms of wake turbulence safety interval, the RECAT-CN operation standard is used. The calculation results show that when the number of small-scale flights (23 flights) is small, the maximum landing time of the proposed model is 55 s earlier than the first-come-first-served method and 271 s earlier than the non-optimized one; when the number of large-scale flights (104 flights) is large, the solver alone cannot find a feasible solution within 3600 s, and the proposed algorithm finds a solution within 128.65 s. The proposed model and algorithm are effective and can be applied to actual flight scheduling optimization.

Flight Arrival Scheduling via Large Language Model

The flight arrival scheduling problem is one of the critical tasks in air traffic operations, aiming to ensure that the flight arrive in the correct sequence safely. Existing methods primarily focus on the terminal area and often overlook the presence of training flight at the airport. Due to the limited generalization of traditional methods and varying control practices at different airports, training flight at airports still rely on manual control for arrival sorting. To effectively address these issues, we propose a novel method for slot allocation that leverages the strong reasoning capabilities and generalization potential of large language models (LLMs). Our method conceptualizes the dynamic scheduling problem for training flight as a language modeling problem, a perspective not previously explored. Specifically, we represent the allocator’s inputs and outputs as language tokens, utilizing LLMs to generate conflict-free results based on a language description of requested landing information and assigned training flight information. Additionally, we employ a reset strategy to create a small dataset for scenario-specific samples, enabling LLMs to quickly learn allocation schemes from the dataset. We demonstrated the capability of LLMs in addressing time conflicts by evaluating metrics such as answer accuracy, conflict rate, and total delay time (without the wrong answer). These findings underscore the feasibility of employing LLMs in the field of air traffic control.

Machine Learning approach in the prediction of Fog: An Early Warning System.

The aviation sector is extremely vulnerable to fog. Thus, accurate fog predictions are essential foraviation sector efficiency, particularly airport management and flight scheduling. Even with numerical weather predictionmodels and guiding systems, fog prediction is challenging. The difficulty of fog prediction is due to the inability to graspthe micro-scale factors that cause fog to form, intensify, augment and dissipate in the boundary layer. This study looks athow well machine learning (ML) tools can predict fog (Visibility <1000 m) and dense fog (Visibility <200 m) at India'sJay Prakash Narayan International Airport (ICAO Index-VEPT), a representative station of the Indo-Gangetic Plains(IGP). The proposed ensemble ML-based model was trained using hourly synoptic data from 2014 to 2020 and testedusing data from 2021 to 2022 (December to February). Once the features are chosen and the forecasters' local knowledgeis taken into account, the dry bulb temperature (°C), dew point temperature (°C), relative humidity (%), cloud amount(octa), wind direction (degrees from true north) and wind speed (knots) are used to build the proposed ML models. MLalgorithms were trained on meteorological data from 1500 to 2200 UTC to predict fog (Visibility <1000 m) and densefog (Visibility <200 m) for the next day at 0000 UTC, with a two-hour lead time. For fog forecasting at 0000 UTC with a 4-hour lead time, ML models were trained with data from 1300 to 2000 UTC and so on. This study evaluates parameter tuning in six level 0 ML models: distributed random forest (DFR), deep learning (DL), gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized tree (XRT), XG Boost and stacked ensemble at level 1. The performance metrics and statistical skill scores indicate that DRF and DL models perform well for lead times of 2 and 4 hours at level 0 for fog (visibility <1000 m) and dense fog (visibility <200 m). But the proposed ensemble models outperform all the base models at level 0 and are recognized as the best instrument for predicting fog at Patna Airport.

AI-Driven FlightOps: Revolutionizing Airline Operations Control with Predictive Analytics and NLP

Abstract: Flight operations control centers (OCCs) are the nerve centers of airline operations, tasked with managing a wide range of activities such as flight scheduling, crew assignments, maintenance coordination, and dealing with disruptions. While traditional approaches have relied heavily on human decision-making and manual analysis of operational data, the increasing complexity of modern airline operations demands more efficient solutions. This paper presents FlightOps AI, a cutting-edge AIpowered system that integrates advanced natural language processing (NLP), deep neural networks (DNNs), reinforcement learning (RL), and computer vision to provide OCC staff with actionable insights and predictive capabilities. The system processes massive amounts of structured and unstructured data in real time, enabling smarter decision-making and automation of routine tasks. By adopting these technologies, FlightOps AI transforms the operational landscape, optimizing airline performance while minimizing human errors

Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study.

Modern ballistocardiography (BCG) and seismocardiography (SCG) use acceleration sensors to measure oscillating recoil movements of the body caused by the heartbeat and blood flow, which are transmitted to the body surface. Acceleration artifacts occur through intrinsic sensor roll, pitch, and yaw movements, assessed by the angular velocities of the respective sensor, during measurements that bias the signal interpretation. This observational study aims to generate hypotheses on the detection and elimination of acceleration artifacts due to the intrinsic rotation of accelerometers and their differentiation from heart-induced sensor accelerations. Multimodal data from 4 healthy participants (3 male and 1 female) using BCG-SCG and an electrocardiogram will be collected and serve as a basis for signal characterization, model modulation, and location vector derivation under parabolic flight conditions from µg to 1.8g. The data will be obtained during a parabolic flight campaign (3 times 30 parabolas) between September 24 and July 25 (depending on the flight schedule). To detect the described acceleration artifacts, accelerometers and gyroscopes (6-degree-of-freedom sensors) will be used for measuring acceleration and angular velocities attributed to intrinsic sensor rotation. Changes in acceleration and angular velocities will be explored by conducting descriptive data analysis of resting participants sitting upright in varying gravitational states. A multimodal data set will serve as a basis for research into a noninvasive and gentle method of BCG-SCG with the aid of low-noise and synchronous 3D gyroscopes and 3D acceleration sensors. Hypotheses will be generated related to detecting and eliminating acceleration artifacts due to the intrinsic rotation of accelerometers and gyroscopes (6-degree-of-freedom sensors) and their differentiation from heart-induced sensor accelerations. Data will be collected entirely and exclusively during the parabolic flights, taking place between September 2024 and July 2025. Thus, as of June 2024, no data have been collected yet. The data will be analyzed until December 2025. The results are expected to be published by June 2026. The study will contribute to understanding artificial acceleration bias to signal readings. It will be a first approach for a detection and elimination method. Deutsches Register Klinische Studien DRKS00034402; https://drks.de/search/en/trial/DRKS00034402. PRR1-10.2196/63306.

Analisis Penggunaan Helicopter Stand dalam Menunjang Keamanan dan Keselamatan Penerbangan di Bandar Udara Tjilik Riwut Palangka Raya

Tjilik Riwut Palangka Raya Airport is one of the main gateways in Central Kalimantan province. In 2023, the airport will serve around 40,000 passengers, indicating an increase in aviation activity that requires high efficiency in facility management. Helicopters at Tjilik Riwut Airport have an important role in various operations, including medical evacuation, goods delivery and government missions. Currently, helicopters only land in regular commercial aircraft parking areas, which are not specifically designed for helicopter needs. This can cause a buildup of activity, disrupting flight schedules. This research aims to analyze the needs and benefits of building a Helicopter Stand at Tjilik Riwut Airport, as well as assessing its impact on the smooth running of flights. This research uses qualitative methods and the types of data used are primary data and secondary data. The data collection techniques used in this research are observation, interviews and documentation. By using analytical techniques in the form of data reduction, data presentation and conclusions. Testing the validity of the data used in this research is technical triangulation and source triangulation. The results of this research show that the helicopter stand facilities at Tjilik Riwut Airport are currently not fully adequate, it is necessary to reorganize the area to ensure clearer separation between helicopters and commercial aircraft in order to reduce the potential for accidents and avoid the potential for overlapping with other aircraft in the parking stand area.

Flight Schedule Optimization Considering Fine-Grained Configuration of Slot Coordination Parameters

In response to the rapid growth of air passenger and cargo transportation services and the sharp increase in congestion at various airports, it is necessary to optimize the allocation of flight schedules. On the basis of reducing the total airport delay time and ensuring the total deviation of flight schedules applied by airlines, it is necessary to consider finely configuring flight schedules with slot coordination parameters, introducing a 5 min slot coordination parameter, and optimizing airport flight schedules in different time periods. This article considers factors such as flight schedule uniqueness, corridor flow restrictions, and time adjustment range limitations to establish a three-objective flight-schedule refinement configuration model, which is solved using the NSGA-II algorithm based on the entropy weight method. Taking Beijing Capital International Airport as an example, the optimized results show that the total flight delay was reduced from 4130 min to 1142 min, and the original delay of 389 flights was reduced to 283 flights. Therefore, flight schedule optimization considering the fine-grained configuration of slot coordination parameters can effectively reduce airport delays, fully utilize time resources, and reduce waste of time slot resources.

Energy-Efficient Flight Scheduling and Trajectory Optimization in UAV-Aided Edge Computing Networks

Energy-Efficient Flight Scheduling and Trajectory Optimization in UAV-Aided Edge Computing Networks

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.

Exploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective

China's rapid expansion of civil aviation has led to an increase in pollution-related issues, causing adverse health effects on populations near airports and downwind. Accurately quantifying aviation emissions is essential for effective emission management. Here, we developed a high-resolution aviation emissions inventory for China by employing a bottom-up approach that relied on daily flight schedules. By using the Aeronautical Information Publication (AIP) to reproduce real-world flight routes rather than conventional great-circle routes, we improved the accuracy of emissions and investigated the potential for reducing these emissions. Our findings demonstrated substantial variations in domestic civil aviation emissions both spatially and temporally. Emissions peaked in most provinces during Chinese holidays, particularly the Chinese Lunar New Year and summer holidays, highlighting the importance of detailed activity data for accurate emissions calculations. Therefore, we recommend extensive utilization of real-world flight routes, particularly in areas with limited Automatic Dependent Surveillance-Broadcast (ADS-B) coverage since they provide more accurate representations of actual flight trajectories. Our study also identified regions like Shaanxi, Sichuan, Beijing, and their surroundings having considerable potential for emission reduction due to substantial deviations from great-circle routes. This approach can enhance the accuracy and spatiotemporal resolution of aviation emissions at national and global scales throughout the year, without relying on extensive, long-term real-time flight trajectories. Additionally, it provides a unique way to quantify the potential for emission reductions across provinces in civil aviation, ultimately contributing to mitigating pollution-related health impacts from aviation emissions and promoting a more sustainable aviation industry.

Evaluating Airline Passengers’ Satisfaction during the COVID-19 Pandemic: A Case Study of AirAsia Services through Sentiment Analysis and Topic Modelling

AirAsia has emerged as a dominant force among prominent low-cost airlines in recent years. However, the COVID-19 pandemic outbreak has severely impacted airline services, including AirAsia. There is a strong need for airline services to monitor customer experience and satisfaction from online customer reviews on the website to keep pace with changing customer perceptions toward their service quality. A growing number of travelers choose to express their experiences and emotions on online customer review platforms, resulting in substantial online airline service evaluations. The research analyzes 796 online customer reviews from Skytrax, a well-known online airline review website. The information hidden in customer-generated reviews is analyzed with the text mining technique, including topic modeling and sentiment analysis. The research uses the Latent Dirichlet Allocation (LDA) model for topic analysis and the Valence Aware Dictionary for Sentiment Reasoning (VADER) model for sentiment analysis. The sentiment ratio for AirAsia’s online reviews is approximately 59% positive and 41% negative. Only four reviews are neutral. The findings indicate that the online review of AirAsia has a greater proportion of positive sentiments than negative sentiments. In addition, the topic modeling shows hidden topics with the top high-probability keywords concerned with interior and seat, baggage, online service, staff service, flight schedule, and refund. The research demonstrates using sentiment analysis and topic modeling on customer review data as a more thorough alternative to survey-based models for researching airline service. The research contributes to the methodological advancements in text mining analysis and expands the current knowledge of customer review data.

Evaluating high-resolution aviation emissions using real-time flight data

Amidst robust global economic growth and advancing globalization, the aviation market is poised for significant expansion. Consequently, the environmental impact of aviation emissions is growing in significance. However, due to limitations in real flight data and aviation emissions index models, further clarification of the emission characteristics throughout entire flights is necessary. To better assess the emission characteristics of entire flights, this study employs real Quick Access Recorder (QAR) data and a high-precision aviation emissions index model, yielding four-dimensional emission data (time, longitude, latitude, altitude) from flights. The analysis compares QAR data with emissions from scheduled flight data (SFD) and Broadcast Automatic Correlation Monitoring (ADS-B) projections, explores seasonal variations in aviation emissions, and assesses the impact of sustainable aviation fuels (SAFs) on emissions reductions. For both number and mass of nvPM emissions, as well as nitrogen oxide emissions, the rankings are: ADS-B-E > SFD-E > QAR-E; for CO, SFD-E > ADS-B-E > QAR-E, particularly during the climb-cruise-descent (CCD) cycle. There are significant differences in the emission of aviation pollutants in airport area and high-altitude area in different seasons. Employing four types of Sustainable Aviation Fuels (SAFs) significantly reduces both the mass and the number of nvPM emissions. Therefore, it is recommended to utilize more QAR data to refine the assessment of the environmental impact of aviation emissions.

Research on Irregular Flight Recovery Strategy Under Different Flight Route Types With Big Data Computing

During periods of extreme weather conditions, airport closures, or other unforeseen circumstances, air-lines frequently encounter disruptions in their flight schedules, posing flight recovery as a recurrent operational challenge. This paper aims to explore the impact of passenger transfer costs on flight recovery across various route types. Drawing on established methods used to calculate aircraft maintenance and crew assignment costs, this study utilizes an improved large-scale neighborhood search algorithm to perform a large number of calculations on a realistic dataset of airlines, meticulously analyzes the amalgamation of passenger transfer costs across different route types and proposes tailored strategies to mitigate disruptions. Through simulation experiments, the research evaluates the influence of various passenger transfer methods on flight recovery across diverse route types, with the goal of furnishing airlines with more adaptable and targeted decision support.

Analisis Pencapaian Ground Time Pesawat Airbus A350-900 Maskapai Singapore Airlines Terhadap Kinerja Petugas Ramp Handling di Bandar Udara Internasional Juanda Surabaya

Ground time is one of the important things in handling aircraft while in the apron area and Ramp handling officers are responsible for block on to block off activities and play an important role in improving service functions in the apron area. Ground time that does not comply with company standards can affect flight schedules and operational costs for an airline. This research aims to analyze the ground time achievements of the Airbus A350-900 aircraft on the performance of ramp handling officers. The method used in this research is a quantitative approach with data collection techniques, namely observation and interviews. The data analysis technique uses SPSS 25 which includes descriptive analysis by processing primary data in the form of ground time records for the Airbus A350-900 aircraft for two months which has been adjusted to secondary data, namely company documents to determine the average (mean) ground time each month.Based on the results of descriptive analysis to find out the average ground time, the results obtained in August were 55.53 minutes and in September it was 55.93 minutes, meaning that in general the ground time was still achieved and the performance of the ramp handling officers was very good because they reached the ground time on time. . If ground time is looked at based on the daily schedule, there are still several delays caused by several factors, namely delays in the arrival of planes from the origin airport to the destination airport, weather factors and delays for passengers with baggage.

Sustainability with Limited Data: A Novel Predictive Analytics Approach for Forecasting CO2 Emissions

AbstractUnforeseen events (e.g., COVID-19, the Russia-Ukraine conflict) create significant challenges for accurately predicting CO2 emissions in the airline industry. These events severely disrupt air travel by grounding planes and creating unpredictable, ad hoc flight schedules. This leads to many missing data points and data quality issues in the emission datasets, hampering accurate prediction. To address this issue, we develop a predictive analytics method to forecast CO2 emissions using a unique dataset of monthly emissions from 29,707 aircraft. Our approach outperforms prominent machine learning techniques in both accuracy and computational time. This paper contributes to theoretical knowledge in three ways: 1) advancing predictive analytics theory, 2) illustrating the organisational benefits of using analytics for decision-making, and 3) contributing to the growing focus on aviation in information systems literature. From a practical standpoint, our industry partner adopted our forecasting approach under an evaluation licence into their client-facing CO2 emissions platform.

A Method for Air Route Network Planning of Urban Air Mobility

Urban air mobility is an effective solution to address the current issue of ground traffic congestion in future cities. However, as the user scale continues to expand, the current civil aviation flight scheduling and control methods are becoming inadequate to meet the high-volume flight guarantee demands of future urban air transportation. In order to effectively handle and resolve potential issues in this field in the future, this paper proposes a method for planning urban air mobility route networks. The planning process is divided into two stages: construction and optimization. Methods for constructing urban air mobility route networks based on flight routes and global optimization methods based on node movement are proposed in each stage. In the construction stage, a complete construction process is designed to generate routes based on existing flight routes, in line with the trend of urban air transportation development. In the optimization stage, inspired by the ant colony algorithm, node transfer rules and information transfer rules are incorporated to design a global optimization process and algorithm for route networks. Experimental results demonstrate the effectiveness and advancement of the proposed planning method.

Airline safety: Still getting better?

We consider worldwide aviation safety on scheduled passenger flights over the years 2018–22, distinguishing the 34 weeks during the Covid-19 pandemic from the prior 26 weeks. Although the pandemic caused convulsions in airline operations, it caused no deviation from the trend under which global passenger death risk from accidents and deliberate acts dropped by about 7% per year. The nations of the world continued to differ substantially in passenger mortality risk, though nations in the previous “intermediate risk” category performed slightly better than those in the earlier “lowest risk” group. The difference, however, fell far short of statistical significance. Consistent with the previous pattern, passenger death risk in the “higher risk” nations was more than an order of magnitude higher than that in other nations. The transmission of Covid-19 aboard commercial flights may have taken thousands of lives worldwide over 3/20-12/22, which would have considerably exceeded passenger deaths over that period caused by accidents and deliberate attacks. However, this increase could be considered a transient effect if the pandemic is essentially over as of the mid-2020's.