Conference Agenda

The integration of autonomous vehicles into airport cargo ground operations faces regulatory and other challenges that hinder progress. Testing and deploying emerging technologies at airports (as critical infrastructure) require approvals that are difficult to obtain because airport ground operations are dynamic, largely unstructured, and characterized by mixed traffic. Despite the growing number of prototypes and solutions, many stakeholders remain hesitant to invest, resulting in missed opportunities to improve efficiency.

This paper presents a survey concept for 30 semi-structured interviews with international aviation experts, focusing on processes, technologies, safety and security, economics, and sustainability. The interviewees are members of a working group contributing to the revision of IATA’s Airport Handling Manual, Section 908 (AHM908), on autonomous ground support equipment (GSE). They are based in 13 countries across four IATA regions - Americas, Europe, Middle East and Asia-Pacific - and they represent seven IATA organization types - aircraft manufacturers, airports, civil aviation authorities, ground handling service providers, ground support equipment manufacturers, IATA airline members, and solution providers. Data collection and analytics are sensitive to safeguard anonymity and to preserve IATA’s neutrality in policymaking.

Main results will include structured perceptions of relevant stakeholder groups, organized by topic clusters, focus airports, and regulatory scenarios. We will explore strategies, approval processes, best practices, investment calculations, technical capabilities, barriers, and hesitation, among other topics. Several local automation initiatives should be mentioned by interviewees, providing valuable perspectives as well as different shades of uncertainty and hesitation. Our holistic approach contributes a novel perspective to automation research in airport operations. Having exclusive access to such a specialized industry working group allows for in-depth discussions with early movers from carefully selected visionary stakeholder organizations. This work will inform policymakers about frameworks to support the development and implementation of autonomous solutions, aiding adaptation to evolving demands and labor shortages in airport logistics. It is complemented by an exemplary investment calculation concept to explore the technology-innovation-business nexus as a central element linking technology development and innovation with operations and business realities at airports.

Purpose:

This study proposes an explainable machine learning workflow for taxi-in time prediction at Frankfurt Airport to support transparent, data-driven ground operations. Accurate taxi-in time prediction is important for the scheduling of ground operations. Given the persistent labor shortages in this sector avoiding waiting times based on more accurate scheduling optimizes ground crew utilization but also avoids delays at the beginning of the aircraft turnaround.

Prior studies have investigated aircraft taxi times at various airports using ADS-B (Automatic Dependent Surveillance – Broadcast), A-CDM (Airport Collaborative Decision Making) and AODB (Airport Operational Databases) data. Most of the studies thereby focus on the taxi-out-times or the full turnaround from the perspective of an airline or airport with the objective of reducing delays or maximizing system efficiency. However few studies have predicted taxi-in-times specifically for optimized ground operations.

Methodology:

The study uses ADS-B-based aircraft movement data from Frankfurt Airport, covering approximately 67,000 arrival events between July 2025 and April 2026, combined with operational and contextual information. Compared with prior studies using broader feature sets, the workflow refines the input space to a compact set of 17 meaningful variables by retaining core operational predictors and adding a few relevant new features. This design improves interpretability, reduces redundancy, and relies on information that is practically available around landing, including landing runway, parking position, aircraft type, local traffic load, time-related variables, weather conditions, and airline-related information. Several supervised regression models are trained and compared, including Linear Regression, MLP, Random Forest, XGBoost, LightGBM, and CatBoost. CatBoost achieved the best overall performance and was selected for detailed evaluation. Model performance is assessed using mean absolute error and operationally interpretable accuracy thresholds. A bootstrap-based uncertainty estimation approach is used to estimate prediction confidence, while subgroup analyses identify operational conditions where taxi-in time is harder to predict.

Findings:

The CatBoost model achieves a mean absolute error of about 105 seconds (≈1.75 min). Around 86% of taxi-in events are predicted within 3 minutes and 96% within 5 minutes, showing useful accuracy under real airport operating conditions. However, subgroup analysis reveals substantial variation, suggesting that specific runway-stand combinations and traffic conditions are systematically more difficult to predict. SHAP-based explanation identifies landing runway and parking position as the most influential predictors. In addition to point predictions, the model provides a confidence score for each flight, indicating how reliable the prediction is expected to be.

Originality:

This study contributes to airport surface operations research in two main ways. First, it extends taxi-in time prediction beyond point estimates by introducing a confidence-aware workflow. Each prediction is accompanied by a confidence score that indicates its expected reliability. Second, the study examines prediction difficulty across operational subgroups, making it possible to identify the conditions under which larger errors are more likely. This provides a more operational view of model reliability than aggregate accuracy measures alone. Together with the explainable ADS-B-based modelling approach, these contributions can support practical planning decisions, such as dispatching ground crews to assigned stands under specific runway, parking, and traffic conditions.

In rail systems, maintenance activities in one subsystem may influence incidents in other subsystem. However, these relationships are rarely analyzed using integrated operational and maintenance data. Besides, existing predictive maintenance studies mainly focus on individual components or sensor-based monitoring, while event-based operational data and maintenance decision support remain underexplored. Thus, this research proposes a machine-learning (ML) based predictive maintenance framework for rail systems using integrated event-based operational and maintenance data. The framework processes include data preprocessing, descriptive analysis, feature engineering and predictive modelling, as well as model performance evaluation. Operational incident and maintenance records from an urban railway signalling system consisting of 98 train sets over the period of 2024–2025 was used as a case study in this study. Incident records, maintenance data, and operational information are firstly integrated. Since maintenance and incident records are largely text-based and subjective depending on maintainer interpretation, preprocessing techniques were applied to transform unstructured records into structured and interpretable datasets suitable for machine-learning based analysis and modelling. Furthermore, integrated operational and maintenance-related datasets provide opportunities to investigate hidden relationships and operational dependencies between railway subsystems. Descriptive analysis is subsequently performed to identify recurring operational incident patterns and relationships between maintenance activities and incident occurrences, especially wheel re-profiling activities and overspeed incidents. Linear Regression and several machine learning techniques including Support Vector Regression (SVR), Random Forest, and Gradient Boosting, were applied to build rail-incident forecasting models. Model performance evaluation results showed that Gradient Boosting achieved the best predictive performance, with a Mean Absolute Error (MAE) of 8.14 days and an R-squared (R²) value of 0.82. Moreover, feature importance analysis also indicated that wheel position was the most influential feature in the predictive model. It significantly supported that maintenance-related and event-based operational data contain meaningful information in forecasting cross-system incidents in rail systems for predictive maintenance.