Title: Innovative airport solutions: using AI, machine learning, and robotics for optimised ground handling and passenger experience

Authors: Priya Jindal; Eshika Jain; Pratham Kaushik; Namrata Nanda

Addresses: Chitkara Business School, Chitkara University, Rajpura-140401, Punjab, India ' Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura-140401, Punjab, India ' Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura-140401, Punjab, India ' Lincoln University College, Kuala Lumpur 47301, Malaysia

Abstract: This paper explores how artificial intelligence and robotics can transform airport processes by optimising functions such as baggage handling and check-in using AI models and robotics. Nine machine-learning algorithms were applied. Key findings include a 25% reduction in flight delays using linear regression, a 15% decrease in lost revenue from no-shows with logistic regression, and a 20% reduction in baggage mishandling through decision trees. K-means clustering insights resulted in a 10% increase in ancillary revenue via targeted marketing. Principal component analysis (PCA) accounted for 85% of operational data variance, improving decision-making and reducing predictive maintenance costs by 18% with 92% accuracy and an F1 score of 0.91. Gradient-boosting reduced passenger check-in wait times by 30%. Convolutional neural networks (CNNs) improved security efficiency by 15% with 94% accuracy. Recurrent neural networks enhanced passenger flow forecasting, reducing congestion with a MAPE of 4.2% and an R-squared of 0.79. Overall, AI and robotics show potential.

Keywords: aviation industry; passenger management; baggage handling; AI; artificial intelligence; ML; machine learning; robotics; operational efficiency; cyber security.

DOI: 10.1504/IJSOI.2025.152349

International Journal of Services Operations and Informatics, 2025 Vol.13 No.2/3/4, pp.352 - 384

Received: 14 Oct 2024
Accepted: 17 Jul 2025
Published online: 16 Mar 2026 *

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