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

Priya Jindal et al.

International Journal of Services Operations and Informatics2025https://doi.org/10.1504/ijsoi.2025.152349article
AJG 1
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijsoi.2025.152349

Or copy a formatted citation

@article{priya2025,
  title        = {{Innovative airport solutions: using AI, machine learning, and robotics for optimised ground handling and passenger experience}},
  author       = {Priya Jindal et al.},
  journal      = {International Journal of Services Operations and Informatics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijsoi.2025.152349},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.