Machine learning

Deep learning

Data mining

Beijing

Computer science

Political science

China

Aviation

Channel (broadcasting)

Aerospace engineering

Key (lock)

Synchronization (alternating current)

Computer network

Air transport

Computer security

Artificial neural network

Real-time computing

Engineering

Artificial intelligence

Transport engineering

Transportation Research Part E Logistics and Transportation Review

This study analyzes high-dimensional data from Beijing International Airport and presents a practical flight delay prediction model. Following a multifactor approach, a novel deep belief network method is employed to mine the inner patterns of flight delays. Support vector regression is embedded in the developed model to perform a supervised fine-tuning within the presented predictive architecture. The proposed method has proven to be highly capable of handling the challenges of large datasets and capturing the key factors influencing delays. This ultimately enables connected airports to collectively alleviate delay propagation within their network through collaborative efforts (e.g., delay prediction synchronization).

Flight delay prediction for commercial air transport: A deep learning approach