NEW YORK : The dreaded last-minute flight delays may soon become a thing of the past, thanks to a new computer model developed by scientists that can more accurately and quickly predict airline delays than traditional networks.
“Our proposed method is better suited to analyse datasets with categorical variables (qualitative variables such as weather or security risks instead of numerical ones) related to flight delays,” said Sina Khanmohammadi, lead author of the study, from Binghamton University in the US.
“We have shown that it can outperform traditional networks in terms of accuracy and training time (speed),” said Khanmohammadi.
Currently, flight delays are predicted by artificial neural network (ANN) computer models that are backfilled with delay data from previous flights.
ANN is an interconnected group of computerised nodes that work together to analyse a variety of variables to estimate an outcome – in this case flight delays – much like the way a network of neurons in a brain works to solve a problem.
These networks are self-learning and can be trained to look for patterns. The more variables an ANN has to process, the more categorical those variables are, and collecting historical data slows down an ANN to make flight delay predictions.
The team, including researchers from State University of New York in the US, introduced a new multilevel input layer ANN to handle categorical variables with a simple structure to help airlines easily see the relationships between input variables (such as weather) and outputs (flight delays).
The research would not eliminate delays, but it will help airlines inform travellers quicker and more accurately about problems, researchers said.
The new model could also help smaller regional airports become more efficient and able to handle more flights per day.
“Airlines can use the proposed method to provide more accurate delay information to the customers, and hence gain customer loyalty,” said Khanmohammadi.
“Air traffic controllers at a busy airport can also use this information as a supplement to improve the management the airport traffic,” said Khanmohammadi.
Researchers trained the new model to pick up on 14 different variables – including day of the week, origin airport, weather and security – that affected arrival times for 1,099 flights from 53 different airports to John F Kennedy airport in New York City.
The new system then predicted delays for hypothetical flights projected to arrive at JFK at 6:30 pm on January 21 from a variety of origins and under a variety of conditions.
The new model predicted the length of delays with about 20 per cent more accuracy than traditional models and required about 40 per cent less time to come to those conclusions.
The study was published in the journal Procedia Computer Science. (AGENCIES)
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