Airplane weight prediction has a big impact on efficient flying. Bit teamed up with a multinational airline company to prototype an AI model that accurately predicts the total weight of all passengers.

Challenge

Improve the prediction of airplane weight

Challenge

The sustainability goals of the aviation sector ask for innovative ways to reduce CO2. One of the routes, is improving the alignment of fuel, speed and flying height to the weight of the airplane. The better the weight is predicted, the better all parameters can be calculated. Passengers weight is the only weight variable not being measured, but estimated by calculating the sum of the average weight of men, women and children on board. This means the biggest improvement can be obtained by reducing the inaccuracy of the passengers weight estimate. Our challenge was to improve the prediction of passengers’ weight without disturbing the boarding process or violating people’s privacy.

Outcome

670

people voluntarily recorded

20.100

frames of data

15

hours of data collection

Hypothesis

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Predict passengers’ weight using a depth sensor

To find out someone’s weight without using a weighing scale, we assumed the best route would be to predict a person’s body volume with a depth sensor. Research has shown that the use of this technology enabled the predictions of one’s BMI by analyzing a face. Our reason to believe there would be equal possibilities in the area of weight prediction.

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Research and prototyping

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Transforming a 3D human model into AI data

We started the prototyping phase by collecting weight and visual data on the airport. The main challenge in creating the prototype was how to transform the camera images into data to train our AI model. We created features based on the images, like length, age and volume. We tried and tested until we came to a model that predicts weight relatively well.

Bit_prototyping

Outcome

Bit created and trained an AI model that reduced weight deviation by 88%. The prototype shows there is potential in using computer vision to improve weight calculation, leading to reduced CO2 emission and six figure business cost savings.

AI model

Reduced weight deviation of 88%

Proven potential

Computer vision can be used for weight calculation

Cost reduction

Estimation of six figure business cost savings