Statistics Means Shorter Elevator Wait Times

October 2, 2020

in A World Without Statistics

Elevators RESIZED

Believe it or not, statistics helps elevators run efficiently

The first computer-controlled machines were created soon after World War II with milling tools developed as an early application of the new technology.

Today, an endless list of devices are run by computer software—from cars to appliances to telephones to airplanes to traffic lights to medical instruments to elevators—using statistical algorithms to guide decisionmaking at their core.

Designing an algorithm that operates a bank of elevators optimally involves knowing the arrival times of people coming to operate them along with the matrix of probabilities that the next operator will request a ride from any particular floor to another at that time of day.

Optimality criteria then involve making the wait-time probability distribution for operators and the energy usage or runtime distribution for the elevator as small as possible (e.g. small expected value, small probability of going above some threshold, etc.).

As the data necessary to fully know this matrix of probabilities is unavailable at the time of installation, methods that include a “learning” component, such as those applying Bayesian updates, are a recent approach.

Without the statistical modeling and computational advances that underlie these algorithms, wait times for elevators and their energy consumption would increase.

Without statistics, we’d have to take the stairs!