This document summarizes a project to optimize passenger waiting times in elevators using machine learning. Data was collected from elevators and analyzed, revealing subgroups that could be improved by establishing parking floors. Machine learning models were developed and one was implemented, achieving an 8% reduction in waiting times for hotel elevators. Further improvements to the models are planned, along with expanding the use of machine learning to other applications like predictive maintenance and demand forecasting.

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Optimization of passengers
waiting time in elevators
using ML
Delio Tolivia
R&D&i, Talento Corporativo

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Company

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Business lines
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Project background

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Background
Internet

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Project

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Project
IALift
Optimization of passengers
waiting time in elevators using ML

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Project

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Project
• Data: We got data from the elevators
• Service: Thyssenkrupp wants to improve their services
• Prediction: We can use ML to predict
• Waiting time: The time that an user of the elevator waits
untill it arrives.
Can we improve the waiting time and make an “intelligent” elevator?

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Project
Data Exploration
We have taken data from 5 street elevators and a
group of 2 elevators from an hotel.
On the Street elevators we can see
two groups (call count/seconds wait):
• Four of them are very similar
(1,2,3,4)
• One of them has 4 floors and it´s
behaviour is quite different from
the others (5)

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Have aslo divides the Street elevator group into two
more subgroups (1,4 and 2,3)
Data Exploration

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In one of the subgroups (2,3) we have detected that all
calls came from the same floor
Data Exploration

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For the another subgroup (1,4) we realized that the
elevator was receiving the 90% of the calls from the
same floor
Data Exploration

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The elevator whose behaviour is different (5) is more
difficult to model
Data Exploration

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We have also studied the time distribution by hour, day, week.
Domingo 3 Domingo 10
Domingo 24
Domingo 17
Data Exploration

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Taken all previous into account… what have we done
to solve the problem and improve the tk elevators
system?
• In the subgroup (2,3): we decided to establish a “parking” at floor 0
• In the subgroup (1,4) we also decided to establish a “parking” at floor 0
• In the more complicated one (5) we decided to make an ML model

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Project
Results:
• Thanks to the data analysys carried out we detected a malfunction in the
subgroup of elevators (2,3) -> Success
• We have achive an improvement of 12% in terms of reducing waitting time
for the other subgroup (1,4). The energy measures confirm that with the
new parking system the elevators consume the same-> Success
Data exploration is very important!

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Project
ML Model (I):
• We have taken the data, and have created some models using
OptiML. We keep on working and have created some new variables to
improve the models:
ü SecondsWait_1: The seconds that the user have wait in the previous call.
ü last_hour_calls_2: Number of calls from the second floor in the last hour (It is the
floor with the largest number of calls)
ü last_three_minutes_calls_0: Number of calls from the floor 0 in the last 3 minutes
We have also added data from work calendar
and meteorology o improve models OPTIML

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ML Model (II):
The best model was an ensemble (We focus on accuracy)

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Implementation:
Whe have put into production a docker containing an API. The elevator
control application make calls trough this API to get the probability of
each floor for the next call so the system can choose the best option to
move the elevator.

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Results:
• At the time we were going to test the model, the elevator was out of
service, so we couldn´t use it. That´s when we decided to use the
same type of model and variables for the hotel elevators. We have
taken the new data, have trained the model and have tried it in the real
installation.
• We achive an 8% of waiting times improvement!.
• However this model isn't perfectly designed for this group of elevators
and we would love to improve it by calculating, adding and testing
new variables.

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What´s next?

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Forthcoming use cases
We keep on working with the elevators but we also have
another use cases:
• Predictive Maintenance (PdM)
• Production planning
• Quality control
• Demand forecasting
• Etc.
• .
What’s next?:

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Thank you!