Automatic building detection and classification using elevator/escalator/stairs modeling-user profiling
A system, a method and a computer program product are provided to determine user profile of one or more users in one or more buildings, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain mobility features associated with the one or more buildings in a geographic region. The processor may be configured to determine using a trained machine learning model, one or more transport modes for the one or more buildings. The processor may be further configured to obtain indoor mobility data of the one or more users based on the one or more transport modes for the one or more buildings. The processor may be further configured determine the user profile of the one or more users in the one or more buildings.
1. A system to determine a user profile of one or more users in one or more buildings, comprising:
at least one memory configured to store computer executable instructions; and
at least one processor configured to execute the computer executable instructions to:
obtain a plurality of mobility features associated with the one or more buildings in a geographic region based on sensor data captured by one or more sensors of one or more mobile devices,
wherein the plurality of mobility features comprises at least one of one or more vertical mobility signals, or one or more transport mode usage;
determine, using a trained machine learning model, one or more transport modes for the one or more buildings, based on the plurality of mobility features;
obtain indoor mobility data associated with the one or more users based on the one or more transport modes for the one or more buildings; and
determine, using the trained machine learning model, the user profile of the one or more users in the one or more buildings based on the indoor mobility data.
2. The system of claim 1 , wherein the user profile of the one or more users comprises a health aware user, a user with a stroller, a recurrent visitor and a new visitor.
3. The system of claim 1 , wherein the indoor mobility data indicates user preferred commuting modes within a building.
4. A method to determine a user profile of one or more users in one or more buildings, comprising:
obtaining a plurality of mobility features associated with the one or more buildings in a geographic region based on sensor data captured by one or more sensors of one or more mobile devices,
wherein the plurality of mobility features comprises at least one of one or more vertical mobility signals, or one or more transport mode usage;
determining, using a trained machine learning model, one or more transport modes for the one or more buildings, based on the plurality of mobility features;
obtaining indoor mobility data of the one or more users based on the one or more transport modes for the one or more buildings; and
determining, using the trained machine learning model, the user profile of the one or more users in the one or more buildings based on the indoor mobility data.
5. The method of claim 4 , wherein the user profile of the one or more users comprises a health aware user, a user with a stroller, a recurrent visitor and a new visitor.
6. A computer program product comprising a non-transitory computer readable medium having stored thereon computer executable instructions, which when executed by one or more processors, cause the one or more processors to carry out operations to determine user profile of one or more users in one or more buildings, the operations comprising:
obtaining a plurality of mobility features associated with the one or more buildings in a geographic region based on sensor data captured by one or more sensors of one or more mobile devices,
wherein the plurality of mobility features comprises at least one of one or more vertical mobility signals, or one or more transport mode usage;
determining, using a trained machine learning model, one or more transport modes for the one or more buildings, based on the plurality of mobility features;
obtaining indoor mobility data of the one or more users based on the one or more transport modes for the one or more buildings; and
determining, using the trained machine learning model, the user profile of the one or more users in the one or more buildings based on the indoor mobility data.
7. The computer program product of claim 6 , wherein the indoor mobility data indicates user preferred commuting modes within a building.