  Next location prediction is a discipline that involves predicting a users
next location. Its applications include resource allocation, quality of
service, energy efficiency, and traffic management. This paper proposes an
energy-efficient, small, and low parameter machine learning (ML) architecture
for accurate next location prediction, deployable on modest base stations and
edge devices. To accomplish this we ran a hundred hyperparameter experiments on
the full human mobility patterns of an entire city, to determine an exact ML
architecture that reached a plateau of accuracy with the least amount of model
parameters. We successfully achieved a reduction in the number of model
parameters within published ML architectures from 202 million down to 2
million. This reduced the total size of the model parameters from 791 MB down
to 8 MB. Additionally, this decreased the training time by a factor of four,
the amount of graphics processing unit (GPU) memory needed for training by a
factor of twenty, and the overall accuracy was increased from 80.16% to 82.54%.
This improvement allows for modest base stations and edge devices which do not
have a large amount of memory or storage, to deploy and utilize the proposed ML
architecture for next location prediction.
