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.

翻译：下一位置预测是一门预测用户下一位置的学科，其应用包括资源分配、服务质量、能量效率及交通管理。本文提出了一种节能、小型化且低参数的机器学习架构，用于实现高精度下一位置预测，并可在普通基站和边缘设备上部署。为此，我们针对整个城市的完整人类移动模式开展了数百次超参数实验，以确定在达到准确率稳定水平时所需模型参数最少的机器学习架构。我们成功将已发表机器学习架构中的模型参数数量从2.02亿减少至200万，使模型参数总存储量从791 MB降至8 MB。此外，训练时间缩短为原来的四分之一，训练所需图形处理器内存降低为原来的二十分之一，且整体准确率从80.16%提升至82.54%。这一改进使得内存或存储资源有限的普通基站和边缘设备能够部署并使用所提出的机器学习架构进行下一位置预测。