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涉及预测用户下一位置的学科，其应用包括资源分配、服务质量、能源效率和交通管理。本文提出了一种节能、小型且低参数的机器学习架构，用于精确的下一位置预测，可部署在普通基站和边缘设备上。为此，我们对整个城市的完整人类移动模式进行了一百次超参数实验，以确定在达到精度平台的同时使用最少模型参数的最佳机器学习架构。我们成功地将已发表机器学习架构中的模型参数数量从2.02亿减少到200万。这使模型参数的总大小从791 MB减少到8 MB。此外，训练时间缩短至原来的四分之一，训练所需的图形处理器内存减少至原来的二十分之一，且整体精度从80.16%提高到82.54%。这一改进使得内存或存储容量有限的普通基站和边缘设备能够部署和利用所提出的机器学习架构进行下一位置预测。