This paper proposes a deep multi-task learning framework to predict the next location from trajectories that are captured by external sensors (e.g., traffic surveillance cameras, or speed radars). The reported positions in such trajectories are sparse, due to the sparsity of the sensor distribution, and incomplete, because the sensors may fail to register the passage of objects. In this framework, we propose different preprocessing steps to align the trajectories representation and cope with a missing data problem. The multi-task learning approach is based on Recurrent Neural Networks. It utilizes both time and space information in the training phase to learn more meaningful representations, which boosts the learning performance of location prediction. The multi-task learning model, together with the preprocessing step, substantially improves the prediction performance. We conduct several experiments using a real dataset, and they demonstrate the validity of our multi-task learning model in terms of accuracy of 85.20%, which is more than 20% better than using a single-task learning model.

中文翻译：

---

位置预测：从外部传感器数据中进行深度时空学习

本文提出了一种深度多任务学习框架，以根据外部传感器（例如，交通监控摄像头或测速雷达）捕获的轨迹预测下一个位置。由于传感器分布的稀疏性，此类轨迹中报告的位置是稀疏的，并且不完整，因为传感器可能无法记录物体的通过。在这个框架中，我们提出了不同的预处理步骤来对齐轨迹表示并处理缺失数据问题。多任务学习方法基于循环神经网络。它在训练阶段利用时间和空间信息来学习更有意义的表示，从而提高位置预测的学习性能。多任务学习模型，连同预处理步骤，大大提高了预测性能。我们使用真实数据集进行了多次实验，结果证明了我们的多任务学习模型的有效性，准确率为 85.20%，比使用单任务学习模型高 20% 以上。