With the development of GPS technology, GPS-equipped taxicabs and users with GPS-enabled devices interact each other, and form a new mobile Internet of things (M-IoT). This paper proposes a M-IoT service framework to predict multiusers’ mobility pattern by solving a cubical user-spatio-temporal probability map arising from heterogeneous sensor data. Then, the framework provides services for the traffic participants based on the prediction. To solve the stationary probability map, a novel tensor based iterative algorithm is proposed and proved to be convergent. Furthermore, the existence and uniqueness of the stationary probability is proved. The multivariate multistep transition tensor (${M^2}{T^2}$) model is proposed to merge massive sensor data from multisource, including time, space, and social network, so on. The eigentensor concept is proposed as the theoretical basis of the ${M^2}{T^2}$ model. The context-aware ${M^2}{T^2}$ model takes into account comprehensive context factors to improve the accuracy of prediction. In the end, extensive experiments based on real GPS data are conducted to evaluate efficiency of the proposed model. The results show that the propose model has highly improved the prediction accuracy.

中文翻译：

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${M^2}{T^2}$：用于用户移动模式预测的多元多步转换张量

随着 GPS 技术的发展，配备 GPS 的出租车与配备 GPS 设备的用户相互交互，形成新的移动物联网（M-IoT）。本文提出了一种 M-IoT 服务框架，通过求解由异构传感器数据产生的立方用户-时空概率图来预测多用户的移动模式。然后，该框架根据预测为交通参与者提供服务。为了求解平稳概率图，提出了一种新的基于张量的迭代算法，并证明是收敛的。进一步证明了平稳概率的存在唯一性。多元多步过渡张量 (${M^2}{T^2}$) 模型用于合并来自多源的海量传感器数据，包括时间、空间和社交网络等。特征张量概念被提出作为${M^2}{T^2}$模型。上下文感知${M^2}{T^2}$模型考虑了综合上下文因素，以提高预测的准确性。最后，基于真实 GPS 数据进行了大量实验，以评估所提出模型的效率。结果表明，所提出的模型大大提高了预测精度。