Objectives  In recent years, trajectory data generated by various location-aware devices have been widely used in a host of fields such as urban planning, intelligent transportation, public health, and be-havior analysis, and have produced huge social and economic value. However, the conventional vector rep-resentation model and the analysis algorithms based on it have high computational complexity and cannot meet the high time-efficient application requirements of large-scale trajectory data. To address the above problems, we propose a trajectory data management and analysis framework based on a geographical grid model, which provides a new technical framework for the entire chain of "representation-manage-ment-analysis-application" of trajectory data mining.

  Methods  This framework includes five parts: (1) Con- struction of geographical grid model. The Earth's surface space is subdivided level by level according to some rules(e. g., quadtree, octree), and the spatiotemporal position is represented by grid coding.(2) Multi-scale coding representation and organization of trajectories. The trajectory data are mapped onto different spatial resolution grid levels according to the spatial position information and precision requirements to im-plement the spatial one-dimensional multi-scale coding representation instead of multi-dimensional vector coordinates, to reduce the difficulty of organization and management on massive trajectory data. (3) Calcula-tion and analysis of trajectories. The concept of geographical grid calculation is proposed, and the existing complex algorithms are modified by using low complexity coding operations (including intrinsic index, multi-scale, set operations, etc.) to accelerate the process of trajectory data mining analysis. (4) High-per-formance computing technologies. We further utilize the discreteness of geographical grid model, and com-bine the current parallel and distributed high-performance computing frameworks to achieve distributed sto-rage and concurrent computing on trajectory big data. (5) Applications of trajectory mining. Based on this framework, a series of high time-efficient applications are proposed to better serve the fields of urban manage-ment, intelligent transportation, and public safety. In addition, we found several advantages of combining geographical grid model and trajectory data, including large-scale data storage and management efficiency and flexibility, suitability for high-performance computing technology, and meeting the needs of automatic control and intelligent computing.

  Results  Supported by the above theoretical methods, the reliability and effectiveness of the technical framework theory and technical methods have been verified by two applica-tions-multi-level real-time visualizing of urban traffic flow and similarity analysis based on geographical grid model. The results show that the spatiotemporal query and similarity measure under this framework are one to two order of magnitude higher than classic algorithms under the vector coordinates. It has achieved to re-sponse within seconds and real-time visualization while processing billions of trajectory data, which proves the reliability and validity of the theories and technical methods. We explain the content of the proposed tra-jectory data management and analysis framework from the perspectives of framework design, theoretical analysis, advantages summary, and comparative experiments.

  Conclusions  It has been verified that the framework has the potential for high-performance management and analysis of massive trajectory data. In the future, issues such as the tradeoffs between data volume and accuracy, complex network analysis, and multi-source element association analysis need to be further explored and studied, to build a complete trajec-tory data management and mining technology system in a geographical grid space.