## 1 Introduction
They proposed an efficient organizational and storage model that effectively enhances data query speed by capitalizing on the HBase database's robust scalability and real-time read features. [PERSON] et al. [16] proposed the MR-DBSCAN algorithm, which partitions data based on computation cost to accelerate query performance. Additionally, MapReduce is employed to execute parallel local clustering for each partition. [PERSON] et al. [17] presented a storage and sharing algorithm for massive data in a distributed heterogeneous environment leveraging HBase. This algorithm effectively addresses the challenge of high storage occupancy and significantly reduces sharing latency. [PERSON] et al. [18] introduced a multi-dimensional spatial data storage framework called MD-HBase, built upon HBase. This framework employs quad-trees or K-d trees for spatial partitioning and Z-encoding, storing the encoded outcomes as row keys within HBase. [PERSON] et al. [19] devised a trajectory big data model integrating data partitioning and spatiotemporal multi-angle hierarchical organization. This model addresses the storage skewness and hot write issues in the distributed HBase database, thereby boosting the write rate of massive data. The research mentioned above, based on the Hadoop distributed storage and computing platform, has all, to some extent, addressed the challenges of storing and managing massive data. The time information of trajectory data is a crucial characteristic of space-time data. However, these studies have yet to leverage the time information of trajectory data to address the issues. Hence, to address this gap, this research introduces a multi-level time encoding storage model based on the time information of trajectory data. This model is designed for the flexible and efficient storage and management of massive trajectory data. Introducing this model can resolve data storage issues arising from the lack of uniformity in time formats and enhance the computational efficiency for multi-scale time. Furthermore, compared to the studies mentioned earlier, this research has also optimized the storage structure of the time encoding storage model based on real-world usage scenarios. This optimization aims to enhance data retrieval efficiency within the same time frame. With the widespread adoption of positioning technologies like GPS in various aspects of daily life, the acquisition of information from traffic big data has become remarkably facile [3]. Grasping the underlying regularities within this voluminous data and comprehending them intuitively have emerged as current research focal points and challenges. Consequently, the field of data visualization has flourished. Data visualization is an interdisciplinary realm encompassing human-computer interaction, computer graphics, image science, statistical analysis, and geographic information. It synthesizes various knowledge domains and skills, encompassing data processing, algorithm design, software development, and human-computer interaction. It employs visual forms such as images, charts, and animations to showcase relationships and trends within data, thereby enhancing the efficiency of data reading and comprehension. As for data types, contemporary visualization research increasingly delves into areas such as multidimensional data, time-series data, network data, and hierarchical data [20; 21; 22; 23]. Given the colossal scale of traffic trajectory big data, directly visualizing massive datasets could lead to visual clutter, hindering the extraction of key insights. For instance, when generating heatmaps, excessive data volume might result in color gradients that are ill-suited to the dataset, causing issues of indistinct contrast and even generating heat kernel phenomena [24] due to high local point densities. Moreover, querying sizable data could engender extended rendering times, dampening information interactivity. Hence, undertaking appropriate data processing can enhance the support for data visualization.