## 1 Introduction  
With the continuous development of smart cities and the widespread use of mobile networks [1] and terminal devices, people have been able to conveniently access and record trajectory data of mobile objects such as taxis, buses, and shared bicycles that are used in their daily urban lives [2]. A substantial amount of trajectory point data can be collected in real-time through GPS terminals [3]. The volume of this data has already exceeded terabytes (TB) and petabytes (PB) and has even reached the scale of zettabytes (ZB). Consequently, exploring a novel technology that can effectively handle storage, management, and computation of transportation trajectory big data has become an urgent problem to address in GIS applications [4]. Current trajectory big data exhibits characteristics of Volume, Velocity, Variety, Value, and Veracity [5]. However, it also possesses certain traits and limitations, such as sorting based on time, inconsistent data time intervals, and low data quality [6]. Each trajectory point contains location information and is sorted based on time, providing a detailed depiction of the spatiotemporal dynamics of mobile objectsand encompassing a wealth of undiscovered spatiotemporal regularity information [7]. Visualization techniques are indispensable to fully explore the potential geographic spatial information embedded within massive trajectory data. They enable individuals to analyze, express, and communicate a vast amount of complex and unobservable spatial information more efficiently [8]. However, more than conventional relational database storage is needed to provide sufficient technical support for visualizing massive taxi trajectory data. Furthermore, issues may arise when dealing with large-scale data for visualization, such as long mapping times, low interactivity, and cluttered visual structures, which can hinder users' comprehension of critical information [9]. Therefore, researching and implementing a solution suitable for the storage and rapid visualization of massive trajectory data is of paramount importance. The design of a high-performance data-storage model is a prerequisite for achieving rapid visualization of massive trajectory data. In recent years, with the rapid development of distributed storage and computing capabilities, high-quality platforms have emerged for storing, managing, and retrieving spatiotemporal data. Due to its comprehensive ecosystem and its characteristics of low cost and high scalability, Hadoop has gained widespread recognition and adoption by developers both domestically and internationally [10]. HBase, known for its scalability, fault tolerance, and high-speed concurrency, has been extensively utilized for storing spatiotemporal data, laying the foundation for spatiotemporal data querying, processing, and big data analysis [11]. Within the realm of contemporary commercial spatial data memory management products, GeoMesa [12] integrates various prevalent distributed computing database technologies such as HBase, Accumulo, Google Bigtable, and Cassandra to facilitate spatial searches involving points, lines, surfaces, or polygons. Additionally, GeoMesa harnesses Kafka for real-time stream management of spatial data. [PERSON] et al. [13], building upon the Hadoop architecture, proposed the innovative ST-Hadoop framework that supports the storage and management of temporal data. They constructed multiple hierarchical time index structures based on different temporal dimensions, providing a novel approach to temporal retrieval using spatial index structures, thereby mitigating superfluous data retrieval during querying. [PERSON] et al. [14], rooted in the HBase database, engineered and materialized a prototype system for segmented storage of trajectory data. Compared to the GeoMesa system, this segmented architecture offers superior query speed and memory utilization performance. [PERSON] et al. [15], abstracting geographic space as meta-semantic entities, stored them as minimal storage units within HBase.