## 5 Conclusions and Outlook  
Based on the trajectory data of taxis in Xiamen City, this paper constructs a Hadoop-distributed storage and computing platform. Leveraging the MapReduce programming model and HBase database, the research has performed the cleansing and distributed storage of taxi trajectory point data. This paper introduces a multi-level time encoding method and devises a trajectory big data storage model based on various time scales. This innovation addresses storage challenges arising from the non-uniformity of time formats and enhances the computational efficiency of multi-scale time. Furthermore, we have also refined the storage structure of the original storage model based on real-world usage scenarios to enhance data retrieval efficiency. Experimental results demonstrate that as the volume of retrieved data continues to increase, the retrieval efficiency of this storage model becomes increasingly superior to that of the original storage model. Moreover, it also outperforms the retrieval efficiency of the space-time-coded storage model. The intro  
Figure 10: Visualization of the heat map after clustering. Figure 9: Visualization of the heat map before clustering. duction of this model has significantly improved the retrieval efficiency of the database and provided critical technology for information mining and visual presentation of trajectory big data. This model not only rectifies the shortcomings in utilizing time information within existing research but also flexibly optimizes data storage structures and holds potential applications in fields such as trajectory data analysis, IoT applications, data warehousing, and big data analytics. It is expected to enhance storage efficiency, accelerate data analysis, and drive innovation in related domains. This paper also introduces a novel density-based partitioning clustering algorithm that incorporates road coefficients to circumvent architectural obstacles, yielding superior clustering results compared to traditional methods. By avoiding the impact of architectural obstacles on the authenticity of trajectory data clustering outcomes, this algorithm circumvents the transmission of numerous redundant data. (1) It notably enhances the rendering speed of taxi trajectory data heatmaps, consequently alleviating stuttering during map panning and zooming, which addresses prolonged map generation times and subpar interactivity stemming from the issue of excessive data volume. (2) Furthermore, it has the added capability to more authentically portray the data clustering effects within the actual geographical space. The outcomes post-clustering not only retain the positional attributes of data points but also address the heat kernel phenomenon arising from localized high data volume during visualization, the substantial enhancement of which significantly elevates the overall visualization impact. This algorithm has filled a void in existing research, which lacked generality within the transportation industry, and combines visual analysis methods to present clustering results intuitively. It can be crucial in urban transportation planning and management, urban vehicle scheduling and management, GIS applications, and more. It enables city decision-makers and planners to extract valuable geographic spatial information from a large volume of valid trajectory data, providing decisive support for urban decision-making and analysis. However, this paper still has its limitations. Firstly, the approach employed in this study involves storing vast taxi trajectory data using the units of \"minutes\", \"hours\", and \"days\". As such, the same data instances are stored three times, resulting in unnecessary storage consumption. Future research endeavors could explore optimizing storage space by adopting a unified storage unit. Secondly, while effective, the clustering algorithm proposed in this study exhibits relative complexity and demands significant computational resources.