Congestion status evaluation is a vital link and tool in urban traffic congestion management. To timely warn and evaluate the congestion status of urban hot spots, a new congestion situation visualization model based on vector field is proposed. The calculation method of congestion status evaluation index is simplified based on this method. At the model building level, combining the advantages of OD flow diagram and OD matrix diagram, a dynamic visualization model of traffic congestion situation monitoring is established by constructing grid division and extracting action quantities. A method for identifying the trend of traffic congestion characteristics in urban areas is proposed. Simplify the algorithm level, combine the model visualization and the calculation process of the congestion evaluation index, and calculate the average actual travel time according to the vector field data structure, to realize the rapid calculation of the congestion index. The research shows that the model has the ability of trend analysis based on the visual expression of many basic traffic flow parameters such as flow, flow direction, speed, and spatial distribution, and realizes effective identification and early warning of hotspot congestion areas. The simplified calculation results of congestion index Compared with the traditional traffic index algorithm, the average error is 0.016. While the calculation efficiency is optimized, the effectiveness of the regional congestion index is guaranteed. This method can apply the research results to urban traffic congestion monitoring and management.

Trajectory big data is suitable for distributed storage retrieval due to its fast update speed and huge data volume, but currently there are problems such as hot data writing, storage skew, high I/O overhead and slow retrieval speed. In order to solve the above problems, this paper proposes a trajectory big data model that incorporates data partitioning and spatio-temporal multi-perspective hierarchical organization. At the spatial level, the model partitions the trajectory data based on the Hilbert curve and combines the pre-partitioning mechanism to solve the problems of hot writing and storage skewing of the distributed database HBase; at the temporal level, the model takes days as the organizational unit, finely encodes them into a minute system and then fuses the data partitioning to build spatio-temporal hybrid encoding to hierarchically organize the trajectory data and solve the problems of efficient storage and retrieval of trajectory data. The experimental results show that the model can effectively improve the storage and retrieval speed of trajectory big data under different orders of magnitude, while ensuring relatively stable writing and query speed, which can provide an efficient data model for trajectory big data mining and analysis.

Current trajectory data grows rapidly in a dynamic and streaming form, and unreasonable data organization causes the problems of skewed data storage and high overhead, as well as slow retrieval speed and page lag during visualization. To achieve effective spatial data organization, this paper proposes a data storage model with multi-level spatio-temporal organization. Spatially, the trajectory data is partitioned based on Hilbert curve, combined with pre-partitioning mechanism to solve the storage skewing problem of distributed database HBase; temporally, borrowing from the organization of spatio-temporal cube, the spatio-temporal hybrid coding is constructed by using the method of slicing by day and minute system coding to solve the retrieval of trajectory data into maps. The experiment proves that the organization model can effectively improve the data storage and retrieval efficiency, enhance the overall effect of trajectory visualization, and provide effective technical support for data mining and analysis.