Research On Mass Data Visualization Method Based On Vehicle Monitoring System

In the Age of Big Data, data information is increasing in a geometric progression. How to deal with massive amounts of data and information, how to implement valuable information extraction from mass data and achieve comprehending of the data, has become challenges in each profession and domain. In the field of Intelligent Transportation, the research on massive vehicle data has an extraordinary realistic significance.According to the latest JT/T808 and JT/T809 protocol issued by the State Ministry of Transportation, the upload data of municipal public transport vehicles will be submitted to the superior transportation department for purpose of the transport vehicles integrated supervision and management. This policy cause the explosion of vehicle traffic data, and put forward to a stricter requirement to the monitoring efficiency of the existing monitoring platform. Applying data visualization theory to settle display problems of existing monitoring platform, could provide stronger decision support for vehicle management.This article utilize the uploaded data from twenty thousand public transport vehicles in Dalian as the experimental data, focusing on critical technology and method in mass data visualization process oriented by vehicle monitoring system.On the basis of the analysis of the existing vehicle monitoring platform, this paper propose an improvement project of data query and real-time update for supporting the huge amounts of data visualization. Then apply Baidu Map API and EasyUI as development tools, implement a GIS map based visualization experiment platform. Through analysis and comparison of various existing marker rendering methods, this article reveal the critical factor to interface response delay when loading huge numbers of markers, and present a fast marker aggregation approach based on grid partitioning, designed a historical trajectory visualization scheme for providing the foundation of mass vehicle data visualization.This article verify the effectiveness of the proposed approach by a vehicle monitoring system. The running effect has been proved that the improved vehicle monitoring system combined with the proposed marker aggregation approach and the historical trajectory visualization scheme, could sharply speed up the interface response, improve user experience, and the massive amounts of location marker could be rendered dynamically, achieve the goal of the massive vehicle data visualization.