Lightweight Web Visualization of Massive Road Traffic Data
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摘要: 为提高网络环境下大规模道路交通数据动态可视化效率，提出一种在网络环境下使用流光线表达交通态势的轻量化可视化方法. 首先，提出顾及道路属性的路网分层组织方法，并绘制在不同视角高度下的道路；然后，提出基于WebGL (Web graghics library)技术线段索引特点的多道路合并方法，并结合视域剔除以及多线程技术对场景进行优化，设计实现了支持道路拥堵状态实时变换的渐变、动态流光线等动画效果；最后，研发了原型系统，并以全国道路网数据为例进行案例实验与分析. 实验结果表明：在网络环境下进行大规模道路交通数据动态可视化，本文方法可视化帧率稳定在40帧/秒以上，相较于百度ECharts GL可视化方法提高了20～30帧/秒.Abstract: In order to further improve the dynamic visualization efficiency of large-scale road traffic data under the network environment, a lightweight visualization method using streaminglight is proposed to express traffic situation under the network environment. The hierarchical organization method of road network that allows road attributes is proposed, and the roads are drawnunder different view heights. The multi-road merging method based on the characteristics of line segment index in WebGL (Web graghics library) technology is presented, and the scene is optimized by combining the visual field elimination and multithreading technology. The animation effects such as gradual change and dynamic streaming light that support the real-time transformation of road congestion state are designed and realized. Finally, the prototype system is developed, and the case experiment and analysis are carried out with the national road network data. Experimental results show that this method can dynamically visualize large-scale road traffic data under the network environment, and its visualization frame rate is stable above 40 frames/s, which is 20−30 frames/s higher than that of the Baidu ECharts GL visualization method.
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Key words:
- road traffic data /
- large-scale scene /
- streaming light /
- scene optimization /
- visualization
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图 1 大规模流光线网络轻量化可视化方法
Figure 1. Lightweight visualization method of large-scale streaminglight network
图 9 大规模流光线可视化实时渲染帧率
Figure 9. Real-time rendering frame rate of large-scale streaming light visualization
表 1 不同颜色的流光线
Table 1. Different colors of streaming light
车道颜色 流光线头部颜色 流光线尾部颜色 交通态势 （0，175，0） （154，255，154） （124，205，124） 不拥堵 （218，218，0） （255，255，180） （205，233，90） 比较拥堵 （238，64，0） （255，114，86） （205，91，69） 拥堵 
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表 2 不同场景下参数记录
Table 2. Parameter records in different scenarios
场景 视角高/km 道路数/条 分段后道路点数/个 道路点索引数/个 一级 二级 三级 四级 Ⅰ层 Ⅱ层 Ⅲ层 Ⅳ层 Ⅰ层 Ⅱ层 Ⅲ层 Ⅳ层 1 7000.0 205 0 0 0 43125 0 0 0 257520 0 0 0 2 40.0 6 60 0 0 4913 14739 0 0 29442 88074 0 0 3 5.0 1 16 59 0 846 7526 16282 0 5070 45060 97338 0 4 0.5 0 0 0 2 0 0 0 437 0 0 0 2610
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