Lightweight Web Visualization of Massive Road Traffic Data

ZHU Jun; WU Sihao; ZHANG Yunhao; HUANG Huaping; GUO Yukun; CHEN Yidong; LI Weilian

doi:10.3969/j.issn.0258-2724.20200107

Volume 56 Issue 5

Oct. 2021

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Journal of Southwest Jiaotong University > 2021 > 56(5): 905-912.

ZHU Jun, WU Sihao, ZHANG Yunhao, HUANG Huaping, GUO Yukun, CHEN Yidong, LI Weilian. Lightweight Web Visualization of Massive Road Traffic Data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 905-912. doi: 10.3969/j.issn.0258-2724.20200107

Citation:

ZHU Jun, WU Sihao, ZHANG Yunhao, HUANG Huaping, GUO Yukun, CHEN Yidong, LI Weilian. Lightweight Web Visualization of Massive Road Traffic Data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 905-912. doi: 10.3969/j.issn.0258-2724.20200107

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Lightweight Web Visualization of Massive Road Traffic Data

doi: 10.3969/j.issn.0258-2724.20200107

Received Date: 30 Mar 2020
Rev Recd Date: 03 Aug 2020

Available Online: 25 Aug 2020

Publish Date: 15 Oct 2021

Abstract

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.
- road traffic data,
- large-scale scene,
- streaming light,
- scene optimization,
- visualization

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References

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