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Research and Application of GPS Trajectory Data Visualization

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Abstract

Taxi trajectory data is a kind of massive traffic data with spatial–temporal dimensions, and plays a key role in traffic management, travel analysis and route recommendation for residents. Analyzing trajectory data with traditional methods is complicated, but visualization techniques can intuitively reflect the change trend of spatial–temporal data and facilitate the mining of knowledge and laws in the data. A novel taxi trajectory data visualization and analysis system, TaxiVis, has been designed and developed in this study. This system not only displays the traveling routes of every taxi on the map at the micro-level, dynamically analyzing every taxi’s operating indicators with varying time, but also displays the operating statistics of every taxi company at the macro-level. In addition, the TaxiVis provides route inquiry recommendation functions for users by GLTC algorithm. Implementation of front-end functions of this system are based on Node.js, D3.js and Baidu map, and the trajectory data has been stored in MySQL database. We evaluate TaxiVis with the trajectory dataset collected from 6599 taxis in Kunming. Experimental results show that the system can effectively process and analyze trajectory data, and provide precise data supporting and presentation for the comprehensive evaluation of taxi operation efficiency and mining the drivers’ intelligence.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61663047 and No. 61363084.

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Authors and Affiliations

  1. School of Computer and Science, Fudan University, Shanghai, 200433, China

    Li Cai

  2. School of Software, Yunnan University, Kunming, 650091, China

    Li Cai, Yifan Zhou, Yu Liang & Jing He

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  1. Li Cai
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  2. Yifan Zhou
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  3. Yu Liang
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  4. Jing He
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Correspondence to Li Cai.

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Cai, L., Zhou, Y., Liang, Y. et al. Research and Application of GPS Trajectory Data Visualization. Ann. Data. Sci. 5, 43–57 (2018). https://doi.org/10.1007/s40745-017-0132-1

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  • DOI: https://doi.org/10.1007/s40745-017-0132-1

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