TZVis: visual analysis of bicycle data for traffic zone division


Traffic zone is a geographical unit which is proposed to reduce the complexity of traffic control and management systems in modern urban road networks. Traffic zone could be generated by different division approaches (e.g., rule-based approaches, data-driven approaches), which may lead to different diverse division results and characteristics. The traditional division approaches usually rely on human experience or static constraints (e.g., political boundaries or natural environment) while neglecting the traveling pattern among different regions. Data-driven approaches are also utilized in recent years; however, domain knowledge and rules are not well integrated and represented in such systems. This paper proposes a new approach that both combines patterns that is hidden in the data and user-defined rules. It not only fully employs the traffic hot spots and traveling patterns among different regions, but also considers the realistic constraints such as road network. We also design TZVis, a visual analysis system that fully integrates with the division approach. The system allows users to generate multiple traffic zone division schemes under different requirements, analyze the schemes in multiple views and select an optimal traffic zone scheme. It provides a map overview to show the traffic zone division result, a matrix view to show the traffic relationship of zones, a parallel coordinates plot for displaying the specific traffic records, a LineUp view to display all the division results and a radar chart to show the features of each zones. We further test our division approach and demonstrate the usefulness of our system based on the shared bicycle dataset.

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  1. Adrienko N, Adrienko G (2010) Spatial generalization and aggregation of massive movement data. IEEE Trans Vis Comput Graph 17(2):205–219

    Article  Google Scholar 

  2. Allsop RE (1974) Some possibilities for using traffic control to influence trip distribution and route choice. In: Proceedings of the transportation and traffic theory, vol 6

  3. Dong H, Wu M, Ding X, Chu L, Jia L, Qin Y, Zhou X (2015) Traffic zone division based on big data from mobile phone base stations. Transp Res C Emerg Technol 58:278–291

    Article  Google Scholar 

  4. Gratzl S, Lex A, Gehlenborg N, Pfister H, Streit M (2013) Lineup: visual analysis of multi-attribute rankings. IEEE Trans Vis Comput Graph 19(12):2277–2286

    Article  Google Scholar 

  5. Jegou H, Douze M, Schmid C, Perez P (2010) Aggregating local descriptors into a compact image representation. Proc CVPR 238(6):3304–3311

    Google Scholar 

  6. Liu D, Weng D, Li Y, Bao J, Zheng Y, Qu H, Wu Y (2016) SmartAdP: visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans Vis Comput Graph 23(1):1–10

    Article  Google Scholar 

  7. Lu M, Lai C, Ye T, Liang J, Yuan X (2017) Visual analysis of multiple route choices based on general GPS trajectories. IEEE Trans Big Data 3(2):234–247

    Article  Google Scholar 

  8. Lu M, Wang Z, Yuan X (2015) TrajRank: exploring travel behaviour on a route by trajectory ranking. In: 2015 IEEE pacific visualization symposium (PacificVis). IEEE, pp 311–318

  9. Ma C, Wang R, Wang Y, Yan B (2007) Calculating method of traffic zone radius in city based on inner trip proportion. J Traffic Transp Eng 7(1):68–72

    Google Scholar 

  10. Maaten Lvd, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(Nov):2579–2605

    MATH  Google Scholar 

  11. Martínez LM, Viegas JM, Silva EA (2009) A traffic analysis zone definition: a new methodology and algorithm. Transportation 36(5):581–599

    Article  Google Scholar 

  12. Park JS, Oh SJ (2012) A new concave hull algorithm and concaveness measure for n-dimensional datasets. J Inf Sci Eng 28(3):587–600

    MathSciNet  Google Scholar 

  13. Rosen E, Jansson E, Brundin M (2014) Implementation of a fast and efficient concave hull algorithm. Uppsala Universitet, Uppsala

    Google Scholar 

  14. Sun GD, Wu YC, Liang RH, Liu SX (2013) A survey of visual analytics techniques and applications: state-of-the-art research and future challenges. J Comput Sci Technol 28(5):852–867

    Article  Google Scholar 

  15. Sun G, Liang R, Qu H, Wu Y (2017) Embedding spatio-temporal information into maps by route-zooming. IEEE Trans Vis Comput Graph 23(5):1506–1519

    Article  Google Scholar 

  16. Sun G, Li S, Cao D, Liu C, Jiang X, Liang R (2018) DiffusionInsighter: visual analysis of traffic diffusion flow patterns. Chin J Electron 27(5):942–950

    Article  Google Scholar 

  17. Torgerson WS (1958) Theory and methods of scaling. Biometrika 46(3/4):247–297

    Google Scholar 

  18. Wenchao W, Jiayi X, Haipeng Z, Yixian Z, Huamin Q, Bing N, Mingxuan Y, Ni LM (2015) TelCoVis: visual exploration of co-occurrence in urban human mobility based on telco data. IEEE Trans Vis Comput Graph 22(1):935–944

    Google Scholar 

  19. Whitson R, White B, Messer C (1973) A study of system versus isolated control as developed on the mockingbird pilot study. Prepared for the City of Dallas. Texas Transportation Institute, College Station and Dallas, Texas

  20. Xing X, Huang W, Song G, Xie K (2014) Traffic zone division using mobile billing data. In: 2014 11th international conference on fuzzy systems and knowledge discovery (FSKD). IEEE, pp 692–697

  21. You J, Nedović-Budić Z, Kim TJ (1998) A GIS-based traffic analysis zone design: implementation and evaluation. Transp Plan Technol 21(1–2):69–91

    Article  Google Scholar 

  22. Zheng Y, Zhao G, Liu J (2015) A novel grid based k-means cluster method for traffic zone division. In: Second international conference on cloud computing and big data in Asia. Springer, pp 165–178

Download references


This work is partly supported by National Natural Science Foundation of China (No. 61972356), Zhejiang Provincial Natural Science Foundation of China (No. LY19F020026), National Natural Science Foundation of China (No. 61602409), Zhejiang Provincial Key Research and Development Program of China No. 2019C01009) and Fundamental Research Funds for the Provincial Universities of Zhejiang (No. RF-C2019001).

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Correspondence to Guodao Sun or Ronghua Liang.

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Sun, G., Chang, B., Zhu, L. et al. TZVis: visual analysis of bicycle data for traffic zone division. J Vis 22, 1193–1208 (2019).

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  • Traffic zone division
  • Shared bicycle data
  • Road network constraint
  • Visual analysis
  • Interactive visualization