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Journal of Visualization

, Volume 21, Issue 5, pp 873–883 | Cite as

BVis: urban traffic visual analysis based on bus sparse trajectories

  • Wenqi Pei
  • Yadong Wu
  • Song Wang
  • Lili Xiao
  • Hongyu Jiang
  • Abdul Qayoom
Regular Paper
  • 113 Downloads

Abstract

Public urban transport network has the characteristic of wide coverage, while buses have the features of stable running routes and static parking places, which is helpful to study urban traffic and bus station congestion patterns. Unlike the common GPS trajectory data, our data includes the pertinent records of the buses arrival and departure from the relevant bus stations due to data compression. In this paper, a visual analysis system called BVis is presented to analyze the urban traffic applying the large-scale real sparse buses dataset. This system covers the four modules of bus data visualization, first, the sparse trajectory data cleaning and mapping, second, the global traffic states and section traffic patterns analysis of roads, third, the bus station congestion patterns analysis using the station parking time, finally, an importance analysis of bus stations in the complex public transport network. Furthermore, an enhanced node importance evaluation algorithm is presented, which combines the dynamic properties of the bus station, such as traffic volume of station and station parking time. Using the real bus GPS dataset, three cases are described to demonstrate the performance and effectiveness of the system.

Graphical Abstract

Keywords

Visual analysis Urban traffic Bus station congestion patterns Bus station importance 

Notes

Acknowledgements

This work is supported by National Key Research and Development Program of China 317 (2016QY04W0801), the fund of Fundamental Sichuan Civil-Military Integration Institute (no. JMRH01) and the fund of 318 Sichuan Province Science and Technology Program (2017TJPT0200, 2017KZ0023, 2017GZ0186).

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Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  • Wenqi Pei
    • 1
  • Yadong Wu
    • 1
    • 2
  • Song Wang
    • 1
  • Lili Xiao
    • 1
  • Hongyu Jiang
    • 1
  • Abdul Qayoom
    • 1
    • 3
  1. 1.School of Computer Science and TechnologySouthwest University of Science and TechnologyMianyangChina
  2. 2.Sichuan Civil-Military Integration Institute SydneySouthwest University of Science and TechnologyMianyangChina
  3. 3.Department of Computer ScienceLasbela University of Agriculture, Water and Marine SciencesUthaPakistan

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