Skip to main content
Log in

VEFP: visual evaluation of flight procedure in airport terminal

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

The optimization of terminal airspace can provide more airport capacity to meet the growing aviation demand. Improving flight procedures is an important prerequisite for optimizing airspace. The existing air-route network visualization cannot fully meet the assessment needs of the terminal flight procedure. In this research, we introduce an analysis tool, VEFP, that provides multiple visualizations based on unlabeled flight trajectory data. The system can help domain experts to evaluate the terminal flight procedure from multiple perspectives, respectively. First, we provide a time series-based statistical information view to help determine the usage status of the flight procedure per unit time. Second, we evaluated the controller’s use of space for flight procedures based on the location information in the data. Third, combined with the visualization method after data processing, the visual complexity is reduced and necessary details are displayed. Then, the users can directly observe the actual flight procedure. We evaluate flight procedures in actual use through cases and experiments and then discuss with users about the observations provided by the system. These results confirm that our system can help domain experts evaluate flight procedures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Andrienko, G., Andrienko, N., Fuchs, G., Garcia, J.M.C.: Clustering trajectories by relevant parts for air traffic analysis. IEEE Trans. Vis. Comput. Gr. 24(1), 34–44 (2018)

    Article  Google Scholar 

  2. Andrienko, G., Andrienko, N., Hurter, C., Rinzivillo, S., Wrobel, S.: From movement tracks through events to places: extracting and characterizing significant places from mobility data. In: IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 161–170. IEEE (2011)

  3. Andrienko, G., Andrienko, N., Hurter, C., Rinzivillo, S., Wrobel, S.: Scalable analysis of movement data for extracting and exploring significant places. IEEE Trans. Vis. Comput. Gr. 19(7), 1078–1094 (2013)

    Article  Google Scholar 

  4. Andrienko, G., Andrienko, N., Schumann, H., Tominski, C.: Visualization of trajectory attributes in space–time cube and trajectory wall. In: Cartography from Pole to Pole, pp. 157–163. Springer (2014)

  5. Andrienko, G.L., Andrienko, N.V., Chen, W., Maciejewski, R., Sentis, L.: Visual analytics of mobility and transportation: state of the art and further research directions. IEEE Trans. Intell. Transp. Syst. 18, 2232–2249 (2017)

    Article  Google Scholar 

  6. Bach, B., Dragicevic, P., Archambault, D.W., Hurter, C., Carpendale, M.S.T.: A review of temporal data visualizations based on space–time cube operations. In: EuroVis (2014)

  7. Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)

    Article  Google Scholar 

  8. Buschmann, S., Trapp, M., Döllner, J.: Real-time animated visualization of massive air-traffic trajectories. In: International Conference on Cyberworlds (CW), pp. 174–181. IEEE (2014)

  9. Cai, Z., Zhang, K., Hu, D.N.: Visualizing large graphs by layering and bundling graph edges. Vis. Comput. 35, 739–751 (2018)

    Article  Google Scholar 

  10. Chen, W., Guo, F., Wang, F.Y.: A survey of traffic data visualization. IEEE Trans. Intell. Transp. Syst. 16(6), 2970–2984 (2015)

    Article  Google Scholar 

  11. Chen, W., Huang, Z., Wu, F., Zhu, M., Guan, H., Maciejewski, R.: Vaud: a visual analysis approach for exploring spatio-temporal urban data. IEEE Trans. Vis. Comput. Gr. 99, 1–1 (2017)

    Google Scholar 

  12. Chen, W., Sentis, L., Gu, T., Gao, S., Bao, H.: Adaptively exploring population mobility patterns in flow visualization. IEEE Trans. Intell. Transp. Syst. 18, 2250–2259 (2017)

    Article  Google Scholar 

  13. Cherniavsky, E.A., Abrahamsen, T.R.: Aviation System Performance Metrics: Airport Utilization. MITRE Center for Advanced Aviation System Development (2000)

  14. Demšar, U., Virrantaus, K.: Space-time density of trajectories: exploring spatio-temporal patterns in movement data. Int. J. Geogr. Inf. Sci. 24(10), 1527–1542 (2010)

    Article  Google Scholar 

  15. Doraiswamy, H., Ferreira, N., Damoulas, T., Freire, J., Silva, C.T.: Using topological analysis to support event-guided exploration in urban data. IEEE Trans. Vis. Comput. Gr. 20(12), 2634–2643 (2014)

    Article  Google Scholar 

  16. Ferstl, F., Kanzler, M., Rautenhaus, M., Westermann, R.: Time-hierarchical clustering and visualization of weather forecast ensembles. IEEE Trans. Vis. Comput. Gr. 23(1), 831–840 (2017)

    Article  Google Scholar 

  17. Holten, D., van Wijk, J.J.: Force-directed edge bundling for graph visualization. Comput. Gr. Forum 28, 983–990 (2009)

    Article  Google Scholar 

  18. Hong, S., Kocielnik, R., Yoo, M.J., Battersby, S., Kim, N.J., Aragon, C.: Designing interactive distance cartograms to support urban travelers. In: Pacific Visualization Symposium (PacificVis), pp. 81–90. IEEE (2017)

  19. Hurter, C., Brenier, Y., Ducas, J., Le Guilcher, E.: Cap: collaborative advanced planning, trade-off between airspace management and optimized flight performance: demonstration of en-route reduced airspace congestion through collaborative flight planning. In: IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), pp. 1–9. IEEE (2016)

  20. Hurter, C., Tissoires, B., Conversy, S.: Fromdady: spreading aircraft trajectories across views to support iterative queries. IEEE Trans. Vis. Comput. Gr. 15(6), 1017–1024 (2009)

    Article  Google Scholar 

  21. Johansson, J., Forsell, C.: Evaluation of parallel coordinates: overview, categorization and guidelines for future research. IEEE Trans. Vis. Comput. Gr. 22(1), 579–588 (2016)

    Article  Google Scholar 

  22. Klein, T., Van Der Zwan, M., Telea, A.: Dynamic multiscale visualization of flight data. In: International Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 104–114. IEEE (2014)

  23. Lhuillier, A., Hurter, C., Telea, A.: Ffteb: Edge bundling of huge graphs by the fast fourier transform. In: IEEE Pacific Visualization Symposium (PacificVis), pp. 190–199. IEEE (2017)

  24. Li, X., Lv, Z., Wang, W., Zhang, B., Hu, J., Yin, L., Feng, S.: Webvrgis based traffic analysis and visualization system. Adv. Eng. Softw. 93, 1–8 (2016)

    Article  Google Scholar 

  25. Li, Y., Liu, R.W., Liu, J., Huang, Y., Hu, B., Wang, K.: Trajectory compression-guided visualization of spatio-temporal ais vessel density. In: 8th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–5. IEEE (2016)

  26. Liu, R., Guo, H., Zhang, J., Yuan, X.: Comparative visualization of vector field ensembles based on longest common subsequence. In: IEEE Pacific Visualization Symposium (PacificVis), pp. 96–103. IEEE (2016)

  27. Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Vis. Comput. 30, 1373–1393 (2013)

    Article  Google Scholar 

  28. Lu, H.P., Sun, Z.Y., Qu, W.C.: Big data-driven based real-time traffic flow state identification and prediction. Discrete Dyn. Nat. Soc. 2015(9), 1–11 (2015)

    MathSciNet  MATH  Google Scholar 

  29. Ma, Y., Lin, T., Cao, Z., Li, C., Wang, F., Chen, W.: Mobility viewer: an Eulerian approach for studying urban crowd flow. IEEE Trans. Intell. Transp. Syst. 17(9), 2627–2636 (2016)

    Article  Google Scholar 

  30. Mahboubi, Z., Kochenderfer, M.J.: Learning Traffic Patterns at Small Airports From Flight Tracks. IEEE Press, New York (2017)

    Book  Google Scholar 

  31. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856 (2002)

  32. Nguyen, M.H., Alam, S.: Airspace collision risk hot-spot identification using clustering models. IEEE Trans. Intell. Transp. Syst. 99, 1–10 (2017)

    Google Scholar 

  33. Organization, I.C.A.: Performance Based Navigation (PBN) Manual. International Civil Aviation Organization (2008)

  34. Peysakhovich, V., Hurter, C., Telea, A.: Attribute-driven edge bundling for general graphs with applications in trail analysis. In: IEEE Pacific Visualization Symposium (PacificVis), pp. 39–46. IEEE (2015)

  35. Saini, R., Roy, P.P., Dogra, D.P.: A novel point-line duality feature for trajectory classification. Vis. Comput. 35(3), 415–427 (2019). https://doi.org/10.1007/s00371-018-1473-2

    Article  Google Scholar 

  36. Salvador, S., Chan, P.: Fastdtw: Toward accurate dynamic time warping in linear time and space (2004)

  37. Scheepens, R., Hurter, C., Van De Wetering, H., Van Wijk, J.J.: Visualization, selection, and analysis of traffic flows. IEEE Trans. Vis. Comput. Gr. 22(1), 379–388 (2016)

    Article  Google Scholar 

  38. Senaratne, H., Mueller, M., Behrisch, M., Lalanne, F., Bustos-Jiménez, J., Schneidewind, J., Keim, D., Schreck, T.: Urban mobility analysis with mobile network data: a visual analytics approach. IEEE Trans. Intell. Transp. Syst. (2017)

  39. Tominski, C., Schumann, H., Andrienko, G., Andrienko, N.: Stacking-based visualization of trajectory attribute data. IEEE Trans. Vis. Comput. Gr. 18(12), 2565–2574 (2012)

    Article  Google Scholar 

  40. Vuckovic, A., Sanderson, P., Neal, A., Gaukrodger, S., Wong, B.W.: Relative position vectors: an alternative approach to conflict detection in air traffic control. Hum. Fact. 55(5), 946–964 (2013)

    Article  Google Scholar 

  41. Wang, Z., Lu, M., Yuan, X., Zhang, J., Van De Wetering, H.: Visual traffic jam analysis based on trajectory data. IEEE Trans. Vis. Comput. Gr. 19(12), 2159–2168 (2013)

    Article  Google Scholar 

  42. Xu, R., Wunsch, D.C.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 645–678 (2005)

    Article  Google Scholar 

  43. Zheng, X., Chen, W., Wang, P., Shen, D., Chen, S., Wang, X., Zhang, Q., Yang, L.: Big data for social transportation. IEEE Trans. Intell. Transp. Syst. 17, 620–630 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank experts in Civil Aviation University of China for helpful feedback and accurate datasets. This paper is supported by National Science Foundation Project of China (Grant No. 61672237 and No. 61802128).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Changbo Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Files, data, and codes related to this research can be downloaded and viewed as appendixes at https://github.com/cchen1-08/VEFP-appendix

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, C., Li, C., Qi, Y. et al. VEFP: visual evaluation of flight procedure in airport terminal. Vis Comput 37, 2139–2155 (2021). https://doi.org/10.1007/s00371-020-01975-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-020-01975-6

Keywords

Navigation