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.
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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)
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)
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)
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)
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)
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)
Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)
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)
Cai, Z., Zhang, K., Hu, D.N.: Visualizing large graphs by layering and bundling graph edges. Vis. Comput. 35, 739–751 (2018)
Chen, W., Guo, F., Wang, F.Y.: A survey of traffic data visualization. IEEE Trans. Intell. Transp. Syst. 16(6), 2970–2984 (2015)
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)
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)
Cherniavsky, E.A., Abrahamsen, T.R.: Aviation System Performance Metrics: Airport Utilization. MITRE Center for Advanced Aviation System Development (2000)
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)
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)
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)
Holten, D., van Wijk, J.J.: Force-directed edge bundling for graph visualization. Comput. Gr. Forum 28, 983–990 (2009)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Vis. Comput. 30, 1373–1393 (2013)
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)
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)
Mahboubi, Z., Kochenderfer, M.J.: Learning Traffic Patterns at Small Airports From Flight Tracks. IEEE Press, New York (2017)
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)
Nguyen, M.H., Alam, S.: Airspace collision risk hot-spot identification using clustering models. IEEE Trans. Intell. Transp. Syst. 99, 1–10 (2017)
Organization, I.C.A.: Performance Based Navigation (PBN) Manual. International Civil Aviation Organization (2008)
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)
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
Salvador, S., Chan, P.: Fastdtw: Toward accurate dynamic time warping in linear time and space (2004)
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)
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)
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)
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)
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)
Xu, R., Wunsch, D.C.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 645–678 (2005)
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)
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).
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Appendix
Files, data, and codes related to this research can be downloaded and viewed as appendixes at https://github.com/cchen1-08/VEFP-appendix
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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
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DOI: https://doi.org/10.1007/s00371-020-01975-6