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Journal of Networking Technology

Use of GPS Data and Visual Summaries for Traffic Planning

Bram Custers, Wouter Meulemans, Bettina Speckmann, Kevin Verbeek
Eindhoven University of Technology The Netherlands

Abstract: In the road networks, the GPS data provide valuable traffic data for planning. In this process, more volume of data is impactful that provide challenges while observing significant features. The GPS and the road network can be integrated to arrive a viable solution and generating visual summaries. Now we outlined acoordinated fully-automated pipeline for computing a schematic overview of mobility patterns from a collection of trajectories on a street network. The proposed framework used the known building blocks from GIS, automated cartography, and trajectory analysis: map matching, road selection, schematization, movement patterns, and metro-map style rendering. The propositions are experimented by subjecting two cities where the real-world data is used to assess the pattern.

Keywords: Trajectories, Visualization, Schematization Use of GPS Data and Visual Summaries for Traffic Planning

DOI:https://doi.org/10.6025/jnt/2021/12/4/108-122

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References:

[1] Adrienko, N., Adrienko. G. (2010). Spatial generalization and aggregation of massive movement data. IEEE TVCG, 17(2) 205–219. doi:10.1109/TVCG.2010.44.
[2] Alt, H., Efrat, A., Rote, G., Wenk., C. (2003). Matching planar maps. Journal of Algorithms, 49 (2) 262–283, 2003. doi:10.1016/S0196-6774(03)00085-3.
[3] Andrienko, G., Andrienko, N., Chen, W., Maciejewski, R., Zhao, Y. (2017). Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions. IEEE Transactions on Intelligent Transportation Systems, 18 (8) 2232–2249. doi:10.1109/TITS.2017.2683539.
[4] Arnold, M., Ohlebusch, E. (2011). Linear time algorithms for generalizations of the longest common substring problem. Algorithmica, 60 (4) 806–818. doi:10.1007/s00453-009-9369-1.
[5] Buchin, K., Buchin, M., van Kreveld, M., Speckmann, B., Staals. F. (2013). Trajectory grouping structure. In WADS, pages 219–230. doi:10.1007/978-3-642-40104-6_19.
[6] Buchin, K., Driemel, A., Gudmundsson, J., Horton, M., Kostitsyna, I., Löffler, M., Struijs. M. (2019). Approximating (k, l)-center clustering for curves. In: Proceedings SODA, pages 2922–2938. doi:10.1137/1.9781611975482.181.
[7] Cabello, S., de Berg, M., van Kreveld, M. (2005). Schematization of networks. Computational Geometry, 30(3):223– 238, 2005. doi:10.1016/j.comgeo.2004.11.002.
[8] Chao, P., Xu, Y., Hua, W., Zhou. X. (2020). A survey on map-matching algorithms. In ADC, pages 121–133, 2020. doi:10.1007/978-3-030-39469-1_10.
[9] Chen, J., Hu, Y., Li, Z., Zhao, R., Meng. L. (2009). Selective omission of road features based on mesh density for automatic map generalization. IJGIS, 23 (8) 1013–1032, 2009. doi: 10.1080/13658810802070730.
[10] Chen, W., Guo, F., Wang. F.-Y. (2015). A survey of traffic data visualization. IEEE Transactions on Intelligent Transportation Systems, 16 (6) 2970–2984. doi:10.1109/TITS.2015.2436897.
[11] Garey, M., Johnson, D., Miller, G., Papadimitriou. C. (1980). The complexity of coloring circular arcs and chords. SIAM Journal on Algebraic Discrete Methods, 1 (2), 216–227. doi: 10.1137/0601025.
[12] Imai, H., Iri, M. (1986). Computational-geometric methods for polygonal approximations of a curve. Computer Vision, Graphics, and Image Processing, 36 (1), 31–41. doi:10.1016/S0734-189X(86)80027-5.
[13] Kraak, M.-J. (2003). The Space-Time cube revisited from a Geovisualization perspective. In Proc. ICC, pages 1988– 1996
14] Kulik, L., Duckham, M., Egenhofer, M. (2005). Ontology-driven map generalization. Journal of Visual Languages & Computing, 16 (3) 245–267. doi:10.1016/j.jvlc.2005.02.001.
[15] Lampe, O., Hauser. H. (2011). Interactive visualization of streaming data with kernel density estimation. In Proc. IEEE PacificVis, pages 171–178. doi:10.1109/PACIFICVIS.2011. 5742387.
[16] Lee, J.-G., Han, J., Whang, K.-Y. (2007). Trajectory clustering: a partition-and-group framework. In Proc. 2007 ACM SIGMOD, pages 593–604. doi:10.1145/1247480.1247546.
[17] Meulemans. W. (2014). Similarity Measures and Algorithms for Cartographic Schematization. PhD thesis, TU Eindhoven, doi:10.6100/ir777493.
[18] Nath, A., Taylor, E. (2020). k-Median clustering under discrete Fréchet and Hausdorff distances, 2020. arXiv:2004.00722.
[19] Scheepens, R., Willems, N., van de Wetering, H., van Wijk, J. (2011). Interactive visualization of multivariate trajectory data with density maps. In IEEE PacificVis, pages 147–154, 2011. doi:10.1109/PACIFICVIS.2011.5742384.
[20] Šuba, R., Meijers, M., van Oosterom, P. (2016). Continuous road network generalization throughout all scales. ISPRS Journal of Geo-Information, 5 (8) 145, 2016. doi:10.3390/ijgi5080145.
[21] Thomson, R. (2006). The ’stroke’ concept in geographic network generalization and analysis. In Progress in Spatial Data Handling, pages 681–697. Springer, 2006. doi:10.1007/3-540-35589-8_43.
[22] Tominski, C., Schumann, H., Andrienko, G., Andrienko. N. (2012). Stacking-based visualization of trajectory attribute data. IEEE TVCG, 18 (12) 2565–2574. doi:10.1109/TVCG.2012.265.
[23] van de Kerkhof, M., Kostitsyna, I., van Kreveld, M., Löffler, M., Ophelders, T. (2020). Routepreserving road network generalization. In: Proceedings ACM SIGSPATIAL, pages 381–384, 2020. doi:10.1145/3397536.3422234.
[24] van den Elzen, S., van Wijk. J. (2014). Multivariate network exploration and presentation: From detail to overview via selections and aggregations. IEEE TVCG, 20 (12), 2310–2319. doi:10.1109/TVCG.2014.2346441.
[25] van Dijk, T., van Goethem, A., Haunert, J.-H., Meulemans, W., Speckmann. B. (2014). Map schematization with circular arcs. In GIScience, pages 1–17. doi:10.1007/978-3-319-11593-1_1.
[26] van Goethem, A., Meulemans, W., Reimer, A., Haverkort, H., Speckmann. B. (2013). Topologicallysafe curved schematisation. The Cartographic Journal, 50 (3), 276–285. doi:10.1179/1743277413Y.0000000066.
[27] van Kreveld. M. (2001). Smooth generalization for continuous zooming. In IGU, pages 2180–2185, 2001. URL: https://icaci.org/files/documents/ICC_proceedings/ICC2001/icc2001/file/f13042.pdf.
[28] van Oosterom. P. (2005). Variable-scale topological data structures suitable for progressive data transfer: The GAPface tree and GAP-edge forest. Cartography and Geographic Information Science, 32 (4) 331–346. doi:10.1559/152304005775194782.
[29] Weiss, R., Weibel, R. (2014). Road network selection for small-scale maps using an improved centrality-based algorithm. Journal of Spatial Information Science, (9) 71–99, doi:10.5311/JOSIS.2014.9.166.
[30] Wolff, A. (2007). Drawing subway maps: A survey. Informatik-Forschung und Entwicklung, 22 (1), 23–44, 2007. doi:10.1007/s00450-007-0036-y.
[31] Wood, J., Dykes, J., Slingsby, A. (2010). Visualisation of Origins, Destinations and Flows with OD maps. The Cartographic Journal, 47 (2) 117–129. doi:10.1179/ 000870410X12658023467367.
[32] Wu, H.-Y., Niedermann, B., Takahashi, S., Nöllenburg, M. (2019). A survey on computing schematic network maps: The challenge to interactivity. In: Proceedings Schematic Mapping Workshop. URL: https://www.cg.tuwien.ac.at/research/publications/2019/wu-2019-smw/.
[33] Yang, B., Luan, X., Li, Q. (2011). Generating hierarchical strokes from urban street networks based on spatial pattern recognition. IJGIS, 25 (12) 2025–2050. doi:10.1080/13658816. 2011.570270.
[34] Yang, Y., Dwyer, T., Goodwin, S., Marriott, K. (2016). Many-to-many geographically-embedded flow visualisation: An evaluation. IEEE TVCG, 23 (1) 411–420, 2016. doi:10.1109/TVCG. 2016.2598885.
[35] Yu, W., Zhang, Y., Ai, T., Guan, Q., Chen, Z., Li, H. (2020). Road network generalization considering traffic flow patterns. IJGIS, 34 (1) 119–149. doi:10.1080/13658816.2019.1650936.
[36] Yuan, G., Sun, P., Zhao, J., Li, D., Wang, C. (2017). A review of moving object trajectory clustering algorithms. Artificial Intelligence Review, 47 (1) 123–144. doi:10.1007/ s10462-016-9477-7.
[37] Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y. (2009). Mining interesting locations and travel sequences from GPS trajectories. In Proc. WWW, pages 791–800. doi:10.1145/1526709.1526816.

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