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GIScience for Dynamic Transportation Systems, GIScience Colloquium, University of Zurich, 2017

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presentation from the University of Zurich GIScience Colloquium on October 10th, 2017 (http://www.agenda.uzh.ch/record.php?id=35880&group=58)

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GIScience for Dynamic Transportation Systems, GIScience Colloquium, University of Zurich, 2017

  1. 1. Anita Graser Scientist, Center for Mobility Systems – AIT Austrian Institute of Technology GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS
  2. 2. ABOUT Anita Graser Scientist @ AIT Austrian Institute of Technology  QGIS user since 2008  MSc in Geomatics 2010  QGIS Project Steering Committee since 2013  OSGeo Director since 2015  Moderator on GIS.StackExchange.com  Author of „Learning QGIS“ (1st ed 2013), „QGIS Map Design“ (2016) & „QGIS 2 Cookbook“ (2016) @underdarkGIS
  3. 3. ABOUT – AIT MOBILITY SYSTEMS
  4. 4. Movement ContextNetwork GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS
  5. 5. VISUALIZE ALL THE THINGS! Matejka, J., & Fitzmaurice, G. (2017, May). Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1290-1294). ACM.
  6. 6. Network GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS
  7. 7. ASSESSING NETWORK DATA QUALITY Graser, A., Straub, M., & Dragaschnig, M. (2013). A comparative study of OpenStreetMap and the official Austrian reference graph for vehicle routing. ICA Workshop on Street Networks and Transport, 26th International Cartographic Conference, Dresden, Germany. OSM (black dashed) – GIP (grey solid)
  8. 8. ASSESSING NETWORK DATA QUALITY Graser, A., Straub, M., & Dragaschnig, M. (2014). Towards an open source analysis toolbox for street network comparison: indicators, tools and results of a comparison of OSM and the official Austrian reference graph. Transactions in GIS, 18(4), 510-526. doi:10.1111/tgis.12061. Turn restrictions One-ways
  9. 9. ROUTING PEDESTRIANS 911.10.2017Graser, A. (2016) Integrating Open Spaces Into OpenStreetMap Routing Graphs for Realistic Crossing Behavior in Pedestrian Navigation. GI_Forum ‒ Journal for Geographic Information Science, 1-2016, 217-230, doi:10.1553/giscience2016_01_s217.
  10. 10. Pedestrian-centered navigation instructions  using information from globally available OpenStreetMap database  automatic selection of most suitable landmark PROVIDING LANDMARK-BASED INSTRUCTIONS Graser, A. (2017). Towards landmark-based instructions for pedestrian navigation systems using OpenStreetMap, AGILE2017, Wageningen, Netherlands.
  11. 11. http://bit.do/perron DEMO WEBSITE
  12. 12. Context GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS
  13. 13. Asamer, J., Graser, A., Heilmann, B., & Ruthmair, M. (2016) Sensitivity Analysis for Energy Demand Estimation of Electric Vehicles. Transportation Research Part D: Transport and Environment, Volume 46, Pages 182-199 Graser, A., Asamer, J., & Ponweiser, W. (2015). The elevation factor: Digital elevation model quality and sampling impacts on electric vehicle energy estimation errors. In Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015 International Conference on (pp. 81-86). PREDICTING E-VEHICLE ENERGY CONSUMPTION Example route profile estimated difference: +12.96kWh (EU-DEM) +31.94kWh (SRTM3.0)
  14. 14. PROVIDING PLANNING INPUT Graser, A. (2017). Tessellating Urban Space based on street intersections & movement barriers. GI_Forum ‒ Journal of Geographic Information Science, 1-2017, 114-125, Network Planning area Network nodes Movement barriers Demand forecastShopping POIsPopulationTessellation cells
  15. 15. Movement GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS
  16. 16. 16 MEASURING MOVEMENT Widhalm, P., Nitsche, P., & Brändie, N. (2012, November). Transport mode detection with realistic smartphone sensor data. In Pattern Recognition (ICPR), 2012 21st International Conference on (pp. 573-576). IEEE.
  17. 17. MEASURING MOVEMENT 17 Nitsche, P., Widhalm, P., Breuss, S., & Maurer, P. (2012). A strategy on how to utilize smartphones for automatically reconstructing trips in travel surveys. Procedia-Social and Behavioral Sciences, 48, 1033-1046.
  18. 18. TRACKING ACTIVE MOBILITY
  19. 19. dts.ait.ac.at/projects/cycletripmap MEASURING POPULARITY
  20. 20. Which routes are popular for commuting, which for leisure trips? How does actual bicycle traffic compare to the official bicycle route network? Do cyclists choose alternative routes or avoid certain junctions / regions? MEASURING POPULARITY Straub, M., & Graser, A. (2015). Learning from Experts: Inferring Road Popularity from GPS Trajectories. GI_Forum ‒ Journal for Geographic Information Science, 1-2015, 41-50, doi:10.1553/giscience2015s41.
  21. 21. ANALYZING URBAN TRAFFIC Time Manager – https://anitagraser.com/projects/time-manager/
  22. 22. MONITORING & FORECASTING TRAFFICS Ulm, M., Heilmann, B., Asamer, J., Graser, A., & Ponweiser, W. (2015). Identifying Congestion Patterns in Urban Road Networks Using Floating Car Data. In Transportation Research Board 94th Annual Meeting (No. 15-1231).
  23. 23. Making sense of data – https://anitagraser.com/2016/09/18/movement-data-in-gis-issues-ideas/ MAKING SENSE OF DATA
  24. 24. MAKING SENSE OF DATA https://github.com/dts-ait/qgis-edge-bundling Graser, A., Schmidt, J., Roth, F. & Brändle, N. (accepted) Untangling Origin-Destination Flows in Geographic Information Systems. Information Visualization ‒ Special Issue on Visual Movement Analytics. Raw OD flows Edge bundling
  25. 25. ANALYZING MASSIVE MOVEMENT DATA Widhalm, P., Yang, Y., Ulm, M., Athavale, S., & González, M. C. (2015). Discovering urban activity patterns in cell phone data. Transportation, 42(4), 597-623. Length of stay Arrival time Length of stay Lengthofstay Arrival time Count CountCummulative
  26. 26. Office & administrationSparse residential (mixed) ANALYZING MASSIVE MOVEMENT DATA Widhalm, P., Yang, Y., Ulm, M., Athavale, S., & González, M. C. (2015). Discovering urban activity patterns in cell phone data. Transportation, 42(4), 597-623.
  27. 27. ANALYZING MASSIVE MOVEMENT DATA
  28. 28. “future work on trajectory data mining should be scalable to handle massive data." (Mazimpaka & Timpf 2016) "we routinely come up against the limits of traditional mapbased overviews of big data" (Robinson et al. 2017) Research themes include: scalability of visualization solutions and data, data size and multi-dimensionality, data filtering, visualizing time-dependent/temporal data, and visualizing qualitative data. (Çöltekin et al. 2017) ANALYZING MASSIVE MOVEMENT DATA Mazimpaka, J. D., & Timpf, S. (2016). Trajectory data mining: A review of methods and applications. Journal of Spatial Information Science, 2016(13), 61-99. Robinson, A. C., Demšar, U., Moore, A. B., Buckley, A., Jiang, B., Field, K., ... & Sluter, C. R. (2017). Geospatial big data and cartography: research challenges and opportunities for making maps that matter. International Journal of Cartography, 1-29. Arzu Çöltekin, Susanne Bleisch, Gennady Andrienko & Jason Dykes (2017). Persistent challenges in geovisualization – a community perspective, International Journal of Cartography.
  29. 29. OSGeo Stack ANALYZING MASSIVE MOVEMENT DATA https://anitagraser.com/2017/08/27/getting-started-with-geomesa-using-geodocker/
  30. 30. CONTACT Anita Graser – anita.graser@ait.ac.at @underdarkGIS

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