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Mapping City Wide Travel Times

Mapping City Wide Travel Times. Andrew Hardin. Project Goal. Encouraging alternate transportation NYC- Bike Share Boulder’s Transportation Management Why? Is using public transit and walking efficient ? in terms of time?.

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Mapping City Wide Travel Times

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  1. Mapping City Wide Travel Times Andrew Hardin

  2. Project Goal • Encouraging alternate transportation • NYC- Bike Share • Boulder’s Transportation Management • Why? • Is using public transit and walking efficient? • in terms of time? intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  3. Go to http://Iskander/TravelTime/ intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  4. GIS Side Data Preprocessing vs. Simulation web side network side intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  5. Data: GTFS • GTFS = “General Transit Feed Specification”. • Describes transit routes, stops, times, etc. • Google Maps Routing intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  6. Data: OSM • OSM = “Open Street Map”. • “Crowd Sourced”, open source map data. • Downloaded as plain text. intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  7. Why OSM? Source: Boulder County Source: OSM intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  8. Preprocessing OSM • Convert text to a raster grid that represents the friction of distance. • Theory: it’s easier to walk on / near streets. • Extract OSM paths. • Rasterize. • Skeletonize. • Transform with smoothstep intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  9. 1. Extract Paths • OSM contains different types of paths. • Extract all the “highways”, including • Highways • Residential streets • Bike paths • Sidewalks • … intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  10. 2. Rasterize • Convert the vector paths into a tessellation. intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  11. 2. Rasterize • Convert the vector paths into a tessellation. * Brensenham’s Line Algorithm * intersects intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  12. 3. Skeletonize • Goal: get distance (in tiles) from nearest path. • Also called “Medial Axis Transformation”. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 2 2 2 1 0 0 1 2 2 2 1 0 0 1 1 1 1 1 0 0 1 2 3 2 1 0 0 1 2 2 2 1 0 0 1 1 1 1 1 0 0 1 2 2 2 1 0 0 1 2 2 2 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Iterate: For each cell, set its value equal to the minimum of its neighbours + 1. Step 1: Fill raster 1s. Set roads to 0. intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  13. 4. Transform w/ smoothstep • Goal: Convert distance from road (in tiles) to factors of friction. • It takes x times longer to cross this cell. 3 Friction smoothstep function 1 0 75 Distance from nearest path (m) intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  14. Preprocessing: Micro Scale Smoothstep OSM Paths Rasterize Skeletonize 1 0 10 3x intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  15. Preprocessing: Micro Scale Smoothstep OSM Paths Rasterize Skeletonize 1 0 10 3x intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  16. Preprocessing: Square (macro) Smoothstep OSM Paths Rasterize Skeletonize 1 3x intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  17. Preprocessing: Hexagon (macro) Smoothstep OSM Paths Rasterize Skeletonize 1 3x intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  18. Preprocessing: Hexagon (macro) Smoothstep OSM Paths Rasterize Skeletonize 1 3x intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  19. Simulation Parameters • City? (Boulder ,CO) • Where? (latitude, longitude) • When? (December 1, 2013 at 2:30 PM) • Grid Type? (square or hexagon) • Walking Speed? (3.1 m/s) intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  20. Simulation Steps • Construct a connected graph of nodes from our smoothstep grid.

  21. Grid to Graph Node Smoothstep Link * Queen Contiguity intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  22. Grid to Graph (hex) Node Smoothstep Link intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  23. Simulation Steps 2. Given a starting node, walk across the graph finding the fastest path to each node. Weight or Cost Time = friction * walking speed

  24. Friction Cost + Public Transit Constant Cost intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  25. Friction Cost + Public Transit Constant Cost intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  26. Hexagons Squares (Hexagons - Squares) • (Static Differences) intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  27. Hexagons vs. Squares • Computing Cost • Hexagons: 2.5 times longer • Visualization • Simulation Differences • Preprocessing • Contiguity intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

  28. Wrap-up • Alternate forms of transportation • Is public transit and walking efficient? -in terms of time? intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

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