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City Travel Time Mapping Project: Encouraging Alternate Transportation in NYC and Boulder

This project aims to analyze travel times in cities like NYC and Boulder to encourage the use of alternate transportation methods. By utilizing GIS data preprocessing and simulations, the efficiency of public transit and walking routes in terms of time is evaluated. The project includes extracting paths from General Transit Feed Specification (GTFS) and Open Street Map (OSM) data, rasterizing vector paths, skeletonizing, and transforming distance data. By converting road distances into factors of friction, the simulation calculates the fastest paths considering walking speed and public transit costs. Various scales of preprocessing like micro and macro smoothstep methods are implemented. The project explores the difference between hexagon and square grids in terms of cost computation and visualization. The goal is to promote sustainable and time-efficient modes of urban transportation.

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City Travel Time Mapping Project: Encouraging Alternate Transportation in NYC and Boulder

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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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