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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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Presentation Transcript
project goal
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

slide3

Go to http://Iskander/TravelTime/

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

gis side
GIS Side

Data

Preprocessing

vs.

Simulation

web side

network side

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

data gtfs
Data: GTFS
  • GTFS = “General Transit Feed Specification”.
  • Describes transit routes, stops, times, etc.
  • Google Maps Routing

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

data osm
Data: OSM
  • OSM = “Open Street Map”.
  • “Crowd Sourced”, open source map data.
  • Downloaded as plain text.

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

why osm
Why OSM?

Source: Boulder County

Source: OSM

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preprocessing osm
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

1 extract paths
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

2 rasterize
2. Rasterize
  • Convert the vector paths into a tessellation.

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

2 rasterize1
2. Rasterize
  • Convert the vector paths into a tessellation.

* Brensenham’s Line Algorithm

* intersects

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

3 skeletonize
3. Skeletonize
  • Goal: get distance (in tiles) from nearest path.
    • Also called “Medial Axis Transformation”.

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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

4 transform w smoothstep
4. Transform w/ smoothstep
  • Goal: Convert distance from road (in tiles) to factors of friction.
    • It takes x times longer to cross this cell.

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Friction

smoothstep function

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Distance from nearest path (m)

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

preprocessing micro scale
Preprocessing: Micro Scale

Smoothstep

OSM Paths

Rasterize

Skeletonize

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3x

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

preprocessing micro scale1
Preprocessing: Micro Scale

Smoothstep

OSM Paths

Rasterize

Skeletonize

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3x

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

preprocessing square macro
Preprocessing: Square (macro)

Smoothstep

OSM Paths

Rasterize

Skeletonize

1

3x

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

preprocessing hexagon macro
Preprocessing: Hexagon (macro)

Smoothstep

OSM Paths

Rasterize

Skeletonize

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3x

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

preprocessing hexagon macro1
Preprocessing: Hexagon (macro)

Smoothstep

OSM Paths

Rasterize

Skeletonize

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3x

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

simulation parameters
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

simulation steps
Simulation Steps
  • Construct a connected graph of nodes from our smoothstep grid.
grid to graph
Grid to Graph

Node

Smoothstep

Link

* Queen Contiguity

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

grid to graph hex
Grid to Graph (hex)

Node

Smoothstep

Link

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

simulation steps1
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

slide24

Friction Cost

+ Public Transit

Constant Cost

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

slide25

Friction Cost

+ Public Transit

Constant Cost

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

slide26

Hexagons

Squares

(Hexagons - Squares)

  • (Static Differences)

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

hexagons vs squares
Hexagons vs. Squares
  • Computing Cost
    • Hexagons: 2.5 times longer
    • Visualization
  • Simulation Differences
    • Preprocessing
    • Contiguity

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions

wrap up
Wrap-up
  • Alternate forms of transportation
    • Is public transit and walking efficient?

-in terms of time?

intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions