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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
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?
intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions
Go to http://Iskander/TravelTime/
intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions
GIS Side
Data
Preprocessing
vs.
Simulation
web side
network side
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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
- OSM = “Open Street Map”.
- “Crowd Sourced”, open source map data.
- Downloaded as plain text.
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Why OSM?
Source: Boulder County
Source: OSM
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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
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1. Extract Paths
- OSM contains different types of paths.
- Extract all the “highways”, including
- Highways
- Residential streets
- Bike paths
- Sidewalks
- …
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2. Rasterize
- Convert the vector paths into a tessellation.
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2. Rasterize
- Convert the vector paths into a tessellation.
* Brensenham’s Line Algorithm
* intersects
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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.
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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)
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Preprocessing: Micro Scale
Smoothstep
OSM Paths
Rasterize
Skeletonize
1
0
10
3x
intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions
Preprocessing: Micro Scale
Smoothstep
OSM Paths
Rasterize
Skeletonize
1
0
10
3x
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Preprocessing: Square (macro)
Smoothstep
OSM Paths
Rasterize
Skeletonize
1
3x
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Preprocessing: Hexagon (macro)
Smoothstep
OSM Paths
Rasterize
Skeletonize
1
3x
intro \ demo \ gis side \ data \ preprocessing \ simulation \ conclusions
Preprocessing: Hexagon (macro)
Smoothstep
OSM Paths
Rasterize
Skeletonize
1
3x
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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)
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Simulation Steps
- Construct a connected graph of nodes from our smoothstep grid.
Grid to Graph
Node
Smoothstep
Link
* Queen Contiguity
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Grid to Graph (hex)
Node
Smoothstep
Link
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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
+ Public Transit
Constant Cost
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+ Public Transit
Constant Cost
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Squares
(Hexagons - Squares)
- (Static Differences)
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Hexagons vs. Squares
- Computing Cost
- Hexagons: 2.5 times longer
- Visualization
- Simulation Differences
- Preprocessing
- Contiguity
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Wrap-up
- Alternate forms of transportation
- Is public transit and walking efficient?
-in terms of time?
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