mapping city wide travel times
DESCRIPTION
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?. - PowerPoint PPT PresentationTRANSCRIPT
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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Go to http://Iskander/TravelTime/
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GIS Side
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Data
Preprocessing
Simulation
vs.
web sidenetwork side
Data: GTFS• GTFS = “General Transit Feed
Specification”.
• Describes transit routes, stops, times, etc.
• Google Maps Routing
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Data: OSM• OSM = “Open Street Map”.
• “Crowd Sourced”, open source map data.
• Downloaded as plain text.
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Why OSM?
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Source: Boulder County Source: OSM
Preprocessing OSM• Convert text to a raster grid that
represents the friction of distance.– Theory: it’s easier to walk on / near
streets.
1. Extract OSM paths.2. Rasterize.3. Skeletonize.4. 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
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• Convert the vector paths into a tessellation.
2. Rasterize
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• Convert the vector paths into a tessellation.
* intersects * Brensenham’s Line Algorithm
3. Skeletonize• Goal: get distance (in tiles) from
nearest path.– Also called “Medial Axis Transformation”.
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Step 1: Fill raster 1s. Set roads to 0.
Iterate: For each cell, set its value equal to the minimum of its neighbours + 1.
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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3
1
Distance from nearest path (m)0 75
Frict
ion
smoothstep function
Preprocessing: Micro Scale
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OSM Paths Rasterize Skeletonize Smoothstep
0 10 1 3x
Preprocessing: Micro Scale
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OSM Paths Rasterize Skeletonize Smoothstep
0 10 1 3x
Preprocessing: Square (macro)
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OSM Paths Rasterize Skeletonize Smoothstep
1 3x
Preprocessing: Hexagon (macro)
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OSM Paths Rasterize Skeletonize Smoothstep
1 3x
Preprocessing: Hexagon (macro)
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OSM Paths Rasterize Skeletonize Smoothstep
1 3x
Simulation Parameters
1. City? (Boulder ,CO)
2. Where? (latitude, longitude)
3. When? (December 1, 2013 at 2:30
PM)
4. Grid Type? (square or hexagon)
5. Walking Speed? (3.1 m/s)
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Simulation Steps1. Construct a connected graph of
nodes from our smoothstep grid.
Grid to Graph
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* Queen Contiguity
Node
Link
Smoothstep
Grid to Graph (hex)
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Node
Link
Smoothstep
Simulation Steps2. Given a starting node, walk across
the graph finding the fastest path to each node.
Weight or CostTime = friction * walking speed
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Constant Cost Friction Cost + Public Transit
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Constant Cost Friction Cost + Public Transit
• (Static Differences)
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(Hexagons - Squares)SquaresHexagons
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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