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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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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