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?

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imulat ion \ conc lus ions

Go to http://Iskander/TravelTime/

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imulat ion \ conc lus ions

GIS Side

i n t ro \ demo \ g i s s ide \ da ta \ p reprocess ing \ s imula t ion \ conc lus ions

Data

Preprocessing

Simulation

vs.

web sidenetwork side

Data: GTFS• GTFS = “General Transit Feed

Specification”.

• Describes transit routes, stops, times, etc.

• Google Maps Routing

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imula t ion \ conc lus ions

Data: OSM• OSM = “Open Street Map”.

• “Crowd Sourced”, open source map data.

• Downloaded as plain text.

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imula t ion \ conc lus ions

Why OSM?

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imula t ion \ conc lus ions

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

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

1. Extract Paths• OSM contains different types of

paths.

• Extract all the “highways”, including– Highways– Residential streets– Bike paths– Sidewalks–…

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

2. Rasterize

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

• Convert the vector paths into a tessellation.

2. Rasterize

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

• 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”.

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

00000

0

01111

0

01111

0

01111

0

01111

0

00000

0

01111

00 1 1 1 1 01

00000

0

01111

0

01222

0

01222

0

01222

0

00000

0

01111

00 1 1 1 1 01

00000

0

01111

0

01222

0

01232

0

01222

0

00000

0

01111

00 1 1 1 1 01

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.

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

3

1

Distance from nearest path (m)0 75

Frict

ion

smoothstep function

Preprocessing: Micro Scale

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

OSM Paths Rasterize Skeletonize Smoothstep

0 10 1 3x

Preprocessing: Micro Scale

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

OSM Paths Rasterize Skeletonize Smoothstep

0 10 1 3x

Preprocessing: Square (macro)

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

OSM Paths Rasterize Skeletonize Smoothstep

1 3x

Preprocessing: Hexagon (macro)

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

OSM Paths Rasterize Skeletonize Smoothstep

1 3x

Preprocessing: Hexagon (macro)

i n t ro \ demo \ g i s s ide \ data \ preprocess ing \ s imula t ion \ conc lus ions

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)

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imula t ion \ conc lus ions

Simulation Steps1. Construct a connected graph of

nodes from our smoothstep grid.

Grid to Graph

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imula t ion \ conc lus ions

* Queen Contiguity

Node

Link

Smoothstep

Grid to Graph (hex)

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imula t ion \ conc lus ions

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

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imula t ion \ conc lus ions

Constant Cost Friction Cost + Public Transit

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imula t ion \ conc lus ions

Constant Cost Friction Cost + Public Transit

• (Static Differences)

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imulat ion \ conc lus ions

(Hexagons - Squares)SquaresHexagons

Hexagons vs. Squares• Computing Cost– Hexagons: 2.5 times longer – Visualization

• Simulation Differences– Preprocessing– Contiguity

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imulat ion \ conc lus ions

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

-in terms of time?

i n t ro \ demo \ g i s s ide \ data \ p reprocess ing \ s imulat ion \ conc lus ions