urban data challenge - christopher a. pangilinan

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Urban Data Challenge 02 | 06 | 2013 Swissnex SFMTA Municipal Transportation Agency Image: Historic Car number 1 and 162 on Embarcadero Visualizing Transportation Data

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Page 1: Urban Data Challenge - Christopher A. Pangilinan

Urban Data Challenge

02 | 06 | 2013 Swissnex

SFMTA Municipal Transportation Agency Image: Historic Car number 1 and 162 on Embarcadero

Visualizing Transportation Data

Page 2: Urban Data Challenge - Christopher A. Pangilinan

Outline

•  System Overview and Challenges •  The Datasets •  Open Government Data •  What Insights Are We Looking For?

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•  SFMTA operates over 3 million service hours annually

•  5 distinct transit modes (bus, trolley, cable car, light rail, historic streetcar)

•  710,000 daily boardings 225 million annually

•  4th highest usage in the nation (passengers per capita)

Muni Overview

Page 4: Urban Data Challenge - Christopher A. Pangilinan

Street Level Challenges

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Transit First City •  Safe and efficient movement of

people and goods

•  Promote public transit, bicycle and pedestrian travel as attractive alternatives

•  Encourage innovative solutions to meet public transportation needs

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Striving for Reliability

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Better Market Street

Central Subway

Van Ness/Geary BRT

Signal Priority TEP

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Do these policies and projects work?

•  Reliable and faster transit service

•  Reliable: On-time and/or expected headways

•  Reliable: Consistent travel time

•  Faster: Less time to travel

•  Faster: More frequent service at same cost

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Let’s look at the data for answers Automatic Vehicle Location (AVL) Data •  Time stamp and Lat/long

every 90 sec or 200 m. •  Scheduled and actual data. •  100 percent coverage. Automatic Passenger Counter (APC) Data •  Passenger on/off data. •  “Dwell time” and traffic delay

at stops. •  Scheduled and actual data. •  Covers 30 percent of bus

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Travel Time Reliability

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Terminal Departures Performance

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Geographically Targeted Supervision

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Terminal Departures by Time of Day

Large number of late departures

Early departures after 10 p.m.

General unreliability during Owl hours

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Ridership

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Damn it Jim! I’m a transit

engineer, not a data scientist!

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Desired Insights and Visualizations

•  Service – What does the transit map look like by time

of day? – What does ridership look like by time of

day? – Where are people coming from and going

to?

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Desired Insights and Visualizations

•  Interaction with traffic – Reliability, speed,

delays – Heartbeat of the City:

How does the blood flow by time of day, day of week?

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Desired Insights and Visualizations

•  Transit Project Benefits

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Be Creative! But also help answer the key policy and

operational questions

22 http://www.nytimes.com/interactive/2010/04/02/nyregion/taxi-map.html?ref=nyregion

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Go Forth and Visualize! Chris Pangilinan @cap_transport

Felipe Robles @fliprobles San Francisco Municipal Transportation Agency