• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Urban Data Challenge - Christopher A. Pangilinan
 

Urban Data Challenge - Christopher A. Pangilinan

on

  • 485 views

 

Statistics

Views

Total Views
485
Views on SlideShare
485
Embed Views
0

Actions

Likes
0
Downloads
2
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Urban Data Challenge - Christopher A. Pangilinan Urban Data Challenge - Christopher A. Pangilinan Presentation Transcript

    • SFMTA Municipal Transportation Agency Image: Historic Car number 1 and 162 on Embarcadero Urban Data Challenge Visualizing Transportation Data 02 | 06 | 2013 Swissnex
    • Outline•  System Overview and Challenges•  The Datasets•  Open Government Data•  What Insights Are We Looking For? 2
    • Muni Overview•  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) 3
    • Street Level Challenges
    • 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
    • Striving for Reliability 6
    • TEP Signal Priority Central Subway 7
    • 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 8
    • Let’s look at the data for answersAutomatic 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 9
    • Travel Time Reliability 10
    • Terminal Departures Performance 12
    • Geographically Targeted Supervision
    • Terminal Departures by Time of Day Large number of late departures Early departures after 10 p.m. General unreliability during Owl hours
    • Ridership 15
    • Damn it Jim! I’m a transitengineer, not a data scientist!
    • 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? 19
    • 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? 20
    • Desired Insights and Visualizations•  Transit Project Benefits 21
    • Be Creative!But also help answer the key policy and operational questions http://www.nytimes.com/interactive/ 22 2010/04/02/nyregion/taxi-map.html?
    • Go Forth and Visualize! Chris Pangilinan @cap_transport Felipe Robles @fliproblesSan Francisco Municipal Transportation Agency