Post on 28-Nov-2014
description
DAIYING CHEN DAVID LEVINSONADDAM HALL KAREN MESKOLISA HALL EI EI MIN THUNOLAN LEAVITT SUDHEER SOMESHWARA
Fall 2009
Heinz College, Carnegie Mellon University
a a real-time pilot for the CMU Shuttle
Agenda
History Planning Implementation Results Going Forward
Stakeholders
Starting Problem Advancing knowledge
within CMU community, in line with Traffic21
Benefiting area residents and commuters
Make significant and substantial contributions to public policy and non-profit management
Stakeholders
Our Changing World
Our Solution: Real-Time Information
Real-Time Transportation Information Cutting-edge technology Novel solution to reliability problems
Many benefits To riders To transit providers To community
Agenda
History Planning Implementation Results Going Forward
Deliverables
Port Authority Technical Capabilities Report Public Transit Ridership Surveys myRide website - http://myride.heinz.cmu.edu Funding Request for permanent system Future coursework plans Android Phone GPS tracking application Google Transit Feed Specification compliant database Mobile Webpage Project Document Report
Benchmarking
University of Michigan TransLoc
~60-bus fleet covers 10 routes~Magic Bus was designed by students~Maintained by staff and students~Funded by Transportation Dept.
~Newer company based in Raleigh~Provides services for 15 schools, including Princeton, Auburn, and Yale
Internal CMU Ridership Survey
Goal: identify the most effective and desireddissemination methods for the CMU shuttle
Small, N= 51 Conducted in person at CMU Shuttle stops and on
the Shuttle. Time frame: Weekdays at various times in mid-
October.
CMU Ridership Survey Takeaways
Shuttle riders do have issues with the timeliness of service.
A wide range of people use the shuttle. Shuttle riders have very high levels of access to
Internet and Text plans. iPhones would not be the most effective way to
reach the largest number of people. Focus on a webpage that can be viewed on
mobile devices.
Pittsburgh Community Survey
Goal: Measure attitudes and perceptions in regards to public transit and technology.
Key factors we wanted to measure: Ridership habits Factors affecting demand elasticity for public transit Access to information dissemination methods Receptiveness to various real-time services Perceived value of a real-time system
The questions posed to respondents were modeled after a series of questions used in a 2006 study by the FTA in estimating benefits of a real-time system.
Source: Real-time Bus Arrival Systems Return on Investment Study. Federal Transit Administration, 2006.
Pittsburgh Community Survey Methodology
Our survey was limited in breadth and depth by a limited timeframe and limited resources. The sample size is not intended to be arandom sampling of Allegheny County residents; instead, it attemptsto measure riders and advocates in the Oakland-Downtowncorridor.
N=148 Survey conducted in-person and online 31% Random sample of pedestrians and bus riders in the
Oakland corridor and downtown 35% Students, faculty and professionals in the Higher Education
field 34% Developmental, cultural and transportation advocacy groups
via text message via an automated telephone system
via a standard website via a website for mobile devices/
smartphones
via a smart phone application
30.2
11.1
26.7
8.9
28.3
32.6
22.2
11.1
22.2
13
7
8.9
46.7
33.34.3
16.3
17.8
8.9
22.2
30.4
14
40
6.713.4
23.8
Respondents ranking of dissemination methods1 (Most Prefer) 2 3 4 (5) Least Prefer
Pittsburgh Community SurveyPreferred Delivery Methods
97.1% 90.1% 72.8% 21.3%Access to method:
Pittsburgh Community SurveyPerceived Value
56.5 %34.8 %
6.5 %
2.2 %
Percentage of respondents who felt that "real-time bus information that was accurate, free and accessible
to me would...."
Increase Ridership
Greatly Increase Ridership
No effect on Rid-ership
Negative effect on Ridership
Pittsburgh Community SurveyPerceived Value
Improve my perception of Public Transporta-tion
Make Public Transit more accessible
Make my commute or trip easier
Make Public Transit more appealing
Improve our region's reputation
97.9 %
89.4 %
85.1 %
76.6 %
57.4 %
Percentage of respondents who felt that a real-time Bus System would...
Pittsburgh Community Survey Takeaways
When compared with other metro regions, the Oakland-Downtown corridor has:
The FTA estimated that a system wide real-time system would increase ridership by 6%-8%.
National Average (2006)
Community Survey
Access to Internet 53.3% 72.8%
Access to Mobile Internet 7.2% 21.3%
Dissatisfaction w/ On Time Performance
42.8% 58.4%
Dissatisfaction with Frequency of Service.
51.3% 60.5%
Source: Real-time Bus Arrival Systems Return on Investment Study. Federal Transit Administration, 2006.
Web server
Transmitting Real-time Bus Location Part
Accessing Real-time BUS Location Part
Bus with GPS
Send GPS data to Web serverWeb Application
Mobile Web
G-phone & T-mobile
Riders
Scope Framework
GTFS Data Schema
Estimated Time ModuleLocation Retrieval Module
Map Plug In
Use Case Diagram
Driver
Transport Admin
RiderGeneral User
Start auto-GPS transmission for any Route
Stop auto-GPS transmission for any Route
Change the Route
myRide SystemAdd new Alert for
riders
Add another Admin user
View Current Bus location on
the map
View full schedule for each route
View myRide on
their Mobile Phone
Use Twitter to follow, share the updates
View estimated
arrival time for their bus
stop
Agenda
History Planning Implementation Results Going Forward
Logistics
Graphic Design
Graphic Design
http://myride.heinz.cmu.edu
Marketing Roll-out
Highlighted tables are GTFS-compliant schema Improve scalability and future enhancement with Google
GTFS-Compliant Database Schema
Data Source Challenges
Bus stop information not available Collected bus stop information Obtained GPS longitude/latitude from Google Maps
Collaborated with drivers to get accurate schedule Route and schedule data population
3 Routes 23 Stops 78 Trips 1140 records of Stop-times
Route Stops Population Data
Runs as background service on Google Android Phones Transmits GPS data every 5 seconds Easy to use for different routes User-friendly User Interface (UI) for Shuttle Drivers
GPS Transmission
GPS Transmission Challenges
Learning curve of Android Platform GPS providers
Network vs. GPS satellite provider Adjusting GPS transmission interval
1 minute or 50 seconds or 5 seconds Performance vs. Accuracy
Deployment to real phone Versions crisis
GPS background service challenge Reliability of hidden service Does phone screen lock stop our application?
Get GPSTransmit
to Web Server
Background Service
Main Web Interface
Challenges Behind the Scene
Geographic Information System Calculating distance by Vincenty’s formula with
ellipsoidal model of earth Accuracy within 0.5mm[1]
Route distances vs. straight-line distance Mapping raw GPS to nearest bus stop
Geocoding with Google Map Reverse Geocoding Encoded Geopolyline mapping Ajax and timer for updating real-time
Cross-Browser Compatibility[1] Source: http://www.movable-type.co.uk/scripts/latlong-vincenty.html
Challenges: Estimated Time Prediction
Inaccurate schedule stop times
Exponentially Weighted Moving Average Problem with frequent stop times
Kalman-Filter Prediction Algorithms[1]
Consider dwelling times
Various Scenarios Select stop time Schedule time Last trip
[1] Source: Prediction Models of Bus Arrival and Departure Times, University of Toronto
Challenges: Estimated Time Prediction
Is Schedule running?
Get Latest GPS data
Is GPS data
outdated?
Get Next Schedule Time
Get the distance and speed to selected stop
Yes
Yes
No
Display Not Running Now
No
No speed? Or Morewood is in between?
Yes
Predict time
No
Last Trip of the day and passed by?
Display Location without Time
Yes
Mobile Phone Interface
Challenges
Display and bandwidth limitations Layout changes for mobile Decrease page load time
Request redirection Device detection
Request
Users
Transport Admin Interface
User Location Detection
Detecting nearest stop based on user’s current location
Google Gears – Geolocation API
Agenda
History Planning Implementation Results Going Forward
Test Cases
Test Reports
Written Time Stamp
Written Location Desc
Server Time Stamp
Server Location
Notes
2:16 Morewood Garden
2009-11-08 14:18:09
Morewood Ave 1076
2:18 Craig and Forbes Corner (Traffic)
2009-11-08 14:19:28
Forbes Ave 4899
100m
2:19 Forbes and Dithridge
2009-11-08 14:21:23
Forbes and Dithridge
50m
2:20 Webster Hall
2009-11-08 14:12:00
S Dithridge St 155
10m
Test for Route AB – by Ei Ei Min Thu 11/08/09Procedure: attached the phone on bus window without interaction. Phone is charged with laptop on.
Web Counter
Thanksgiving Holiday
Accomplishments
Android deployment GIS (Geographic Information
Systems) Challenges Estimating bus arrival time Mobile Compatibility
Agenda
History Planning Implementation Results Going Forward
Next Steps: Future Enhancements
Enhance System Admin module to update the Route and the stop times on the UI
Improve the Estimated Time algorithm Incorporate the CMU Escort and PTC Shuttle
Route Add advertisements and school announcements
on the website
Next Steps
Continue the Pilot Install more robust hardware Create an iPhone application
Devise traffic monitoring system based on sensor and server capabilities
Pursue funding opportunities Advertisements University Funding Traffic21
Acknowledgements
Robert Hampshire (team advisor) (donation of G1 Phones) CMU Shuttle: Lt. Gary Scheimer, Jim Heverly, Jim
McNeil, Colton Brown, James Collins, & Jason Brown
Ramayya Krishnan, Rick Stafford, Dave Roger, Steve Bland, and Joe Hughes (advisory board)
Hillman Foundation Gary Franko (design and printing support)
Contact Information
Addam Hall (project manager): aehall@andrew.cmu.edu
Ei Ei Min Thu (IT manager): eiei@cmu.edu Robert Hampshire (advisor): hamp@cmu.edu