Real-time data & mobile
Increasing the uptake of public transport
Rupert Hanson
Developer, AppJourney
@rpy
NSW Transport I...
Outline
•The case for real-time data
•How real-time data works
•Emergence and challenges
•Implications
The case for real-time data
• Service reliability is difficult
• Agencies need to communicate with
customers in a timely, ...
Top 4 customer information needs
• Departure, arrival times
• Trackwork, station closures
• Delays, cancellations
• Wayfin...
Customer information needs
“Obtaining train delay information is:
• Most difficult when you are on the train
and before th...
Voice of the customer
“If the train is 15 minutes late,
tell me it’s going to be 15
minutes late”
Source: Customer intervi...
Customer experience
• 80% of transit users indicated uncertainty
as to the arrival time of their service
caused frustratio...
Why open data?
• Technology landscape changing rapidly
• Public can contribute services that are
cost/time prohibitive for...
How does real-time data work?
How does real-time data work?
Collect data Publish feeds Real-time apps
But where from?
• Scheduling systems
• Vehicle telemetry
• Operations staff
• Network infrastructure
Collect data Publish ...
Data feed types
• Schedules
• Vehicle positions
• Service alerts
• Trip updates
Collect data Publish feeds Real-time apps
Data interchange formats
• GTFS
• GTFS-realtime
• TransXChange
• SIRI
• Proprietary APIs (NextBus, REST, SOAP)
Collect dat...
Collect data Publish feeds Real-time apps
GTFS: Google Transit
Feed Specification
• Schedules, shapes, fares
• CSV based f...
Collect data Publish feeds Real-time apps
TransXChange
• Schedule data format
• XML based
• UK standard sponsored by
Depar...
• GTFS smaller, works as relational data, contains shapes
• Volumes for schedule data in Sydney:
0
225
450
675
900
GTFS Tr...
Collect data Publish feeds Real-time apps
GTFS-realtime
• Vehicle position, delays and
service alerts
• Protobuf binary fo...
Collect data Publish feeds Real-time apps
SIRI: Service Interface for Real-
time Information
• Complex, broad ranging XML ...
Proprietary APIs
• Some agencies have rolled their own API
• NextBus commercial XML based system
popular in US
• Proprieta...
Collect data Publish feeds Real-time apps
Sydney
Trains
PTIPS
131500
TDX
TfNSW infrastructure Application
servers
Real-tim...
Emergence of real-time data
Source: 131500.com.au, February 2001
NSW: PTIPS
• Real-time bus tracking system
• Grants traffic signal priority
• Provides feedback to bus operators
• Project...
NSW: PTIPS
Vehicles send GPS position at waypoints
NSW: TDX
• Transport Data
Exchange
• Launched 2009
• Open access to
schedule data,
RMS traffic alerts
NSW: real-time bus data
• 0488TXTBUS launched 2010
• Temporary access to real-time vehicle positions in
March 2011 at apps...
NSW: real-time train data
• Developer hothouse in January 2013
• GTFS daily and long term schedule data
• GTFS-realtime ve...
NSW: real-time train data
• Signalling reports
track circuit
occupation
• Passenger
information
systems correlate
to runs ...
NSW: real-time train data
• Vehicle positions updated every 10
seconds
• RMC staff report alerts at network, route,
trip a...
NSW: The future
• More PTIPS enabled bus agencies
• More rail network coverage
• Real-time ferries
• Light rail data
• Acc...
In Australia
Sydney
GTFS, TransXChange,
GTFS-realtime
Brisbane GTFS,
real-time in
development
Canberra GTFS, real-
time in...
In Australia
0
10000
20000
30000
40000
Sydney Brisbane Perth Adelaide Canberra
Bus Train Ferry Light Rail
Services schedul...
Around the world
• 200+ US transit agencies publishing GTFS
• 330+ GTFS feeds globally
• GTFS-realtime in US, New Zealand,...
Around the world
Sources: Various agencies, gtfs-data-exchange.com, code.google.com/p/googletransitdatafeed/wiki/PublicFee...
Challenges
• Data quality and service reliability
• Data longevity
• Integrating disparate systems
• Resistance to transpa...
Implications
• Customer experience
• Increased transit ridership
• Multi-modal integration
• Reduced technology costs
• An...
Customer experience
• 92% of transit users somewhat more or
much more satisfied
• 91% of users reported spending less time...
Customer experience
• Real-time information reduced perceived
wait time by 20-26%
• 46% of night time transit users felt s...
Customer experience
• Milwaukee: complaints decreased 24%
• Denver: complaints decreased 26%
• Portland: complaints decrea...
Increased transit ridership
Source: Ferris, Watkins & Borning, 2010, ‘OneBusAway: results from providing real-time arrival...
Increased transit ridership
• Finland: 25% of bus passengers reported
increased use because of real-time data
• Liverpool:...
Multi-modal integration
• Customers can make real-
time multi-modal decisions
• Decentralised demand
management following
...
Multi-modal integration
• 78% customers more likely to walk to a
different stop than previously
• Users with access to rea...
Reduced technology costs
• Decreased procurement requirements for
customer facing applications
• Draw on developer communi...
Reduced technology costs
“We’re small and we can’t provide
every customised solution people
ask for... it’s like having an...
Analytics for agencies and customers
• Network coverage
• On-time running, service reliability
• Network bottlenecks
• Tra...
Analytics
Network coverage
Analytics
On-time running
Analytics
Network bottlenecks
• Analyse vehicle
movements, compare
against scheduled
timings
• Aggregate data over
short a...
Analytics
Travel demand patterns
Real-time data & mobile
Increasing the uptake of public transport
Rupert Hanson
Developer, AppJourney
@rpy
NSW Transport I...
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Real-time data and smart phone technology increasing the uptake of public transport

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Rupert Hanson, Web/ IOS Developer, Triptastic delivered this presentation at the 2013 NSW State Transport Infrastructure Summit.
The State Transport Infrastructure Series of events represent the leading forums in Australia to assess the future plans for transport infrastructure development and financing across Australia. For more information, please visit www.statetransportevents.com.au

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Real-time data and smart phone technology increasing the uptake of public transport

  1. 1. Real-time data & mobile Increasing the uptake of public transport Rupert Hanson Developer, AppJourney @rpy NSW Transport Infrastructure Summit August 2013
  2. 2. Outline •The case for real-time data •How real-time data works •Emergence and challenges •Implications
  3. 3. The case for real-time data • Service reliability is difficult • Agencies need to communicate with customers in a timely, relevant way • Fulfil customer information needs • Increase customer satisfaction
  4. 4. Top 4 customer information needs • Departure, arrival times • Trackwork, station closures • Delays, cancellations • Wayfinding Source: Stancombe Research & Planning, December 2011
  5. 5. Customer information needs “Obtaining train delay information is: • Most difficult when you are on the train and before the journey • Acceptable when you are at the station • Not relevant when you arrive at the destination” Source: Stancombe Research & Planning, December 2011
  6. 6. Voice of the customer “If the train is 15 minutes late, tell me it’s going to be 15 minutes late” Source: Customer interviews conducted by PwC, October 2012
  7. 7. Customer experience • 80% of transit users indicated uncertainty as to the arrival time of their service caused frustration • Transit users overestimate wait time by 24-30% Source: A Stated Preference Analysis of Real-Time Public Transit Stop Information, Journal of Public Transportation, Vol. 12, No. 3, 2009
  8. 8. Why open data? • Technology landscape changing rapidly • Public can contribute services that are cost/time prohibitive for public sector • Developers want to innovate • Ensure a single, quality source of truth
  9. 9. How does real-time data work?
  10. 10. How does real-time data work? Collect data Publish feeds Real-time apps
  11. 11. But where from? • Scheduling systems • Vehicle telemetry • Operations staff • Network infrastructure Collect data Publish feeds Real-time apps
  12. 12. Data feed types • Schedules • Vehicle positions • Service alerts • Trip updates Collect data Publish feeds Real-time apps
  13. 13. Data interchange formats • GTFS • GTFS-realtime • TransXChange • SIRI • Proprietary APIs (NextBus, REST, SOAP) Collect data Publish feeds Real-time apps
  14. 14. Collect data Publish feeds Real-time apps GTFS: Google Transit Feed Specification • Schedules, shapes, fares • CSV based flat files • Low level, but easy to generate and consume • Created by Google and TriMet in 2005
  15. 15. Collect data Publish feeds Real-time apps TransXChange • Schedule data format • XML based • UK standard sponsored by Department of Transport since 2000 • Follows CEN Transmodel, interoperable with SIRI
  16. 16. • GTFS smaller, works as relational data, contains shapes • Volumes for schedule data in Sydney: 0 225 450 675 900 GTFS TransXChange 58MB 105MB 883MB 439MB TDX Sydney Buses Sydney Trains Collect data Publish feeds Real-time apps
  17. 17. Collect data Publish feeds Real-time apps GTFS-realtime • Vehicle position, delays and service alerts • Protobuf binary format • Provides complete snapshot of the network • Introduced by Google in August 2011 header { gtfs_realtime_version: "1.0" incrementality: FULL_DATASET timestamp: 1375286551 } entity { id: "75035903_20130701_11954" vehicle { trip { trip_id: "75035903_20130701_11954" } position { latitude: -33.651478 longitude: 151.3231 bearing: 16.0 speed: 34.4 } timestamp: 1375286537 vehicle { id: "75035903_20130701_11954" label: "1721" } } }
  18. 18. Collect data Publish feeds Real-time apps SIRI: Service Interface for Real- time Information • Complex, broad ranging XML API • European standard developed by France, Germany, Scandinavia, UK • Covers schedule data, real-time position, delays, service alerts, performance metrics, managing connecting services
  19. 19. Proprietary APIs • Some agencies have rolled their own API • NextBus commercial XML based system popular in US • Proprietary implementations limit potential for innovation Collect data Publish feeds Real-time apps
  20. 20. Collect data Publish feeds Real-time apps Sydney Trains PTIPS 131500 TDX TfNSW infrastructure Application servers Real-time apps GTFS GTFS-RT GTFS GTFS-RT GTFS TransXCh
  21. 21. Emergence of real-time data Source: 131500.com.au, February 2001
  22. 22. NSW: PTIPS • Real-time bus tracking system • Grants traffic signal priority • Provides feedback to bus operators • Project commenced 2004
  23. 23. NSW: PTIPS Vehicles send GPS position at waypoints
  24. 24. NSW: TDX • Transport Data Exchange • Launched 2009 • Open access to schedule data, RMS traffic alerts
  25. 25. NSW: real-time bus data • 0488TXTBUS launched 2010 • Temporary access to real-time vehicle positions in March 2011 at apps4nsw competition • App developer hothouse held October 2012 • GTFS-realtime feeds for vehicle position, delay forecasts generated from PTIPS • Real-time apps launched December 2012
  26. 26. NSW: real-time train data • Developer hothouse in January 2013 • GTFS daily and long term schedule data • GTFS-realtime vehicle position, service alert feeds • Real-time apps launched April 2013
  27. 27. NSW: real-time train data • Signalling reports track circuit occupation • Passenger information systems correlate to runs and GPS coordinates
  28. 28. NSW: real-time train data • Vehicle positions updated every 10 seconds • RMC staff report alerts at network, route, trip and station levels • Delay forecasting based on waypoints and dwell times
  29. 29. NSW: The future • More PTIPS enabled bus agencies • More rail network coverage • Real-time ferries • Light rail data • Accuracy improvements
  30. 30. In Australia Sydney GTFS, TransXChange, GTFS-realtime Brisbane GTFS, real-time in development Canberra GTFS, real- time in development Perth GTFS Adelaide GTFS Melbourne No data; proprietary tram API Hobart No data Darwin No data
  31. 31. In Australia 0 10000 20000 30000 40000 Sydney Brisbane Perth Adelaide Canberra Bus Train Ferry Light Rail Services scheduled to operate Wednesday 7 August 2013 Source: TfNSW, TransLink, PTAWA, Adelaide Metro, ACTION GTFS data feeds
  32. 32. Around the world • 200+ US transit agencies publishing GTFS • 330+ GTFS feeds globally • GTFS-realtime in US, New Zealand, France • NextBus API popular in US, Canada • SIRI used in Norway, UK, Germany, Sweden
  33. 33. Around the world Sources: Various agencies, gtfs-data-exchange.com, code.google.com/p/googletransitdatafeed/wiki/PublicFeedsNonGTFS GTFS TransXChange Proprietary API GTFS-realtime NextBus SIRI
  34. 34. Challenges • Data quality and service reliability • Data longevity • Integrating disparate systems • Resistance to transparency • Engagement with developers
  35. 35. Implications • Customer experience • Increased transit ridership • Multi-modal integration • Reduced technology costs • Analytics for agencies and customers
  36. 36. Customer experience • 92% of transit users somewhat more or much more satisfied • 91% of users reported spending less time waiting Source: Ferris, Watkins & Borning, 2010, ‘OneBusAway: results from providing real-time arrival information for public transit’, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, ACM, pp. 1807-1816
  37. 37. Customer experience • Real-time information reduced perceived wait time by 20-26% • 46% of night time transit users felt safer knowing when the service will arrive Source: A Stated Preference Analysis of Real-Time Public Transit Stop Information, Journal of Public Transportation, Vol. 12, No. 3, 2009
  38. 38. Customer experience • Milwaukee: complaints decreased 24% • Denver: complaints decreased 26% • Portland: complaints decreased 53% on one route Source: Enhancing the Rider Experience: The Impact of Real-Time Information on Transit Ridership, 2005, National Center for Transit Research, Unviersity of South Florida
  39. 39. Increased transit ridership Source: Ferris, Watkins & Borning, 2010, ‘OneBusAway: results from providing real-time arrival information for public transit’, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, ACM, pp. 1807-1816 Trips per week taken by real-time transit users
  40. 40. Increased transit ridership • Finland: 25% of bus passengers reported increased use because of real-time data • Liverpool: ridership increased 5-6% on lines with real-time data • Belgium: ridership increased 6% on lines with real-time data Source: Enhancing the Rider Experience: The Impact of Real-Time Information on Transit Ridership, 2005, National Center for Transit Research, Unviersity of South Florida
  41. 41. Multi-modal integration • Customers can make real- time multi-modal decisions • Decentralised demand management following service interruptions • Adjust planned connections on multi-stage journeys
  42. 42. Multi-modal integration • 78% customers more likely to walk to a different stop than previously • Users with access to real-time data walk 6.9 more blocks per week • In Seattle and San Francisco, 5-10% users changed modes as a result of real-time data Sources: Enhancing the Rider Experience: The Impact of Real-Time Information on Transit Ridership, 2005, National Center for Transit Research, Unviersity of South Florida; Ferris, Watkins & Borning, 2010, ‘OneBusAway: results from providing real-time arrival information for public transit’, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, ACM, pp. 1807-1816
  43. 43. Reduced technology costs • Decreased procurement requirements for customer facing applications • Draw on developer community resources • Focus on core business of service delivery
  44. 44. Reduced technology costs “We’re small and we can’t provide every customised solution people ask for... it’s like having an army of developers available to us.” -- Tim McHugh, CTO of Portland’s TriMet
  45. 45. Analytics for agencies and customers • Network coverage • On-time running, service reliability • Network bottlenecks • Travel demand patterns
  46. 46. Analytics Network coverage
  47. 47. Analytics On-time running
  48. 48. Analytics Network bottlenecks • Analyse vehicle movements, compare against scheduled timings • Aggregate data over short and long term • Determine congested paths within network
  49. 49. Analytics Travel demand patterns
  50. 50. Real-time data & mobile Increasing the uptake of public transport Rupert Hanson Developer, AppJourney @rpy NSW Transport Infrastructure Summit August 2013
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