real-time data and smart phone technology increasing the uptake of public transport
DESCRIPTION
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.auTRANSCRIPT
Real-time data & mobileIncreasing the uptake of public transport
Rupert HansonDeveloper, AppJourney@rpy
NSW Transport Infrastructure SummitAugust 2013
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, relevant way
• Fulfil customer information needs
• Increase customer satisfaction
Top 4 customer information needs
• Departure, arrival times
• Trackwork, station closures
• Delays, cancellations
• Wayfinding
Source: Stancombe Research & Planning, December 2011
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
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
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
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
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 feeds Real-time apps
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 data Publish feeds Real-time apps
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
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
• GTFS smaller, works as relational data, contains shapes
• Volumes for schedule data in Sydney:
0
225
450
675
900
GTFS TransXChange
58MB105MB
883MB
439MB
TDX Sydney Buses Sydney Trains
Collect data Publish feeds Real-time apps
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" } }}
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
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
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
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 commenced 2004
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 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
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
NSW: real-time train data
• Signalling reports track circuit occupation
• Passenger information systems correlate to runs and GPS coordinates
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
NSW: The future
• More PTIPS enabled bus agencies
• More rail network coverage
• Real-time ferries
• Light rail data
• Accuracy improvements
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
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
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
Around the world
Sources: Various agencies, gtfs-data-exchange.com, code.google.com/p/googletransitdatafeed/wiki/PublicFeedsNonGTFS
GTFSTransXChange
Proprietary API
GTFS-realtimeNextBus
SIRI
Challenges
• Data quality and service reliability
• Data longevity
• Integrating disparate systems
• Resistance to transparency
• Engagement with developers
Implications
• Customer experience
• Increased transit ridership
• Multi-modal integration
• Reduced technology costs
• Analytics for agencies and customers
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
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
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
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
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
Multi-modal integration
• Customers can make real-time multi-modal decisions
• Decentralised demand management following service interruptions
• Adjust planned connections on multi-stage journeys
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
Reduced technology costs
• Decreased procurement requirements for customer facing applications
• Draw on developer community resources
• Focus on core business of service delivery
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
Analytics for agencies and customers
• Network coverage
• On-time running, service reliability
• Network bottlenecks
• Travel demand patterns
Analytics
Network coverage
Analytics
On-time running
Analytics
Network bottlenecks
• Analyse vehicle movements, compare against scheduled timings
• Aggregate data over short and long term
• Determine congested paths within network
Analytics
Travel demand patterns
Real-time data & mobileIncreasing the uptake of public transport
Rupert HansonDeveloper, AppJourney@rpy
NSW Transport Infrastructure SummitAugust 2013