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