Big data and real-time information as game changer? Lauren Sager Weinstein Head of Analytics

1. Big data and real-time information as game changer? Lauren Sager Weinstein Head of Analytics Customer Experience

2. Today’s presentation - agenda • Changing expectations • Open data – travel information • Big data • Key considerations

3. Our purpose ‘Keep cities working and growing and make life better’ Plan ahead to meet the challenges of a growing population Unlock economic development and growth Meet the rising expectations of our customers and users

4. ‘Any place, any device’ access to all the tools and services customers need • Tasks are made simpler, to save customers time • Self-service is quick and easy • Staff can fix customer problems easily • A personalised service – my transport provider knows me • Transport seamless - integrated information and ticketing • Customers can easily find help if needed • Information is easy to find - real-time and accurate • Customer experience reflects customer expectations and needs Customers’ Technology Expectations 4

5. TfL Travel information as open data Live data and APIs include; • Bus arrivals – stream and API • Tube movements, departures, status • Cycle hire docking station status • River boat status and arrivals • Roads status • Journey Planner API Reference data includes • Stations, stops and piers locations • Timetables • Future works on Tube, Roads All available in our Developers’ Area of tfl.gov.uk

6. Why open data – travel information Reach Public data Optimal use of transport network Economic benefit Innovation

7. Our work in Big Data Journeys on London Underground, typical week Given our number of customers, we have big data– it becomes ‘Big Data Analytics’ when we combine it...

8. Inferring destination for bus trips A customer taps an Oyster card on the reader, which records the location and time Can we infer the exit point? Stop B Stop A Boarding Stop Bus events are recorded in the iBus system and we can match this with our Oyster data bus route of current journey segment

9. Where is the next tap? From the location of the next tap (if there is one), we can infer where a customer alights Underground line busroute R Station Y Stop X • If next trip begins at stop X, the current segment is inferred to end at stop A Stop B Stop A bus route of current journey segment • If next trip begins at station Y, the current segment inferred to end at stop B

10. Using our Big Data tools We use our analysis to monitor congestion so that we can tailor our bus services where needed We also use this for our bus route groupings so we can design good transfer points

11. Our Big Data plans Many more topics and questions to explore! • Integrating ticketing, bus, traffic congestion, and incident data for better performance of the bus and road networks • Integrating social media with our customer data for deeper understanding • Looking at weather data to see how it affects transport use

12. Big Data + Open Data = insight together • Our strategy is to make as much data openly available as possible • Where data has a sensitive element (e.g. some journey data) we aggregate to publish summary statistics and provide some sample data that is open to all • We also have a controlled area for researchers to access disaggregate sensitive data • As a result, much of our work has been informed by our collaboration with academia • And we are exploring some further opportunities to bring our data and other data together.

13. THANK YOU