Big data and real-time information as game changer?

Lauren Sager WeinsteinHead of AnalyticsCustomer Experience

Big data and real-time information as game changer?

Lauren Sager WeinsteinHead of AnalyticsCustomer Experience

2

Today’s presentation - agenda

1.1. Changing expectationsChanging expectations

2.2. Open data – travel informationOpen data – travel information

3.3. Big dataBig data

4.4. Key considerationsKey considerations

1.1. Changing expectationsChanging expectations

2.2. Open data – travel informationOpen data – travel information

3.3. Big dataBig data

4.4. Key considerationsKey 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

Customers’ Technology Expectations

• ‘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

4

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

5

6

Why open data – travel information

Public data

Reach

Optimal use of transport network

Economic benefit

Innovation

Our work in Big Data

Given our number of customers, we have big data– it becomes‘Big Data Analytics’ when we combine it...

Journeys on London Underground, typical week

050,000

100,000150,000200,000250,000300,000350,000400,000450,000

04:0

0 -

05:0

0

05:0

0 -

06:0

0

06:0

0 -

07:0

0

07:0

0 -

08:0

0

08:0

0 -

09:0

0

09:0

0 -

10:0

0

10:0

0 -

11:0

0

11:0

0 -

12:0

0

12:0

0 -

13:0

0

13:0

0 -

14:0

0

14:0

0 -

15:0

0

15:0

0 -

16:0

0

16:0

0 -

17:0

0

17:0

0 -

18:0

0

18:0

0 -

19:0

0

19:0

0 -

20:0

0

20:0

0 -

21:0

0

21:0

0 -

22:0

0

22:0

0 -

23:0

0

23:0

0 -

24:0

0

24:0

0 -

25:0

0

25:0

0 -

26:0

0

S unday

Monday

Tues day

Wednes day

Thurs day

F riday

S aturday

7

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?

bus route of current journey

segment

Stop B

Stop A

Boarding Stop

Bus events are recorded in the iBus system and we can match this with our Oyster data

8

bus route of current journey

segment

bus route R

Station Y

Underground line

Stop X

Stop B

Stop A

Where is the next tap?

From the location of the next tap (if there is one), we can infer where a customer alights

If next trip begins at stop X, the current segment is inferred to end at stop A

If next trip begins at station Y, the current segment inferred to end at stop B

9

Using our Big Data tools

10

Time ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###LEWISHAM CENTRE 0 0 4 2 3 3 3 2 3 4 1 3 8 0 5 10 12 0 0 6 0 4 3 8 1 5 7 2LOCKMEAD ROAD 2 0 5 4 3 6 4 2 4 5 3 8 7 6 5 18 7 0 0 7 0 6 3 9 2 8 8 5THE SQUIRRELS 2 0 5 4 3 6 4 2 5 5 3 7 7 6 5 18 8 2 0 7 0 6 3 9 3 8 8 2ST MARGARET'S CHURCH / BRANDRAM ROAD 2 0 5 4 3 6 5 2 5 5 3 7 8 6 6 10 2 2 4 8 0 6 3 9 4 8 6 2BLACKHEATH HOSPITAL 2 1 6 4 3 7 5 2 5 5 4 7 8 5 8 11 3 3 5 8 0 6 3 9 4 7 6 2BLACKHEATH STATION # 2 1 6 3 4 11 7 2 6 11 8 11 8 7 10 18 9 7 8 9 0 9 5 12 6 14 4 5ROYAL PARADE 2 2 6 3 4 12 7 1 6 11 8 12 9 8 10 19 10 7 7 12 0 12 5 14 5 14 4 3MONTPELIER ROW 2 2 6 3 5 12 7 1 6 11 8 14 12 8 10 20 11 8 6 11 0 14 5 15 8 15 3 4ST GERMANS PLACE 2 2 6 3 5 12 9 1 6 11 8 16 12 9 13 21 11 9 12 15 0 15 6 15 8 16 3 4STRATHEDEN ROAD / SHOOTERS HILL ROAD 2 2 6 3 7 14 11 2 11 17 13 23 20 12 19 37 13 24 17 18 0 21 6 18 8 18 3 4BLACKHEATH / ROYAL STANDARD 4 3 10 9 15 29 23 10 26 43 25 40 48 27 27 54 28 43 29 22 10 21 8 20 10 17 11 6KIRKSIDE ROAD 4 6 11 11 15 33 26 11 32 43 27 43 50 34 28 54 29 43 32 23 10 24 9 22 8 17 11 6WESTCOMBE PARK STATION # 4 7 12 11 16 33 26 12 32 43 27 45 50 37 30 54 31 43 32 24 10 24 10 21 9 18 11 7WESTERDALE ROAD 4 7 12 11 16 32 27 11 33 43 26 45 50 38 30 54 28 43 31 23 11 23 10 21 8 17 10 5SAINSBURY'S AT GREENWICH PENINSULA 5 7 16 16 18 30 43 18 36 43 34 46 50 40 44 56 34 43 32 22 11 27 9 24 11 18 11 5ODEON CINEMA 5 8 15 16 19 29 46 25 38 42 36 47 50 40 43 54 40 43 31 22 11 27 10 24 11 18 11 5MILLENNIUM VILLAGE SOUTH 5 8 15 18 19 29 47 25 39 42 37 47 50 40 43 56 40 43 31 22 11 26 11 22 11 19 11 5MILLENNIUM VILLAGE / OVAL SQUARE 5 8 16 19 18 34 48 28 44 43 45 47 50 40 42 55 40 47 30 31 12 30 12 26 14 23 15 7NORTH GREENWICH STATION <> 8 8 2 8 15 7 8 2 5 20 3 8 5 7 5 4 6 5 3 7 9 10 6 21 4 9 4 2BOORD STREET 8 7 2 8 14 7 8 1 6 20 3 8 5 7 5 5 6 4 3 7 9 10 6 21 4 9 4 2BLACKWALL LANE 8 8 3 8 14 7 8 0 9 16 3 9 4 8 9 6 6 3 3 7 11 9 6 24 3 12 5 2MORDEN WHARF ROAD 8 8 3 8 14 7 9 0 9 16 3 9 4 8 9 6 6 3 3 7 11 9 6 24 3 12 5 2BLACKWALL TUNNEL / EAST INDIA DOCK ROAD 8 6 3 7 12 7 6 0 6 13 3 9 5 8 9 7 6 3 3 7 9 8 6 23 4 11 5 2ANDREW STREET 9 6 3 7 12 7 6 0 6 14 4 13 8 11 10 9 6 7 4 8 8 8 6 23 3 14 5 3ST LEONARD'S WHARF 9 6 4 9 13 5 11 3 9 16 6 18 10 15 11 12 6 9 6 10 9 8 7 23 4 14 8 3COVENTRY CROSS ESTATE 9 7 4 10 13 5 12 3 9 16 6 17 11 15 11 11 6 8 6 9 9 8 7 23 4 14 9 3BROMLEY BY BOW STATION <> 5 6 4 7 12 4 9 1 7 13 4 10 15 10 9 7 5 6 4 9 9 7 8 22 4 14 6 3GRACE STREET 5 6 4 7 12 4 9 1 7 13 4 10 15 10 9 7 6 6 4 8 9 7 8 22 4 13 5 3BOW INTERCHANGE 5 5 2 6 9 4 7 1 7 10 2 8 11 9 8 6 6 6 4 8 8 7 7 18 5 12 4 3MARSHGATE LANE 4 5 2 6 8 4 7 1 7 10 2 8 11 9 8 6 6 6 4 8 7 7 6 18 5 11 4 3WARTON ROAD 4 6 9 9 15 10 9 3 13 17 12 21 9 18 20 8 3 6 4 12 9 6 8 17 6 11 7 3STRATFORD HIGH ST STN [DLR] / CARPENTERS ROAD 4 6 10 10 16 9 7 3 13 19 14 24 12 21 23 10 3 7 4 12 9 8 10 16 6 10 10 3STRATFORD BUS STATION <> # [DLR] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0

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

Our Big Data plans

11

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

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.

12

THANK YOU

13