Innovations in London’s Transport: Big Data for a Better Customer Experience

Andrew Hyman - Analytics Research ManagerCustomer Experience, Transport for London

High Performance Computing & Big Data Conference - February 4th 2016

1. Who are TfL?

2. Act on Fact

3. Case studies

Session Overview

Who are TfL?

Buses Taxi-Private Hire

Coaches Cycles

River Dial-A-Ride

Underground Overground

DLR Trams

Air-Line TfL Rail

Owner and operator of the largest integrated transport network in Europe

Surface Transport Rail and Underground

Our Purpose

“Keep London working and growing to make life in the Capital better”

Plan ahead to meet the challenges of a growing population

Unlock economic development and growth

Meet rising expectations of our customers and users

Every penny of our revenue is reinvested in running and improving services on the

transport network

London is ‘Big’, so our data is ‘Big’, too...

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Customers are at the heart of our businessEvery Journey Matters

On the London Underground....

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On the London Underground....

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Almost twice as many journeys are made on London Buses...

1st transport authority to introduce contactless vastly improving convenience for customers

Future demand will be even greater....

Act on Fact

The “Act on Fact” Journey – revealing patterns / trends to enable action to be taken

Data

Sense Making

Information Intelligence

Story Telling

Knowledge

Take Action

Impact

Inspired by Stephen Few

Case Studies

Customers have rising expectations for personalised journey planning

12 million users visit tfl.gov.uk every month

Our open data feeds whizzy apps that provide further sources of real time info on demand

Data from Oyster and Contactless cards help TfL understand how people behave

and their transport needs

Insight from our data helps ensure we are prepared for increased demand during events

Hyde Park's Winter Wonderland opened on 20 November 2015 at 17:00.During opening times (10am-10pm), nearby stations are much busier than normal.

We can visualise, understand and look for ways to influence travel demand

Tap In Tap Out

On London Underground ticketing data tells us where you start and end your journey

Tap In No Tap Out

On buses people don’t need to tap outso there is no record of where you get off

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 BStop

A

Boarding Stop

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

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Big Data informs our Bus Network Planning

Working with MIT we built an algorithm we call ODX that joins iBus and ticketing data together

bus route of current journey

segment

bus route R

Station Y

Underground line

Stop X

Stop B

Stop A

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

ODX enables us to infer the alighting stop and to build complete journeys

This helps TfL understand how crowded buses are, plan interchanges and minimise walk times

ODX has informed Interchange Analysis as part of the Better Junctions programme

Putney Bridge closed for emergency repair work in 2014.

Bus services had to stop either side of bridge. People could only walk or cycle across.

Big Data / ODX has also helped us look after customers during major bridge works

Targeted e-mails

Bus transfer card

Route diversions

Big Data / ODX has also helped us look after customers during major bridge works

What Next? Our Future Aims

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Many more topics and questions to explore!

Integrating ticketing, bus, traffic congestion, and incident data for better performance of the bus and road networks

Developing further personalised services for those customers who want tailored information

Predicting platform and train congestion at stations

Understanding walking, cycling and driving journeys alongside public transport

Using new data mining tools, machine learning and geo-spatial visualisations to bring data to life

Proof-of-concept real-time crowding information to help customers to plan travel

57% of customers say they would change their journeys to avoid crowdsReal-time information would be most effective at influencing behaviour

Designs and use of channels are for illustration purposes only

Plan a less crowded journey before leaving or on route

using Journey Planner

View real-time crowding information and predicted journey time via information screens at stations

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