How London Public Transport is Benefiting From Big Data & The Internet of Things

Many companies have used the insights gleaned from big data analysis and IoT to vastly improve the day to day experiences of their customers. Transport for London (TfL) is one such company.

London is one of the busiest cities in the world with millions of people using the transport network of buses, trains, trams and ferries to the cycle paths and footpaths every day. TfL has to run this network and as such must be at the very top of their game to ensure any issues cause minimum amounts of disruption.

Data collected from ticketing systems and a number of vehicle/traffic light sensors help TfL to run this vast network, in addition to the enormous amounts of data collated from social media, as well as surveys and via focus groups. The introduction of the Oyster card back in 2003 also helped TfL an enormous amount and has provided the company with huge amounts of precise date, and will continue to do so.

The number of people commuting and travelling through London is growing at an extremely fast rate; therefore TfL has recognised the need to step up its game in order to continue to provide passengers with excellent levels of service and value for money.

Head of Analytics at TfL, Lauren Sager-Weinstein emphasises this need, stating: “The population is currently 8.6 million and is expected to grow to 10 million very quickly. We have to understand how they behave and how to manage their transport needs.”

Using big data and IoT, enables TfL to gain hugely detailed insight to each user journey that takes place, and allows the company to track any issues and/or emergencies and provide users with a personalised update service.

Each of these points is explored further below:

1. Detailed journey analysis

TfL uses big data analysis in order to understand the journeys their customers are taking and gain insight into how crowded certain services are at specific times of the day. In this way, services can be adapted in order to meet the specific needs of the customers. For instance, if a particular bus service is extremely crowded and overloaded on the same day at the same time, it shows the need for additional services to be put on.

These days, by using big data and IoT technology, TfL is able to gain an incredibly accurate picture of the individual journeys people are taking, even across the range of different transport services. This insight allows TfL to understand how far people have to walk in-between different services, thus allowing the company to work on improvements, i.e. minimising walk times or introducing new transport routes.

2. Tracking and identifying issues and/ or emergencies

With a huge number of people using its services, it is vital that TfL is able to respond quickly and effectively to any emergencies or transport issues, not only in terms of safety but also for customer satisfaction.

The analysis from big data enables TfL to respond in the manner that is needed, as quickly as they can and provide as little disruption to users as possible. In this way TfL are able to suggest alternative methods to destinations, as well as put on additional or alternative transport methods in order to respond to the specific needs of their customers.

For example Sager-Weinstein explains TfL’s actions when Wandsworth Council had to close Putney Bridge in order to carry out some emergency repairs. With the help of data analysis they worked out that the majority of user journeys started or ended very close to Putney Bridge, whilst the other half were only at the half-way point of their journey.

To service the needs of the latter customers TfL “were able to set up a transport interchange and increase bus service on alternate routes.” The company also sent these customers personalised messages detailing how journeys might be affected.

3. Tailored travel updates

One of the greatest advantages of big data is that it allows TfL to understand the specific routes people use regularly. They can therefore send useful updates that are specifically tailored to the customers.  Sager-Weinsten explains further: “If we know a customer frequently uses a particular station, we can include information about service changes at that station in their updates.”

TfL understand that people do not want to be inundated with irrelevant information. Using the analysis from big data ensures their customers receive only what is most relevant: “We understand that people are hit by a lot of data these days and too much can be overwhelming so there is a strong focus on sending data which is relevant”.

It is now one of TfL’s key priorities to implement this personalised approach and as such provide customers with information 24 hours a day, taking into account any number of events, activities, payment technologies, transport routes and travel methods.

TfL have reported that 83% of their passengers rate their tailored service as ‘useful’ or ‘very useful’, which is an extremely positive result when you consider the sheer amounts of data that most of us are subjected to each and every day.

Ultimately TfL is constantly on the lookout to improve London’s public transport service; ensuring that transport demand is met and that the transport infrastructure runs as efficiently as possible. TfL has embraced open source technology in order to ensure 3rd party app developers can create personalised solutions for TfL’s users and the company is looking to improve their service levels even more by making use of real-time analytics and continuing to integrate insights gleaned from big data and IoT.