Transport for London has announced that they will soon be able to give better journey information to its passengers.

The innovational pilot of collecting depersonalised Wi-Fi data in the year of 2016 has now demonstrated how technology could be used to Lower crowding and also raise priority for transport investment.

Depersonalised Wi-Fi connection data will be used soon to help Transport for London improve the information that it provides to the customers on the London Underground.

The depersonalised data collection, that will begin from the 8th of July 2019, will aim to harness the already existing Wi-Fi connection data from 260 plus Wi-Fi enabled London Underground stations in order to understand how people use and navigate the network.

This information will then be used by Transport for London to ensure that they provide much better, more targeted information to the customers of the TfL network as they navigate around the city of London, helping them plan their route better in a way that is best to avoid congestion and delays.

The system, which has been in-house developed by Transport for London, will depersonalise the data automatically, with no browsing or historical data being collected from any of the devices used to receive and obtain the data.

At the current time, TFL uses data from its system used for ticketing in order to understand how journeys are made across their network. While this is accurate for people entering and exiting the stations of the network, this data that is obtained through the ticketing system can’t show the flow of movement through a station. Using the depersonalised Wi-Fi data will give a much more accurate, almost instant and real-time, understanding of the flow of people through stations or interchanging between the different services.

In the year of 2016, Transport for London held a pilot to test Wi-Fi data collection technology across 54 stations within Zones 1-4 of which lasted four weeks long. When a device has Wi-Fi enabled, it will continually search for a Wi-Fi network by sending out a unique identifier, this unique identifier is known as a Media Access Control Address (MACA) – to nearby routers as customers with these devices pass through stations.

This trial collected these Wi-Fi connection requests, these Wi-Fi connection requests were depersonalised automatically, and then were analysed by the in-house analytics team of TFL, this was to help understand where customers were at particular points of their journeys. There were, in fact, more than 509 million pieces of depersonalised data, collected from 5.6 million mobile devices that were making around 42 million different journeys which revealed to Transport For London a number of results, that could not have been detected from ticketing data or paper-based surveys that have priorly been used. An example of this is that analysis taken showed that customers travelling between King’s Cross St Pancras station and Waterloo station take at least 18 different routes, with around 40 per cent of customers not taking one of the two most popular routes that are available between these stations.

Since this pilot took place, TfL has been working to understand how this data could be used in a useful manner to provide the customers with newer, more specifically tailored information about the journeys they are undertaking – both before they begin the journey and while they are travelling. Transport for London also undertook work closely with key stakeholders and the Information Commissioner’s Office to ensure that any concerns about privacy and transparency were considered and addressed actively.

Detailed digital mapping of all London Underground stations has also been undertaken. This is to allow TfL to identify where Wi-Fi routers are located on their network and to allow TfL to understand in detail how their customers move across the network and through the stations on the network.

Later this year, both customers and The staff of Transport for London will begin to see the first benefits from the data obtained. These benefits could include:

• Providing crowding data via the TFL website in order to help customers plan their route across London better;
• Incorporating crowding data into TFL’s free open-data API, which could allow app developers, academics and businesses to utilise the data obtained further for new products and services;
• Early warnings via the Transport For London website and the social media channels about any congestion at ticket halls or platforms, this will allow customers to alter their route and their travel plans so that their travel experience will be better;
• Helping TFL staff at their stations have the latest and most accurate information to hand when they are giving customer assistance (particularly those with small children or with accessibility needs) as well as advising them about the travel conditions on other parts of the TfL network.

As well as providing benefits to both the customers and the staff, the data obtained will also enable Transport for London to understand customer flows throughout stations to a better extent, highlighting the effectiveness and accountability of its advertising estate based on accurate and real customer volumes that this data was obtained. Being able to demonstrate this in a reliable manner should improve commercial revenue. This commercial revenue can then be reinvested back into the transport network.

Clear signage, based on TFL’s signs on CCTV across the TfL network, signs will shortly be installed across the London Underground network, ahead of the data collection Start date, to inform the customers and direct them to a web page with more information, including how the data obtained through this technology will be depersonalised automatically and  stored securely.

Following the start of the data collection on 8th of July 2019, any customers who do not wish for their Wi-Fi connection data to be collected and stored will need to turn Wi-Fi off on their devices in order to opt out of this process.

What did officials say?

Lauren Sager Weinstein, Chief Data Officer at Transport for London said:

‘The benefits this new depersonalised dataset could unlock across our network—from providing customers with better alerts about overcrowding to helping station staff have a better understanding of the network in near-real time — are enormous. By better understanding overall patterns and flows, we can provide better information to our customers and help us plan and operate our transport network more effectively for all.

‘While I am excited about the potential of this new dataset, I am equally mindful of the responsibility that comes with it. We take our customers’ privacy extremely seriously and will not identify individuals from the Wi-Fi data collected. Transparency, privacy and ethics need to be at the forefront of data work in society and we recognise the trust that our customers place in us, and safeguarding our customers’ data is absolutely fundamental.’

London’s Chief Digital Officer, Theo Blackwell, said:

‘TfL is a world leader in the use of data to improve services. Today nearly half of all Londoners use an app derived from TfL’s open data feeds. Greater insight into how people use the Tube helps better transport planning but also enables the development of new apps to give customers further advance information about their planned journey in order to plan their best, and least congested route.’

Sue Daley, Associate Director Technology & Innovation TechUK, said:

‘Using Wi-Fi technology to gain real-time insights into how we are traveling around London’s transport system brings to life how data collection and analysis can make a real difference to people’s everyday lives. It is also a great example of how different technologies, such as Wi-Fi, data analytics and sensors, can work together to help reduce overcrowding, increase service efficiency and provide customised information for travellers that will make our journeys and lives that little bit easier.

‘The transparency shown by TfL around the data collection taking place and the steps taken to make customers aware of its purpose is welcome and should be seen as an example for others. If we are to realise the full potential and value of real-time data, it is vital that we bring the public on this journey and build a culture of data trust and confidence.’

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