The Innovation Exchange programme is supporting Transport for London (TfL) to find innovators who can provide a solution to define and trial a location-driven data collection process that maximises the opportunity to generate insights on how and when people use TfL services and power tailored customer experiences. It is important that the solution must also minimise the collection of TfL’s customers’ personal data and support positive customer experiences.

Background

Transport for London (TfL) is responsible for the day-to-day running of the capital’s transport system. TfL must cater for more than 3.6 billion passenger journeys made each year.

TfL is seeking applicants to demonstrate effective use of mobile device location data and to develop a standard process for data collection and processing. The aim is to enhance existing TfL data sets at both an individual and aggregated level, generating valuable insights into how Londoners travel and identifying opportunities for optimisation.

The TfL Go app was launched in 2020 and features a digital Tube map, network status updates, a multi-modal journey planner, and real-time bus arrival information. It currently has more than 1.3 million monthly users and is growing at a rate of 30% per year. This user base provides TfL with an opportunity to generate a meaningful location-based dataset via the app.

TfL wishes to understand how services can be improved by analysing users’ location data, enabling the identification of journey patterns at both individual and aggregate levels. Gaining insight into individual movements and identifying the modes of transport used—across public transport, walking, cycling, and driving—will support the creation of customer profiles and allow for tailored journey advice, ultimately enhancing customer experience. TfL confirms that data from this challenge will not be used for any charging or pricing models.

Applying privacy-enhancing technologies and techniques—such as anonymisation and pseudonymisation—to large-scale data sets will provide a comprehensive view of movement across London and inform solutions for efficiency and achieving net zero, all while protecting individual identities.

The challenge

TfL would like to better understand how they might make use of tools and methods built into smartphone hardware and available to an app, to collect mobile device location data at an appropriate level of granularity and process that data to prepare it for analysis. This will help TfL to quickly understand the potential of collecting high quality location data, and how it can be used to deliver greater customer and business value.

Challenge – part 1
How might we define a location data collection standard process that maximises the opportunity for TfL to generate network insights and power customer-facing features, whilst minimising personal data collection?

Capture specification including but not limited to:

• When to start capturing, when to stop capturing location,
• Top/tail journey (privacy zones),
• Location data capture underground on the TfL network.

Challenge – part 2
How might we process, transform, and maintain the dataset defined through part 1 to ensure it is ready for analysis by TfL?

Areas to be covered include but are not limited to:

• Inferred mode of travel,
• Identification of end-to-end journeys including multi-modal travel,
• Implementation of privacy enhancing technologies and techniques (including, but not limited to, anonymisation and pseudonymisation processes),
• Demographic inference,
• Ensuring separation of the aggregated and personalised datasets.

TfL would also like to understand more about the system architecture, data processing rules, and data storage, management and retention rules that a live service would require.