TfL Research and Development
Exploring new data APIs to ease customer journeys
TfL is the government body responsible for the London Underground. As part of an open brief Conjure were tasked with providing innovative and practical ways to make use of TfL’s newly available, open database.
With over 5 million daily riders the tube can often be a crowded and unpleasant experience. For our concept we wanted to explore how data could be used to create a more comfortable experience for journeys across London.
With convenience being essential to busy commuters this presented the perfect opportunity to create a wearables application to provide visual information as quickly as telling the time.
- Apple Watch and digital display application
- Visualise how crowded the tube will be by carriage
- Accurate, real-time data advising the best place to board
How do you make complex data easy to understand?
As access to data continues to get better, along with mobile devices becoming more embedded into our lives, we’re seeing a correlated increase in services and wearable devices that make use of that data, to improve our experiences.
By providing public access to its data, TFL opened up a whole new world of opportunities for improving customer journeys. But, a danger with greater access to data is that we can easily become overwhelmed. As users of the tube ourselves, we were all to familiar with the discomfort and frustrations that can be caused by overcrowding and therefore we decided this would be our focus.
Our solution to this problem was a digital application to be displayed on screens inside the platform and via smart watches to provide a visual representation of incoming tube services and how crowded they are.
In doing so we created a really quick and easy way of allowing travellers to assess whether to board a busy train, or whether to wait for the next one.
Our concept solution was delivered in two weeks using our innovation sprint programme. We began by interviewing our own staff as to their biggest frustrations using the underground and the problems they wish could be solved.
A common theme revolved around overcrowding. From our survey we found that the majority of people we spoke to would prefer to wait for the next train if they felt it would be less busy, or they had a higher chance of getting a seat. This was particularly the case for people travelling longer distances, with children or with luggage. The problem is that waiting for the next train often feels like a gamble with no guarantee that the next service will be any less busy than the last. For those in a hurry to work this tends to mean squeezing onto the first available train, with doors failing to close properly delaying the service and further exacerbating the problem.
From previous projects we knew that wifi could be used to measure density of people within a space, and so we decided to explore whether this data could be used to predict how busy tube services would be. By combining this data with real-time data on the position of each tube in the network we were able to make accurate predictions about the number of passengers on-board.
After several iterations our user experience designers came up with a very simple design that conveys a lot of information at just a glance. Individual tubes are shown travelling along a tube line which is in the usual format of a line with stations distributed evenly along it. Real-time information shows where each tube is in relation to the stations and a simple red, amber, green colour status indicates how busy the tube is.
By providing accurate data at the right moment, we can improve our daily journeys by easing stress, minimise delays, and reducing overcrowding on platforms, which is a major safety issue.
The overall result was an accurate solution that can be used by passengers and TFL controllers alike, who can use the data to help plan their journeys and regulate services.
At Conjure we want to see how access to better data can improve our journeys travelling around the city.