Big Data Keeps London Moving
A journey planner to help passengers easily move through London’s metro network.
Transport for London, United Kingdom
Like every major city around the world, London has a huge transport infrastructure that is not without its challenges as it tries to move millions of people every day. In fact, the UK capital's road network supports more than 20 million journeys each day, which represents around 80 percent of all trips in the city.
In addition to private cars, motorbikes, taxis and bicycles approximately 6.5 million people are ferried on buses each day. And everyone who travels around the metropolis has to navigate hundreds of thousands of roadworks each year. With the city's population set to increase by more than one people by 2030 traveling in London is going to become even more complicated and challenging.
According to Transport for London (TfL) the government body responsible for the transport system in Greater London, in 25 years the network will need to support an additional five million daily trips. To help mitigate the effects of growing passenger numbers and to make city travel as seamless as possible TfL has turned to analytics and collects reams of passenger data to gain meaningful insights.
For example, the millions of daily transactions that are seen through the public transportation ticketing system are fed into models to help transport planners predict future demand. This type of data is also combined with other data sets such as bus location data to plan the bus network. Data is also used to manage unexpected events and provide passengers with personalized travel information.
But that’s not all. Realizing that much more can be achieved with data TfL held its first hackathon in 2017.
Data in Motion
The Data in Motion Hack Week was designed to find solutions to common transport problems by using open data. Among the specific challenges, participants were asked to focus their attention on were maximizing capacity on the roads and improving air quality. Anyone could enter and form teams and they were given access to TfL's open data. Concepts were judged on innovation, commercial viability and relevance to key challenges.
The overall winner was team WSO2 and its members came up with a prototype for a live journey planner. The working model used a map to explore the crowding level at different underground stations on the network. It made use of data from several modes of transport and overlaid passenger flow/train loading and pollution data. This would allow passengers to plan a route based on how busy the stations are while also taking into account the air quality. According to WSO2 its machine learning model predicted traffic five to ten minutes in the future and got it right 88% of the time.
This was the first time that TfL involved itself in such a concentrated effort to explore opportunities with its data. The hackathon showed the government body some of the things that are possible when you cast your net wide to engage innovators to come up with new ways to ease congestion and make journeys more pleasant for passengers.
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