Skip to main content

London leads on open transport data

London has come out on top of an analysis of the performance of several major cities in providing open data on transport and mapping. The Future Spaces Foundation, a charity that studies living spaces, has said in its Vital Cities: Transport Systems Scorecard that London’s record of providing open access to real time transport data is the best example of data sharing. The Scorecard analyses the transport networks of 12 cities around the world on indicators ranging from breathability to the density of
May 16, 2016 Read time: 2 mins
London has come out on top of an analysis of the performance of several major cities in providing open data on transport and mapping.

The Future Spaces Foundation, a charity that studies living spaces, has said in its Vital Cities: Transport Systems Scorecard that London’s record of providing open access to real time transport data is the best example of data sharing.

The Scorecard analyses the transport networks of 12 cities around the world on indicators ranging from breathability to the density of cycle and pedestrian networks to the use of data and apps.

London scored top marks for facilitating the creation of multi-modal apps with the open availability of its live transit feeds. But it came second to Singapore in converting data into the most user-friendly and informative travel apps.

The research found that there is still room for developers in London and elsewhere in the world to improve the services they offer by taking their lead from Singapore’s Land Transport Authority, which provides its own web and mobile based route planning tool and app. It includes features not yet available in London, such as information about standing and seating room on public transport, as well as disabled access and the availability of parking spaces close to the passenger’s chosen destination.

1466 Transport for London recently announce plans for the release of new groups of open data feeds, taking in the London Trams network and historical crowding data from the Underground.

In light of the research, the Future Spaces Foundation is calling on governments all over the world to implement effective open data policies that encourage everyone – including web and app developers, residents and tourists – to make use of the data available.

Related Content

  • January 23, 2012
    Reducing transport energy use with real time travel information
    The In-Time project is looking at the effect that multi-modal real-time traveller information services can have of reducing transport's energy consumption levels. By Martin Böhm, AustriaTech GmbH. Around the world, significant research and development effort is currently directed towards reducing energy consumption by addressing those areas where the biggest savings can be expected. European studies have shown that the transport sector has the potential to reduce its energy consumption by up to 26 per cent
  • June 10, 2015
    UITP reveals promising growth in public transport modal share
    Back in 2009, the public transport sector set itself a goal: double its market share worldwide by 2025 to make cities more liveable and more productive. Today, in 2015, on the occasion of the biennial UITP World Congress & Exhibition in Milan this week, UITP presented a report to illustrate the urban policies that are moving cities closer to that goal. In a report presented at the plenary session of the World Congress, UITP research points to a general increase in public transport modal share thanks to
  • September 2, 2021
    Arriva MaaS app unifies Dutch transport 
    Passengers can sort the app’s ‘suggested routes’ via total level of CO2
  • November 23, 2018
    Cubic: predictive analytics is putting fortune tellers out of business
    The rise of machine learning and artificial intelligence means that fortune tellers will soon be out of business. Ed Chavis takes a behind the scenes look at the world of predictive analytics ver since organisations started taking advantage of insights derived from Big Data, data scientists concentrated their efforts on the ability to make correct assumptions about the future. A few years later, with the help of automation, developments in machine learning (ML) and advancements in the application of a