Many major cities around the world are seeing rapid population growth, resulting in increased strain on existing road and public transportation network infrastructure as the numbers on the daily commute swell. Smart mobility – putting data, information, and options in the hands of the travelling public – has been beneficial to many of these cities, allowing better use of fixed resources and more efficient movement around the urban space. Opening up live and static datasets for public consumption can be inexpensive and straightforward relative to the cost of building new physical infrastructure, particularly where sensor information can be easily accessed through existing control systems and carefully specified new “smart city” infrastructure.
London’s Open Data Portal for Transport
Being based in London, I am fortunate to be a data researcher in a city proactively releasing huge amounts of open data. Transport for London (TfL) is the city administration’s transport manager for many modes (e.g. buses, bikeshare, trams, light rail), operator for some (e.g. metro, cable car, major roads) and regulator for others (e.g. taxis, private hire). It has worked with its divisions and private operators to release large amounts of data, both dynamic (updating live, e.g. metro departure boards and traffic cameras) and static (e.g. infrastructure locations, roadworks and safe taxi lists), as open data, for consumption and augmentation by anyone, including commercial concerns who can create potential business with such data.
The data is available through an Open Data Portal section on its website. Live running and timetable information allows multi-modal journey planners (see below) to be easily created and quickly react to disruption and show alternatives. Visualizations of such data can inform both transport planners and the general public as to the use and operation of transport modes, in the long term and short term respectively. Fine-grained temporal capacity information can be used to encourage changes in travel habits. As both a user of TfL’s transit systems, and a visualizer of its data, I have seen first-hand the benefits of the easy access and utilisation of these datasets. I developed TubeHeartbeat, for example, which uses an open dataset from TfL’s portal, on passenger volumes by quarter-hour, to visualize the short but intense commute periods on London’s “Underground” metro network. I also curated an exhibition “Big Data Here”, which projected live running bus information and traffic camera videos, amongst other hyperlocal open data, onto a screen positioned right by the corresponding bus stop and camera itself.