Key policy objectives of the City of Edinburgh Council (CEC) include reducing CO2 emissions, improving air quality and promoting active travel. Active travel is at the heart of the Council’s Transport 2030 Vision and Local Transport Strategy 2017-21 and contributes to improving health, the environment and economic development. Such multi-dimensional challenges require novel approaches, and these in turn will depend on better use of digital technologies combined with rich data streams to support smart and sustainable urban transport systems.
In order to develop and test measures that contribute to these goals, and to communicate policy to the public, it is crucial to have a detailed analysis of the current landscape for active travel in Edinburgh. At present, access to relevant data on walking and cycling behaviour in the city is partial, heterogeneous, and fragmented. As a step towards addressing this gap in understanding, we are focussing on cycling data; more specifically, carrying out a project to gather the fine-grained data required for developing an accurate picture of cycling issues and attitudes. This initially involves finding out what’s available: sourcing and analysing information from across the public, private and third sectors. Second, we intend to curate relevant datasets; clean them if required; and where possible publish them as open data. Most importantly, we will look for patterns across the combined datasets and build interactive visualisations to help communicate new insights.
As part of this initiative, Andreea Pascuhas already started work on one valuable repository of data, namely the information provided by Council-maintained counters on the 19 off-road cycling paths in Edinburgh. The data has been collected by the CEC Cycling Team, and in some cases dates back as far as 2008. The automatic counters given hourly totals for each direction of travel on a 24/7 basis. The data offers an opportunity to develop a longitudinal perspective on usage of the cycling paths, and can yield insights about the effects of adding new paths to the network of the period in question. We can also start to answer questions about temporal variations: What hours of the day see most traffic? How does usage differ across days of the week?
Despite the richness of the counter data, it is also obviously partial. In particular, it tells us little about journeys across the cycling paths, and nothing about cycling on roads in the city. However, we expect that by combining the cycling path data with other datasets, it will be possible to detect correlations which give us a much richer and more complete picture.
As far as we know, no city has carried out a comprehensive active travel data audit and curation initiative of the kind proposed here. The European Cyclists Federation highlights that European cities have only just started to started to consider how rich data on cycling can be used to support policy and advocacy. The EU is at the early stages of harmonising a set of indicators for an Urban Mobility Scoreboard, while according to an authoritative UN report on Sustainable Cities, no reliable baselines exist for measuring sustainable transport.