Mapping and monitoring of cyclists (Big Cyclist Data) have been absent in the implementation of Intelligent Transport Systems (ITS) in cities. Big data aims to provide information that could support the decision making in urban planning. To ensure that big data provides valuable information in the future sustainable development of cities it is important to define what information the data provides and how it should be utilised. This thesis examines how big cyclist data contribute to urban planning and to what extent cyclist data provides an understanding of bicycle practices. The research consists of different methods of data collection. The thesis presents a case study that investigates how big cyclist data have been utilised in Oregon, USA. Additionally, cyclist data for Copenhagen have been examined, where the data represent the bicycle activity for one month in the city of Copenhagen and Greater Copenhagen. The data is received from the IT company Strava Metro, who has developed a smartphone application to collect cyclist data. The users of this application represent the data sample. Finally, conducting an experiment in Copenhagen enabled to collect cyclist data from ten voluntary citizens that mapped their bicycle routes through a smartphone application. The experiment enabled a deeper investigation of to what extent cyclist data gives an understanding of cyclists’ practices. In-depth interviews were conducted with each participant, which gave insight into the diverse opinions the cyclists present. By using Shove, Pantzar and Watson's perspective on practice theory, we integrate the elements we believe is elementary for understanding mobility practices.

The research identified that quantitative cyclist data to a large extent highlights the most heavily used routes, and to some extent illustrates where the alternative routes exist. The data enable to form assumptions, which could support the identification of additional research projects. People’s bicycle practices tend to be diverse, and the qualitative interviews made it possible to identify different types of common factors that influence cyclists’ usage of the infrastructure. Big cyclist data in the form of visualisations on open street maps, numbers on counts, and time of the day profiles are a valuable supplement in urban planning. To achieve a deeper understanding of the practices of urban cyclists, other information sources are necessary such as in-depth interviews. Different methods for collecting cyclist data seems to support decision makers and planners in finding solutions to influence sustainable mobility practices.