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Tool Inventory

Big Data in Bicycle Traffic
Big Data in Bicycle Traffic

Application Area

  • Data gathering
  • Analysis, scenarios and measure selection
  • Appraisal and assessment

Tool Type

  • Guidance document / Manual
  • Indicator set
  • Method / Approach

Target Audience

  • Rural areas
  • Small cities
  • Medium-sized cities
  • Large cities
  • Metropolitan regions
  • Other


The present guide builds on two extremely positive trends:

  1. Cycling is fun, healthy, easy on the wallet and environmentally friendly. Moreover, the bicycle is an extremely fexible and efcient means of transport. Cycling thus enhances not only the quality of life of cyclists themselves, but also the quality of life of a city as a whole.
  2. The onward march of the digital revolution is opening up new possibilities for data collection and analysis, new participative data contents and the reorganization of existing processes by means of digital data.

Why not combine these two positive trends and use the obvious advantages of digitalization for local authority cycling planning?

Good cycling infrastructure – a prerequisite for a high share of cycling in the modal split – has to be exactly where cyclists need it. It is amazing, but it is a fact, that so far, many local authority transport planners have not had any robust data on the precise routes taken by cyclists. This is where digitalization can help.

In the research project entitled „Smartphone-generated behavioural data in cycling“, data of cycling movements were thoroughly examined and used as a model for cycling planning in Dresden. The findings confirmed that the movement data not only provide an interesting insight into the local cycling flows but can also help to identify the needs and expectations of cyclists and incorporate them into transport planning.

The present guide provides local authority planners and interested members of the public with an easy-to-use practical introduction to the topic of GPS data, and in doing so identifies potential for cycling planning and possible stumbling blocks in the use of these data. It encourages the use of digitalization in this sector as well and addresses key issues such as data protection or the challenges involved in interpreting the data.

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Basic Information

September 2018


Technische Universität Dresden

Thematic Areas