Research Output
Considerations about the Analysis of ITS Data of Bicycle Sharing Systems.
  Handling and managing data automatically collected by Intelligent Transport Systems (ITS) is a major opportunity and challenge for transport professionals nowadays. This study guides the management of smartcard data from public bikes by providing criteria to detect travel patterns that describe the specific use of bike-share systems and which cannot be encountered in other transport modes. The guidelines have been put into practice with data from the TusBic system in Santander, Spain.

The major discovery that has resulted from the analysis of the data is the high number of records that describe very short trips that show the same terminal at origin and destination. An algorithm is proposed that assumes the users try and return the bike if this is not working properly, and pick a new one from the same terminal. In such cases, the records are joined to describe a unique trip by considering the origin as the pick-up instant of the first bike, and the destination, the instant at which the second bike has been returned.

The indications presented in this article should be considered in future studies of demand and level of service of public bicycle systems since not only they can make a big difference in the accuracy of the results, but also they can provide interesting information regarding the management and design of the system. Therefore, they are of interest for different stakeholders such as politicians and decision makers, service planners, and agencies responsible for the operations and direct management of public bicycle systems.

  • Type:


  • Date:

    31 December 2014

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  • Library of Congress:

    HE Transportation and Communications

  • Dewey Decimal Classification:

    388 Transportation; ground transportation


Bordagaray, M., Fonzone, A., dell’Olio, L., & Ibeas, A. (2014). Considerations about the Analysis of ITS Data of Bicycle Sharing Systems. Procedia Social and Behavioral Sciences, 162, 340-349.



Intelligent Transport Systems; smartcards; data mining; travel behaviour; demand analysis

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