PETRA: The PErsonal TRansport Advisor Platform and Services

  • Michele Berlingerio
  • Veli Bicer
  • Adi BoteaEmail author
  • Stefano Braghin
  • Francesco Calabrese
  • Nuno Lopes
  • Riccardo Guidotti
  • Francesca Pratesi
  • Andrea Sassi
Part of the Complex Networks and Dynamic Systems book series (CNDS, volume 4)


Smart Cities applications are fostering research in many fields including Computer Science and Engineering. Data Mining is used to support applications such as the optimization of a public urban transit network and event detection. The aim of the PErsonal TRansport Advisor (PETRA) EU FP7 project is to develop an integrated platform to supply urban travelers with smart journey and activity advices, on a multi-modal network, while taking into account uncertainty, such as delays in time of arrivals, and variations of the walking speed.

In this chapter, we describe the architecture of the PETRA platform, and present results obtained by applying PETRA to two different use cases, namely journey planning under uncertainty, and smart tourism advisor with crowd balancing.

We present the main modules of the platform, namely data acquisition and integration, mobility mining, journey planning under uncertainty, activity planning, with details on design and interfacing choices. We then present the results of applying PETRA in two cities, Rome and Venice, corresponding to the above two use cases. In Rome, we demonstrate how our multi-modal journey planner under uncertainty can cope with the intrinsic uncertainty in the transport network and can integrate private transport into the public one. In Venice, we demonstrate how our crowd-balancing tourism activity planner can schedule visit activities in order to reduce pedestrian congestions occurring in the historical city centre.

Our results are presented also as demonstrators to the EU as part of the results of the PETRA FP7 EU project.



This work has been partially supported by the EC under the FET-Open Project n. FP7-ICT-609042, PETRA.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Michele Berlingerio
    • 1
  • Veli Bicer
    • 2
  • Adi Botea
    • 1
    Email author
  • Stefano Braghin
    • 1
  • Francesco Calabrese
    • 3
  • Nuno Lopes
    • 4
  • Riccardo Guidotti
    • 5
  • Francesca Pratesi
    • 5
  • Andrea Sassi
    • 6
  1. 1.IBM Research IrelandDublinIreland
  2. 2.Core MediaDublinIreland
  3. 3.VodafoneMilanItaly
  4. 4.TopQuadrantLondonUK
  5. 5.KDDLab Department of Computer ScienceUniversity of PisaPisaItaly
  6. 6.EpocaModenaItaly

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