A Process Mining Approach to the Identification of Normal and Suspect Traffic Behavior

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 728)

Abstract

Born and typically exploited in the business and industrial application domain, automatic process management, and in particular process mining, might be profitably applied also to the very different domain of traffic understanding. In facts, previous successful experiences in other movement-oriented applications have been reported in the literature. However, some peculiarities of these special domains require powerful techniques to be available. For this reason, these experience exploit the WoMan framework for workflow management, that has proved to be able to handle complex processes. This paper describes the WoMan framework along with its features and functionality, explains why it is more suitable than other process mining approaches and systems available in the current literature, and proposes a number of ways in which it might be applied to the traffic understanding domain. It also highlights possible shortcomings of the WoMan system, that might need adjustments before it can be applied at full scale on real-world traffic data.

Keywords

Traffic understanding Process mining Activity prediction Process prediction 

Notes

Acknowledgements

Thanks to Amedeo Cesta and Gabriella Cortellessa for providing the GPItaly dataset, and to Riccardo De Benedictis for translating it into WoMan format. This work was partially funded by the Italian PON 2007–2013 project PON02_00563_3489339 ‘Puglia @Service’.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità di BariBariItaly
  2. 2.Centro Interdipartimentale per la Logica e sue Applicazioni, Università di BariBariItaly
  3. 3.Artificial Brain S.r.l.BariItaly

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