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Collaborative Data Analysis in Hyperconnected Transportation Systems

  • Mohammad Nozari ZarmehriEmail author
  • Carlos Soares
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 480)

Abstract

Taxi trip duration affects the efficiency of operation, the satisfaction of drivers, and, mainly, the satisfaction of the customers, therefore, it is an important metric for the taxi companies. Especially, knowing the predicted trip duration beforehand is very useful to allocate taxis to the taxi stands and also finding the best route for different trips. The existence of hyperconnected network can help to collect data from connected taxis in the city environment and use it collaboratively between taxis for a better prediction. As a matter of fact, the existence of high volume of data, for each individual taxi, several models can be generated. Moreover, taking into account the difference between the data collected by taxis, this data can be organized into different levels of hierarchy. However, finding the best level of granularity which leads to the best model for an individual taxi could be computationally expensive. In this paper, the use of metalearning for addressing the problem of selection of the right level of the hierarchy and the right algorithm that generates the model with the best performance for each taxi is proposed. The proposed approach is evaluated by the data collected in the Drive-In project. The results show that metalearning helps the selection of the algorithm with the best performance.

Keywords

Hyperconnected world Machine learning Metalearning Data mining Intelligent transportation systems Collaborative data analysis 

Notes

Acknowledgment

This research work has received funding from the ECSEL Joint Undertaking, the framework programme for research and innovation horizon 2020 (2014–2020) under grant agreement number 662189-MANTIS-2014-1.

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Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.INESC TEC, Faculdade de EngenhariaUniversidade do Porto (FEUP)PortoPortugal

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