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Using Metalearning for Prediction of Taxi Trip Duration Using Different Granularity Levels

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Advances in Intelligent Data Analysis XIV (IDA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9385))

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Abstract

Trip duration is an important metric for the management of taxi companies, as it affects operational efficiency, driver satisfaction and, above all, customer satisfaction. In particular, the ability to predict trip duration in advance can be very useful for allocating taxis to stands and finding the best route for trips. A data mining approach can be used to generate models for trip time prediction. In fact, given the amount of data available, different models can be generated for different taxis. Given the difference between the data collected by different taxis, the best model for each one can be obtained with different algorithms and/or parameter settings. However, finding the configuration that generates the best model for each taxi is computationally very expensive. In this paper, we propose the use of metalearning to address the problem of selecting the algorithm that generates the model with the most accurate predictions for each taxi. The approach is tested on data collected in the Drive-In project. Our results show that metalearning can help to select the algorithm with the best accuracy.

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Acknowledgment

This work is financed by the ERDF - European Regional Development Fund through the COMPETE programme (operational programme for competitiveness) within project GNOSIS, cf. “FCOMP-01-0202-FEDER-038987”. It is also funded by the North Portugal Regional Operational Programme (ON.2 – O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT) through projects “NORTE-07-0124-FEDER-000057” and “NORTE-07-0124-FEDER-000059”. The work is also financed by the ERDF – European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects “FCOMP-01-0124-FEDER-037281” and “SFRH/BD/71438/2010”. The research leading to these results has also received funding from the ECSEL Joint Undertaking, the framework programme for research and innovation horizon 2020 (2014–2020) under grant agreement \(\mathrm{n}^{\circ }\) 662189-MANTIS-2014-1.

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Authors and Affiliations

  1. INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 378, Porto, Portugal

    Mohammad Nozari Zarmehri & Carlos Soares

Authors
  1. Mohammad Nozari Zarmehri
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  2. Carlos Soares
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Corresponding author

Correspondence to Mohammad Nozari Zarmehri .

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Editors and Affiliations

  1. Université de Saint-Etienne, Saint-Etienne, France

    Elisa Fromont

  2. Intelligent Systems Lab, University of Bristol Intelligent Systems Lab, Bristol, United Kingdom

    Tijl De Bie

  3. Informatics Section, Katholieke Universiteit Leuven, Leuven, Belgium

    Matthijs van Leeuwen

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Nozari Zarmehri, M., Soares, C. (2015). Using Metalearning for Prediction of Taxi Trip Duration Using Different Granularity Levels. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-24465-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24464-8

  • Online ISBN: 978-3-319-24465-5

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