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Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City

  • Santi Phithakkitnukoon
  • Marco Veloso
  • Carlos Bento
  • Assaf Biderman
  • Carlo Ratti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6439)

Abstract

Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In this paper, we present a predictive model for the number of vacant taxis in a given area based on time of the day, day of the week, and weather condition. The history is used to build the prior probability distributions for our inference engine, which is based on the naïve Bayesian classifier with developed error-based learning algorithm and method for detecting adequacy of historical data using mutual information. Based on 150 taxis in Lisbon, Portugal, we are able to predict for each hour with the overall error rate of 0.8 taxis per 1x1 km2 area.

Keywords

Mutual Information Inference Engine Smart City Taxi Driver Human Mobility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Steventon, A., Wright, S.: Intelligent Spaces: The Application of Pervasive ICT (Computer Communications and Networks). Springer, New York (2005)Google Scholar
  2. 2.
    Reades, J., Calabrese, F., Sevtsuk, A., Ratti, C.: Cellular census: Explorations in urban data collection. IEEE Pervasive Computing 6(3), 30–38 (2007)CrossRefGoogle Scholar
  3. 3.
    Song, C., Qu, Z., Blumm, N., Barabsi, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Calabrese, F., Pereira, F.C., Lorenzo, G.D., Liu, L.: The geography of taste: analyzing cell-phone mobility and social events. In: Proceedings of IEEE Inter. Conf. on Pervasive Computing (PerComp) (2010)Google Scholar
  5. 5.
    Phithakkitnukoon, S., Horanont, T., Lorenzo, G.D., Shibasaki, R., Ratti, C.: Activity-aware map: Identifying human daily activity pattern using mobile phone data. In: Inter. Conf. on Pattern Recognition (ICPR 2010), Workshop on Human Behavior Understanding (HBU), pp. 14–25. Springer, Heidelberg (2010)Google Scholar
  6. 6.
    Chang, H., Tai, Y., Hsu, J.Y.: Context-aware taxi demand hotspots prediction. Int. J. Bus. Intell. Data Min. 5(1), 3–18 (2010)CrossRefGoogle Scholar
  7. 7.
    Yamamoto, K., Uesugi, K., Watanabe, T.: Adaptive routing of multiple taxis by mutual exchange of pathways. Int. J. Knowl. Eng. Soft Data Paradigm. 2(1), 57–69 (2010)CrossRefGoogle Scholar
  8. 8.
    Ziebart, B.D., Maas, A.L., Dey, A.K., Bagnell, J.A.: Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. In: UbiComp 2008: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 322–331. ACM, New York (2008)Google Scholar
  9. 9.
    Liu, L., Andris, C., Bidderman, A., Ratti, C.: Revealing taxi drivers mobility intelligence through his trace. In: Movement-Aware Applications for Sustainable Mobility: Technologies and Approaches, pp. 105–120 (2010)Google Scholar
  10. 10.
  11. 11.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  12. 12.
    WeatherUnderground, http://www.wunderground.com/
  13. 13.
    Cover, T.M., Thomas, J.A.: Elements of information theory. Wiley-Interscience, New York (1991)CrossRefzbMATHGoogle Scholar
  14. 14.
    Phithakkitnukoon, S., Dantu, R.: Adequacy of data for characterizing caller behavior. In: Proceedings of KDD Inter. Workshop on Social Network Mining and Analysis (SNAKDD 2008) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Santi Phithakkitnukoon
    • 1
  • Marco Veloso
    • 2
    • 3
  • Carlos Bento
    • 2
  • Assaf Biderman
    • 1
  • Carlo Ratti
    • 1
  1. 1.SENSEable City LabMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Departamento de Engenharia InformáticaUniversidade de CoimbraPortugal
  3. 3.Escola Superior de Tecnologia e Gestão de Oliveira do HospitalInstituto Politécnico de CoimbraPortugal

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