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Predicting Taxi Passenger Demands Based on the Temporal and Spatial Information

  • Sang Ho Kang
  • Han Bin Bae
  • Rhee Man KilEmail author
  • Hee Yong Youn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

This paper presents a new method of predicting taxi passenger demands in the central city areas of Seoul and New York based on the temporal and spatial information on predicted values. For the efficiency of the city’s taxi system, investigating the taxi passenger demands is required mainly in the large scaled cities. From this context, this paper proposes a prediction model of combining the conditional transition distribution and the neighboring information on taxi passenger demands. As a result, the proposed method provides higher prediction performances than other methods of homogeneous prediction models.

Keywords

Taxi passenger demands Poisson process Co-occurrence matrix Temporal and spatial information 

Notes

Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. B0717-17-0070).

References

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sang Ho Kang
    • 1
  • Han Bin Bae
    • 1
  • Rhee Man Kil
    • 1
    Email author
  • Hee Yong Youn
    • 1
  1. 1.College of SoftwareSungkyunkwan UnivesityJangan-gu, Suwo-siKorea

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