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.
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Acknowledgments
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. B0717-17-0070).
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Kang, S.H., Bae, H.B., Kil, R.M., Youn, H.Y. (2017). Predicting Taxi Passenger Demands Based on the Temporal and Spatial Information. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_27
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DOI: https://doi.org/10.1007/978-3-319-70139-4_27
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