ETRI Journal

  • 제44권4호
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  • Pages.624-640
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  • 2022
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Taxi-demand forecasting using dynamic spatiotemporal analysis

  • Gangrade, Akshata (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology) ;
  • Pratyush, Pawel (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology) ;
  • Hajela, Gaurav (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology)
  • 투고 : 2021.04.07
  • 심사 : 2021.09.27
  • 발행 : 2022.08.10
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초록

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.

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