Taxi-demand forecasting using dynamic spatiotemporal analysis
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 n...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Electronics and Telecommunications Research Institute (ETRI)
2022-08-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2021-0123 |
| _version_ | 1811223701518024704 |
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| author | Akshata Gangrade Pawel Pratyush Gaurav Hajela |
| author_facet | Akshata Gangrade Pawel Pratyush Gaurav Hajela |
| author_sort | Akshata Gangrade |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T08:37:03Z |
| format | Article |
| id | doaj.art-2231d3ba43e8407592c7367ec8f6a98f |
| institution | Directory Open Access Journal |
| issn | 1225-6463 |
| language | English |
| last_indexed | 2024-04-12T08:37:03Z |
| publishDate | 2022-08-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj.art-2231d3ba43e8407592c7367ec8f6a98f2022-12-22T03:39:59ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632022-08-0144462464010.4218/etrij.2021-012310.4218/etrij.2021-0123Taxi-demand forecasting using dynamic spatiotemporal analysisAkshata GangradePawel PratyushGaurav HajelaTaxi-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.https://doi.org/10.4218/etrij.2021-0123combined covariates modelensemble regression modelslinear regressionspatiotemporal analysistaxi demand forecasting |
| spellingShingle | Akshata Gangrade Pawel Pratyush Gaurav Hajela Taxi-demand forecasting using dynamic spatiotemporal analysis ETRI Journal combined covariates model ensemble regression models linear regression spatiotemporal analysis taxi demand forecasting |
| title | Taxi-demand forecasting using dynamic spatiotemporal analysis |
| title_full | Taxi-demand forecasting using dynamic spatiotemporal analysis |
| title_fullStr | Taxi-demand forecasting using dynamic spatiotemporal analysis |
| title_full_unstemmed | Taxi-demand forecasting using dynamic spatiotemporal analysis |
| title_short | Taxi-demand forecasting using dynamic spatiotemporal analysis |
| title_sort | taxi demand forecasting using dynamic spatiotemporal analysis |
| topic | combined covariates model ensemble regression models linear regression spatiotemporal analysis taxi demand forecasting |
| url | https://doi.org/10.4218/etrij.2021-0123 |
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