Ride-sharing Determinants: Spatial and Spatio-temporal Bayesian Analysis for Chicago Service in 2022
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arXiv
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| Format: | Preprint |
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2024
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| author | Elkhouly, Mohamed Alhadidi, Taqwa |
| author_facet | Elkhouly, Mohamed Alhadidi, Taqwa |
| contents | The rapid expansion of ride-sharing services has caused significant disruptions in the transpor-tation industry and fundamentally altered the way individuals move from one place to another. Accurate estimation of ride-sharing improves service utilization and reliability and reduces travel time and traffic congestion. In this study, we employ two Bayesian models to estimate ride-sharing demand in the 77 Chicago community areas. We consider demographic, scoio-economic, transportation factors as well as land-use characteristics as explanatory variables. Our models assume conditional autoregression (CAR) prior for the explanatory variables. Moreover, the Bayesian frameworks estimate both the unstructured random error and the struc-tured errors for the spatial and the spatiotemporal correlation. We assessed the performance of the estimated models and the residuals of the spatial regression model have no left-over spatial structure. For the spatiotemporal model, the squared correlation between actual ride-shares and the fitted values is 0.95. Our analysis revealed that the demographic factors (populations size and registered crimes) positively impact the ride-sharing demand. Additionally, the ride-sharing demand increases with higher income and increase in the economically active propor-tion of the population as well as the residents with no cars. Moreover, the transit availability and the walkability indices are crucial determinants for the ridesharing in Chicago. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ride-sharing Determinants: Spatial and Spatio-temporal Bayesian Analysis for Chicago Service in 2022 Elkhouly, Mohamed Alhadidi, Taqwa Applications Other Statistics The rapid expansion of ride-sharing services has caused significant disruptions in the transpor-tation industry and fundamentally altered the way individuals move from one place to another. Accurate estimation of ride-sharing improves service utilization and reliability and reduces travel time and traffic congestion. In this study, we employ two Bayesian models to estimate ride-sharing demand in the 77 Chicago community areas. We consider demographic, scoio-economic, transportation factors as well as land-use characteristics as explanatory variables. Our models assume conditional autoregression (CAR) prior for the explanatory variables. Moreover, the Bayesian frameworks estimate both the unstructured random error and the struc-tured errors for the spatial and the spatiotemporal correlation. We assessed the performance of the estimated models and the residuals of the spatial regression model have no left-over spatial structure. For the spatiotemporal model, the squared correlation between actual ride-shares and the fitted values is 0.95. Our analysis revealed that the demographic factors (populations size and registered crimes) positively impact the ride-sharing demand. Additionally, the ride-sharing demand increases with higher income and increase in the economically active propor-tion of the population as well as the residents with no cars. Moreover, the transit availability and the walkability indices are crucial determinants for the ridesharing in Chicago. |
| title | Ride-sharing Determinants: Spatial and Spatio-temporal Bayesian Analysis for Chicago Service in 2022 |
| topic | Applications Other Statistics |
| url | https://arxiv.org/abs/2406.11590 |