  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.
