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In shared micromobility networks, such as bike-share and scooter-sharenetworks, using trip data to accurately estimate demand in docked and docklesssystems is critical to analyzing how the system is operating, such asidentifying the number of dissatisfied users, operational costs, and equity inaccess, especially for city officials. However, the distribution of availablebikes affects the distribution of observed trips. Users may walk from anunobserved cell location to an available bike masking the true location of userdemand, and users may look for a bike and not find one, which is unobserveduser demand. In collaboration with city planners from Providence, R.I., wepresent a flexible and interpretable framework to estimate spatial-temporaldemand as a spatial non-homogeneous Poisson process that explicitly models howusers choose a bike, bridging the gap between the docked and docklessmethodology. Further, we present computational experiments highlighting thatour method provides more accurate estimates of demand when there is incompleteavailability compared to previous methods, and we comment on the results of ouralgorithm on data from Providence's dockless scooter-share network. Ourestimation algorithm is publicly available through an efficient anduser-friendly application designed for other city planners and organizations tohelp inform system planning.

Estimating Censored Spatial-Temporal Demand with Applications to Shared Micromobility