Characterizing the Distributions of Taxi Demand: Is Poisson the Right Model?

Abstract

Statistical distribution of taxi demand is essential for modeling the dynamics of taxi services. An accurate demand prediction not only helps the drivers lessen their searching time but also helps the passengers shorten their waiting time. Moreover, the temporal distribution of taxi demand is a critical component in traffic simulation. Obviously, a simulator needs to know how many new taxi pickup events to schedule after the others. In most studies, a poisson distribution is often used to model the temporal distribution of taxi pickups. However, this assumption has mostly been used without validation with empirical data. Therefore, it is unclear whether such an assumption is appropriate for modeling the statistical distribution of taxi demand. In this study, we characterize the temporal distribution of taxi pickups based on real taxi trip data from Bangkok, Thailand, and Chicago, IL, USA. It is shown that, in most cases, the poisson distribution is not suitable for modeling the temporal distribution of taxi pickups. On the contrary, this study demonstrates that a geometric distribution is more appropriate in modeling the temporal distribution of taxi pickups. To our knowledge, this has not been discovered in any prior studies.