  Ride-hailing is a sustainable transportation paradigm where riders access
door-to-door traveling services through a mobile phone application, which has
attracted a colossal amount of usage. There are two major planning tasks in a
ride-hailing system: (1) matching, i.e., assigning available vehicles to pick
up the riders, and (2) repositioning, i.e., proactively relocating vehicles to
certain locations to balance the supply and demand of ride-hailing services.
Recently, many studies of ride-hailing planning that leverage machine learning
techniques have emerged. In this article, we present a comprehensive overview
on latest developments of machine learning-based ride-hailing planning. To
offer a clear and structured review, we introduce a taxonomy into which we
carefully fit the different categories of related works according to the types
of their planning tasks and solution schemes, which include collective
matching, distributed matching, collective repositioning, distributed
repositioning, and joint matching and repositioning. We further shed light on
many real-world datasets and simulators that are indispensable for empirical
studies on machine learning-based ride-hailing planning strategies. At last, we
propose several promising research directions for this rapidly growing research
and practical field.
