8
Predicting Rideshare Demand
8.1 Introduction - Rideshare
This last chapter returns to spatial problem solving to predict space/time demand for rideshare in Chicago. Companies like Uber and Lyft generate and analyze tremendous amounts of data to incentivize rideshare use; to employ dynamic or ‘surge' pricing; to solve routing problems; and to forecast rideshare demand to minimize driver response times. This last use case is the focus of this chapter.
The model developed here is similar to the other geospatial machine learning models built thus far, with two exceptions. First, this chapter focuses on time effects, adding additional complexity to our models, and two, social costs are less important here than ...
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