In order for the service to be reliable, AutoTel has to make sure that supply and demand are geospatially balanced, meaning cars are where and when they are needed. This task is extremely difficult since cars are driven and parked by customers who are not aligned at all with this optimization task. For the most part, the distribution of cars is uncorrelated with the demand: one reason is that if a car is parked in a suburban neighborhood, it may take a long time before another user may drive it to the city center, where high demand for the cars exists; thus clusters of unused cars are very often present on the outskirts of the city.
Using machine learning, AutoTel can predict the geospatial availability of cars at given times, and use predictions to modify their business model. They could, for example, modify prices so that it would be cheaper to park cars in high demand areas, or plan the the maintenance program so that cars will be collected from high-supply-low-demand areas and returned to areas of high demand. This is an example.
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