A future in which there is a shared resource of a fleet of autonomous vehicles
poses numerous challenges in making effective use of the fleet. As a point of
reference, it is interesting to consider the experiences to date in developing
the logisitics management tools for the similar context of bike-sharing, and
to consider ways in which the key elements are both similar and different.
We will lean on our experience with Citibike and their parent company Motivate, using analytics and optimization to change how they manage the system. As one would see in AV management, huge rush-hour usage imbalances the system - and we provide answers to the following two questions: where should bikes be at the start of a day and how can we mitigate the
imbalances that develop? We will outline the algorithmic tools we have employed for the former question, where we developed an approach based on continuous-time Markov chains combined with integer programming models to compute stocking levels for the bikes, as well as methods employed for optimizing (and re-optimizing) the capacity of the stations. We have also guided the development of Bike Angels, a program to incentivize users to crowdsource “rebalancing rides”, which might also play a role in managing a fleet of autonomous vehicles.
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