  Mobility-on-demand (MoD) systems have recently emerged as a promising
paradigm of one-way vehicle sharing for sustainable personal urban mobility in
densely populated cities. In this paper, we enhance the capability of a MoD
system by deploying robotic shared vehicles that can autonomously cruise the
streets to be hailed by users. A key challenge to managing the MoD system
effectively is that of real-time, fine-grained mobility demand sensing and
prediction. This paper presents a novel decentralized data fusion and active
sensing algorithm for real-time, fine-grained mobility demand sensing and
prediction with a fleet of autonomous robotic vehicles in a MoD system. Our
Gaussian process (GP)-based decentralized data fusion algorithm can achieve a
fine balance between predictive power and time efficiency. We theoretically
guarantee its predictive performance to be equivalent to that of a
sophisticated centralized sparse approximation for the GP model: The
computation of such a sparse approximate GP model can thus be distributed among
the MoD vehicles, hence achieving efficient and scalable demand prediction.
Though our decentralized active sensing strategy is devised to gather the most
informative demand data for demand prediction, it can achieve a dual effect of
fleet rebalancing to service the mobility demands. Empirical evaluation on
real-world mobility demand data shows that our proposed algorithm can achieve a
better balance between predictive accuracy and time efficiency than
state-of-the-art algorithms.
