Learning Periodic Human Behaviour Models from Sparse Data for
  Crowdsourcing Aid Delivery in Developing Countries
  In many developing countries, half the population lives in rural locations,
where access to essentials such as school materials, mosquito nets, and medical
supplies is restricted. We propose an alternative method of distribution (to
standard road delivery) in which the existing mobility habits of a local
population are leveraged to deliver aid, which raises two technical challenges
in the areas optimisation and learning. For optimisation, a standard Markov
decision process applied to this problem is intractable, so we provide an exact
formulation that takes advantage of the periodicities in human location
behaviour. To learn such behaviour models from sparse data (i.e., cell tower
observations), we develop a Bayesian model of human mobility. Using real cell
tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we
find that our model outperforms the state of the art approaches in mobility
prediction by at least 25% (in held-out data likelihood). Furthermore, when
incorporating mobility prediction with our MDP approach, we find a 81.3%
reduction in total delivery time versus routine planning that minimises just
the number of participants in the solution path.
