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In many developing countries, half the population lives in rural locations,where access to essentials such as school materials, mosquito nets, and medicalsupplies is restricted. We propose an alternative method of distribution (tostandard road delivery) in which the existing mobility habits of a localpopulation are leveraged to deliver aid, which raises two technical challengesin the areas optimisation and learning. For optimisation, a standard Markovdecision process applied to this problem is intractable, so we provide an exactformulation that takes advantage of the periodicities in human locationbehaviour. To learn such behaviour models from sparse data (i.e., cell towerobservations), we develop a Bayesian model of human mobility. Using real celltower data of the mobility behaviour of 50,000 individuals in Ivory Coast, wefind that our model outperforms the state of the art approaches in mobilityprediction by at least 25% (in held-out data likelihood). Furthermore, whenincorporating mobility prediction with our MDP approach, we find a 81.3%reduction in total delivery time versus routine planning that minimises justthe number of participants in the solution path.

Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries