Learning periodic human behaviour models from sparse data for crowdsourcing aid delivery in developing countries


McInerney, James, Rogers, Alex and Jennings, Nicholas R. (2013) Learning periodic human behaviour models from sparse data for crowdsourcing aid delivery in developing countries At Conference on Uncertainty in Artificial Intelligence (UAI), United States. 11 - 15 Jul 2013. , pp. 401-410.

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Description/Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Venue - Dates: Conference on Uncertainty in Artificial Intelligence (UAI), United States, 2013-07-11 - 2013-07-15
Related URLs:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Organisations: Electronics & Computer Science
ePrint ID: 352319
Date :
Date Event
11 July 2013Published
Date Deposited: 09 May 2013 14:27
Last Modified: 17 Apr 2017 15:33
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/352319

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