Please use this identifier to cite or link to this item: https://doi.org/10.1109/WI-IAT.2012.216
Title: Hierarchical Bayesian nonparametric approach to modeling and learning the wisdom of crowds of urban traffic route planning agents
Authors: Yu, J.
Low, K.H. 
Oran, A.
Jaillet, P.
Keywords: crowdsourcing
hierarchical Dirichlet and Pitman-Yor process
intelligent transportation systems
route prediction
sequential decision making
wisdom of crowds
Issue Date: 2012
Citation: Yu, J., Low, K.H., Oran, A., Jaillet, P. (2012). Hierarchical Bayesian nonparametric approach to modeling and learning the wisdom of crowds of urban traffic route planning agents. Proceedings - 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012 2 : 478-485. ScholarBank@NUS Repository. https://doi.org/10.1109/WI-IAT.2012.216
Abstract: Route prediction is important to analyzing and understanding the route patterns and behavior of traffic crowds. Its objective is to predict the most likely or "popular" route of road segments from a given point in a road network. This paper presents a hierarchical Bayesian non-parametric approach to efficient and scalable route prediction that can harness the wisdom of crowds of route planning agents by aggregating their sequential routes of possibly varying lengths and origin-destination pairs. In particular, our approach has the advantages of (a) not requiring a Markov assumption to be imposed and (b) generalizing well with sparse data, thus resulting in significantly improved prediction accuracy, as demonstrated empirically using real-world taxi route data. We also show two practical applications of our route prediction algorithm: predictive taxi ranking and route recommendation. © 2012 IEEE.
Source Title: Proceedings - 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/78170
ISBN: 9780769548807
DOI: 10.1109/WI-IAT.2012.216
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