Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents
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Jaillet_Hierarchical bayesian.pdf
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1.65 MB
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Author(s)
Yu, Jiangbo
Low, Kian Hsiang
Oran, Ali
Jaillet, Patrick
Date Issued
December 2012
Journal
Proceedings of the 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yu, Jiangbo, Kian Hsiang Low, Ali Oran, and Patrick Jaillet. “Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents.” 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (n.d.).
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/WI-IAT.2012.216
