Abstract
Most traffic management and optimization tasks, such as accident detection or optimal vehicle routing, require an ability to adequately model, reason about and predict irregular and stochastic behavior. Our goal is to create a probabilistic model of traffic flows on highway networks that is realistic from the point of applications and at the same time supports efficient learning and inference. We study several multivariate probabilistic models and analyze their respective strengths. To balance accuracy and efficiency, we propose a novel learning model, mixture of Gaussian trees, and show its advantages in learning and inference. All models are evaluated on real-world traffic flow data from highways of the Pittsburgh area.
Chapter PDF
Similar content being viewed by others
Keywords
- Mutual Information
- Bayesian Network
- Bayesian Information Criterion
- Highway Network
- Conditional Autoregressive
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Belomestny, D., Jentsch, V., Schreckenberg, M.: Completion and continuation of nonlinear traffic time series: a probabilistic approach. Journal of Physics A: Math. Gen. 36, 11369–11383 (2003)
Besag, J., York, J., Mollie, A.: Bayesian Image Restoration With Two Applications In Spatial Statistics. Annals of the Institute of Statistical Mathematics 43(1), 1–59 (1991)
Lauritzen, S.L.: Graphical Models. Oxford University Press, Oxford (1996)
Hastie, T., Tibshirani, R., Friedman, J.: Elements of Statistical Learning. Springer, Heidelberg (2001)
Chellappa, R., Jain, A. (eds.): Markov Random Fields - Theory and Applications. Academic Press, London (1993)
Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)
Jensen, F.V.: An Introduction to Bayesian Networks. Springer, New York (1996)
Meilă-Predoviciu, M.: Learning with mixtures of trees. PhD thesis, MIT (1999)
Shachter, R., Kenley, R.: Gaussian influence diagrams. Management Science 35(5), 527–550 (1989)
Chow, C.J.K., Liu, C.N.: Approximating discrete probability distributions with dependence trees. IEEE Trans. on Inf. Theory 14(3), 462–467 (1968)
Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978)
Meilă, M., Jaakkola, T.: Tractable Bayesian learning of tree belief networks. Technical Report CMU–RI–TR–00–15, Carnegie Mellon University Robotics Institute (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Šingliar, T., Hauskrecht, M. (2007). Modeling Highway Traffic Volumes. In: Kok, J.N., Koronacki, J., Mantaras, R.L.d., Matwin, S., Mladenič, D., Skowron, A. (eds) Machine Learning: ECML 2007. ECML 2007. Lecture Notes in Computer Science(), vol 4701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74958-5_74
Download citation
DOI: https://doi.org/10.1007/978-3-540-74958-5_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74957-8
Online ISBN: 978-3-540-74958-5
eBook Packages: Computer ScienceComputer Science (R0)