Forecasting Multivariate Road Traffic Flows Using Bayesian Dynamic Graphical Models, Splines and Other Traffic Variables
DOI10.1111/anzs.12026zbMath1337.62289OpenAlexW2124238881WikidataQ57976986 ScholiaQ57976986MaRDI QIDQ2802832
Casper J. Albers, Catriona M. Queen, Osvaldo Anacleto
Publication date: 27 April 2016
Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://pure.rug.nl/ws/files/111521822/Anacleto_et_al_2013_Australian_New_Zealand_Journal_of_Statistics.pdf
occupancycubic splinesstate space modelsspeeddynamic linear modelheadwaylinear multiregression dynamic model
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Uses Software
Cites Work
- Bayesian forecasting and dynamic models.
- Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network
- ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORK
- Regression modeling strategies. With applications to linear models, logistic regression and survival analysis
- The elements of statistical learning. Data mining, inference, and prediction
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