Predicting travel times with context-dependent random forests by modeling local and aggregate traffic flow

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 1357-1359

  Hamner, Benjamin ^a  

^a DUKE UNIVERSITY   (United States)

Author keywords

Random forest; Traffic prediction

Indexed keywords

AGGREGATE MODEL; AGGREGATE TRAFFIC; CONTEXT DEPENDENT; GPS NAVIGATION; RANDOM FORESTS; ROAD SEGMENTS; SIMULATION FRAMEWORK; TRAFFIC FLOW; TRAFFIC PREDICTION; TRAVEL TIME;

ALGORITHMS; DATA MINING; DECISION TREES; ROADS AND STREETS; TRAFFIC CONTROL; TRAFFIC SURVEYS;

FORECASTING;

EID: 79951746147     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2010.128     Document Type: Conference Paper

References (3)

1. W. Min, L. Wynter, and Y. Amemiya. "Road traffic prediction with spatio-temporal correlations," Proc. of the Sixth Triennial Symposium on Transportation Analysis, Jun. 2007.
2. I. T. Jolliffe. Principal Component Analysis. New York: Springer-Verlag, 1986.
3. L. Breiman, "Random forests," Machine Learning, 45(1):5-32, 2001. (Pubitemid 32933532)