Ensemble learning approach for freeway short-term traffic flow prediction

2007 IEEE International Conference on System of Systems Engineering, SOSE

Volumn , Issue , 2007, Pages

  Chen, Long ^a   Chen, C L Philip ^a  

^a University of Texas at San Antonio   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARSENIC COMPOUNDS; CONTROL THEORY; EDUCATION; FEEDFORWARD NEURAL NETWORKS; FEES AND CHARGES; FINANCE; FORECASTING; HIGHWAY SYSTEMS; INDUSTRIAL ENGINEERING; INTELLIGENT SYSTEMS; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; MILITARY DATA PROCESSING; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; REAL TIME SYSTEMS; SYSTEMS ENGINEERING; TRAFFIC SURVEYS; TRANSPORTATION; TRANSPORTATION CHARGES; VEHICLE LOCATING SYSTEMS;

BEIJING , CHINA; DATA GATHERING SYSTEMS; ENSEMBLE LEARNING; ENSEMBLE LEARNING APPROACH; FILTER METHODS; HISTORIC DATA; INTELLIGENT TRANSPORTATION SYSTEMS (ITS); INTERNATIONAL CONFERENCES; METROPOLITAN AREAS; NON PARAMETRIC METHODS; PREDICTION ACCURACY; PREDICTION ERRORS; PREDICTION METHODS; RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN); REAL TRAFFIC; REAL-TIME DATA; SHORT-TERM TRAFFIC FLOW; SHORT-TERM TRAFFIC FLOW FORECASTING; SYSTEM-OF-SYSTEMS ENGINEERING; TIME RESOLUTIONS; TIME SERIES; TRAFFIC FLOWING; TRAFFIC SITUATIONS; TRANSPORTATION NETWORKS;

STREET TRAFFIC CONTROL;

EID: 47249085676     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SYSOSE.2007.4304282     Document Type: Conference Paper

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