Computational intelligence-based congestion prediction for a dynamic urban street network

Neurocomputing

Volumn 72, Issue 10-12, 2009, Pages 2710-2716

  Srinivasan, Dipti ^a   Wai Chan, Chee ^a   Balaji, P G ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Evolutionary computation; Fuzzy logic; Neural networks; Traffic flow prediction

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; CENTRAL BUSINESS DISTRICTS; COMPARISON ANALYSIS; COMPUTATIONAL INTELLIGENCES; CONGESTION PREDICTIONS; DATA CLUSTERING; EVOLUTION STRATEGIES; EVOLUTIONARY COMPUTATION; FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; FUZZY INPUTS; FUZZY OUTPUTS; HYBRID METHODS; HYBRID MODELS; INPUT DATUM; INPUT-OUTPUT RELATIONS; MAIN MODULES; MEAN ABSOLUTE PERCENTAGE ERRORS; MULTI LAYERS; SHORT TERMS; SHORT-TERM FORECASTS; SHORT-TERM TRAFFIC FLOW FORECASTING; SINGAPORE; SINGLE POINTS; TEST DATUM; TRAFFIC FLOW PREDICTION; TRAFFIC FLOWS; TRAFFIC PREDICTIONS; URBAN STREETS; URBAN TRAFFIC NETWORKS; VALUE-BASED;

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; CLUSTERING ALGORITHMS; EVOLUTIONARY ALGORITHMS; FUZZY LOGIC; FUZZY NEURAL NETWORKS; FUZZY SETS; HYBRID SENSORS; HYBRID SYSTEMS; NETWORK LAYERS; STATISTICAL TESTS; TRAFFIC SURVEYS;

STREET TRAFFIC CONTROL;

ACCURACY; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; FUZZY LOGIC; MATHEMATICAL COMPUTING; PREDICTION; PRIORITY JOURNAL; SINGAPORE; STATISTICAL MODEL; TRAFFIC;

EID: 67349116014     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.01.005     Document Type: Article

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