Exploring the value of traffic flow data in bus travel time prediction

Journal of Transportation Engineering

Volumn 138, Issue 4, 2012, Pages 436-446

  Mazloumi, Ehsan ^a   Moridpour, Sara ^b   Currie, Graham ^a   Rose, Geoff ^a  

^a MONASH UNIVERSITY   (Australia)

^b RMIT UNIVERSITY   (Australia)

Author keywords

Artificial neural network; Bus travel time prediction; Traffic flow

Indexed keywords

ACCURATE PREDICTION; ARTIFICIAL NEURAL NETWORK MODELS; BUS ROUTE; INDEPENDENT VARIABLES; INPUT VARIABLES; MELBOURNE , AUSTRALIA; PASSENGER DEMANDS; PREDICTION ACCURACY; PREDICTION MODEL; PREDICTION PERFORMANCE; TRAFFIC FLOW; TRAVEL TIME PREDICTION;

BUS TRANSPORTATION; FORECASTING; MATHEMATICAL MODELS; NEURAL NETWORKS; REGRESSION ANALYSIS; TIME VARYING CONTROL SYSTEMS; TRAVEL TIME;

TRAFFIC CONTROL;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; BUS TRANSPORT; PREDICTION; TRAVEL TIME;

AUSTRALIA; MELBOURNE; VICTORIA [AUSTRALIA];

EID: 84860015840     PISSN: 0733947X     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)TE.1943-5436.0000329     Document Type: Article

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