Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting...

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Bibliographic Details
Main Authors: Seyed Hadi Hosseini, Behzad Moshiri, Ashkan Rahimi-Kian, Babak Nadjar Araabi
Format: Article
Language:English
Published: University of Zagreb, Faculty of Transport and Traffic Sciences 2014-10-01
Series:Promet (Zagreb)
Subjects:
ITS
mutual information
traffic flow forecasting
noisy data
data loss
Online Access:http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/1429

Internet

http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/1429

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