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
Description
Summary: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 methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.
ISSN:0353-5320
1848-4069

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