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
- Neue Suche nach: Seyed Hadi Hosseini
- Neue Suche nach: Behzad Moshiri
- Neue Suche nach: Ashkan Rahimi-Kian
- Neue Suche nach: Babak Nadjar Araabi
- Neue Suche nach: Seyed Hadi Hosseini
- Neue Suche nach: Behzad Moshiri
- Neue Suche nach: Ashkan Rahimi-Kian
- Neue Suche nach: Babak Nadjar Araabi
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.
Dokumentinformationen
Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction
2014
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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