Seyed Hadi Hosseini
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Behzad Moshiri
Control and Intelligent Processing Center of Excellence, School of ECE, University of Tehran, Tehran, Iran.
Ashkan Rahimi-Kian
Control and Intelligent Processing Center of Excellence, School of ECE, University of Tehran, Tehran, Iran.
Babak Nadjar Araabi
Control and Intelligent Processing Center of Excellence, School of ECE, University of Tehran, Tehran, Iran.
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.
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