Author/Authors :
Zareian, Mohammad Mahdi Department of Civil Engineering - University of Calgary, Canada , Mesbah, Mahmoud Department of Civil and Environmental Engineering - Amirkabir University of Technology, Tehran, Iran , Moradi, Sepehr Department of Mathematics and Computer Science - Amirkabir University of Technology, Tehran, Iran , Ghatee, Mehdi Department of Mathematics and Computer Science - Amirkabir University of Technology, Tehran, Iran
Abstract :
This paper proposes an integrated system to control ramps and adjust variable speed limits. It includes three essential modules to predict the starting time of congestion and a fuzzy controller to determine the parameters and a model predictive control. An Apriori algorithm that is a powerful tool for frequent pattern mining is used in the first module. The proposed system is neither sensitive to the traffic distribution nor computationally intensive. Two traffic simulators of Aimsun and CTMSIM are applied to validate the results. Compared with the most recent algorithms, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), this system improves prediction accuracy up to 2.63%. The results of ramp metering and variable speed limit subsystems are also promising. The embedded controller shows 0.6% and 4% overall and rush hour improvement in the total travel time.
Keywords :
Data mining , Frequent pattern mining , Apriori algorithm , Ramp metering , Variable speed limit
