ROBUSTNESS EVALUATION OF THE 3-SATISFIABILITY REVERSE ANALYSIS METHOD WITH DISCRETE HOPFIELD NEURAL NETWORK AND GENETIC ALGORITHM FOR TRAFFIC FLOW DATASET
Abstract
Traffic flow congestion is a pervasive global phenomenon. Nonetheless, the systematic analysis and identification of traffic flow patterns remain a challenge as the volume of traffic data increases. Consequently, robust data extraction methods are required to uncover underlying data patterns. This paper proposes a 3-Satisfiability logic mining approach using a Discrete Hopfield Neural Network, develops the 3-Satisfiability Reverse Analysis method by integrating the Discrete Hopfield Neural Network with a Genetic Algorithm, and implements this method on traffic flow datasets, comparing its accuracy with existing approaches. The 3-Satisfiability Reverse Analysis method employs 3-Satisfiability for logical representation and integrates a Discrete Hopfield Neural Network with a Genetic Algorithm as its learning system. A simulation was conducted using the Urban Traffic dataset for São Paulo, Brazil. The robustness of the method in extracting relationships within traffic flow data was evaluated using selected performance metrics. The results indicated that the proposed 3-Satisfiability Reverse Analysis method, which integrates the Discrete Hopfield Neural Network and Genetic Algorithm, achieved promising performance with an accuracy rate of 80%, outperforming existing methods
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References
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