Research on Traffic Identification Algorithm Based on FCM Cluster Analysis

In our code implementation, we employ FCM (Fuzzy C-Means) cluster analysis that integrates both Euclidean distance methodology and fuzzy algorithm approaches to identify traffic conditions. The Euclidean distance method, implemented through geometric distance calculation functions, serves as a common spatial measurement technique for estimating distances between data points in feature space. The fuzzy algorithm component, coded using membership functions and uncertainty handlers, effectively addresses ambiguous characteristics inherent in traffic condition data. By combining these two methodologies in our FCM implementation - where cluster centers are iteratively optimized using distance minimization and fuzzy membership updates - we achieve more accurate and reliable traffic condition classification. The algorithm structure includes centroid initialization, iterative membership updating using fuzzy coefficients, and convergence checking through objective function optimization.