DBSCAN-Natural Break Hybrid Clustering for Traffic State Classification

This project implements a hybrid clustering approach combining DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Natural Breaks (Jenks) to classify traffic states. The algorithm is designed to extract meaningful traffic patterns from speed data across road segments by determining relative speed thresholds for various traffic states and analyzing the inter-segment relationships in traffic behavior.
The main functions include:

Determining relative speed thresholds for traffic state classification using hybrid clustering.
Computing statistical metrics (mean, standard deviation, etc.) of relative speeds in each cluster.
Analyzing the correlation of traffic states between adjacent road segments, including:

Linear regression slope and intercept.
Pearson correlation coefficient (R).
p-value of statistical significance.
Standard error of regression coefficients.