Clustering Urban Roads Using Local Binary Patterns to Enhance the Accuracy of Traffic Flow Prediction

Many studies employ a dynamic statistical approach to model the relationship between roads for predicting traffic flow. Statistical relationships among roads pertain to the associations between road segments in nearby areas. Roads with similar traffic patterns usually define the relationship. Previous studies have shown that adjacent roads demonstrated a similar traffic pattern on the same day and time interval. Studying similar patterns between roads in surrounding areas provides information about the traffic state among roads in a cluster. Furthermore, the results of this finding may be considered to increase the performance of prediction of traffic conditions. In general, road segment roads are correlated with other road segments. They are connected upstream, downstream, or both; they are connected upstream, downstream, or both. To address this, we propose a Local Binary Pattern (LBP) to explore road connections by clustering congestion features between the target roads and their neighboring roads. The outcomes show that the roads with high connectivity obtained from the clustering process were utilized to predict traffic conditions using the SVM approach. The David-Bouldin Index of LBP with k-means shows the lowest compared with PCA k-means and k-means only. By evaluating the clustering result using the Davies-Bouldin Index and assessing the prediction results with SVM, we discovered that incorporating LBP with K-Means improved outcomes in identifying highly connected roads using K-Means.