Abstract
Service and total delay are considered for classifying MLOS and intersections, respectively. GPS is used to collect travel time and speed data for turning movements that are transformed into average delay values. Thirteen junctions from eight different cities in India form the dataset. Divisive followed by agglomerative clustering (DAC-HAC) algorithm is applied as a two-step process for obtaining the service and total delay ranges. Validation of clusters is performed based on the Davies-Bouldin score, Calinski-Harabasz index, and Silhouette gaps. Based on DAC-HAC, uncontrolled intersections are classified into six categories (Cat-I, II, III, IV, V, and VI). Results indicate MLOS classes “D”, “E” and “F” have significantly higher service delay ranges as compared to Highway capacity manual “control” delay ranges indicating mixed traffic conditions. Most of the uncontrolled intersections under mixed traffic fall under Cat-IV, V, and VI, having higher total delay ranges (> 60 s/ vehicle/ approach). Finally, validation of the clustering results is done for geometric and roadside environmental features.
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