Bao, J.; Jiang, Y.; Li, S. Determination of Safety-Oriented Pavement-Friction Performance Ratings at Network Level Using a Hybrid Clustering Algorithm. Lubricants2023, 11, 275.
Bao, J.; Jiang, Y.; Li, S. Determination of Safety-Oriented Pavement-Friction Performance Ratings at Network Level Using a Hybrid Clustering Algorithm. Lubricants 2023, 11, 275.
Bao, J.; Jiang, Y.; Li, S. Determination of Safety-Oriented Pavement-Friction Performance Ratings at Network Level Using a Hybrid Clustering Algorithm. Lubricants2023, 11, 275.
Bao, J.; Jiang, Y.; Li, S. Determination of Safety-Oriented Pavement-Friction Performance Ratings at Network Level Using a Hybrid Clustering Algorithm. Lubricants 2023, 11, 275.
Abstract
Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement friction performance rating across a statewide road network. A large, safety-oriented dataset is first generated by integrating network friction and vehicle crash data based on the attributes contributing possibly to friction related crashes. One-, two-, and multi-dimensional clustering analyses, respectively, are then performed to rate pavement friction. The Chi-square test is further employed to validate and identify the practical ratings. It is shown that by effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.
Keywords
pavement friction rating; network level; road safety attributes; hybrid clustering; density-based spatial clustering of applications with noise (DBSCAN); Gaussian mixture model (GMM); Chi-square test
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
