Establishment of a Composite Safety Index for Pavement ManagementStephen A Arhin* and Asteway Ribbiso
Department of Civil and Environmental Engineering, Howard University, Washington, DC, 20059, USA
- *Corresponding Author:
- Stephen A. Arhin
Assistant Professor, Department of Civil and Environmental Engineering
Howard University, Washington, DC, 20059
Tel: +1 202-806-6100
E-mail: [email protected]
Received date: April 04, 2017; Accepted date: April 14, 2017; Published date: April 18, 2017
Citation: Arhin SA, Ribbiso A (2017) Establishment of a Composite Safety Index for Pavement Management. J Civil Environ Eng 7: 273. doi: 10.4172/2165-784X.1000273
Copyright: © 2017 Arhin SA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
One of the goals of local transportation agencies is to improve the quality of life for citizens and visitors by ensuring the efficient and safe movement of people and goods through the roadway system. Maintenance and rehabilitation of pavements are necessary to ensure that roadway networks continue to perform at their optimum. Currently, maintenance and rehabilitation of roadway networks depend on several factors including pavement condition indices, funding availability, among others. Previous studies have established relationships between crash frequency and pavement condition indices. However, the combined influence of speed, volume, and crash frequency on pavement indices, and thereby pavement management efforts has not been thoroughly examined. In this paper, a multinomial logistic regression was employed for 193 arterial segments to establish a new categorical variable: Composite Safety Index (CSI). The CSI values or ratings were based on pavement indices, crash frequency, traffic volumes and vehicular speeds to help categorize pavement sections for either maintenance or rehabilitation. The results indicated that the selected independent variables were statistically reliable in ranking pavement sections for rehabilitation or maintenance based on their CSI values.