Selection of Automated Data Collection Technologies Using Multi Criteria Decision Making Approach for Pavement Management Systems

摘要

Data collection is a critical aspect of any management system. Pavement Management Systems (PMSs) rely on consistent and repeatable data collection. Traditionally, such data has been collected through manual surveys, which are subjective, tedious, and time consuming. However, over the past two decades, there has been ongoing development of automated data collection technologies. The literature reveals a large number of services and distress data collection technologies each with different features and levels of automation and complexity. These technologies provide fast and improved methods to collect, process, and analyze data. The key is to identify and collect the most useful, reliable, cost-effective information and use it to make informed decisions for asset management. The accuracy and reliability of automated data collection can significantly impact pavement decisions at project and network levels; therefore, it is important that agencies ensure that high quality pavement condition data is collected and processed. Ultimately, this can be assured by carefully selecting the automated technology to be used for data collection. This paper presents a framework for the evaluation and selection of the appropriate automated data collection technologies for pavement management systems when there are several factors. The framework is used to facilitate the selection of the most appropriate automated data collection technologies by the aid of a Multi Criteria Decision Making (MCDM) computational approach. A case study of the province of Ontario is presented to illustrate how the framework can be implemented.