An economic approach to road condition assessment using road user feedback: A new model and its application

ABSTRACT

Assessing roadway assets condition is the prerequisite of an efficient road management system. It requires participation from the top management, equipment, trained human resources, and dedicated funding. Newfoundland and Labrador have 13,500 lane kilometers of roads, of which almost 7,700 kilometers belong to the local jurisdictions. Local agencies typically consult the Transportation Association of Canada's pavement management guidelines for managing the road networks. But, municipality roads require more specified guidelines considering issues like lack of human resources, equipment, inadequate funding, environmental factors, and public expectations. To better maintain these roads, evaluation of road conditions is the first step. However, a proper evaluation system needs considerable funding, a trained workforce, and necessary equipment. Hence, the idea of using road users’ feedback is introduced in this paper. Citizens from 108 municipalities of the province participated in a feedback survey where they were asked questions about roadway assets condition. The survey resulted in a significant amount of data. First, an exploratory analysis of the road users’ feedback data was conducted. Then, a simple distress-based pavement performance model was developed. This model can be adopted by the local agencies as a simple decision-making tool. To make the model practical, a smartphone application called MUNPave is also introduced in this paper.

KEYWORDS:

• Road management system
• pavement management system
• pavement condition data
• pavement distress
• municipality
• low volume road
• local road
• prediction
• pavement performance model
• smartphone application
• pandemic

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by Natural Sciences and Engineering Research Council of Canada.