Towards Road Sustainability—Part I: Principles and Holistic Assessment Method for Pavement Maintenance Policies

Abstract

Assessing the holistic sustainability of public policies remains a challenge rarely taken up due to a lack of adequate assessing methods. Frequently, only environmental and/or financial aspects are addressed, rather than the three pillars, including macro- and micro-economic as well as social performance. This paper presents an assessment method to fully compare the performance of pavement resurfacing policies for all its stakeholders and considering pavement–vehicle interactions. First, an analytical and then systemic approach to road maintenance highlights all its stakeholders, and a complete set of sustainability indicators is proposed to quantify the various impacts of maintenance programs: tax revenues, road operator’s and users’ savings, domestic production and employment, net present value, users’ time savings and noise reduction health benefits, as well as protection of natural resources, biodiversity and human health. Second, specific physical models of road condition (International Roughness Index) and its role in pavement–vehicle interaction in terms of vehicle consumption and wear as well as traffic noise are introduced. Then, equations to calculate these indicators are presented based on a comparison of existing assessment methods. The final transdisciplinary method pulls from road engineering, industrial ecology, acoustics and economics. It especially combines environmental and economic life cycle assessments and economic input–output analysis, as well as financial and socioeconomic appraisals. Finally, this article takes up the interdisciplinary challenge of building a fully holistic assessment method to help decision makers properly address sustainability, and its general algorithm can be adapted to assess a variety of transportation policies.

Towards Road Sustainability—Part II: Applied Holistic Assessment and Lessons Learned from French Highway Resurfacing Strategies

“…Roads are key infrastructures that support most of the world's transportation activity [1,2], explaining the wide corpus of research on their sustainability [3]. However, the studies carried out around sustainability solutions are fragmentary, often approached through the prism of a single discipline, as shown in the literature review of part I of this double article on pavement sustainability assessment [4]. Examining these solutions quantitatively through a holistic assessment to quantify the levers of sustainability is needed to develop sound public policy recommendations.…”

“…Examining these solutions quantitatively through a holistic assessment to quantify the levers of sustainability is needed to develop sound public policy recommendations. This is the challenge that we address in this article on the issue of road maintenance, by applying the holistic method developed in part I of this double article [4].…”

A method for intelligent generation of decision information for urban road maintenance

“…Pn Pl Ci floor Cl (5) where Pl is the image capture interval, Cl is the road unit length, Pn is the image sequence number, and floor indicates rounding down.…”

“…In recent years, propelled by the rapid evolution of artificial intelligence technology, deep learning techniques have found extensive application in the intelligent identification of road defects [1][2][3][4] , emerging as a significant tool to complement urban road maintenance decision-making [5][6][7][8] . Xiao Liyang et al [9] enhanced the Mask R-CNN model, achieving precise localization and extraction of road surface cracks under high thresholds through a cascade of multiple detectors.…”

Road Maintenance Planning of the Fier-Vlore Road Axis, based on the Assessment and Forecast of AADT and Los Service Level

“…Given that VOP and LOS also show a low level of productivity, it can be concluded that options should be considered to increase the capacity of the road, for example, by adding a lane or building an alternative road (Figure 3). AADT forecast for the period 2023-2032 [12]. Referring to the results from Table 3, the Service Level for 2032 would be 0.79, or referring to Table 1, the Service Level is in Service Level (LOS) D conditions, which stands for "Long Manoeuvring Restrictions".…”