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Integrating Environmental and Social Performance into Asset Management Practices within Pavement Management Systems


Pavement management systems over the last decade have emerged as an effective tool for allocating resources for the maintenance and rehabilitation of pavement networks. The current paradigms search for allocation policies that maximize the performance of a series of roadway segments subject to resources made available to the governing department of transportation (DOT) or metropolitan planning organization (MPO). In its current form, many transportation agencies typically search for an allocation policy that maximizes the current serviceability (oftentimes measured per pavement condition) of a pavement network over an assumed time-horizon. With that said, current Moving Ahead for Progress in the 21st Century (MAP-21) legislation now requires the development of performance-oriented models go beyond infrastructure condition and address issues such as roadway safety, congestion, and environmental impact. This suggests that a new multi-objective framework is needed for DOTs and MPOs that can begin to quantify the environmental and social repercussions of alternative investment strategies and levels of funding.

An increasingly common framework to measure the environmental burden of an individual pavement segment is life-cycle assessment (LCA), a modeling paradigm that considers the impact associated with the raw materials production, construction, maintenance, use, and end-of-life of an individual pavement segment. Most frequently, existing studies tend to use LCA to compare the global warming impact of alternative pavement design and maintenance strategies for project-level decisions. Of course, LCA can also be used to quantify other environmental impact categories (e.g., resource depletion) as well as indicators that measure social equity (e.g., human health). In spite of the progress by the research community to augment the present pavement LCA frameworks, several opportunities remain to further enhance the current paradigms. This includes a better understanding of the impact associated with the use phase (pavement rolling resistance and deflection, albedo, leachate, and tire wear), traffic delays for maintenance events, and asphalt fumes on the health of the construction labor force.

Perhaps more importantly, however, current pavement LCAs are typically constructed for project-level decisions that use information that might not necessarily be available to a DOT or MPO across an entire network of roadways. Therefore, to begin to introduce LCA for creating more socially equitable and environmentally sustainable roadway systems, approximate models are needed that are scalable and applicable to the types of datasets used in pavement management systems.


i. Develop a common set of environmental and social performance metrics for asset management in pavement management systems.

ii. Characterize and synthesize the important life-cycle phases that affect the environmental and social performance of roadway networks

iii. Establish a set of approximate models that capture the key mechanisms in LCA while conforming to the current datasets available to DOTs and MPOs.

iv. Develop multi-objective frameworks for pavement management systems that quantify the environmental burden and social impact of allocation and funding decisions.


LCA presents a tremendous opportunity to create multi-objective pavement management systems that adhere with the current guidelines set forth in MAP-21 legislation. Unfortunately, current methods for pavement LCA are geared towards project-level decisions that require data (e.g., the transportation distance of raw materials) that is not necessarily available nor feasible when managing thousands of roadway segments. This suggests that ‘scalable’, approximate models are needed for network-level LCAs that capture the important mechanisms that affect the environmental (and social) impact of different funding and allocation choices for a roadway network. Furthermore, the pavement management community should collaborate with pavement LCA researchers that are trying to enhance our understanding of the relationship between important life-cycle phases (in particular the use phase) and the built-environment.

The aim of this research project is to introduce frameworks that allow decision-makers for DOTs and MPOs to be aware of the environmental and social repercussions associated with current, and alternative, asset management policies. Developing a set of common performance goals and quantifying the important life-cycle stages (and their mechanisms) will help center future research efforts for the pavement LCA community and lead to scalable network models that can be used for pavement management systems. This will allow for the introduction of multi-objective frameworks, consistent with the goals set forth in MAP-21 legislation, to augment the maintenance and preservation of roadway systems.


Task 1. Conduct a thorough literature review to synthesize previous LCA studies in terms of their scope of analysis, performance metrics, and methods to quantify the impact of difficult to measure life-cycle phases (e.g., use phase).

Task 2. Conduct a survey of state DOTs and MPOs to identify current data needs to conduct a network-level LCA and establish a consensus on important environmental and social metrics for transportation agencies.

Task 3. Work with pavement LCA researchers to develop approximate LCA models that are scalable for pavement management systems and fit within the limited data that is available to planning agencies.

Task 4: Prepare case studies to show the applicability of the models developed in Task 3.

Sponsoring Committee:AKT10, Pavement Management Systems
Research Period:12 - 24 months
Research Priority:High
RNS Developer:Omar Abdullah Swei
Date Posted:02/10/2016
Date Modified:02/16/2016
Index Terms:Pavement management systems, Asset management, Environmental impacts, Social impacts, Performance measurement, Life cycle analysis,
Cosponsoring Committees: 
Maintenance and Preservation
Planning and Forecasting

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