Developing a Sustainable Pavement Management Plan: Tradeoffs in Road                    Condition, User Costs, and Greenhouse Gas Emissions

France-Mensah, Jojo; O’Brien, William J.

doi:10.1061/(asce)me.1943-5479.0000686

Cited by 54 publications

(32 citation statements)

References 40 publications

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“…While recognizing the importance of flexibility at the local scale, resilient planning argues for the need of a regional and national perspective [2,[9][10][11]. In the planning of road networks, this integrated outlook makes it possible to pursue overall objectives such as overall condition improvement [12][13][14][15][16] or safety performance [17], as well to contribute to the mitigation of the system's structural imbalances [18] and vulnerabilities [4,19]. Given the link between road networks and other essential facilities, such as hospital or schools, and their role in induced community vulnerability [20], reducing these networks' vulnerabilities and structural imbalances should be a primary objective for infrastructure planning.…”

Section: Implementation Planning and Decentralizationmentioning

confidence: 99%

“…For example, in the case of preservation, a treatment with a service life of 2.5 years, this action was considered to be applied four times over the 10 years of the analysis period (Table 1, column "Treatment/Period"). Finally, we formulated the road network's condition improvement objective as the sum of the pavement condition index condition score (PCI-CS) improvements of all the entities of the road network being analyzed (Matin et al, 2017 [12]):…”

Section: Planning Module: Generation Of Planning Alternativesmentioning

confidence: 99%

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Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Salas

Piqueras

2020

IJERPH

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Resilient planning demands not only resilient actions, but also resilient implementation, which promotes adaptive capacity for the attainment of the planned objectives. This requires, in the case of multi-level infrastructure systems, the simultaneous pursuit of bottom-up infrastructure planning for the promotion of adaptive capacity, and of top-down approaches for the achievement of global objectives and the reduction of structural vulnerabilities and imbalances. Though several authors have pointed out the need to balance bottom-up flexibility with top-down hierarchical control for better plan implementation, very few methods have yet been developed with this aim, least of all with a multi-objective perspective. This work addressed this lack by including, for the first time, the mitigation of urban vulnerability, the improvement of road network condition, and the minimization of the economic cost as objectives in a resilient planning process in which both actions and their implementation are planned for a controlled, sustainable development. Building on Urban planning support system (UPSS), a previously developed planning tool, the improved planning support system affords a planning alternative over the Spanish road network, with the best multi-objective balance between optimization, risk, and opportunity. The planning process then formalizes local adaptive capacity as the capacity to vary the selected planning alternative within certain limits, and global risk control as the duties that should be achieved in exchange. Finally, by means of multi-objective optimization, the method reveals the multi-objective trade-offs between local opportunity, global risk, and rights and duties at local scale, thus providing deeper understanding for better informed decision-making.

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Section: Implementation Planning and Decentralizationmentioning

confidence: 99%

Section: Planning Module: Generation Of Planning Alternativesmentioning

confidence: 99%

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Salas

Piqueras

2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…Chen et al [1] proposed a dichotomic approach to maximize the PV benefit (and minimize the PV cost) under annual budgetary and condition requirements. France-Mensah and O'Brien [14] developed a sustainable pavement management plan that incorporates road conditions, user costs, and environmental considerations. e aforementioned literature on multiobjective optimization for pavement network maintenance tends to focus on M&R planning for pavement networks, but principally from the perspective of a department of transportation.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiobjective Optimization for Pavement Network Maintenance and Rehabilitation Programming: A Case Study in Shanghai, China

Sun

Zhou

et al. 2020

Mathematical Problems in Engineering

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This study investigates the pavement network maintenance and rehabilitation (M&R) programming problem, over a certain planning horizon and in the context of limited funding. We designed an integer programming model to fulfill three purposes, namely, optimize the road conditions, minimize user disturbance costs, and minimize agency costs. We present a case study in which this model is applied to the pavement network of Shanghai. We investigate the results through the use of five M&R strategies, to identify the Pareto-optimal trade-offs inherent in developing pavement network M&R planning. The results demonstrate that there is a positive relationship between PCI improvement and user disturbance costs and between PCI improvement and agency costs. Additionally, we conduct a comparative analysis between agency and government-oriented strategies to evaluate the effectiveness and equity consideration. The findings suggest that the government-oriented strategy improves the pavement condition effectively with low user disturbance costs, and the agency-oriented strategy accounts for more equity consideration. Finally, we formulate an extension model that considers multiple road types, for application to pavement network M&R programming. The results show that light rehabilitation and preventive maintenance are the most frequently implemented treatments on arterial roads and secondary trunk roads from the case network implementation. This study helps decision-makers identify the trade-offs made when developing a pavement network M&R program.

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“…The Maintenance, Rehabilitation, and Reconstruction (MR&R) program of pavement network is a multidimensional decision-making process that takes into account several considerations. Highway agencies generally focus on two issues: maximizing the efficiency of the pavement network or minimizing agency costs [1]. Both of these issues require the estimation of operating conditions of the pavement network to set up pavement management and maintenance plans.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

et al. 2020

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Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., Levenberg-Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS model outperforms other models with the promising results of APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.

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Developing a Sustainable Pavement Management Plan: Tradeoffs in Road Condition, User Costs, and Greenhouse Gas Emissions

Cited by 54 publications

References 40 publications

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Multiobjective Optimization for Pavement Network Maintenance and Rehabilitation Programming: A Case Study in Shanghai, China

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

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