Coordination of User and Agency Costs Using Two-Level Approach for Pavement Management Optimization

Pavement maintenance and rehabilitation programming requires the consideration of conflicting objectives to optimize its life-cycle costs. While there are several approaches to solve multiobjective problems for pavement management systems, when user costs or environmental impacts are considered the optimal solutions are often impractical to be accepted by road agencies, given the dominating share of user costs in the total life-cycle costs. This paper presents a two-stage optimization methodology that considers maximization of pavement quality and minimization of agency costs as the objectives to be optimized at the pavement section level, while at the network level, the objectives are to minimize agency and user costs. The main goal of this approach is to provide decision makers with a range of optimal solutions from which a practically implementable one could be selected by the agency. A sensitivity analysis and some trade-off graphics illustrate the importance in balancing all the objectives to obtain reasonable solutions for highway agencies. Multiobjective optimization problems at both levels are solved using genetic algorithms. The results of a case study indicate the applicability of the methodology.

[1]  Bei Zhou,et al.  A Disaggregated Approach for the Computation of Network-Level Highway User Costs , 2010 .

[2]  J. Kalbfleisch,et al.  The Analysis of Panel Data under a Markov Assumption , 1985 .

[3]  Glaucio H. Paulino,et al.  Integration of Pavement Cracking Prediction Model with Asset Management and Vehicle-Infrastructure Interaction Models , 2015 .

[5]  Hwasoo Yeo,et al.  Algorithms for bottom-up maintenance optimisation for heterogeneous infrastructure systems , 2013 .

[6]  Tien Fang Fwa,et al.  Assessment of different genetic algorithms for pavement management systems , 2016 .

[7]  Adelino Ferreira,et al.  Pavement maintenance programming considering two objectives: maintenance costs and user costs , 2013 .

[8]  Tien Fang Fwa,et al.  A decentralized multidistrict optimization framework for system-wide pavement maintenance resource allocation , 2016 .

[9]  Karim Chatti,et al.  Estimating the Effects of Pavement Condition on Vehicle Operating Costs , 2012 .

[10]  James C. Chu,et al.  Optimal threshold-based network-level transportation infrastructure life-cycle management with heterogeneous maintenance actions , 2012 .

[11]  Kiyoshi Kobayashi,et al.  A statistical deterioration forecasting method using hidden Markov model for infrastructure management , 2012 .

[12]  Khaled A. Abaza.  Back-calculation of transition probabilities for Markovian-based pavement performance prediction models , 2016 .

[13]  Joaquim Agostinho Barbosa Tinoco,et al.  An application of Markov chains to predict the evolution of performance indicators based on pavement historical data , 2018 .

[14]  William G. Buttlar,et al.  Effect of Pavement Roughness on User Costs , 2012 .

[15]  Reus Salini,et al.  Application of Artificial Intelligence for Optimization in Pavement Management , 2015 .

[16]  Hongchao Liu,et al.  A risk-based optimisation for pavement preventative maintenance with probabilistic LCCA: a Chinese case , 2017 .

[17]  Weng Tat Chan,et al.  Multiobjective Optimization for Pavement Maintenance Programming , 2000 .

[18]  Mostafa A. Abo-Hashema,et al.  Development of maintenance decision model for flexible pavements , 2009 .

[19]  B Leben,et al.  The Way Forward for Pavement Performance Indicators Across Europe. COST Action 354: Performance Indicators for Road Pavements , 2008 .

[20]  Christopher H. Jackson,et al.  Multi-State Models for Panel Data: The msm Package for R , 2011 .

[21]  Zhanmin Zhang,et al.  Network-Level Multi-objective Optimal Maintenance and Rehabilitation Scheduling , 2010 .

[22]  Charles-Antoine Robelin,et al.  Reliability-Based System-Level Optimization of Bridge Maintenance and Replacement Decisions , 2008, Transp. Sci..