Eco-based pavement lifecycle maintenance scheduling optimization for equilibrated networks. (September 2020)

• Record Type: Journal Article
• Authors: Liu, Chenglong; Du, Yuchuan; Wong, S.C.; Chang, Guangzhao; Jiang, Shengchuan
• Is Part Of: Transportation research. Volume 86(2020)
• Publication Date: 2020-09
• Subjects: Maintenance scheduling -- Fuel consumption -- Equilibrated network -- Optimal deployment; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2020.102471
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlights: This paper proposes a bi-level model for pavement maintenance to minimize fuel cost. Traffic volume, pavement conditions and maintenance interference are considered. The impact of roughness and traffic disruption on fuel cost is investigated. Interactions between user equilibrium and maintenance activities are formulated. A modified active set algorithm is proposed to solve the bi-level programming. The performance is tested by comparing it with other mainstream strategies. Abstract: This paper proposes a bi-level mathematical programming framework for pavement maintenance to minimize fuel consumption in equilibrated networks. The proposed model extends research in the area by formulating the interaction between network equilibrium and various maintenance activities. The volume of traffic, age of the pavement, initial conditions, and interference due to maintenance are considered in developing this long-term deterioration model. The fuel consumption induced by roughness and traffic disruption is further investigated in the optimization process. A modified active set algorithm with nested sub-programming is developed to generate the detailed solutions. The performance of the proposed model was tested by comparing it with two other mainstream strategies: worst first and threshold control. The results show that eco-based method outperformed prevalent models by reducing extra fuel consumption by 20%. They thus show that eco-based optimal scheduling has the potential to aid in long-term maintenance decisions and reduce the energy cost.