Optimal sustainable road plans using multi-objective optimization approach. (July 2016)

• Record Type: Journal Article
• Authors: Chung, Jin-Hyuk; Bae, Yun Kyung; Kim, Jinhee
• Is Part Of: Transport policy. Volume 49(2016)
• Page Start: 105
• Page End: 113
• Publication Date: 2016-07
• Subjects: Transportation and state -- Periodicals; Transportation -- Rates -- Periodicals; 388
• DOI: 10.1016/j.tranpol.2016.04.011
• Languages: English
• ISSNs: 0967-070X

Abstract

Abstract: To construct a sustainable road network, the three dimensions of sustainability—economic efficiency, environmental impact, and social equity—should be significantly and simultaneously taken into account at the planning stage. Because these dimensions have trade-off relationships among them, we developed a multi-objective optimization model for planning optimal road capacity improvement. Three indicators, each measuring one dimension of sustainability, were used as the objectives in the proposed model. The total travel cost, which combines the monetized value of travel time and operating costs, was adopted as the economic indicator. The total emissions cost and the GINI coefficient based on zonal accessibility were adopted as the environmental and equity indicators, respectively. We performed an experimental test with three model scenarios to compare the single- and multi-objective approaches and different objective functions. We obtained Pareto optimal solutions using the elitist non-dominated sorting genetic algorithm. The results show that the proposed model, which is based on the multi-objective approach and considers all three dimensions of sustainability, is more suitable than other options for designing a sustainable road network. In addition, we suggest that the frequency rate of a link within Pareto solutions can be used to prioritize capacity improvement for maximum road network sustainability. Highlights: Multi-objective optimization model of sustainable road network design is developed. The objective functions consist of the three sustainable indicators. Pareto optimal solutions are obtained using a genetic algorithm. The solutions can be used to prioritize capacity improvement for sustainability.