Multi-objective optimization of urban bus network using cumulative prospect theory

Multi-objective optimization of urban bus network can help improve operation efficiency of the transit system and develop strategies for reducing urban traffic congestion in China. The work used cumulative prospect theory, currently the most influential model for decision under uncertainty, to optimize urban bus network. To achieve the research objective, the work developed the theoretical framework of urban bus network optimization, including optimization principle, optimization objectives and constraints. Furthermore, optimization objectives could comprehensively reflect expectations of passengers and bus companies from the dimension of time, space and value. It is more scientific and reasonable compared with only one stakeholder or dimension alone in the previous studies. In addition, the technique for order preference by similarity to ideal solution (TOPSIS) was used to determine the positive and negative ideal alternative. The correlations between the optimization alternatives and the ideal alternatives were estimated by grey relational analysis simultaneously. The cumulative prospect theory (CPT) was used to determine the best alternative by comparing comprehensive prospect value of every alternative, accurately describing decision-making behavior compared with expected utility theory in actual life. Finally, Case of Xi'an showed that the method can better adjust the bus network, and the optimization solution is more reasonable to meet the actual needs.