Optimization path design for urban travel system based on CO2-congestion-satisfaction multi-objective synergy: Case study in Suzhou, China. (June 2022)

• Record Type: Journal Article
• Authors: Xu, Yue; Tian, Shu; Wang, Qingsong; Zhang, Yujie; Yuan, Xueliang; Ma, Qiao; Ma, Haichao; Liu, Chengqing
• Is Part Of: Sustainable cities and society. Volume 81(2022)
• Publication Date: 2022-06
• Subjects: CO2 emission reduction -- Urban travel system -- Multi-objective optimization -- Uncertain optimization -- Path design; GDP gross domestic product -- CNY Chinese Yuan -- TIC transportation infrastructure construction -- STD spatial and temporal distribution -- MOOM multi-objective optimization model -- IVIA interval-valued interactive algorithm -- IVIFEW interval-valued intuitionistic fuzzy entropy weight method -- NSGA-II non-dominated sorting genetic algorithm-II -- TOPSIS technique for order preference by similarity to an ideal solution -- MG management policy group -- TG technology policy group -- RG resource policy group -- MTG management policy & technology policy group -- MRG management policy & resource policy group -- TRG technology policy & resource policy group -- MTRG management policy & technology policy & resource policy group -- BAU business as usual (basic scenario); Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2022.103863
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Optimizing urban travel system from government-resident perspective. MOOM with CO2 -congestion-satisfaction objectives synergy is proposed. Uncertainty of results is reduced by introducing IVIFEW and IVIA. The decreasing marginal utility relationship between dS and dC, dCG is found. An optimal policy implementation path (MG→TG→RG) is designed. Abstract: The optimization of urban travel system can effectively reduce CO2 emissions and alleviate road congestion. It can also improve resident satisfaction. Thus, a multi-objective optimization model from the government-resident perspective is developed to coordinate the needs of both sides for urban travel system. Meanwhile, a two-step uncertain multi-objective optimization algorithm combining interval-value interaction algorithm and non-dominated ranking genetic algorithm is designed to solve the model. Results showed that in the scenario based on the travel conditions of Suzhou in 2019, shifting the travel mode from Private car, Bike/e-bike to Bus, Metro is the optimal solution. The value of CO2 emissions, Road congestion and Satisfaction are reduced by (-10.63%, -9.66%, -3.15%) in the best case and (-1.56%, -2.72%, -4.33%) in the worst case. To further analyze the effect of policy implementation on optimization results, management group (MG), technology group (TG) and resource group (RG) policies are introduced into the original model. It is found that the average optimization effect of MG is the best, but different policies have emphases on different objectives. According to the optimization features of different policies, a policy implementation path is proposed: MG→TG→RG. The above conclusions can provide theoretical guidance for the optimization of urban travel system.