An Optimization Model of Urban Transportation Travel Carbon Footprint Based on Game Theory
This article is part of Special Issue:
Xiaoyu Wu,
Aiguo Lei,
Lishuang Bian,
Corresponding Author
Xiaoyu Wu
School of Automation , Nanjing University of Science and Technology , Nanjing , 210094 , China , njust.edu.cn
Search for more papers by this authorAiguo Lei
School of Automation , Nanjing University of Science and Technology , Nanjing , 210094 , China , njust.edu.cn
Search for more papers by this authorLishuang Bian
School of Automation , Nanjing University of Science and Technology , Nanjing , 210094 , China , njust.edu.cn
Search for more papers by this authorXiaoyu Wu,
Aiguo Lei,
Lishuang Bian,
Corresponding Author
Xiaoyu Wu
School of Automation , Nanjing University of Science and Technology , Nanjing , 210094 , China , njust.edu.cn
Search for more papers by this authorAiguo Lei
School of Automation , Nanjing University of Science and Technology , Nanjing , 210094 , China , njust.edu.cn
Search for more papers by this authorLishuang Bian
School of Automation , Nanjing University of Science and Technology , Nanjing , 210094 , China , njust.edu.cn
Search for more papers by this authorFirst published: 29 April 2025
Academic Editor: Stefano de Luca
Abstract
To address climate change and promote green and low-carbon development, this study proposes an urban travel carbon footprint optimization method for transportation structures. Considering the environmental friendliness and efficiency of travel and combining carbon incentive policies and regret mechanisms, the travel preference model is constructed using game theory. Through the comprehensive perceived benefit function and multidimensional analysis, the effective reduction of travel carbon footprint is achieved. Taking Beijing as an example, the optimized transportation structure reduces the carbon footprint of travel by 17.17% and the total carbon emissions by 13.04%. Research has shown that to achieve the optimal carbon footprint, the green travel preference weight p1 in the multiobjective optimization model needs to be no less than 0.48, which verifies that this method can effectively alleviate the problem of transportation carbon emissions. Although this study has certain limitations in dynamic traffic demand applications, it has good practical application value for travel carbon footprint optimization under static demand conditions and contributes to the sustainable development of urban transportation and the realization of the “dual carbon” goals in China.
Conflicts of Interest
The authors declare no conflicts of interest.
Open Research
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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