A Tripartite Evolutionary Game Model for Air-Rail Intermodal Transportation Stakeholders Based on Perspective of Airspace Congestion
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Abstract:
A tripartite evolutionary game model of enterprise, air traffic control (ATC) and passengers in an air-rail intermodal transport (ARIT) system was developed and investigated. The optimal interaction among enterprise, ATC and passengers was explored based on the congestion charging mechanism, as presented in terms of the payoffs and decision-making behaviors of three participants. Payoff matrices were established for three game players, wherein fare, mileage cost, en-route charge and generalized travel cost were taken into consideration. After that, the replicated dynamic equations were derived and employed to analyze the reliability of the proposed model and the dynamic behaviors of each game player under initial conditions. Eventually, the Beijing-Shanghai, Beijing-Guangzhou and Beijing-Kunming corridors were used as practical cases to clarify the impact of key factors (e.g., distance, en-route charge and passenger sharing ratio) on the evolutionary trend and final strategy. The results showed that three players tend to choose the strategy which is always profitable. The enterprises would choose to introduce the ARIT strategy in medium-distance route, but not in short- and long-distance route, ATC chose to implement the congestion charging strategy, and passengers preferred the ARIT strategy. In addition, the final strategies were affected by any changes in key factors, and enterprises were more sensitive and likely to introduce the ARIT strategy out of individual interest.
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This work was supported in part by the Natural Science Foundation of Tianjin (No.20YJCZH176), and the National Natural Science Foundation of China (No.U2333206).
SUN Bo, XU Zehui, GAO Han. A Tripartite Evolutionary Game Model for Air-Rail Intermodal Transportation Stakeholders Based on Perspective of Airspace Congestion[J]. Transactions of Nanjing University of Aeronautics & Astronautics,2026,(2):203-224