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摘 要：“共享汽车+轨道交通”可有效改善其他公共交通换乘轨道交通的不足，大大减少私家车数量，缓解城市道路交通堵塞。本文致力于解决“共享汽车+軌道交通”中用户可能面临的在轨道交通站点“无车可用”或“无位可停”的问题，提出自适应自主迁移调度策略，包含共享汽车自主迁移与用户自主迁移两种情况，进行用户预约时间窗设计，进一步优化共享汽车停放位置。构建带时间窗的“共享汽车+轨道交通”调度模型，采用遗传算法提高模型求解效率。通过系统随机生成的数据验证了模型的有效性和可行性。研究结果为推进共享汽车与轨道交通换乘接驳的发展提供理论依据。
关键词：“共享汽车+轨道交通” 用户预约时间窗 迁移调度策略 遗传算法
Proposing and Optimizing the Dispatching Strategy of "Car Sharing + Rail Transit"
Zhou Xinlin，Wei Ao，Wang Ningning
Abstract：“Car sharing + rail transit” can effectively improve the shortcomings of other public transportation transferring to rail transit， greatly reducing the number of private cars， and alleviating urban road traffic congestion. This article is dedicated to solving the problems of “car-free” or “no-stop” in rail transit stations that users may face in “car-sharing + rail transit”， and proposes an adaptive autonomous migration scheduling strategy， including autonomous migration of shared cars and users autonomous migration in both cases， designing the time window for user appointments， and further optimizing the parking location of shared cars. The study constructs a “shared car + rail transit” scheduling model with time windows， and uses genetic algorithms to improve the efficiency of model solving. The data generated randomly by the system verifies the validity and feasibility of the model. The research results provide a theoretical basis for advancing the development of shared cars and rail transit transfers.
Key words：“car sharing + rail transit”， user appointment time window， migration scheduling strategy， genetic algorithm
关键词：“共享汽车+轨道交通” 用户预约时间窗 迁移调度策略 遗传算法
Proposing and Optimizing the Dispatching Strategy of "Car Sharing + Rail Transit"
Zhou Xinlin，Wei Ao，Wang Ningning
Abstract：“Car sharing + rail transit” can effectively improve the shortcomings of other public transportation transferring to rail transit， greatly reducing the number of private cars， and alleviating urban road traffic congestion. This article is dedicated to solving the problems of “car-free” or “no-stop” in rail transit stations that users may face in “car-sharing + rail transit”， and proposes an adaptive autonomous migration scheduling strategy， including autonomous migration of shared cars and users autonomous migration in both cases， designing the time window for user appointments， and further optimizing the parking location of shared cars. The study constructs a “shared car + rail transit” scheduling model with time windows， and uses genetic algorithms to improve the efficiency of model solving. The data generated randomly by the system verifies the validity and feasibility of the model. The research results provide a theoretical basis for advancing the development of shared cars and rail transit transfers.
Key words：“car sharing + rail transit”， user appointment time window， migration scheduling strategy， genetic algorithm