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发表刊物：Neural Computing and Applications

摘要：Traditional intercity passenger transportation is inefficient, inflexible, and financially unrewarding, failing to meet the demands of intercity travel. To address these issues, this study utilizes historical carpooling order data, extracts travel patterns, and devises a comprehensive plan for intercity customized passenger transport services. Firstly, a formalized description of issues related to long-distance cross-city travel, multiple lines, and scheduling around highway entry and exit points is addressed. Secondly, a single-objective integer linear programming model is constructed, aimed at maximizing the total profit for the operating company. Finally, from a spatial-temporal network perspective, a refined balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm is designed for alternative station selection. To achieve a rapid and effective solution to the model, the approach is combined with a variable neighborhood search algorithm. Experimental results on historical carpooling data from Anxi County and Xiamen City demonstrate that the proposed algorithm, compared to the combination of BIRCH and genetic algorithms, exhibits shorter computation time, higher quality, and stability. Additionally, various parameter analysis and sensitivity experiments demonstrate the effectiveness of parameters used

论文类型：期刊论文

是否译文：否

发表时间：2024-04-24

收录刊物：SCI