The number of passengers in a high-speed railway line normally varies significantly by the time periods, such as the peak and nonpeak hours. A reasonable classification of railway operation time intervals is essential for an adaptive adjustment of the train schedule. However, the passenger flow intervals are usually classified manually based on experience, which is subjective and inaccurate. Based on the time samples of actual passenger demand data for 365 days, this paper proposes an affinity propagation (AP) algorithm to automatically classify the passenger flow intervals. Specifically, the AP algorithm first merges time samples into different categories together with the passenger transmit volume of the stations, which are used as descriptive variables. Furthermore, clustering validity indexes, such as Calinski–Harabasz, Hartigan, and In-Group Proportion, are employed to examine the clustering results, and reasonable passenger flow intervals are finally obtained. A case study of the Zhengzhou-Xi’an high-speed railway indicates that our proposed AP algorithm has the best performance. Moreover, based on the passenger flow interval classification results obtained using the AP algorithm, the train operation plan fits the passenger demand better. As a result, the indexes of passenger demand satisfaction rate, average train occupancy rate, and passenger flow rate are improved by 7.6%, 16.7%, and 14.1%, respectively, in 2014. In 2015, the above three indicators are improved by 5.7%, 18.4%, and 14.4%, respectively.

中文翻译：

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基于亲和传播的聚类方法在高速铁路旅客需求时空动态管理中的应用

高速铁路上的乘客数量通常会随时间段（例如高峰时间和非高峰时间）而显着变化。铁路运行时间间隔的合理分类对于列车时刻表的适应性调整至关重要。但是，通常根据经验手动对乘客流量间隔进行分类，这是主观且不准确的。基于365天的实际乘客需求数据的时间样本，提出了一种亲和力传播（AP）算法来自动对客流间隔进行分类。具体来说，AP算法首先将时间样本与车站的乘客发送量一起合并为不同的类别，它们被用作描述变量。此外，聚类有效性指标，例如Calinski–Harabasz，Hartigan，运用分组内比例法和组内比例法对聚类结果进行检验，最终得到合理的客流区间。对郑西高速铁路的案例研究表明，本文提出的AP算法具有最佳的性能。此外，基于使用AP算法获得的客流间隔分类结果，列车运行计划可以更好地满足客运需求。结果，2014年的旅客需求满意度，平均火车占用率和旅客流量指标分别提高了7.6％，16.7％和14.1％。2015年，上述三个指标提高了5.7。 ％，18.4％和14.4％。对郑西高速铁路的案例研究表明，本文提出的AP算法具有最佳的性能。此外，基于使用AP算法获得的客流间隔分类结果，列车运行计划可以更好地满足客运需求。结果，2014年的旅客需求满意度，平均火车占用率和旅客流量指标分别提高了7.6％，16.7％和14.1％。2015年，上述三个指标提高了5.7。 ％，18.4％和14.4％。对郑西高速铁路的案例研究表明，本文提出的AP算法具有最佳的性能。此外，基于使用AP算法获得的客流间隔分类结果，列车运行计划可以更好地满足客运需求。结果，2014年的旅客需求满意度，平均火车占用率和旅客流量指标分别提高了7.6％，16.7％和14.1％。2015年，上述三个指标提高了5.7。 ％，18.4％和14.4％。