郭文,肖为周,秦菲菲

• 1:苏州大学轨道交通学院

摘要(Abstract)：

为了更精确地预测短期站点客流量,动态调整城市轨道交通的日常客流方案,采用支持向量机模型对预测地铁客流量。首先,通过对AFC数据分析,利用上周同期进站量、前一天同期进站量、当日前两个时段进站量以及高峰和非高峰时段参数作为模型的输入变量;然后,构造支持向量机预测模型并运用粒子群算法优化模型(PSO-SVM模型),实现地铁站点客流量预测,并进行不同模型预测误差的比较分析;最后,以苏州地铁数据为例,预测汾湖路地铁站的进站客流量。结果表明,优化模型能够有效改善预测误差,预测结果更为准确,证明PSO-SVM方法能有效用于地铁进站客流量的预测研究,为地铁进站客流量预测提供了新的方法。

关键词(KeyWords)： 交通运输工程;城市轨道交通;客流预测;支持向量机;进站客流量

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金青年项目(71301112)

作者(Author): 郭文,肖为周,秦菲菲

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