摘要
随着交通网络的快速发展,越来越多的旅客选择空铁联运出行,对空铁联运中转城市推荐方法提出了更高的要求。文章设计了符合空铁联运中转城市数据特点的数据不平衡处理方法,采用能够处理类别型特征的CatBoost算法构造基准模型,在2个不同数据分布的测试集上对该模型进行评估,模型准确率均超过85%。通过与其他算法的对比分析,证明了该模型具有较好的稳定性和更优的性能,提高了空铁联运中转城市的推荐效果,可更好地满足旅客的出行需求;通过对特征贡献度的分析发现,下单人的姓名特征会对模型预测带来影响,从而进一步提高空铁联运中转城市的个性化推荐效果。
With the rapid development of transportation networks,more and more passengers are choosing air-rail intermodal transportation,which puts forward higher requirements for the recommendation method of air-rail intermodal transit cities.This paper designed a data imbalance handling method that conformed to the characteristics of air-rail intermodal transit city data.The CatBoost algorithm,which can handle categorical features,was used to construct a benchmark model.The model was evaluated on two different test sets with different data distributions,and the accuracy of the model exceeded 85%.Through comparative analysis with other algorithms,it was proven that this model had good stability and better performance,improved the recommendation effect of air-rail intermodal transit cities and better met the travel needs of passengers.Through the analysis of feature contribution,it was found that passenger name characteristics could have an impact on model prediction,which could further improve the personalized recommendation effect of air-rail transit cities.
作者
白广栋
朱建军
翁湦元
张鹏
刘仁全
BAI Guangdong;ZHU Jianjun;WENG Shengyuan;ZHANG Peng;LIU Renquan(Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)
出处
《铁路计算机应用》
2024年第6期15-19,共5页
Railway Computer Application
基金
北京经纬信息技术有限公司科研项目(DZYF23-08)。
关键词
空铁联运
中转城市推荐
机器学习
CatBoost模型
数据不平衡
air-rail intermodal transportation
recommendation of transit cities
machine learning
CatBoost model
data imbalance
作者简介
白广栋,工程师;朱建军,正高级工程师。
