摘要
选取培训机构地理位置、培训机构许可培训项目数、培训机构服务质量以及船员年龄、籍贯、已参加培训次数作为影响因子,采用随机森林模型对浙江省船员选择培训机构的影响因素进行了分析.结果表明:(1)地理位置对船员培训机构选择的影响最大,可解释58.7%的船员选择培训机构的行为,年龄对船员培训机构选择的影响最小;(2)影响因素的作用存在区域差异性,同一影响因素对不同区域的船员选择培训机构的影响程度有所不同,地理位置对舟山地区船员选择行为的影响最大,对省内其他地区影响较小.
The geological location of the training institution,the number of training projects licensed by the training authorities,the quality of training services,the age of the ship crew,the place of birth of the crew,and the number of trainings having participated by the crew have been selected as the influencing factors in this study.The random forest model is used to analyze the factors influencing the selection of training institutions by crew members in Zhejiang Province.The results show that:(1)Geographic location has the most significant impact on selecting the training institutions,which may account for 58.7% of the crew’s choice of training institutions;in comparison,age has the least influence.(2)The role of influencing factors varies according to regions.Also noted is that the same influencing factors have different impacts on crew’s selection of training institutions in different regions.Geographic location has the greatest impact on the crew’s selection preference in Zhoushan area,and less is found in other areas across the province.
作者
袁玉峰
郁晓红
郑彭军
YUAN Yufeng;YU Xiaohong;ZHENG Pengjun(Faculty of Maritime and Transportation,Ningbo University,Ningbo 315832,China;Zhejiang Province 2011 Port Economic Collaborative Innovation Center,Ningbo 315832,China;National Traffic Management Engineering&Technology Research Centre Ningbo University Sub-centre,Ningbo University,Ningbo 315832,China;Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies,Nanjing 210096,China)
出处
《宁波大学学报（理工版）》
CAS
2021年第3期79-83,共5页
Journal of Ningbo University：Natural Science and Engineering Edition
基金
国家重点研发计划政府间国合重点专项(2017YFE9134700)
欧盟地平线2020项目(690713)
浙江海事局科技项目(HS2019000170).
关键词
船员选择培训机构行为
影响因素
区域差异
随机森林
crew’s selection of training institutions
influencing factor
regional differences
random forest
