了解并将"粉红领"工人纳入基于就业的旅行需求模型
Yiping Yan1,2,3, Abraham Leung1, Matthew Burke1
1Cities Research Institute, Griffith University, Brisbane, Queensland, Australia.
PloS one
|April 18, 2024
概括
传统的蓝领和白领工人细分在城市交通方面已经过时了. 使用无监督集群的数据驱动方法揭示了三种不同的通勤类型:
科学领域:
- 城市交通规划 城市交通规划
- 劳动力市场细分 劳动力市场细分
- 数据驱动建模数据驱动建模
背景情况:
- 传统的通勤者细分 (蓝领/白领) 对于现代城市交通模式来说是不够的.
- 不断发展的劳动力市场需要更细致的通勤者细分,考虑到诸如女性参与度增加等因素.
研究的目的:
- 采用数据驱动,无监督的集群方法来确定相关的通勤市场细分.
- 通过使用南东昆士兰旅行调查 (SEQTS) 数据,为城市交通建模开发更准确和更强大的细分.
主要方法:
- 在2017-20年SEQTS数据上使用了无监督集群.
- 根据职业,行业和社会人口统计学变量 (性别,年龄,家庭规模,车辆所有权,技能得分) 分组通勤者.
- 比较不同集群号 (k=8和k=3) 的细分结果.
主要成果:
- 一个3集群解决方案 (k=3) 产生了三种不同的通勤类型:"粉红领" (以女性为主),"蓝领" (以男性为主),和"白领" (性别平衡).
- "粉红领"工人,主要是女性的文职,行政,服务和销售角色,表现出最短的中位数通勤时间.
- 在三个已识别的细分市场中观察到旅行行为的显著差异.
结论:
- 数据驱动的集群方法为城市交通模式提供了更准确和更强大的市场细分.
- 这种改进的细分可以提高对各种通勤群体的运输项目和政策影响的预测.
- 这些发现有助于交通规划人员更好地了解并满足各种通勤需求.
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