地理科学 ›› 2022, Vol. 42 ›› Issue (10): 1727-1734.doi: 10.13249/j.cnki.sgs.2022.10.005
收稿日期:
2021-10-19
修回日期:
2022-02-15
接受日期:
2022-07-15
发布日期:
2022-10-20
出版日期:
2022-10-30
作者简介:
付晓(1988−),女,安徽蚌埠人,博士,副教授,主要从事居民活动与出行行为、多模式交通网络建模及交通大数据研究。E-mail: fuxiao@seu.edu.cn
基金资助:
Fu Xiao1(), Chen Zidan1, Huang Jie2
Received:
2021-10-19
Revised:
2022-02-15
Accepted:
2022-07-15
Online:
2022-10-20
Published:
2022-10-30
Supported by:
摘要:
构建考虑多维特征的城市居民非通勤出行群体画像概念模型,提出一种按序结合相关系数矩阵与二阶聚类的方法,以进行非通勤出行群体画像。利用苏州市手机信令数据,基于非通勤出行时空规律和社会属性将城市居民出行者进行群体划分,并结合城市居民非通勤出行群体画像概念模型对不同类型非通勤出行群体进行多维度解析。结果显示:① 城市居民出行者可划分为:活跃?波动?工作日主导型群体、非活跃?稳定?均衡型群体。② 不同类型非通勤出行群体画像在多维特征上存在显著差异。③ 根据群体画像标签关联分析,除显性关联外,群体画像不同标签间存在隐性关联。
中图分类号:
付晓, 陈梓丹, 黄洁. 基于手机信令数据的城市居民非通勤出行群体画像——以苏州市为例[J]. 地理科学, 2022, 42(10): 1727-1734.
Fu Xiao, Chen Zidan, Huang Jie. Non-work Travel Group Profiles of Urban Residents Based on Mobile Phone Signaling Data: A Case of Suzhou[J]. SCIENTIA GEOGRAPHICA SINICA, 2022, 42(10): 1727-1734.
表2
城市居民非通勤出行群体画像标签含义及分类
属性 | 标签 | 含义 | 分类 |
人口属性 | 性别 | 用户性别 | 男、女 |
年龄段 | 用户年龄段 | 未成年、青年、中年、老年 | |
职业 | 用户职业 | 职业类别 | |
位置属性 | 居住地行政区 | 用户居住地所在的行政区 | 根据研究区域行政区划进行分类 |
出行活跃度 | 出行频率 | 调查周期内日平均出行频率 | 高频、中频、低频 |
出行时长 | 调查周期内日平均出行时长 | 长时、中时、短时 | |
出行距离 | 调查周期内日平均出行距离 | 长距离、中距离、短距离 | |
出行规律性 | 出行稳定性 | 调查周期内日出行频次标准差 | 稳定型、波动型 |
工休出行差异 | 根据工作日非通勤出行频次日均值与 休息日非通勤出行频次日均值作差所得 | 工作日主导型、均衡型、节假日主导型 | |
出行习惯及偏好 | 出行时段 | 由用户出行频次占比最高的时段反映/时 | 0~7、7~9、9~12、12~17、17~19、19~24 |
出行方式 | 由用户出行频次占比最高的出行方式反映 | 慢行、汽车、地铁 | |
主导活动 | 根据用户停留点POI属性及停留时间,假定用户停留 时间最长的停留点POI属性为该用户的主导活动 | 公司企业、餐饮服务、交通设施服务 (主要为公共交通)等14类 |
表3
变量的相关系数矩阵
变量 | 性别 | 年龄段 | 职业 | 居住地行政区 | 出行频率 | 出行时长 | 出行距离 | 出行稳定性 | 工休出行差异 | 主导活动 |
性别 | 1.000 | 0.058 | −0.021 | 0.022 | −0.013 | −0.010 | 0.004 | 0.012 | −0.019 | −0.038 |
年龄段 | 1.000 | −0.010 | 0.013 | 0.009 | 0.011 | 0.043 | 0.024 | −0.008 | −0.021 | |
职业 | 1.000 | −0.015 | −0.005 | 0.017 | −0.003 | −0.010 | 0.013 | 0.028 | ||
居住地行政区 | 1.000 | −0.010 | 0.004 | 0.004 | 0.032 | −0.014 | 0.024 | |||
出行频率 | 1.000 | 0.726 | 0.640 | 0.573 | −0.210 | 0.012 | ||||
出行时长 | 1.000 | 0.370 | 0.414 | −0.220 | 0.002 | |||||
出行距离 | 1.000 | 0.471 | −0.130 | −0.018 | ||||||
出行稳定性 | 1.000 | −0.257 | −0.032 | |||||||
工休出行差异 | 1.000 | 0.021 | ||||||||
主导活动 | 1.000 |
表4
处理后的用户数据表样例
编号 | 性别 | 年龄 | 职业 | 居住地 经度(E) | 居住地 纬度(N) | 日均出行 时长/min | 日均出行 距离/km | 出行频率/ (次/d) | 出行频次 方差 | 工休出行 均值差 | 主导 活动 |
1 | 男 | 31 | 其他行业 | 120°30'12" | 31°10'30" | 66.201 | 20.440 | 0.250 | 0.548 | 0.017 | 公司企业 |
2 | 男 | 29 | 餐饮业 | 120°44'44" | 31°39'20" | 15.554 | 2.906 | 0.125 | 0.447 | 0.008 | 餐饮服务 |
3 | 男 | 55 | 其他行业 | 120°31'12" | 31°14'42" | 20.036 | 7.599 | 1.750 | 2.915 | −0.021 | 公司企业 |
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