基于分类决策树的城市内部迁居人群通勤时间预测模型

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (6): 135-140.

基于分类决策树的城市内部迁居人群通勤时间预测模型

吴静娴^*1，杨敏²，韩印¹

1. 上海理工大学管理学院，上海 200093；2. 东南大学交通学院，南京 210096

收稿日期:2019-06-06 修回日期:2019-08-23 出版日期:2019-12-25 发布日期:2019-12-25
作者简介:吴静娴(1987-)，女，江苏盐城人，讲师，博士.
基金资助:
国家自然科学基金/National Natural Science Foundation of China (71771049, 51678132, 71801041).

Commute Time Pattern of Relocated Residents in City Based on Classification Decision Tree

WU Jing-xian¹, YANG Min², HAN Yin¹

1. Management Institute, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. School of Transportation, Southeast University, Nanjing 210096, China

Received:2019-06-06 Revised:2019-08-23 Online:2019-12-25 Published:2019-12-25

摘要/Abstract

摘要：

针对迁居个体长距离、长时间的通勤现象，以南京迁居人群通勤出行多源数据为基础，综合考虑迁居人群个人家庭属性、迁居属性、建成环境属性及通勤属性，建立迁居人群通勤时间多元线性回归模型，分析各属性对通勤时间的影响. 结合多元线性回归和Logistic 回归确定决策树模型入选变量和通勤时间离散值，利用C4.5 算法建立迁居人群通勤时间决策树，以决策树结构揭示不同背景下迁居个体通勤时间模式特征. 结果显示，相较于个人家庭属性，职住同区、通勤出发时刻、住房类型、通勤距离、通勤方式及社区至主干道的便捷程度对迁居个体通勤时长的影响更为显著，侧面反映社区微观建成环境优化政策更利于提升迁居个体通勤效率.

关键词: 交通工程, 迁居通勤者, 建成环境, 通勤时间, 分类决策树

Abstract:

Focusing on the long-distance and long-time commute of relocated residents, this study analyzed the impact of factors including personal and household characteristics, relocation attribute, built environment and commute attribute on commute time of relocated commuters by using multivariate linear regression model. After the identification of significant variables and the discretized value by linear regression model and Logistic regression model, a classification decision tree was developed to interpret the commute time pattern of relocated commuters in different scenarios by using the C4.5 algorithm. The result indicates that same zone of job-housing, departure time, house type, job-housing distance, commute mode, and accessibility of community to arterials are detrimental factors of commute time compared to personal and household characteristics, reflecting that policies that optimize the built environment in micro level contribute more in improving commute efficiency for relocated residents.

Key words: traffic engineering, relocated commuters, built environment, commute time, classification decision tree

中图分类号:

U491.1

吴静娴，杨敏，韩印. 基于分类决策树的城市内部迁居人群通勤时间预测模型[J]. 交通运输系统工程与信息, 2019, 19(6): 135-140.

WU Jing-xian, YANG Min, HAN Yin. Commute Time Pattern of Relocated Residents in City Based on Classification Decision Tree[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(6): 135-140.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19951.shtml

http://www.tseit.org.cn/CN/Y2019/V19/I6/135

参考文献

[1] 盛亦男. 中国的家庭化迁居模式[J]. 人口研究, 2014, 38(3): 41-54. [SHENG Y N. Family migration pattern in China[J]. China Population Today, 2014, 38(3): 41-54.]

[2] 李雪铭, 杜晶玉. 基于居民通勤行为的私家车对居住空间影响研究: 以大连市为例[J]. 地理研究, 2007, 26 (5): 1033-1042. [LI X M, DU J Y. Research on private cars' influence on residential space based on residents' travel behavior: Taking dalian as an example[J]. Geographical Research, 2007, 26(5): 1033-1042.]

[3] SCHWANEN T, DIELEMAN F M, DIJST M. Car use in Netherlands daily urban systems: Does polycentrism result in lower commute times?[J]. Urban Geography, 2003, 24(5): 410-430.

[4] CERVERO R. Jobs-housing balancing and regional mobility[J]. Journal of the American Planning Association, 1989, 55(2): 136-150.

[5] WANG D, CHAI Y. The jobs-housing relationship and commuting in Beijing, China: The legacy of Danwei[J]. Journal of Transport Geography, 2009, 17(1): 30-38.

[6] GIMENEZ-NADAL J I, MOLINA J A. Commuting time and household responsibilities: Evidence using propensity score matching[J]. Journal of Regional Science, 2016, 56(2): 332-359.

[7] FRANK L, BRADLEY M, KAVAGE S, et al. Urban form, travel time, and cost relationships with tour complexity and mode choice[J]. Transportation, 2008, 35 (1): 37-54.

[8] SUN B, HE Z, ZHANG T, et al. Urban spatial structure and commute duration: An empirical study of China[J]. International Journal of Sustainable Transportation, 2016, 10(7): 638-644.

[9] ZHAO P, LU B. Exploring job accessibility in the transformation context: An institutionalist approach and its application in Beijing[J]. Journal of Transport Geography, 2010, 18(3): 393-401.

[10] KASHANI A T, MOHAYMANY A S. Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models[J]. Safety Science, 2011, 49(10): 1314-1320.

[11] ARENTZE T, TIMMERMANS H. Parametric action decision trees: Incorporating continuous attribute variables into rule-based models of discrete choice[J]. Transportation Research Part B: Methodological, 2007, 41(7): 772-783.

[12] BREIMAN L, FRIEDMAN J H, OLSHEN R A, et al. Classification and regression trees[M]. Chapman & Hall (Wadsworth, Inc.), New York, 1984.

[13] QUINLAN J R. Induction of decision trees[J]. Machine Learning, 1986, 1(1): 81-106. 140

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