基于机器学习模型的建成环境对小汽车拥有行为的影响

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (4): 173-177.

基于机器学习模型的建成环境对小汽车拥有行为的影响

王晓全，邵春福^*，管岭，尹超英

北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室，北京 100044

收稿日期:2020-02-14 修回日期:2020-05-13 出版日期:2020-08-25 发布日期:2020-08-25
作者简介:王晓全(1992-)，男，黑龙江宾县人，博士生.
基金资助:
中央高校基本科研业务经费专项资金/The Fundamental Research Funds for the Central Universities (2019YJS101).

Exploring Influences of Built Environment on Car Ownership Based on a Machine Learning Method

WANG Xiao-quan, SHAO Chun-fu, GUAN Ling, YIN Chao-ying

Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China

Received:2020-02-14 Revised:2020-05-13 Online:2020-08-25 Published:2020-08-25

摘要/Abstract

摘要：

为分析家庭小汽车拥有行为，同时考虑居住地和工作地建成环境的影响，构建梯度提升迭代决策树(GBDT)模型；分析社会经济属性，居住地、工作地建成环境属性对小汽车拥有行为的影响程度，并基于长春市居民出行调查数据进行实证研究.结果表明：3类影响因素中，社会经济属性对小汽车拥有行为的影响最大(58.95%)；职住地建成环境属性均对家庭小汽车拥有行为具有显著影响，且居住地建成环境影响(23.77%)高于工作地建成环境(17.28%)；职住地建成环境属性中，除居住地交叉口密度，工作地到中央商务区(CBD)距离及公共交通站点密度外，其他建成环境属性对小汽车拥有行为的影响均大于5%.因此，有必要同时优化职住地的建成环境来抑制小汽车拥有量的增长.

关键词: 交通工程, 职住地建成环境, 小汽车拥有, 影响程度, 梯度提升迭代决策树

Abstract:

To analyze the car ownership behaviors, a gradient boosting decision tree (GBDT) method is employed to explore the effect sizes of residential and workplace built environments on car- ownership decisions. The empirical analysis is conducted based on the Changchun household travel survey data. The results show that the socio-economic factors contribute 58.95% to automobile ownership collectively and rank the first among the three categories of factors. The residential and workplace built environment variables are both associated with car ownership. And the residential built environment is more influential than the workplace built environment. Except for intersection density at residential locations, distance to the central business district(CBD), and bus stop density at workplace locations, all built environment variables have relative importance more than 5%. Therefore, it is of great importance for urban planners and policy makers to optimize the urban built environment to mitigate the increase of car ownership.

Key words: traffic engineering, workplace built environment, car ownership, relative importance, gradient boosting decision tree

中图分类号:

U491

王晓全，邵春福，管岭，尹超英. 基于机器学习模型的建成环境对小汽车拥有行为的影响[J]. 交通运输系统工程与信息, 2020, 20(4): 173-177.

WANG Xiao-quan, SHAO Chun-fu, GUAN Ling, YIN Chao-ying. Exploring Influences of Built Environment on Car Ownership Based on a Machine Learning Method[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 173-177.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract20096.shtml

http://www.tseit.org.cn/CN/Y2020/V20/I4/173

参考文献

[1] 尹超英, 邵春福, 王晓全.考虑停车可用性的建成环境对小汽车通勤出行的影响[J]. 吉林大学报(工学版), 2018, 44(1): 1212-1221. [YIN C Y, SHAO C F, WANG X Q. Influence of urban built environment on car commuting considering parking availability[J]. Journal of Jilin University (Engineering and Technology Edition), 2018, 44(1): 1212-1221.]

[2] 王晓全, 邵春福, 尹超英, 等.考虑交通小区相关性的小汽车拥有行为研究[J]. 交通运输系统工程与信息, 2019, 19(4): 28-32, 71. [WANG X Q, SHAO C F, YIN C Y, et al. Capturing car ownership behavior considering spatial autocorrelation in traffic analysis zones[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(4): 28-32, 71.]

[3] DING C, WANG Y, YANG J, et al. Spatial heterogeneous impact of built environment on household auto ownership levels: Evidence from analysis at traffic analysis zone scales[J]. Transportation Letters, 2016, 8 (1): 26-34.

[4] YIN C, SUN B. Disentangling the effects of the built environment on car ownership: A multi-level analysis of Chinese cities[J]. Cities, 2018, 74: 188-195.

[5] GUO Z. Does residential parking supply affect household car ownership? The case of New York City[J]. Journal of Transport Geography, 2013, 26: 18-28.

[6] DING C, CAO X. How does the built environment at residential and work locations affect car ownership? An application of cross- classified multilevel model[J]. Journal of Transport Geography, 2019, 75: 37-45.

[7] DING C, CAO X, WANG Y. Synergistic effects of the built environment and commuting programs on commute mode choice[J]. Transportation Research Part A: Policy and Practice, 2018, 118: 104-118.

[8] 尹超英, 邵春福, 王聘玺, 等. 基于多层模型的城市建成环境对通勤行为的影响[J]. 交通运输系统工程与信息, 2018, 18(2): 122- 127. [YIN C Y, SHAO C F, WANG P X, et al. Impacts of built environment on commuting behavior based on a multilevel modeling approach[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(2): 122-127.]

[9] MA X, DING C, LUAN S, et al. Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(9): 2303-2310.

[10] WANG X, SHAO C, YIN C, et al. Exploring the influence of built environment on car ownership and use with a spatial multilevel model: A case study of Changchun, China[J]. International Journal of Environmental Research and Public Health, 2018, 15: 1-15.

[1]	林建新, 刘博, 赵霞, 张蕾. 基于混合约束自编码器的机动车工况智能构建方法[J]. 交通运输系统工程与信息, 2022, 22(2): 109-116.
[2]	戢晓峰, 耿昭师. 山区双车道公路货车碰撞预测的双变量冲突极值模型[J]. 交通运输系统工程与信息, 2022, 22(2): 230-238.
[3]	张建波, 孙建平, 徐春玲, 郭镜霞, 温慧敏, 宋国华. 考虑交通运行条件影响的驾驶员特征聚类[J]. 交通运输系统工程与信息, 2022, 22(2): 330-336.
[4]	马景峰, 任刚, 李豪杰, 曹奇, 杜建玮. 电动自行车与机动车事故严重性影响因素分析[J]. 交通运输系统工程与信息, 2022, 22(2): 337-348.
[5]	宗芳, 王猛, 贺正冰. 考虑多车影响的分子动力学智能网联跟驰模型[J]. 交通运输系统工程与信息, 2022, 22(1): 37-48.
[6]	马小龙, 余强, 刘建蓓. 基于路侧毫米波雷达的车辆碰撞概率计算方法[J]. 交通运输系统工程与信息, 2022, 22(1): 57-66.
[7]	廖鹏, 唐铁桥. 基于车辆动力传动和转向系统的换道轨迹优化策略[J]. 交通运输系统工程与信息, 2022, 22(1): 98-105.
[8]	刘永红, 罗霞, 胡剑鹏. 考虑临时和预约需求的共享泊位动态分配方法[J]. 交通运输系统工程与信息, 2022, 22(1): 171-181.
[9]	袁振洲, 郭曼泽, 彭泳鑫, 杨洋. 基于梯度关联规则的老年行人交通事故风险识别[J]. 交通运输系统工程与信息, 2022, 22(1): 195-208.
[10]	张玉婷, 陈波佑, 张双焱, 闫学东, 李晓梦. 抵近信控交叉口分心驾驶识别模型[J]. 交通运输系统工程与信息, 2022, 22(1): 217-224.
[11]	宋浪, 王健, 杨滨毓, 安实. 连续流交叉口左转非机动车交通组织创新设计[J]. 交通运输系统工程与信息, 2022, 22(1): 225-233.
[12]	白婧荣, 唐伯明, 孙宗元, 毕辉云, 向恬君. 毗邻互通立交特长隧道驾驶负荷研究[J]. 交通运输系统工程与信息, 2022, 22(1): 301-310.
[13]	郭彦茹, 罗志雄, 王家川, 何方. 数据驱动的共享单车停放区规划方法研究[J]. 交通运输系统工程与信息, 2021, 21(6): 9-16.
[14]	李君羡, 沈宙彪, 童文聪, 吴志周. 基于分类车头时距的城市道路大型车影响分析[J]. 交通运输系统工程与信息, 2021, 21(6): 55-62.
[15]	卢凯, 周志洁, 张勇刚, 赵世杰, 徐广辉. 面向单向交通路网的绿波协调设计方法[J]. 交通运输系统工程与信息, 2021, 21(6): 74-83.