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

被引：0

作者：

Wang X.-Q. ^{[1
]}

Shao C.-F. ^{[1
]}

Guan L. ^{[1
]}

Yin C.-Y. ^{[1
]}

机构：

[1] Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing

来源：

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2020年 / 20卷 / 04期

关键词：

Car ownership; Gradient boosting decision tree; Relative importance; Traffic engineering; Workplace built environment;

D O I：

10.16097/j.cnki.1009-6744.2020.04.025

中图分类号：

学科分类号：

摘要：

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. Copyright © 2020 by Science Press.

引用

页码：173 / 177

页数：4

共 10 条

[1] YIN C Y, SHAO C F, WANG X Q., Influence of urban built environment on car commuting considering parking availability, Journal of Jilin University (Engineering and Technology Edition), 44, 1, pp. 1212-1221, (2018)
[2] WANG X Q, SHAO C F, YIN C Y, Et al., Capturing car ownership behavior considering spatial autocorrelation in traffic analysis zones, Journal of Transportation Systems Engineering and Information Technology, 19, 4, pp. 28-32, (2019)
[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, Transportation Letters, 8, 1, pp. 26-34, (2016)
[4] YIN C, SUN B., Disentangling the effects of the built environment on car ownership: A multi-level analysis of Chinese cities, Cities, 74, pp. 188-195, (2018)
[5] GUO Z., Does residential parking supply affect household car ownership? The case of New York City, Journal of Transport Geography, 26, pp. 18-28, (2013)
[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, Journal of Transport Geography, 75, pp. 37-45, (2019)
[7] DING C, CAO X, WANG Y., Synergistic effects of the built environment and commuting programs on commute mode choice, Transportation Research Part A: Policy and Practice, 118, pp. 104-118, (2018)
[8] YIN C Y, SHAO C F, WANG P X, Et al., Impacts of built environment on commuting behavior based on a multilevel modeling approach, Journal of Transportation Systems Engineering and Information Technology, 18, 2, pp. 122-127, (2018)
[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, IEEE Transactions on Intelligent Transportation Systems, 18, 9, pp. 2303-2310, (2017)
[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, International Journal of Environmental Research and Public Health, 15, pp. 1-15, (2018)

← 1 →