Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (11): 2568-2573.doi: 10.13229/j.cnki.jdxbgxb20210530

Previous Articles    

Residents' commuting time model under the nonlinear impact of urban built environment

Jing-xian WU1,2(),Hua-peng SHEN1,Yin HAN1(),Min YANG3   

  1. 1.Business School，University of Shanghai for Science and Technology，Shanghai 200093，China
    2.Key Laboratory of Earthquake Engineering and Engineering Vibration，Institute of Engineering Mechanics，China Earthquake Administration，Harbin 150080，China
    3.School of Transportation，Southeast University，Nanjing 210096，China
  • Received:2021-06-18 Online:2022-11-01 Published:2022-11-16
  • Contact: Yin HAN E-mail:wujingxiannn@163.com;hanyin2000@sina.com

RICH HTML

 6   

PDF (PC)

175

Abstract:

To investigate the nonlinear impact of built environment on residents' commuting time， a case study was conducted in Nanjing by using a Gradient Boosting Decision Tree. The model has considered the impact of socio-demographics， trip characteristics， and built environment at traffic analysis zone. The result shows that trip characteristics have the largest cumulative contribution on commuting time as high as 52%， followed by built environment factors（38.88%）. Significant nonlinear impacts of built environment have been presented. The nonlinear thresholds of land-use mix and residential ratio are identified. This study expects to provide supportive scientific references for urban future planning and design at the micro-level.

Key words: engineering of communications and transportation system, built environment, commuting time, nonlinear effects, gradient boosting decision tree

CLC Number: 

  • U491.1

Fig.1

Traffic analysis zones in central city of Nanjing"

Table 1

Sample data description （N=3200）"

属性变量变量定义均值标准差
个人家庭性别是否为男性：0-否（48.2%）；1-是（51.8%）0.5180.500
学历是否具有大专及以上学历：0-否（48.0%）；1-是（52.0%）0.5200.500
年龄

年龄：1-18~29岁（34.9%）；2-30~40岁（24.0%）；

3-40~50岁以上（25.8%）；4-50岁以上（15.3%）

2.2141.080
驾照是否拥有驾照：0-否（40.3%）；1-是（59.7%）0.5970.491
汽车拥有数家庭中小汽车拥有数量（辆）0.7900.590
学龄前儿童家庭中学龄前儿童个数（个）0.1240.332
家庭年收入家庭年收入是否高于10万元：0-否（43.0%）；1-是（57.0%）0.5700.494
通勤出行特征慢行出行是否使用慢行交通方式出行：0-否（44.6%）；1-是（55.4%）0.5540.497
公交出行是否使用公共交通出行：0-否（85.3%）；1-是（14.7%）0.1470.354
机动车出行是否使用私人机动车出行：0-否（70.1%）；1-是（29.9%）0.2990.458
出发时间是否高峰时期出行：0-否（47.0%）；1-是（53.0%）0.5300.499
通勤距离职住交通小区间直线距离（km）8.4207.620
通勤时间居民通勤所需时间（min）34.72019.950

建成环境

距市中心距离居住地交通小区距离市中心的距离（km）9.0597.394
公交站点密度居住地交通小区公交站台个数/交通小区面积（个/km2）20.06111.685
交叉口密度居住地交叉口个数/交通小区面积（个/km2）19.63731.801
混合度居住地交通小区用地混合熵0.6490.169
地铁站密度居住地交通小区地铁站个数/交通小区面积（个/km2）1.1971.979
住宅用地占比居住地交通小区住宅用地面积/交通小区面积0.3160.205
工作用地占比居住地交通小区商业、工业等用地面积/交通小区面积0.2970.181
路网密度居住地交通小区所有道路长度/交通小区面积（km/km2）12.3008.860

Table 2

Relative importance and ranking of explanatory variables"

属性变 量影响程度影响程度/%总计/%
建成环境属性路网密度36.6138.88
工作用地占比45.61
公交站点密度55.52
距市中心距离65.45
住宅用地占比75.22
混合度84.74
交叉口密度94.06
地铁站点密度141.67
通勤出行特征慢行出行221.1752.00
公交出行112.72
私人机动车131.92
通勤距离122.50
出发时间103.69
个人家庭属性性别161.389.12
学历191.03
年龄122.33
家庭小汽车拥有量151.64
家庭收入171.20
学龄前儿童数目200.37
驾照181.17
总计100

Fig.2

Nonlinear impact of land use mixture"

Fig.3

Nonlinear impact of intersection density"

Fig.4

Nonlinear impact of job density"

Fig.5

Nonlinear impact of house density"

1 孙晓雪. 通勤时间对城市居民幸福感的影响研究[D]. 北京: 北京交通大学经济管理学院, 2018.
Sun Xiao-xue. Research on the impact of commute time on urban resident happiness[D]. Beijing: School of Economics and Management, Beijing Jiaotong University, 2018.
2 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 Chao-ying, Shao Chun-fu, Wang Pin-xi, 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.
3 Zhang H, Xu S, Liu X, et al. Near "real-time" estimation of excess commuting from open-source data: evidence from China's megacities[J]. Journal of Transport Geography, 2020, 91(4): 102929.
4 Sun B, Yin C. Impacts of a multi-scale built environment and its corresponding moderating effects on commute duration in China[J]. Urban Studies, 2020, 57(10): 2115-2130.
5 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.
6 尹超英, 邵春福, 黄兆国, 等. 基于梯度提升决策树的多尺度建成环境对小汽车拥有的影响[J]. 吉林大学学报: 工学版, 2022, 52(3): 572-577.
Yin Chao-ying, Shao Chun-fu, Huang Zhao-guo, et al. Investigating influences of multi-scale built environment on car ownership behavior based on gradient boosting decision trees[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(3): 572-577.
7 Koushik A N, Manoj M, Nezamuddin N. Machine learning applications in activity-travel behaviour research: a review[J]. Transport Reviews, 2020, 40: 288-311.
8 Zhao X, Yan X, Yu A, et al. Prediction and behavioral analysis of travel mode choice: a comparison of machine learning and logit models[J]. Travel Behaviour and Society, 2020, 20: 22-35.
9 熊丽芳, 甄峰, 钱前, 等. 基于CHAID决策树方法的城市居民通勤时间影响因素分析——以南京为例[J]. 人文地理, 2013(6): 68-73.
Xiong Li-fang, Zhen Feng, Qian Qian, et al. The influencing factors analysis of city residents' commute time based on CHAIN model of decision tree: a case study of Nanjing[J]. Human Geography, 2013(6): 68-73.
10 Cheng L, Vos J D, Zhao P, et al. Examining non-linear built environment effects on elderly's walking: a random forest approach[J]. Transportation Research Part D: Transport and Environment, 2020, 88:102552.
11 Tao T, Wang J, Cao X. Exploring the non-linear associations between spatial attributes and walking distance to transit[J]. Journal of Transport Geography, 2020, 82: 102560.
[1] Song FANG,Jian-xiao MA,Gen LI,Ling-hong SHEN,Chu-bo XU. Traffic risk analysis of moving work zone on right lane of city expressway [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1786-1791.
[2] Song-xue GAI,Xiao-qing ZENG,Xiao-yuan YUE,Zi-hao YUAN. Parking guidance model based on user and system bi⁃level optimization algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1344-1352.
[3] Hong-feng XU,Hong-jin CHEN,Dong ZHANG,Qian-hui LU,Na AN,Xian-cai Geng. Fully⁃actuated signal timing technique for isolated signalized intersections in connected vehicle environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1324-1336.
[4] Chao-ying YIN,Chun-fu SHAO,Zhao-guo HUANG,Xiao-quan WANG,Sheng-you WANG. Investigating influences of multi⁃scale built environment on car ownership behavior based on gradient boosting decision trees [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 572-577.
[5] Feng XUE,Chuan-lei HE,Qian HUANG,Jian LUO. Coordination degree of multimodal rail transit network [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2040-2050.
[6] Bo PENG,Yuan-yuan ZHANG,Yu-ting WANG,Ju TANG,Ji-ming XIE. Automatic traffic state recognition from videos based on auto⁃encoder and classifiers [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 886-892.
[7] Dian-hai WANG,Xin-yi SHEN,Xiao-qin LUO,Sheng JIN. Offset optimization with minimum average vehicle delay [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 511-523.
[8] Xian-min SONG,Ming-ye ZHANG,Zhen-jian LI,Xin WANG,Ya-nan ZHANG. Setting of dynamic bus lane and its simulation analysis and evaluation [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1677-1686.
[9] Hong-fei JIA,Xin-ru DING,Li-li YANG. Bi-level programming model for optimization design of tidal lane [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 535-542.
[10] Chao-ying YIN,Chun-fu SHAO,Xiao-quan WANG,Zhi-hua XIONG. Influence of built environment on commuting mode choice considering spatial heterogeneity [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 543-548.
[11] Da-wei ZHANG,Hai-tao ZHU. An optimization⁃based evacuation model considering pedestrian heterogeneity [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 549-556.
[12] Yuan-li GU, Yuan ZHANG, Xiao-ping RUI, Wen-qi LU, Meng LI, Shuo WANG. Short⁃term traffic flow prediction based on LSSVMoptimized by immune algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1852-1857.
[13] Yi-ming BIE,Kai JIANG,Ru-ru TANG,Lin-hong WANG,Xin-yu XIONG. Time of interval partition for traffic control at isolated intersection considering impacts of plan transition [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1844-1851.
[14] Guo-zhu CHENG, Si-he FENG, Tian-jun FENG. Setting condition of on⁃street parking space occupied vehicle lane [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1858-1864.
[15] Quan LIANG,Jian-cheng WENG,Wei ZHOU,Jian RONG. Stability identification of public transport commute passengers based on association rules [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1484-1491.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd