Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (7): 1981-1993.doi: 10.13229/j.cnki.jdxbgxb.20211037

Previous Articles    

Travel mode choice of traditional car travelers after implementation of driving restriction policy

Zhuang-lin MA1(),Shan-shan CUI2,Da-wei HU1,Jin WANG3   

  1. 1.College of Transportation Engineering，Chang'an University，Xi'an 710064，China
    2.Shandong Provincial Communications Planning and Design Institute Group Co. ，Ltd. ，Jinan 250031，China
    3.Yunnan Science Research Institute of Communication Co. ，Ltd. ，Kunming 650011，China
  • Received:2021-10-11 Online:2023-07-01 Published:2023-07-20

RICH HTML

 3   

PDF (PC)

268

Abstract:

Taking traditional car travelers in Xi'an as the research object， a questionnaire combining the revealed preference and stated preference was designed to obtain the travel mode choice behavior of car travelers in specific scenarios. The CHAID decision tree method was used to divide the categories of traditional car travelers. The fixed parameter logit and random parameter logit model were used to establish travel mode choice models of different groups under the driving restriction policy（DRP）， and both two models were compared. The results show that traditional car travelers can be divided into four categories， namely young travelers with only one car， middle-aged travelers with only one car， elderly travelers with only one car， and travelers with multiple cars. The fitting effect of random parameter logit models outperform those of fixed parameter logit models. The low-income young and middle-aged travelers with only one car are more likely to choose public transport under the DRP. Improving the service level of public transport is helpful to attract travelers with only one car to choose public transport on restricted days. Uncongested road is helpful to attract travelers with two or more cars to choose public transport on restricted days. The research conclusions can provide theoretical support for traffic management authorities to formulate differentiated DRPs.

Key words: traffic engineering, driving restriction policy, decision tree, random parameter logit model, travel mode choice

CLC Number: 

  • U491

Table 1

Hypothetical scenarios of three situationalfactors"

水平值道路交通状况限行感知效果公共交通服务水平
1非常拥堵效果不明显较差
2有些拥堵略有效果一般
3不拥堵效果显著较好

Table 2

Orthogonal test scheme based on orthogonaltable L9（34）"

序号道路交通状况限行感知效果公共交通服务水平
1111
2122
3133
4212
5223
6231
7313
8321
9333

Fig.1

Sample characteristics of respondents"

Table 3

Description of variables"

类别变量定义
社会经济属性性别男性=0，女性=1
年龄（<25岁）<25岁=1，否则=0
年龄（25-35岁）25-35岁=1，否则=0
年龄（36-45岁）36-45岁=1，否则=0
年龄（>45岁）*>45岁=1，否则=0
职业固定职业=0，自由职业=1
低收入（<5000元）<5000元=1，否则=0
中收入（5000-15000元）5000-15000元=1，否则=0
高收入（>15000元）*>15000元=1，否则=0
家庭结构（单人家庭）*单人家庭=1，否则=0
家庭结构（夫妻家庭）夫妻家庭=1，否则=0
家庭结构（多人家庭）多人家庭=1，否则=0
接送孩子情况不接送=0，接送=1
私家车拥有量1辆=0，2辆及以上=1
出发时间在7：00之前出发*7：00之前=1，否则=0
在7：00–9：00出发7：00–9：00=1，否则=0
在9：00以后出发9：00之后=1，否则=0
出行时间<30分钟*<30 min=1，否则=0
30-60分钟30-60 min=1，否则=0
60-90分钟60-90 min=1，否则=0
>90分钟>90min=1，否则=0
出行距离<3km*<3km=1，否则=0
3-10km以内3-10km=1，否则=0
>10km>10km=1，否则=0
道路交通状况非常拥堵非常拥堵=1，否则=0
有些拥堵有些拥堵=1，否则=0
不拥堵*不拥堵=1，否则=0
限行感知效果效果不明显*效果不明显=1，否则=0
略有效果略有效果=1，否则=0
效果显著效果显著=1，否则=0
公共交通服务水平较差*较差=1，否则=0
一般一般=1，否则=0
较好较好=1，否则=0

Fig.2

Car traveler classification based on CHAID decision tree"

Table 4

Estimated results of FPL and RPL model （model Ⅰ）"

变量FPL模型RPL模型
参数值OR值参数值OR值
截距项-2.428***--3.76***-
月收入（参照项：>15 000元）<5000元1.936***6.934.313***74.66
5000~15 000元1.691**5.423.040**20.91
出行时间（参照项：<60 min）60~90 min1.799***6.044.814***123.22
>90 min2.176***8.816.074***434.41
出行距离（参照项：<3 km）3~10 km-0.648**0.52--
>10 km-1.441***0.24--
公共交通服务水平（参照项：较差）较好2.182***8.685.567***261.65
s.d.--3.002**-
误差项—2.846***
LL（0）-596.1066-596.1066
LL（β）-439.8445-395.2039
AIC895.69806.41
BIC933.74844.46

Table 5

Estimated results of FPL and RPL model （model Ⅱ）"

变量FPL模型RPL模型
参数值OR值参数值OR值
截距项-0.902***--0.908***-
性别（参照项：男性）女性0.498**1.650.452*1.57
月收入（参照项：>15 000元）<5000元0.629**1.880.863***2.37
是否接送孩子（参照项：否）是-0.411**0.66--
出行时间（参照项：<60 min）60~90 min0.995***2.701.114***3.05
出行距离（参照项：<3 km）>10 km-0.485**0.62--
公共交通服务水平（参照项：较差）较好0.706***2.020.864***2.37
s.d.--0.687**-
误差项-0.731***
LL（0）-521.2467-521.2467
LL（β）-408.1907-402.4974
AIC830.38816.99
BIC862.74844.73

Table 6

Estimated results of FPL and RPL model （model Ⅲ）"

变量FPL模型RPL模型
参数值OR值参数值OR值
截距项-2.05**--1.423**-
职业（参照项：固定）自由职业-0.935*0.39-1.693**0.18
家庭结构（参照项：单人家庭）夫妻家庭2.297**9.94--
多人家庭2.752**15.671.199*3.31
出行时间（参照项：<60 min）60~90 min3.096***22.115.024***152.02
>90 min3.738***42.016.172***479.14
出行距离（参照项：<3 km）3~10 km-0.671*0.51--
>10 km-3.041***0.04-3.904***0.02
限行效果（参照项：效果不明显）效果显著1.361***3.901.588***4.89
s.d.--1.547**-
公共交通服务水平（参照项：较差）较好1.009***2.741.752***5.77
误差项-1.293***
LL（0）-216.2619-216.2619
LL（β）-168.6237-162.5274
AIC357.25343.05
BIC394.67376.74

Table 7

Estimated results of FPL and RPL model （model Ⅳ）"

变量FPL模型RPL模型
参数值OR值参数值OR值
截距项-0.763*--0.823*-
出发时间（参照项：7∶00前）7∶00~9∶00-2.117***0.12-2.746**0.06
出行时间（参照项：<60 min）60~90 min1.919***6.813.271***26.33
>90 min1.748***5.743.550***34.81
出行距离（参照项：<3 km）3~10 km-0.989**0.37--
>10 km-1.278***0.28-1.527***0.22
交通拥堵状况（参照项：不拥堵）非常拥堵-1.940***0.14-3.670*0.03
限行效果（参照项：效果不明显）略有效果2.385***10.86--
效果显著4.503***90.283.053***21.18
s.d.--3.249**3.49
误差项-1.298***
LL（0）-288.3492-288.3492
LL（β）-181.6887-165.5023
AIC381.38347
BIC417.65379.25

Table 8

Common influencing factors of different car travelers' travel mode choice"

出行者月收入出发时间出行时间出行距离公共交通服务水平限行效果交通拥堵状况
有1辆车青年√√√
有1辆车壮年√√√
有1辆车中老年√√√√
有多辆车√√√√√
1 国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2020.
2 刘凤喜, 曹弋, 周军. 城市机动车保有量控制及合理使用策略[M]. 北京: 北京交通大学出版社, 2018.
3 Jia N, Zhang Y, He Z, et al. Commuters' acceptance of and behavior reactions to license plate restriction policy: a case study of Tianjin, China[J]. Transportation Research Part D, 2016, 52: 428-440.
4 Zhang L L, Long R Y, Chen H. Do car restriction policies effectively promote the development of public transport?[J]. World Development, 2019, 119: 100-110.
5 Yang J, Lu F, Qin P. How does a driving restriction affect transportation patterns? the medium-run evidence from Beijing[J]. Environment for Development, 2018, 204: 270-281.
6 Yao W B, Ding Y H, Xu F M, et al. Analysis of cars' commuting behavior under license plate restriction policy: a case study in Hangzhou, China[C].∥The 21st International Conference on Intelligent Transportation Systems. USA: ITSC, 2018.
7 Huang H J, Fu D Y, Qi W. Effect of driving restrictions on air quality in Lanzhou, China: Analysis integrated with internet data source[J]. Journal of Cleaner Production, 2017, 142: 1013-1020.
8 Viard V, Fu S. The effect of Beijing's driving restrictions on pollution and economic activity[J]. Journal of Public Economics, 2015, 125: 98-115.
9 Guerra E, Millard-ball A. Getting around a license-plate ban: behavioral responses to Mexico City's driving restriction[J]. Transportation Research Part D, 2017, 55: 113-126.
10 Grange L D, Troncoso R. Impacts of vehicle restrictions on urban transport flows: the case of Santiago, Chile[J]. Transport Policy, 2011, 18 (6): 862-869.
11 Ye J J. Better safe than sorry? evidence from Lanzhou's driving restriction policy [J]. China Economic Review, 2017, 45: 1-21.
12 Cantillo V, Ortúzar J. Restricting the use of cars by license plate numbers: a misguided urban transport policy[J]. Dyna, 2014, 81(188): 75-82.
13 Wang L, Xu J, Qin P. Will a driving restriction policy reduce car trips? —the case study of Beijing, China[J]. Transportation Research Part A, 2014, 67: 279-290.
14 侯现耀, 陈学武, 王卫杰. 多公交信息下居民出行前方式选择意向分析[J]. 交通运输系统工程与信息, 2014, 14(4): 79-84.
Hou Xian-yao, Chen Xue-wu, Wang Wei-jie. Preferences of pre-trip mode choice based on multiple public transit information[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(4): 79-84.
15 Böckenholt U, Dillon W R. Some new methods for an old problem: modeling preference changes and competitive market structures in pretest market data[J]. Journal of Marketing Research, 1997, 34(1): 130-142.
16 Fischer G W, Nagin D. Random versus fixed coefficient quantal choice models[J]. Structural Analysis of Discrete Data with Econometric Applications, 1981: 273-304.
17 Train K E. Recreation demand models with taste differences over people[J]. Land Economics, 1998, 74(2):230-239.
18 Liu Y, Hong Z, Liu Y. Do driving restriction policies effectively motivate commuters to use public transportation?[J]. Energy Policy, 2016, 90: 253-261.
19 戢晓峰, 刘丁硕, 陈方. 考虑需求强度与群体差异的公路旅客出行行为异质性研究[J]. 北京交通大学学报, 2021, 45(1): 47-61.
Ji Xiao-feng, Liu Ding-shuo, Chen Fang. Research on heterogeneity of road passenger travel behavior considering demand intensity and group differences[J]. Journal of Beijing Jiaotong University, 2021, 45(1): 47-61.
20 William H G, David A H. A latent class model for discrete choice analysis: contrasts with mixed Logit[J]. Transportation Research Part B, 2003, 37: 681-698.
21 Zhao W J, Ma Z L, Yang K, et al. Impacts of variable message signs on en-route route choice behavior[J]. Transportation Research Part A, 2020, 139: 335-349.
22 赵鹏, 翟茹雪, 宋文波. 考虑个体异质性的高速铁路旅客选择行为[J]. 北京交通大学学报, 2019, 43(2): 117-123.
Zhao Peng, Zhai Ru-xue, Song Wen-bo. Passenger choice behavior of high speed railway considering individual heterogeneity[J]. Journal of Beijing Jiaotong University, 2019, 43(2): 117-123.
23 Kobayashi D, Takahashi O, Arioka H, et al. A prediction rule for the development of delirium among patients in medical wards: chi-square automatic interaction detector (CHAID) decision tree analysis model[J]. The American Journal of Geriatric Psychiatry, 2013, 21(10): 957-962.
24 何凡, 沈毅, 叶众. 卡方自动交互检测法及其应用[J]. 中华预防医学杂志, 2005, 39(2): 133-135.
He Fan, Shen Yi, Ye Zhong. Chi-square automatic interactive detection method and its application[J]. Chinese Journal of Preventive Medicine, 2005, 39(2): 133-135.
25 云美萍, 刘广洋, 刘芳. 公交服务质量变化对出行方式选择行为的影响[J]. 中国公路学报, 2017, 30(7): 119-125.
Yun Mei-ping, Liu Guang-yang, Liu Fang. Influence of change of public transportation service quality on travel mode choice behavior[J]. China Journal of Highway and Transport, 2017, 30(7): 119-125.
26 Rizzi L I, Ortúzar J D. Stated preference in the valuation of interurban road safety[J]. Accident Analysis & Prevention, 2003, 35(1): 9-22.
27 Ortúzar J D, Willumson L G. Modelling Transport[M]. London:Wiley, 2011.
28 钟礼杰, 高玉堂, 金丕焕. Logistic回归模型的拟合优度检验[J]. 中国卫生统计, 1993, 10(3): 55-59.
Zhong Li-jie, Gao Yu-tang, Jin Pei-huan. The goodness of fit test of Logistic regression model[J]. Chinese Journal of Health Statistics, 1993, 10(3): 55-59.
29 Akaike H T. A new look at the statistical model identification[J]. Automatic Control IEEE Transactions on, 1974, 19(6): 716-723.
30 Kass R E, Wasserman L. A reference Bayesian test for nested hypotheses and its relationship to the schwarz criterion[J]. Publications of the American Statistical Association, 1995, 90(431): 928-934.
[1] Heng-yan PAN,Yong-gang WANG,De-lin LI,Jun-xian CHEN,Jie SONG,Yu-quan YANG. Evaluating and forecasting rear⁃end collision risk of long longitudinal gradient roadway via traffic conflict [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1355-1363.
[2] Kai LU,Guang-hui XU,Zhi-hong YE,Yong-jie LIN. Algebraic method of bidirectional green wave coordination control for the head of the platoon considering the clearance time [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 421-429.
[3] Xin ZHANG,Wei-hua ZHANG. Safety analysis of main line under different traffic conditions in expressway confluence area [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1308-1314.
[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] Da-yi QU,Zi-xu ZHAO,Yan-feng JIA,Tao WANG,Qiong-hui LIU. Car⁃following dynamics characteristics and model based on Lennard⁃Jones potential [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2549-2557.
[6] Chun-jiao DONG,Dai-yue DONG,Cheng-xiang ZHU-GE,Li ZHEN. Trip characteristics and decision⁃making behaviors modeling of electric bicycles riding [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2618-2625.
[7] Jing-xian WU,Hua-peng SHEN,Yin HAN,Min YANG. Residents' commuting time model under the nonlinear impact of urban built environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2568-2573.
[8] Zhuang-lin MA,Shan-shan CUI,Da-wei HU. Urban residents' low⁃carbon travel intention after implementation of driving restriction policy [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2607-2617.
[9] Da-yi QU,Kai-xian HEI,Hai-bing GUO,Yan-feng JIA,Tao WANG. Game behavior and model of lane-changing on the internet of vehicles environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 101-109.
[10] Wen-hui ZHANG,Jing YI,Wei LIU,Qiu-ying YU,Lian-zhen WANG. Injury mechanism of occupants in bus during rear-end crash based on MADYMO [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 118-126.
[11] Xiao-xue SUN,Hui ZHONG,Hai-peng CHEN. Statistical analysis system for students' examination results based on decision tree classification technology [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1866-1872.
[12] Zhi-wei LIU,Jian-rong LIU,Wei DENG. Travelers′ choice behavior of autonomous vehicles based on latent class [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1261-1268.
[13] Jin XU,Cun-shu PAN,Jing-hou FU,Jun LIU,Dan-qi WANG. Speed behavior characteristic on typical driving scenarios and along switched scenarios [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1331-1341.
[14] Wei-xiong ZHA,Qi-yan CAI,Jian LI,Li-xin YAN. Optimization of offset of urban arterial signal coordination under condition of vehicle entry and exit on side road [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 565-574.
[15] LUAN Xin, DENG Wei, CHENG Lin, CHEN Xin-yuan. Mixed Logit model for understanding travel mode choice behavior of megalopolitan residents [J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
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