Figures (4)  Tables (12)

    • Figure 1. 

      Case study map of Yangzhou (Source: the authors).

    • Figure 2. 

      Research design.

    • Figure 3. 

      The likelihood of using different transport modes for different travel times.

    • Figure 4. 

      The probability of using different transport modes for different travel times.

    • Types Urban permanent population
      Small city Type II small city < 0.2 million
      Type I small city > 0.2 million and < 0.5 million
      Medium-sized city > 0.5 million and < 1 million
      Large city Type II large city > 1 million and < 3 million
      Type I large city > 3 million and < 5 million
      Mega city > 5 million and < 10 million
      Super mega city > 10 million
      Source: The State Council of the PRC[33].

      Table 1. 

      Types of Chinese cities.

    • Types Chinese cities Global cities
      Alpha++ / London and New York
      Alpha+ Chinese first-tier cities: Beijing and Shanghai E.g., Paris, Tokyo, and Sydney
      Alpha Chinese first-tier city: Guangzhou E.g., Los Angeles, Frankfurt, and Seoul
      Alpha− Chinese first-tier city: Shenzhen E.g., Brussels, Melbourne, and San Francisco
      Beta+ Chinese new first-tier cities: Chengdu, Tianjin, and Hangzhou E.g., Rome, Doha, and Miami
      Beta Chinese new first-tier cities: Chongqing, Nanjing, Wuhan, Zhengzhou, and Suzhou.
      Chinese second-tier cities: Xiamen, Jinan, Shenyang, and Dalian      		 E.g., Cairo, Oslo, and Abu Dhabi
      Beta− Chinese new first-tier cities: Qingdao, Changsha, Xi'an, and Hefei.
      Chinese second-tier city: Kunming      		 E.g., Casablanca, Denver, and Manchester
      Gamma+ Chinese second-tier cities:Fuzhou and Taiyuan E.g., Austin, Rotterdam, and Adelaide
      Gamma Chinese new first-tier city: Ningbo. Chinese second-tier city: Harbin.
      Chinese third-tier city: Haikou      		 E.g., Osaka, Birmingham, and Detroit
      Gamma− Chinese second-tier cities: Nanchang and Changchun E.g., Pittsburgh, Edinburgh, and Penang
      High sufficiency Chinese second-tier cities: Zhuhai and Shijiazhuang E.g., Glasgow, Phoenix, and Algiers
      Sufficiency Chinese new first-tier cities: Wuxi and Dongguan. Chinese second-tier cities: Guiyang, Nanning, Foshan, Lanzhou, Baoding, and Wenzhou. Chinese third-tier cities: Urumqi, Hohhot, Tangshan, and Yinchuan E.g., Ottawa, Yangon, and Liverpool
      Source: The Rising Lab[6]; GaWC[34].

      Table 2. 

      Comparison of Chinese and global cities.

    • Underground
      construction      		 Light rail
      construction
      Public fiscal budget revenue > 30 billion CNY > 15 billion CNY
      Regional GDP > 300 billion CNY > 150 billion CNY
      Urban permanent population > 3 million > 1.5 million
      Proposed initial passenger traffic intensity > 7,000 passengers per km per d > 4,000 passengers per km per d
      Long-term passenger flow scale > 30,000 passengers per h in one direction at peak time > 10,000 passengers per h in one direction at peak time
      Source: The State Council of the PRC[39]

      Table 3. 

      Standards for a city to build urban rail transit.

    • Categories Frequency Percentage
      Socio-demographics
      Gender Female 3,859 50.34%
      Male 3,807 49.66%
      Age < 25 324 4.23%
      25‒34 2,117 27.62%
      35‒44 2,300 30.00%
      45‒54 1,462 19.07%
      55‒64 884 11.53%
      ≥ 65 579 7.55%
      Annual income (CNY) ≤ 30,000 1,178 15.37%
      30,000‒50,000 1,454 18.97%
      50,000‒80,000 2,174 28.36%
      80,000‒120,000 1,979 25.82%
      > 120,000 881 11.49%
      Living area Downtown area 6,501 84.80%
      Urban fringe area 1,165 15.20%
      Car ownership Yes 3,797 49.53%
      No 3,869 50.47%
      Whether respondents have children Yes 3,515 45.85%
      No 4,151 54.15%
      Travel behaviour
      Travel time (min) ≤ 10 2,085 27.20%
      10‒15 1,286 16.78%
      15‒20 1,628 21.24%
      20‒30 1,919 25.03%
      > 30 748 9.76%
      Walking time to bus stops (min) ≤ 5 4,509 58.82%
      > 5 3,157 41.18%
      Average waiting time for buses (min) ≤ 10 6,717 87.62%
      > 10 949 12.38%
      Whether travelling in the peak period Peak period 4,967 64.79%
      Off-peak period 2,699 35.21%
      Average number of trips per day ≤ 2 4,419 57.64%
      > 2 3,247 42.36%
      Attitudes towards Yangzhou's transport system Satisfied 6,445 84.07%
      Unsatisfied 1,221 15.93%
      Transport mode Bus 409 5.34%
      Car 1,556 20.30%
      Walking 1,109 14.47%
      Traditional bike 228 2.97%
      E-bike 4,364 56.93%

      Table 4. 

      Descriptive statistics (n = 7,684).

    • Category Variable Explanation and measurement
      Socio-demographics Gender Binary variable (1 = male, 0 = female)
      Age Continuous variables
      Annual income Continuous variables
      Living area Binary variable (1 = downtown area, 0 = urban fringe area)
      Car ownership Binary variable (1 = yes, 0 = no)
      Whether respondents have children Binary variable (1 = yes, 0 = no)
      Travel behaviour Travel time Binary variable (1 = travel time ≤ 10 min, 0 = no)
      Binary variable (1 = travel time is between 10 and 15 min, 0 = no)
      Binary variable (1 = travel time is between 15 and 20 min, 0 = no)
      Binary variable (1 = travel time is between 20 and 30 min, 0 = no)
      Binary variable (1 = travel time > 30 min, 0 = no)
      Walking time to bus stops Binary variable (1 = walking time to bus stops ≤ 5 min, 0 = walking time to bus stops > 5 min)
      Average waiting time for buses Binary variable (1 = average waiting time for buses ≤ 10 min, 0 = average waiting time for buses > 10 min)
      Whether travelling in the peak period Binary variable (1 = travelling in the peak period, 0 = travelling in the off-peak period)
      Average number of trips per day Continuous variables
      Attitudes towards Yangzhou's transport system Binary variable (1 = satisfied, 0 = unsatisfied)

      Table 5. 

      Independent variables included in the models.

    • Variable B Standard error Sig. Exp(B) 95% CI for Exp(B)
      Lower Upper
      Socio-demographics
      Gender −0.249 0.110 0.023** 0.780 0.629 0.967
      Age 0.022 0.004 0.000*** 1.022 1.014 1.030
      Annual income −0.104 0.032 0.001*** 0.901 0.846 0.960
      Living area 0.724 0.229 0.002*** 2.063 1.317 3.231
      Car ownership −0.506 0.115 0.000*** 0.603 0.482 0.755
      Whether respondents have children −0.093 0.114 0.415 0.911 0.728 1.140
      Travel behaviour
      Walking time to bus stops 0.217 0.114 0.056* 1.242 0.994 1.552
      Average waiting time for buses 0.412 0.185 0.026** 1.509 1.050 2.170
      Whether travelling in the peak period −0.328 0.115 0.004*** 0.720 0.575 0.902
      Average number of trips per day −0.185 0.057 0.001*** 0.831 0.742 0.930
      Attitudes towards Yangzhou's transport system −0.173 0.325 0.595 0.841 0.445 1.592
      Travel time (min) ≤ 10 −2.782 0.225 0.000*** 0.062 0.040 0.096
      10 < x ≤ 15 −2.203 0.218 0.000*** 0.110 0.072 0.169
      15 < x ≤ 20 −1.432 0.162 0.000*** 0.239 0.174 0.328
      20 < x ≤ 30 −0.763 0.135 0.000*** 0.466 0.358 0.608
      > 30 Control group
      Pseudo R2 = 0.152. The meaning of values in boldface: * p-value < 0.1, ** p-value < 0.05, *** p-value < 0.01.

      Table 6. 

      Results of the binary logistic regression (1 = travelled by bus, 0 = otherwise; n = 7,666).

    • Variable B Standard error Sig. Exp(B) 95% CI for Exp(B)
      Lower Upper
      Travel time (continuous) 0.040 0.003 0.000*** 1.041 1.034 1.048
      The meaning of values in boldface: * p-value < 0.1, ** p-value < 0.05, *** p-value < 0.01. This table only shows the results for travel time, and other indicators are shown in Supplementary Table S1.

      Table 7. 

      Results of the binary logistic regression (1 = travelled by bus, 0 = otherwise; n = 7,666).

    • Categories Car Walking Traditional bike E-bike
      B Sig. Exp(B) B Sig. Exp(B) B Sig. Exp(B) B Sig. Exp(B)
      Socio-demographics
      Gender 1.586 0.000*** 4.883 −0.010 0.939 0.990 0.549 0.002*** 1.731 −0.080 0.478 0.923
      Age −0.061 0.000*** 0.941 0.027 0.000*** 1.027 −0.007 0.276 0.993 −0.029 0.000*** 0.971
      Annual income 0.519 0.000*** 1.680 −0.057 0.135 0.945 −0.198 0.000*** 0.820 0.081 0.016** 1.084
      Living area −1.176 0.000*** 0.309 −1.088 0.000*** 0.337 0.038 0.910 1.039 −0.467 0.045** 0.627
      Car ownership 3.654 0.000*** 38.614 −0.039 0.776 0.962 −0.107 0.561 0.898 −0.172 0.142 0.842
      Whether respondents have children 0.192 0.148 1.211 −0.029 0.831 0.971 −0.346 0.067* 0.708 0.159 0.171 1.173
      Travel behaviour
      Walking time to bus stops −0.201 0.134 0.818 −0.201 0.137 0.818 −0.008 0.967 0.992 −0.274 0.018** 0.760
      Average waiting time for buses −0.539 0.011** 0.583 −0.214 0.323 0.808 0.383 0.239 1.466 −0.441 0.019** 0.643
      Whether travelling in the peak period 0.240 0.085* 1.271 0.184 0.165 1.202 0.132 0.460 1.141 0.372 0.001*** 1.450
      Average number of trips per day 0.086 0.197 1.090 0.320 0.000*** 1.377 0.064 0.455 1.076 0.188 0.001*** 1.207
      Attitudes towards Yangzhou's transport system 0.647 0.085* 1.911 0.447 0.312 1.563 −0.183 0.729 0.833 0.003 0.992 1.003
      Travel time (min) ≤ 10 1.002 0.000*** 2.724 4.134 0.000*** 62.442 2.699 0.000*** 14.861 2.779 0.000*** 16.108
      10 < x ≤ 15 1.054 0.000*** 2.869 3.159 0.000*** 23.556 2.037 0.000*** 7.670 2.321 0.000*** 10.191
      15 < x ≤ 20 0.947 0.000*** 2.577 2.012 0.000*** 7.482 1.543 0.000*** 4.678 1.505 0.000*** 4.504
      20 < x ≤ 30 0.597 0.001*** 1.817 0.956 0.000*** 2.602 0.605 0.044** 1.831 0.852 0.000*** 2.343
      > 30 Control group
      Pseudo R2 = 0.537. The meaning of values in boldface: * p-value < 0.1, ** p-value < 0.05, *** p-value < 0.01.

      Table 8. 

      Results of the multinomial logistic regression (n = 7,666).

    • Travel time (min) Likelihood ranking
      Bus Car Walking Traditional bike E-bike
      1 > 30 10 < x ≤ 15 ≤ 10 ≤ 10 ≤ 10
      2 20 < x ≤ 30 ≤ 10 10 < x ≤ 15 10 < x ≤ 15 10 < x ≤ 15
      3 15 < x ≤ 20 15 < x ≤ 20 15 < x ≤ 20 15 < x ≤ 20 15 < x ≤ 20
      4 10 < x ≤ 15 20 < x ≤ 30 20 < x ≤ 30 20 < x ≤ 30 20 < x ≤ 30
      5 ≤ 10 > 30 > 30 > 30 >3 0
      In this table, the results for bus travel were from Section Binary logistic regression; the results for car, walking, traditional bike, and e-bike travel were from Section Multinomial logistic regression.

      Table 9. 

      The rank of the likelihood of using different transport modes for different travel times.

    • Travel time (min) Probability
      Bus Car Walking Traditional bike E-bike
      ≤ 10 5.93% 10.32% 51.50% 29.38% 28.52%
      10−15 8.05% 11.74% 21.28% 16.91% 18.73%
      15−20 18.70% 24.07% 15.32% 24.26% 21.82%
      20−30 40.51% 37.03% 9.75% 19.96% 23.45%
      > 30 26.81% 16.85% 2.15% 9.48% 7.48%
      Total 100.00% 100.00% 100.00% 100.00% 100.00%
      The values in boldface represent the highest probability of using the bus and car for different travel times.

      Table 10. 

      The probability of using different transport modes for different travel times via Naive Bayes.

    • Bus Car Walking Traditional
      bike      		 E-bike
      Accuracy
      Naive Bayes classifiers 0.37 0.33 0.59 0.33 0.34
      Multinomial logistic regression 0.22 0.25 0.20 0.17 0.12
      DIC
      Naive Bayes classifiers 226.43 934.06 512.49 139.42 2,577.18
      Multinomial logistic regression 13,087.62

      Table 11. 

      Comparison of the performance between multinomial logistic regression and Naive Bayes classifier.

    • Travel time (min) Probability ranking
      Bus Car Walking Traditional bike E-bike
      1 20 < x ≤ 30 20 < x ≤ 30 ≤ 10 ≤ 10 ≤ 10
      2 > 30 15 < x ≤ 20 10 < x ≤ 15 15 < x ≤ 20 20 < x ≤ 30
      3 15 < x ≤ 20 > 30 15 < x ≤ 20 20 < x ≤ 30 15 < x ≤ 20
      4 10 < x ≤ 15 10 < x ≤ 15 20 < x ≤ 30 10 < x ≤ 15 10 < x ≤ 15
      5 ≤ 10 ≤ 10 > 30 > 30 > 30

      Table 12. 

      The rank of the probability of using different transport modes for different travel times.