Figures (11)  Tables (9)

    • Figure 1. 

      General research framework for analyzing spatial heterogeneity in metro–bus transfer behavior.

    • Figure 2. 

      Defining a metro–bus transfer: a 30-min window linking trip legs across modes.

    • Figure 3. 

      Local GWR coefficient of nearest bus stop distance on bus-to-metro transfer rates.

    • Figure 4. 

      Local GWR coefficient of metro ridership on bus-to-metro transfer rates.

    • Figure 5. 

      Local GWR coefficient of road network density on bus-to-metro transfer rates.

    • Figure 6. 

      Local GWR coefficient of parking density on bus-to-metro transfer rates.

    • Figure 7. 

      Local GWR coefficient of distance to CBD on bus-to-metro transfer rates.

    • Figure 8. 

      Local GWR coefficient of distance to nearest bus stop on metro-to-bus transfer rates.

    • Figure 9. 

      Local GWR coefficient of metro ridership on metro-to-bus transfer rates.

    • Figure 10. 

      Local GWR coefficient of parking density on metro-to-bus transfer rates.

    • Figure 11. 

      Local GWR coefficient of distance to CBD on metro-to-bus transfer rates.

    • Category Variable Symbol Min Mean Max SD
      Dependent variables
      Bus$ \rightarrow $Metro transfer rate $ R_{{{\mathrm{B}}\to {\mathrm{M}}}} $ (ratio) 0.000296 0.122 0.696 0.123
      Metro$ \rightarrow $Bus transfer rate $ R_{{{\mathrm{M}}\to {\mathrm{B}}}} $ (ratio) 0.000132 0.113 0.803 0.118
      Transit service and station network
      Metro ridership (monthly) $ MR $ (#) 1,171 436,923 2,567,207 387,063
      Number of metro lines $ N_{\text{line}} $ (lines) 1 1.310 4 0.621
      Number of station exits $ N_{\text{exit}} $ (#) 1 5.143 20 2.826
      Distance to nearest bus stop $ D_{\text{bus\_nn}} $ (km) 0.003 0.256 1.872 0.326
      Bus stop density $ D_{\text{bus}} $ (#/km2) 0 7.065 15.924 3.153
      Built environment
      Parking density $ D_{\text{par}} $ (#/km2) 0 5.118 13.113 3.771
      Bicycle lane density $ D_{\text{bld}} $ (km/km2) 0 0.452 4.023 0.863
      Road network density $ D_{\text{road}} $ (km/km2) 0.036 12.791 32.331 5.540
      Distance to CBD $ D_{\text{CBD}} $ (km) 0.552 13.735 65.984 10.324
      Residential POI share $ P_{\text{res}} $ (ratio) 0.215 0.472 0.798 0.096
      Office POI share $ P_{\text{off}} $ (ratio) 0 0.101 0.288 0.029
      Tourism POI share $ P_{\text{tour}} $ (ratio) 0 0.328 0.667 0.060
      Population density (buffer) $ P_{\text{pop}} $ (pers/km2) 56 18,851.271 44,682 12,126.491
      Employment density (buffer) $ P_{\text{job}} $ (jobs/km2) 0.080 285.476 905.573 246.295
      SD, standard deviation; POI, point of interest; CBD, central business district. #, number (count). Transfer rates and POI shares are expressed as ratios (0–1), not percentages. Metro ridership is the total number of entries recorded at the station over the 1-month study period (April 2015). All built-environment and socio-demographic indicators are measured within a 1 km buffer around each metro station.

      Table 1. 

      Definitions and descriptive statistics of the variables.

    • Variable Coefficient t-statistic p-value Significance
      Road network density −0.139395 −2.187172 0.029495 **
      Distance to nearest bus stop −0.074204 −1.864957 0.063174 *
      Distance to CBD 0.230025 3.210951 0.001468 ***
      Population densitya −0.029297 −0.622181 0.534300 ns
      Proportion of tourist POIs 0.318515 2.044617 0.041763 **
      Parking density −0.136822 −3.438710 0.000681 ***
      Metro ridership 0.154115 3.837729 0.000161 ***
      ***, **, and * indicate that the coefficient is statistically significant at the 1%, 5%, and 10% levels, respectively (i.e., p < 0.01, p < 0.05, and p < 0.10); ns indicates not significant (p ≥ 0.10). a Population density was retained in the OLS results for transparency, but excluded from the GWR estimation due to its insignificant OLS t-statistic.

      Table 2. 

      OLS coefficient estimates and t-tests ($ R_{{\mathrm{B}} \rightarrow {\mathrm{M}}} $).

    • VariableCoefficientt-statisticp-valueSignificance
      Distance to CBD0.3362072.4077800.016641**
      Population density−0.047775−0.7616040.446886ns
      Distance to nearest bus stop−0.122878−2.4905530.013285**
      Parking density−0.160087−3.0493100.002507***
      Metro ridership0.2663194.6897080.000006***
      ***, **, and * indicate that the coefficient is statistically significant at the 1%, 5%, and 10% levels, respectively (i.e., p < 0.01, p < 0.05, and p < 0.10); ns indicates not significant (p ≥ 0.10).

      Table 3. 

      OLS coefficient estimates and t-tests ($ R_{{\mathrm{M}} \rightarrow {\mathrm{B}}} $).

    • Model Moran's $ I $ $ Z $-score p-value Significance
      OLS (B$ \rightarrow $M) 0.113058 4.451014 < 0.00001 ***
      OLS (M$ \rightarrow $B) 0.123467 4.858613 < 0.00001 ***
      ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; ns indicates not significant (p ≥ 0.10).

      Table 4. 

      Global Moran's $ I $ test on OLS residuals.

    • Model Moran's $ I $ $ Z $-score p-value Significance
      GWR (B$ \rightarrow $M) −0.003062 0.016194 0.987079 ns
      GWR (M$ \rightarrow $B) −0.012077 −0.632434 0.527103 ns
      ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; ns indicates not significant (p ≥ 0.10).

      Table 5. 

      Global Moran's $ I $ test on GWR residuals.

    • Variable Moran's $ I $ Expected index $ Z $-score p-value Significance
      Metro ridership 0.258069 −0.003279 10.05420 < 0.000001 ***
      Distance to nearest bus stop 0.452975 −0.003279 17.57602 < 0.000001 ***
      Distance to CBD 0.957835 −0.003279 36.96441 < 0.000001 ***
      Population density 0.786053 −0.003279 30.08828 < 0.000001 ***
      Tourist POI proportion 0.383708 −0.003279 15.59166 < 0.000001 ***
      Road network density 0.646344 −0.003279 24.85657 < 0.000001 ***
      Parking lot density 0.693274 −0.003279 24.90747 < 0.000001 ***
      ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; ns indicates not significant (p ≥ 0.10).

      Table 6. 

      Moran's $ I $ test for spatial autocorrelation ($ R_{{\mathrm{B}} \rightarrow {\mathrm{M}}} $).

    • Variable Moran's $ I $ Expected index $ Z $-score p-value Significance
      Metro ridership 0.258069 −0.003279 10.05420 < 0.000001 ***
      Distance to CBD 0.957835 −0.003279 36.96441 < 0.000001 ***
      Parking lot density 0.693274 −0.003279 24.90747 < 0.000001 ***
      Distance to nearest bus stop 0.452975 −0.003279 17.57602 < 0.000001 ***
      Population density 0.786053 −0.003279 30.08828 < 0.000001 ***
      ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; ns indicates not significant (p ≥ 0.10).

      Table 7. 

      Moran's $ I $ test for spatial autocorrelation ($ R_{{\mathrm{M}} \rightarrow {\mathrm{B}}} $).

    • Model $ R^{2} $ Adj. $ R^{2} $ AICc $ F $-statistic p-value Significance
      OLS 0.388 0.374 −438.513 26.992 < 0.000001 ***
      GWRa 0.548 0.490 −486.286 – – –
      ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; ns indicates not significant (p ≥ 0.10); '–' indicates not applicable, as the GWR model does not report a global $ F $-statistic. a The GWR model was calibrated with an adaptive bandwidth of 191 neighboring features.

      Table 8. 

      GWR outperforms OLS in explaining bus-to-metro transfer rates ($ R_{{\mathrm{B}} \rightarrow {\mathrm{M}}} $).

    • Model $ R^{2} $ Adj. $ R^{2} $ AICc $ F $-statistic p-value Significance
      OLS 0.303 0.291 −290.548 26.064 < 0.000001 ***
      GWRa 0.497 0.418 −329.746 – – –
      ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; ns indicates not significant (p ≥ 0.10); '–' indicates not applicable, as the GWR model does not report a global $ F $-statistic. a The GWR model was calibrated with an adaptive bandwidth of 112 neighboring features.

      Table 9. 

      GWR outperforms OLS in explaining metro-to-bus transfer rates ($ R_{{\mathrm{M}} \rightarrow {\mathrm{B}}} $).