Research Article

Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China

Table 4

Matrix of a bivariate correlation test between independent variables and ridership.

RidershipPopulationEmploymentCom_AreaOffi_AreaLand_use_MixRestaurant_NumCollege_NumHospital_NumShopping_NumFinancial_NumParking_NumRecreational_NumHotel_NumDis_to_centersBus_line_NumDummy_line_transferDummy_CBDJ_AccessibilityP_Accessibility

Ridership10.6180.5910.6950.7770.7220.7880.3160.5070.6890.7550.7140.6320.609−0.5850.4250.5690.5370.5610.555
Population10.5390.5880.5540.4860.6940.3740.3870.7330.8160.7840.8400.671−0.6690.5990.1760.6370.6530.705
Employment10.5670.5770.5010.438−0.0130.1840.4360.6820.6490.5110.347−0.5280.4270.445.4650.5930.588
Com_Area10.5310.6590.7360.0910.2820.7830.7520.6830.5860.564−0.5330.4860.2230.4480.4920.506
Offi_Area10.7970.5870.2870.2600.5050.7250.6410.5760.521−0.5110.2230.3990.3390.4550.443
Land_use_Mix10.5150.2220.3070.5100.6280.5840.4540.465−0.5140.3130.3490.2940.4970.479
Restaurant_Num10.4290.3540.8740.7710.7730.7650.745−0.4970.4120.1330.4810.4100.426
College_Num10.1830.3170.2520.2900.4050.429−0.2870.1440.0430.2350.1300.143
Hospital_Num10.3150.3590.3300.3340.338−0.2710.2110.3140.3660.3220.344
Shopping_Num10.7460.6980.7880.708−0.4570.4780.1650.5840.4550.491
Financial_Num10.8710.8070.596−0.6380.5220.2760.5210.5760.600
Parking_Num10.7210.645−0.6860.5870.1730.5440.6240.640
Recreational_Num10.675−0.5150.3880.1460.5940.4880.527
Hotel_Num1−0.4360.3680.0640.3940.3700.401
Dis_to_centers1−0.565-0.246-0.442-0.830-0.847
Bus_line_Num10.2320.5130.4700.531
Dummy_line_transfer10.3480.3090.303
Dummy_CBD10.4980.539
J_Accessibility10.988
P_Accessibility1

Significant at the 0.05 level. Significant at the 0.01 level.