Research Article

Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information

Table 5

Parameter estimation results.

Departure timeaBStandard error of meanWaldDegree of freedomStatistical significanceExp (B)

1.0Intercept1.2392.2520.30210.582
Gender0.0440.4110.01210.9141.045
Age0.2840.1613.09010.0791.328
Occupation0.6060.4561.96910.1831.834
Income level−0.0340.3070.01210.9130.967
Family car ownership0.6210.4721.73210.1881.861
Frequency of trips to Yangzhou−0.3820.2482.37610.1230.682
Purpose of travel−0.0730.4940.02210.8830.93
Tour schedule−0.7370.4183.10910.0780.478
Self-driving tour or group tour−1.4440.6115.58910.0180.236
Composition of people traveling together1.0290.6742.33610.1262.799
The number of attractions planned to visit0.0270.4210.00410.9481.028
Real-time information acquisition−0.8230.4713.04910.0810.439

2.0Intercept0.0811.8220.00210.965
Gender0.20.320.3910.5321.221
Age0.0340.1390.05910.8071.034
Occupation0.2430.3560.46510.4951.274
Income level0.0390.2470.02410.8761.039
Family car ownership0.3140.3740.70610.4011.369
Frequency of trips to Yangzhou−0.3010.1952.38310.1230.74
Purpose of travel−0.3430.390.77310.3790.709
Tour schedule0.1350.3410.15810.6911.145
Self-driving tour or group tour−1.3470.4538.83810.0030.26
Composition of people traveling together1.140.564.14010.0423.126
The number of attractions planned to visit0.3080.3330.85110.3561.36
Real-time information acquisition−0.5540.3422.62510.1050.575

B is the parameter estimated value; Wald is a chi-square value; Exp (B) is the odds ratio; the reference category is 3.0; the variable is significant.