Research Article

The Research on Ticket Fare Optimization for China’s High-Speed Train

Table 1

Regression analysis results.

Day 1Day 2Day 3Day 4Day 5Day 6Day 7

0.0104−0.0301−0.1274−0.0980−0.06310.01560.0304
4.83970.83911.13230.95840.84241.01300.5160
square0.56450.80330.75610.79570.81520.60570.5474
Adjusted 0.53730.79100.74080.78290.80360.58110.5191
value0.00030.00440.00690.00600.00270.00010.0004

Day 8Day 9Day 10Day 11Day 12Day 13Day 14

0.02970.01400.03460.17870.06300.18060.2385
0.88061.02211.17461.13911.44612.08681.8605
square0.79470.82400.79480.79380.81200.74860.9067
Adjusted 0.78190.81300.78200.78100.80020.73290.9009
value0.00620.00180.00620.00650.00310.00880.0001

Day 15Day 16Day 17Day 18Day 19Day 20

0.44030.86651.43213.082813.283280.4182
2.71533.23794.71706.96778.4014−45.7899
square0.85980.82800.83920.81150.50560.8178
Adjusted 0.85110.81720.82910.79980.47470.8064
value0.00110.00150.00320.00310.00090.0024