Research Article
The Research on Ticket Fare Optimization for China’s High-Speed Train
Table 1
Regression analysis results.
| | Day 1 | Day 2 | Day 3 | Day 4 | Day 5 | Day 6 | Day 7 |
| | 0.0104 | −0.0301 | −0.1274 | −0.0980 | −0.0631 | 0.0156 | 0.0304 | | 4.8397 | 0.8391 | 1.1323 | 0.9584 | 0.8424 | 1.0130 | 0.5160 | square | 0.5645 | 0.8033 | 0.7561 | 0.7957 | 0.8152 | 0.6057 | 0.5474 | Adjusted | 0.5373 | 0.7910 | 0.7408 | 0.7829 | 0.8036 | 0.5811 | 0.5191 | value | 0.0003 | 0.0044 | 0.0069 | 0.0060 | 0.0027 | 0.0001 | 0.0004 |
| | Day 8 | Day 9 | Day 10 | Day 11 | Day 12 | Day 13 | Day 14 |
| | 0.0297 | 0.0140 | 0.0346 | 0.1787 | 0.0630 | 0.1806 | 0.2385 | | 0.8806 | 1.0221 | 1.1746 | 1.1391 | 1.4461 | 2.0868 | 1.8605 | square | 0.7947 | 0.8240 | 0.7948 | 0.7938 | 0.8120 | 0.7486 | 0.9067 | Adjusted | 0.7819 | 0.8130 | 0.7820 | 0.7810 | 0.8002 | 0.7329 | 0.9009 | value | 0.0062 | 0.0018 | 0.0062 | 0.0065 | 0.0031 | 0.0088 | 0.0001 |
| | Day 15 | Day 16 | Day 17 | Day 18 | Day 19 | Day 20 | |
| | 0.4403 | 0.8665 | 1.4321 | 3.0828 | 13.2832 | 80.4182 | | | 2.7153 | 3.2379 | 4.7170 | 6.9677 | 8.4014 | −45.7899 | | square | 0.8598 | 0.8280 | 0.8392 | 0.8115 | 0.5056 | 0.8178 | | Adjusted | 0.8511 | 0.8172 | 0.8291 | 0.7998 | 0.4747 | 0.8064 | | value | 0.0011 | 0.0015 | 0.0032 | 0.0031 | 0.0009 | 0.0024 | |
|
|