Policy-Combination Oriented Optimization for Public Transportation Based on the Game Theory

Table 2

Initial training sets based on LHS (L=20 Km).

Sample	f	P1	P2	P3	TSC(BL)	TSC(NBL)
1	60.40	2.00	1.67	5.00	185777.91	225677.89
2	93.40	3.33	3.00	3.67	186796.77	204368.78
3	80.20	4.00	4.33	0.33	185024.12	213703.29
4	86.80	10.00	2.67	1.67	208164.83	204523.45
5	7.60	4.67	2.33	0.00	184429.85	267614.63
6	67.00	1.33	0.00	1.33	189456.57	219378.16
7	1.00	5.33	4.00	3.33	183540.38	273631.74
8	100.00	7.33	0.33	2.33	202561.09	198485.98
9	47.20	0.67	2.00	2.00	182491.73	238477.61
10	27.40	8.67	3.33	2.67	194053.19	240229.91
11	40.60	8.00	1.33	0.67	204952.75	230611.79
12	34.00	6.00	5.00	1.00	185727.60	245151.34
13	53.80	9.33	1.00	4.67	220477.97	225774.28
14	14.20	2.67	0.67	3.00	185137.01	256741.38
15	73.60	6.67	3.67	4.33	196209.59	208643.40
16	20.80	0.00	4.67	4.00	182221.75	261805.43
lower	1.00	0.00	0.00	0.00	183539.01	273797.40
upper	100.00	10.00	5.00	5.00	202561.09	198485.98
baseline	60.00	5.00	2.00	2.00	194109.49	219819.26