Customized passenger path optimization for airport connections under carbon emissions restrictions

Figures & data

Figure 1. Customized passenger transport map of airport connection.

Table 1. Model parameters.

Decision variable
$w_{ik}$	Start service time of vehicle k to station i
$x_{ijk}$	Does vehicle k depart from station i to station j, Y: $x_{ijk}$=1; N: $x_{ijk}$=0
$y_{ik}$	Does vehicle k provide service for station i,Y: $y_{ik}$=1; N: $y_{ik}$=0
Parameter
K	Vehicle collection
V	Number collection
N	Station collection
${\mathrm{\Delta }}^{+}\left(i\right)$	Collection of arcs emitted from No.i
$Z_1$	Total travel time cost of passengers
$Z_2$	Operating costs of passenger transport enterprises
$t_p$	Unit time value
v	Average vehicle speed
$C_k$	Fixed cost of vehicle k
$f_k$	Unit fuel consumption cost of vehicle k
$d_{ij}$	Distance travelled by vehicles from station i to station j
$q_{ij}$	Passenger capacity of vehicles from station i to station j
$p_{ik}$	Number of passengers served by vehicle k at station i
$a_i$	Left TW of passengers getting off at station i
$b_i$	Right TW of passengers getting off at station i
E	Left TW of the airport
L	Right TW of the airport
$t_s$	Average service time per passenger
$Q_k$	Maximum passenger capacity of vehicle k

Figure 2. Chromosome code $◯1$.

Figure 3. Chromosome code $◯2$.

Figure 4. Chromosome code $◯3$.

Figure 5. Schematic diagram of cross operation for different individual improvement sequences.

Figure 6. Schematic diagram of cross operation for the same individual improvement sequence.

Figure 7. mutation operation diagram.

Figure 8. GA solution flowchart.

Table 2. Data for each node.

No.	X/km	Y/km	demands	left TW	right TW	service time/min
0	50	50	0	8:00	12:00	0
1	25	85	2	10:05	11:11	2
2	10	40	3	9:30	10:57	3
3	2	40	2	8:39	10:41	2
4	42	15	1	9:51	10:42	1
5	35	5	2	8:54	11:04	2
6	88	30	2	8:52	10:39	2
7	67	85	2	9:01	11:05	2
8	60	85	1	8:50	10:29	1
9	40	78	3	8:37	10:57	2
10	72	15	2	9:44	10:37	2
11	30	60	2	9:35	11:34	3
12	15	60	3	10:17	11:22	3
13	35	40	1	8:11	10:20	1
14	35	69	1	9:00	11:30	1
15	5	5	3	9:00	10:57	1
16	65	65	1	9:37	11:42	3
17	63	23	2	9:16	10:07	2
18	80	40	2	9:14	10:07	2
19	51	31	2	8:20	9:24	2
20	31	67	3	9:06	11:30	3
21	61	52	3	9:09	11:28	3
22	37	31	2	8:19	9:27	2
23	2	60	3	9:05	9:58	3
24	65	20	1	8:39	9:48	1
25	20	50	1	8:57	10:34	1

Table 3. GA experimental data.

		Total cost/yuan
Pc	Pm	average value	optimal value	time T/s
0.9	0.01	1109.04	1070.36	49.6
0.9	0.02	1115.06	1095.59	49.3
0.9	0.03	1114.12	1087.79	49.4
0.9	0.04	1097.60	1055.44	49.2
0.9	0.05	1108.30	1024.69	49.2
0.8	0.01	1098.73	1063.50	49.0
0.8	0.02	1109.94	1053.65	48.9
0.8	0.03	1109.31	1093.63	49.2
0.8	0.04	1106.73	1057.08	49.0
0.8	0.05	1105.53	1041.89	49.0
0.7	0.01	1113.18	1083.58	48.7
0.7	0.02	1105.13	1068.38	48.6
0.7	0.03	1100.27	1044.05	48.6
0.7	0.04	1109.76	1085.20	48.7
0.7	0.05	1111.01	1052.33	48.5
0.6	0.01	1113.69	1070.34	48.3
0.6	0.02	1116.04	1085.75	48.5
0.6	0.03	1114.43	1076.83	48.4
0.6	0.04	1103.30	1058.58	48.6
0.6	0.05	1104.41	1058.86	48.4
0.5	0.01	1104.60	1046.69	48.1
0.5	0.02	1111.01	1072.08	48.2
0.5	0.03	1100.38	1053.24	48.1
0.5	0.04	1102.22	1068.79	47.9
0.5	0.05	1118.31	1034.02	48.2

Figure 9. Results under different combinations of crossover mutation probabilities.

Figure 10. Average running time under different cross-mutation probability combinations.

Figure 11. Iteration diagram.

Figure 12. Path scheme roadmap.

Table 4. Seven path information statistics.

Route	Path scheme	Number of station/PCS	Number of passengers served/PCS	Attendance	Carbon emission/kg
1	0$\to$9$\to$8$\to$7$\to$16$\to$0	4	7	0.875	42.51
2	0$\to$21$\to$18$\to$6$\to$0	3	7	0.875	30.09
3	0$\to$17$\to$24$\to$10$\to$4$\to$0	4	6	0.75	32.45
4	0$\to$19$\to$5$\to$15$\to$0	3	7	0.875	49.16
5	0$\to$22$\to$13$\to$23$\to$1$\to$0	4	8	1	53.51
6	0$\to$2$\to$3$\to$11$\to$0	3	7	0.875	37.36
7	0$\to$25$\to$12$\to$20$\to$14$\to$0	4	8	1	33.73