Genetic Algorithm for Biobjective Urban Transit Routing Problem

Table 3

The best results obtained by Fan et al. [3].

Case	Number of routes	Parameters	The best results for passenger	The best routes for passenger	The best results for operator	The best routes for operator
I	4	𝑑0	90.88	14–13–11–10–8–6–4–5	61.08	2–3–6–8–15–7–10–11
		𝑑1	8.35	2–4–12–11–10–7–15–9	36.61	15–9
		𝑑2	0.77	11–10–8–6–3–2–1	2.31	14–13–11–12
		𝑑un	0.00	5–2–3–6–15–7–10	0.00	5–4–2–1
		ATT	10.65		13.88
		𝐶𝑂	126		63
II	6	𝑑0	93.19	13–11–10–8–6–3–2–1	66.09	2–3–6–8–15–7–10
		𝑑1	6.23	7–15–6–3–2–4–5	30.38	9–15
		𝑑2	0.58	10–8–6–4–5	3.53	2–1
		𝑑un	0.00	13–14–10–11–12–4–2–1	0.00	14–13–11–10
		ATT	10.46	10–7–15–9	13.34	2–4–5
		𝐶𝑂	148	12–11–13–14	63	11–12
III	7	𝑑0	92.55	7–15–8	65.64	9–15
		𝑑1	6.68	14–13–11–12–4–2–3	26.60	11–12
		𝑑2	0.77	12–11–10–7–15–9	8.61	4–5
		𝑑un	0.00	14–10–7–15–6–4–5	0.00	14–13–11
		ATT	10.44	10–8–6–4–5–2–3	13.54	2–4
		𝐶𝑂	166	1–2–3–6–8–10–11–13	63	1–2–3–6–8–15–7–10
				4–2–1		11–10
IV	8	𝑑0	91.33	2–4–12–11–13–14–10	59.92	4–2
		𝑑1	8.67	12–11–13–14–10–7–15–6	21.37	3–2
		𝑑2	0.00	5–2–3–6–8–15–9	18.11	2–1
		𝑑un	0.00	1–2–3–6–8–10–11–13	0.00	13–11
		ATT	10.45	12–11–13–14–10–8–6–4	13.57	4–5
		𝐶𝑂	245	4–6–15–9	63	15–9
				5–4–6–8–10–11–13		12–11–10–7–15–8–6–3
				12–11–10–7–15–6–3–2		13–14