Genetic Algorithm for Biobjective Urban Transit Routing Problem

Table 4

Routes and percentage for passengers' demand unsatisfied from the operators' point of view for cases II and IV.

Case	[𝑖,𝑗]	Route	Number of transfers	Demand	𝑑un
II	[1,12]	1–2–3–6–8–15–7–10–11–12	3	50
	[4,12]	4–2–3–6–8–15–7–10–11–12	3	50	0.9
	[5,12]	5–4–2–3–6–8–15–7–10–11–12	3	30
	[9,12]	9–15–7–10–11–12	3	10
IV	[1,9]	1–2–3–6–8–15–9	3	60
	[1,13]	1–2–3–6–8–15–7–10–11–13	3	70
	[1,14]	1–2–3–6–8–15–7–10–11–13–14	4	0
	[2,14]	2–3–6–8–15–7–10–11–13–14	3	10
	[4,9]	4–2–3–6–8–15–9	3	30
	[4,13]	4–2–3–6–8–15–7–10–11–13	3	20
	[4,14]	4–2–3–6–8–15–7–10–11–13-14	4	10
	[5,6]	5–4–2–3–6	3	100
	[5,7]	5–4–2–3–6–8–15–7	3	50	4.75
	[5,8]	5–4–2–3–6–8	3	50
	[5,9]	5–4–2–3–6–8–15–9	4	20
	[5,10]	5–4–2–3–6–8–15–7–10	3	240
	[5,11]	5–4–2–3–6–8–15–7–10–11	3	40
	[5,12]	5–4–2–3–6–8–15–7–10–11–12	3	30
	[5,13]	5–4–2–3–6–8–15–7–10-11–13	4	10
	[5,14]	5–4–2–3–6–8–15–7–10–11–13–14	5	0
	[5,15]	5–4–2–3–6–8–15	3	0
	[9,14]	9–15–7–10–11–13–14	3	0