Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning

Table 6

Optimization results of other sections.

Section name	Minimum energy consumption of actual data(KWh)	After optimization (KWh)	Net energy saving(KWh)	Energy saving (%)	Section length(m)	interval(m)
Xi’erqi-Life Science Park	28	26.94	1.06	3.79	5455	200
Life Science Park-Zhuxinzhuang	19	18.44	0.56	2.95	2405	100
Zhuxinzhaung-Gonghuacheng	19	18.36	0.64	3.39	3810	200
Gonghuacheng-Shahe	20	19.13	0.87	4.35	2037	100
Shahe-Shahe University Park	22	20.88	1.12	5.08	1967	100
Shahe University Park-Nanshao	30	29.45	0.55	1.83	5364	200
Nanshao-Beishaowa	14	13.55	0.45	3.21	2003	100
Beishawa-Changping dongguan	16	15.66	0.34	2.13	1687	100
Changping dongguan-Changping	22	21.58	0.42	1.91	2439	100
Changping-MingTombs	39	38.56	0.44	1.13	3522	200
MingTombs-Changpingxishankou	21	20.35	0.65	3.10	1230	50
Total	250	242.9	7.1	2.84	31964	-
Average value	22.73	22.08	0.65	-	-	-