Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method

Table 5

Relevant data of passenger flow of Dabaishu rail transit station.

Date	Atmospheric temperature	Weather	Working day	Ground index	Elevated road index	Number of entrances	Number of exits
1	22.4	1	1	42.28	37.63	18563	18077
2	31.3	2	1	58.51	43.91	18393	17999
3	14.6	1	1	58.51	38.46	19534	18172
4	17.1	2	2	58.51	20.21	10452	8734
5	13.3	2	2	58.51	35.65	8652	8417
6	11.8	2	2	58.51	34.86	7811	8704
7	8.9	2	1	58.51	50.59	17989	18400
8	11.6	1	1	58.51	34.32	18775	18441
9	13	1	1	58.51	43.33	18664	18220
10	16.1	1	1	51.91	40.28	19911	19259
11	16.8	1	2	45.48	30.99	12972	12097
12	19.6	1	2	41.96	22.9	10486	10601
13	14.4	1	1	54.9	54.82	18279	18098
14	16.1	1	1	52.5	48.02	18463	17956
15	22.4	1	1	54.58	42.4	18722	18302
16	24.3	1	1	52.35	31.62	18524	18265
17	18.8	1	1	51.79	39.17	20147	19200
18	26.3	1	2	44.3	34.64	12552	11750
19	21.7	1	2	37.93	25.85	10370	10509
20	16.2	2	1	58.51	48.96	18158	18108