A Passenger Flow Risk Forecasting Algorithm for High-Speed Railway Transport Hub Based on Surveillance Sensor Networks

Table 1

Computational result of 6 hours (12:00 am–6:00 pm).

Hour	Period	Forecasting risk value	Actual risk value	Gap
12:00 am	1	6.3	6.5	3.08%
	2	6.7	6.7	0.00%
	3	6.7	6.9	2.90%
	4	6.5	6.9	5.80%
	5	6.9	7.3	5.48%
	6	7.2	7.1	1.41%
1:00 pm	7	6.8	6.5	4.62%
	8	6.5	6.5	0.00%
	9	6.7	6.2	8.06%
	10	6.2	6.3	1.59%
	11	6.7	6.4	4.69%
	12	6.8	6.6	3.03%
2:00 pm	13	7.3	6.9	5.80%
	14	7.3	7.2	1.39%
	15	7.5	7.2	4.17%
	16	7.2	7.1	1.41%
	17	7.1	7.2	1.39%
	18	6.9	7.0	1.43%
3:00 pm	19	6.6	6.8	2.94%
	20	6.6	6.6	0.00%
	21	6.3	6.2	1.61%
	22	6.5	6.3	3.17%
	23	6.4	6.5	1.54%
	24	6.4	6.3	1.59%
4:00 pm	25	6.5	6.4	1.56%
	26	6.2	6.2	0.00%
	27	6.0	5.8	3.45%
	28	6.1	5.9	3.39%
	29	5.9	5.8	1.72%
	30	6.0	5.8	3.45%
5:00 pm	31	6.5	6.3	3.17%
	32	6.8	6.9	1.45%
	33	7.0	6.9	1.45%
	34	7.2	7.3	1.37%
	35	7.5	7.3	2.74%
	36	7.6	7.4	2.70%