Demand Prediction of Railway Emergency Resources Based on Case-Based Reasoning

Table 2

The weight values of attributes.

Sequence number	Attribute	Value range	Case ID					The target case T	Weight
			Case 1	Case 2	Case 3	Case 4	Case 5
1	Number of passengers affected	[0, 4000]	2100	1980	1900	1100	1630	[1700, 2000]	0.15
2	Number of deaths	[0, 100]	71	9	15	3	40	[14, 17]	0.13
3	Number injured	[0, 500]	416	40	25	34	172	[24, 29]	0.1
4	Number of vehicles derailed	[0, 40]	5	12	0	11	4	1	0.1
5	Accident type		Derailment	Derailment	Train crash	Derailment	Rear-end collision	Train crash	0.08
6	Emergency response level		I	II	I	II	I	I	0.08
7	Derailment distance	[0, 200]	120	120	25	200	80	[20, 50]	0.08
8	Railway damage		Especially serious	Serious	General serious	Serious	General serious	Missing	0.08
9	Vehicle damage	[0, 40]	14	12	9	11	27	8	0.07
10	Locomotive damage	[0, 8]	1	0	2	0	2	2	0.06
11	Accident location		Railway curves	Fork in bending	Station entrance	Mountain pass	Viaduct	Ground	0.05
12	Weather		Cloudy (in morning)	Fine (in evening)	Fine (in morning)	Strong winds	Lightning stroke	Fine	0.02