Research Article

Risk Influencing Factor Analysis of Urban Express Logistics for Public Safety: A Chinese Perspective

Table 3

Parameter values of 96 case studies are used to support the Bayesian network analysis of factors of urban logistics express delivery for public safety accidents.

SampleMWH1H2H3H4F1F2F3G1G2

110100100000
200000100000
310100100000
410000100000
510100100000
610100100000
700101000000
800101000000
900101000000
1010101000000
1110001000000
1210001100000
1310001100000
1410001100000
1510001000010
1610000100000
1700101000000
1810101100000
1910000100000
2010000100000
2110000100000
2210000100000
2310101000000
2400011000000
2500100000000
2600101000000
2710101000000
2802100000000
2910101000000
3010101011000
3110001011000
3211001011010
3300011000000
3400101000000
3510011000000
3602100000000
3700000000010
3802000000010
3900101000000
4000100000010
4120000011000
4200101000000
4310001100000
4410101001000
4510001110000
4600101000000
4710101000000
4810001100010
4910101001000
5000101000000
5110001100000
5210001000000
5301101000000
5410001000000
5500101000000
5600101000000
5700101000000
5810011000000
5910101000000
6010000011100
6110001000011
6210001011000
6310000000010
6410000000010
6510000100010
6610000000010
6710000000010
6810000000010
6910000000010
7010000000010
7110100000010
7210000000010
7310000000010
7410000000010
7510000000010
7610000000010
7710000000010
7810000000010
7910000000010
8010000000010
8110000000010
8210001000000
8310001000000
8410001100000
8510001000011
8611001000011
8710001000010
8810001000000
8910001000000
9010001000001
9110001000100
9210001000000
9310001000000
9410001100000
9510001000000
9610101000000