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Table 9 Results of the robustness analysis

From: Data mining combined to the multicriteria decision analysis for the improvement of road safety: case of France

  Case1a Case1b Case1c Case2a Case2b Case1c
c1, c0, c2 0.6, 0.7, 0.8 0.7, 0.8, 0.9
d1, d2
Median ranking
50% 70% 60% 80% 70% 90% 50% 70% 60% 80% 70% 90%
1 R6 R6 R6 [R6, R14, R28] [R6, R14, R28] [R6, R14, R28]
2 R14 R14 R14 [R23, R26, R40] [R23, R26, R40] [R23, R26, R40]
3 R28 R28 R40 R15 R15 R15
4 [R15, R23, R40] [R15, R23, R40] R28 [R5, R17, R33, R38] [R5, R17, R33, R38] [R5, R17, R33, R38]
5 [R26, R33] [R26, R33] [R15, R23] [R7, R19, R29, R41] [R7, R19, R29, R41] [R7, R19, R29, R41]
6 [R5, R19, R41] [R5, R19, R41] [R5, R26] [R37, R39] [R37, R39] [R37, R39]
7 [R7, R38] [R7, R38] R33 [R1, R11, R16, R27, R44] [R1, R11, R16, R27, R44] [R1, R11, R16, R27, R44]
8 [R17, R29, R37] [R17, R29, R37] [R7, R17, R19, R38, R41] [R4, R8, R20, R24] [R4, R8, R20, R24] [R4, R8, R20, R24]
9 R1 R1 [R29, R37] [R34, R35] [R34, R35] [R34, R35]
10 [R11, R16, R39, R44] [R11, R16, R39, R44] [R1, R11, R16, R39, R44] [R3, R9, R12, R25, R30, R36, R43] [R3, R9, R12, R25, R30, R36, R43] [R3, R9, R12, R25, R30, R36, R43]
11 [R8, R27] [R8, R27] [R8, R27] [R18, R32, R45] [R18, R32, R45] [R18, R32, R45]
12 [R4, R20, R24, R30, R34, R35] [R4, R20, R24, R30, R34, R35] [R4, R20, R24, R34, R35] [R13, R22, R42] [R13, R22, R42] [R13, R22, R42]
13 [R9, R36, R43] [R9, R36, R43] [R9, R30, R36, R43] [R10, R31, R46] [R10, R31, R46] [R10, R31, R46]
14 [R3, R12, R25] [R3, R12, R25] [R3, R12, R25] R2 R2 R2
15 [R18, R32] [R18, R32] [R18, R32] R21 R21 R21
16 [R13, R45] [R13, R45] [R13, R45] [R6, R14, R28]   
17 [R42, R46] [R42, R46] [R42, R46]    
18 [R10, R22, R31] [R10, R22, R31] [R10, R22, R31]    
19 R21 R21 R21    
20 R2 R2 R2