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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