Table 3 Results of feature importance analysis.
From: Traffic accident duration prediction using text mining and ensemble learning on expressways
Variable | Importance (%) | Cumulative percent (%) | Model input 1 | Model input 2 | Model input 3 | Model input 4 | Model input 5 |
|---|---|---|---|---|---|---|---|
Report times | 17.80 | 17.80 | √ | √ | √ | √ | √ |
Hour | 13.30 | 31.10 | √ | √ | √ | √ | √ |
Text size of first report | 3.50 | 34.60 | ╳ | √ | √ | √ | √ |
W1 | 3.00 | 37.60 | ╳ | √ | √ | √ | √ |
W2 | 2.90 | 40.50 | ╳ | √ | √ | √ | √ |
W3 | 2.40 | 42.90 | ╳ | ╳ | √ | √ | √ |
Mouth | 2.30 | 45.20 | ╳ | ╳ | √ | √ | √ |
W4 | 2.10 | 47.30 | ╳ | ╳ | √ | √ | √ |
W5 | 1.90 | 49.20 | ╳ | ╳ | √ | √ | √ |
Accident type | 1.80 | 51.00 | ╳ | ╳ | √ | √ | √ |
W6 | 1.80 | 52.80 | ╳ | ╳ | ╳ | √ | √ |
W7 | 1.60 | 54.40 | ╳ | ╳ | ╳ | √ | √ |
W8 | 1.60 | 56.00 | ╳ | ╳ | ╳ | √ | √ |
W9 | 1.40 | 57.40 | ╳ | ╳ | ╳ | √ | √ |
W10 | 1.40 | 58.80 | ╳ | ╳ | ╳ | √ | √ |
W11 | 1.40 | 60.20 | ╳ | ╳ | ╳ | √ | √ |
W12 | 1.30 | 61.50 | ╳ | ╳ | ╳ | ╳ | √ |
Weather | 1.20 | 62.70 | ╳ | ╳ | ╳ | ╳ | √ |
W13 | 1.10 | 63.80 | ╳ | ╳ | ╳ | ╳ | √ |
W14 | 1.00 | 64.80 | ╳ | ╳ | ╳ | ╳ | √ |
Weekday | 0.90 | 65.70 | ╳ | ╳ | ╳ | ╳ | √ |
W15 | 0.90 | 66.60 | ╳ | ╳ | ╳ | ╳ | √ |
W16 | 0.90 | 67.50 | ╳ | ╳ | ╳ | ╳ | √ |
W17 | 0.90 | 68.40 | ╳ | ╳ | ╳ | ╳ | √ |
W18 | 0.90 | 69.30 | ╳ | ╳ | ╳ | ╳ | √ |
W19 | 0.80 | 70.10 | ╳ | ╳ | ╳ | ╳ | √ |
W20 | 0.80 | 70.90 | ╳ | ╳ | ╳ | ╳ | √ |
W21 | 0.80 | 71.70 | ╳ | ╳ | ╳ | ╳ | ╳ |