Research Article
A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica
Table 4
Inference results for variables that are associated with “serious injuries” and “total estimated damage” in serious traffic accidents.
| Variable class | Variable name | Serious injuries/% | Total estimated damage (≥10,000 AUD)/% |
| Driver’s apparent error | Fail to stand | 3.54 | 17.5 | Change lanes to endanger | 2.83 | 17.5 | Incorrect turn | 3.61 | 17.1 | Reverse without due care | 4.03 | 17.9 | Follow too closely | 3.91 | 20.0 | Overtake without due care | 3.36 | 18.7 | Disobey traffic lights | 4.15 | 19.4 | Disobey stop sign | 4.81 | 19.5 | Disobey give way sign | 4.68 | 18.9 | DUI | 5.17 | 20.4 | Fail to give way | 3.91 | 17.2 |
| Road geometry | Cross road | 4.47 | 19.6 | Y junction | 3.69 | 18.2 | T junction | 3.27 | 18.0 | Multiple | 4.17 | 18.4 | Divided road | 4.05 | 18.1 | Not divided | 4.12 | 18.3 |
| Weather condition | Raining | 4.22 | 18.4 | Not raining | 3.69 | 18.1 |
| Light condition | Daylight | 3.65 | 18.1 | Night | 3.93 | 18.2 |
| Crash type | Rear end | 3.40 | 19.0 | Hit fixed object | 2.76 | 17.4 | Side swipe | 1.20 | 16.9 | Right angle | 2.48 | 17.6 | Head on | 15.4 | 26.0 | Hit pedestrian | 9.37 | 18.6 | Right turn | 2.93 | 17.2 | Hit parked vehicle | 0.34 | 16.9 |
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