# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Property Damage Only** & **Evident Injury** & **Disabling Injury** & **Total** \\ \hline
**Location** & & & & \\
210–214 & 185 & 172 & 58 & 415 \\
215–219 & 234 & 47 & 56 & 337 \\
220–225 & 238 & 58 & 82 & 378 \\ \hline
**Road-bound** & & & & \\ South & 453 & 99 & 139 & 691 \\ North & 287 & 73 & 79 & 439 \\ \hline
**Accident zone** & & & & \\ Interchange & 14 & 3 & 0 & 17 \\ Junction & & & & \\ Lay-by & 2 & 0 & 1 & 3 \\ Main Route & 666 & 155 & 209 & 1030 \\ North Bound Entry Ramp & 8 & 2 & 0 & 10 \\ North Bound Exit Ramp & 4 & 2 & 0 & 6 \\ Rest and Service Area & 21 & 4 & 2 & 27 \\ South Bound Entry Ramp & 2 & 0 & 1 & 3 \\ South Bound Exit Ramp & 7 & 1 & 3 & 11 \\ Toll Plaza & 16 & 5 & 2 & 23 \\ \hline
**Accident reported cause** & & & & \\ Bad Pavement Condition & 0 & 1 & 0 & 1 \\ Brake Failure & 6 & 2 & 1 & 9 \\ Bump-bump & 37 & 12 & 27 & 76 \\ Dangerous Pedestrian Behaviour & 0 & 0 & 1 & 1 \\ Drunk & 0 & 0 & 1 & 1 \\ Loss of Wheel & 1 & 0 & 2 & 3 \\ Lost control & 75 & 18 & 22 & 115 \\ Mechanical & 5 & 1 & 0 & 6 \\ Mechanical/Electrical Failure & 11 & 0 & 1 & 12 \\ Obstacle & 43 & 12 & 6 & 61 \\ Other Bad Driving & 15 & 1 & 4 & 20 \\ Other Human Factor/Over Load/Over Height & 3 & 0 & 0 & 3 \\ Over speeding & 345 & 61 & 91 & 497 \\ Parked Vehicle & 4 & 4 & 10 & 18 \\ Skidding & 1 & 0 & 0 & 1 \\ Sleepy Driver & 134 & 44 & 42 & 220 \\ Stray Animal & 13 & 1 & 2 & 16 \\ Tire burst & 47 & 15 & 8 & 70 \\ \hline
**Lighting condition** & & & & \\ Dark with Street Light & 47 & 6 & 8 & 61 \\ Dark without Street Light & 225 & 74 & 89 & 388 \\ Dawn/Dusk & 35 & 9 & 9 & 53 \\ Day Light & 433 & 83 & 112 & 628 \\ \hline
**Surface condition** & & & & \\ Dry & 460 & 146 & 190 & 796 \\ Wet & 280 & 26 & 28 & 334 \\ \hline
**Collision type** & & & & \\ Angular Collision & 9 & 2 & 0 & 11 \\ Broken Windscreen & 2 & 0 & 0 & 2 \\ Cross direction & 2 & 0 & 1 & 3 \\ Head-on Collision & 0 & 1 & 4 & 5 \\ Hitting Animal & 12 & 1 & 2 & 15 \\ Hitting Object On Road & 44 & 12 & 7 & 63 \\ Others & 20 & 0 & 6 & 26 \\ Out of Control & 457 & 92 & 107 & 656 \\ Overturned & 33 & 11 & 7 & 51 \\ Rear Collision & 137 & 48 & 81 & 266 \\ Right Angle Side Collision & 11 & 1 & 1 & 13 \\ Side Swipe & 13 & 4 & 2 & 19 \\ \hline
**Accident time** & & & & \\ Day time & 677 & 156 & 203 & 1036 \\ Night time & 63 & 16 & 15 & 94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Driver injury severity distribution according to accident related factors. Before feeding the accident data into the recurrent neural network, the data must be preprocessed. The steps included were: removing missing data, data transformation, and detection of highly correlated factors. Some data were missing in the accident records; therefore, the complete raw data in which a missing data is found was removed. The data transformation included one-hot encoding for the categorical factors. In addition, correlation between predictors was assessed to detect any multicollinearity problem. First, the multiple \(R^{2}\) was calculated for each factor. Second, the Variance Inflation Factor (_VIF_) was calculated from the multiple \(R^{2}\) for each factor (Table 3). The highest correlation of 0.27 was found between lighting condition factor and surface condition factor. However, the highest multiple \(R^{2}\) and _VIF_ were found to be 0.135 and 1.156 for surface condition factor. There was no multicollinearity found among the factors if _VIF_ = 1.0, however when the value exceeded 1.0, then moderate multicollinearity was found. In both cases, no high correlation was found during the model training and testing phase. Therefore, none of the factors was removed.