# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Data  
The traffic accident data for the period 2009-2015 from the North-South Expressway (NSE, Petaling Jaya, Malaysia), Malaysia were used in this study. The NSE is the longest expressway \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper-Parameter** & **Best Value** & **Description** \\ \hline Minibatch size & 32 & Number of training cases over which SGD update is computed. \\ Loss function & Categorical crossentropy & The objective function or optimization score function is also called as multiclass logloss which is appropriate for categorical targets. \\ Optimizer & SGD & Stochastic gradient descent optimizer. \\ Learning rate & 0.01 & The learning rate used by SGD optimizer \\ Gradient momentum & 0.80 & Gradient momentum used by SGD optimizer. \\ Weight decay & 0.9 & Learning rate decay over each update. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The optimized hyperparameters of the proposed RNN model. (772 km) operated by Projek Lebuhraya Usaha Sama (PLUS) Berhad (the largest expressway operator in Malaysia, Petaling Jaya, Malaysia) and links many major cities and towns in Peninsula Malaysia. The data were obtained from the PLUS accident databases. The files used in this study were accident frequency and accident severity files in the form of an Excel spreadsheet. The accident frequency file contains the positional and descriptive accident location and the number of accidents in each road segment of 100 m. The accident records were separated according to the road bound (south, north). In contrast, the accident severity file contains the general accident characteristics such as accident time, road surface and lighting conditions, collision type, and the reported accident cause. To link the two files, the unique identity field (accident number) was used. According to the vehicle type information in the accident data, three scenarios were found: (1) single-vehicle with object accidents, (2) two-vehicle accidents, (3) and multiple vehicle accidents (mostly three vehicles). Training a deep learning model requires many samples to capture the data structure and to avoid model overfitting. Therefore, the analysis in this study did not focus just on a single scenario, but instead, all scenarios were included in training the proposed RNN model. In total, 1130 accident records were reported during 2009-2015. Of these, 740 (approximately 65.4%) resulted in drivers damaging property only. On the other hand, 172 (15.2%) drivers were involved in possible/evident injury, and 218 (19.4%) in disabling injury. The section of the NSE used in this study has a length of 15 km running from Ayer Keroh (210 km) to Pedas Linggi (225 km) (Figure 3). The accident severity data showed that the last section (220-225) of the NSE experienced several accidents resulting in serious injury (82) than the other sections (Table 2). Most accidents have occurred on the main route and southbound of the expressway. During the accident events, the actual accident causes were documented. The data showed that lost control, brake failure, and obstacles were the main accident causes on the NSE. With respect to lighting and surface conditions, most accidents occurred in daylight conditions and with a dry road surface. The main collision types in the accident records were out of control and rear collision. In addition, the accident time factor showed that 91.68% of the accidents occurred during the daytime. Additionally, the data also revealed that two-car accidents, single heavy car with an object and motorcycle with an object were the most recorded crashes on the NSE. Figure 3: Location of the North-South Expressway (NSE) section analyzed in this study.