# Severity Prediction of Traffic Accidents with Recurrent Neural Networks  
[PERSON]  
1 Department of Civil Engineering, Geospatial Information Science Research Center (GISRC),  
Faculty of Engineering, University Putra Malaysia, UPM, Serdang 43400, Malaysia; [EMAIL_ADDRESS]  
1  
[PERSON]ESS] or [EMAIL_ADDRESS]; Tel. : +60-3-8946-6383  
1  
[PERSON]  
1 Department of Civil Engineering, Geospatial Information Science Research Center (GISRC),  
Faculty of Engineering, University Putra Malaysia, UPM, Serdang 43400, Malaysia; [EMAIL_ADDRESS]  
1  
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hierarchal representations of data with multiple levels of abstraction at different processing layers. The advantage of deep learning neural networks over statistical techniques is that they involve a more general mapping procedure i.e., a specific function is not required in model building [8]. However, these techniques can be treated as black box methods, if the network architecture is not carefully designed and its parameters are not optimized. Several studies have investigated Neural Network (NN) and deep learning methods in transportation related applications [9; 10; 11; 12]. For example, [PERSON] and [PERSON] [8] used NN to predict driver injury severity from various accident factors (i.e., driver, vehicle, roadway and environment characteristics). In their study, the NN model developed performed better than the ordered probit model. In another paper, [PERSON] et al. [10; 13] applied NN to model injury severity of road accidents using 17 significant parameters. The NN model was used to predict the injury severity levels, resulting in a low overall accuracy of 40.71%. More recently, [PERSON] and [PERSON] [14] proposed a novel rule-based technique to predict traffic accident severity based on users' preferences instead of conventional data mining methods. The proposed method outperformed the NN and support vector machine methods. In a recent paper, [PERSON] et al. [11] used NN methods to predict the injury severity (minor, moderate, severe, death) of traffic accidents in Abu Dhabi. Their analysis was based on 5973 traffic accident records that had occurred over a 6-years period. The overall accuracy of the model for the training and testing data were 81.6% and 74.6%, respectively. In addition, [PERSON] and [PERSON] [15] proposed a training algorithm and network structure optimization method for crash injury severity prediction. Their results indicated that the proposed training algorithm performed better than the traditional back-propagation algorithm. The optimized NN, which contained less nodes than the fully connected NN, achieved reasonable prediction accuracy. Also, the fully connected and optimized NN models outperformed the ordered logit model. Also, their results showed that optimization of the NN structure could improve the overall performance of model prediction. In recent years, deep learning has become a popular technique in image [16] and natural language processing applications [17]. Many researchers have applied deep learning based techniques in transportation related applications such as traffic flow [18] and accident hotspots prediction [19]. A detailed literature review showed that optimization of network structures is critical for crash severity prediction. Generally speaking, deep learning allows compositionality and the design of flexible network structures with multilayer modules, and therefore it is expected that the performance of deep learning models will be better than the traditional NN models. A deep learning architecture is a multilayer stack of simple modules, most of which are subjected to learning computing nonlinear input-output mappings [20].