## 4 Discussion  
Below, we mention some thoughts presented in the articles to analyze and consider for future research. For instance, traffic accidents are not fortuitous events but events caused by conditions that occur in space and time and under certain circumstances [30]. According to [32], unfavorable traffic characteristics, adverse weather conditions, and driver distraction may lead to a crash. Additionally, the most significant factors on crash severity are vehicle failures, not wearing the seat belt, and unfavorable weather conditions [38]. Meanwhile, others assert that driving drunk and at high speed are serious factors in traffic accidents [45], and the wet pavement is one condition that increases the accident rate significantly [8]. Finally, the situation that causes the highest probability of suffering a traffic accident is the aggressive driving behavior after unusual congestion to recover the time lost [6]. For their part, the authors of [25] determined as follows: high speed is one of the most recurrent causes among fatal vehicle crashes; the traffic during morning peak and the first days of the week increase the risk of property-damage-only crashes; additionally, slopes and proximity to curves are the main road geometry factors that lead to fatal crashes; high speed and proximity to curves are the main causes of fatal-injury type crashes; faulty windshield wippers in rainy weather conditions and not wearing seat belt among young people are the most important causes of injury crashes; and, finally, driving at night without caution during rainy weather conditions increase the risk of property-damage-only crashes. About performance, models based on Deep Neural Networks reduce their accuracy, precision, and F1 score as the learning data size increases [37]. Additionally, the performance of a Support Vector Machine model depends on the learning process, so future efforts must focus on tuning the scale of parameter values and kernel functions selection [42]. Finally, the authors of [26] assert that the performance of the prediction models decreases as the spatio-temporal resolution of the prediction task increases. Regarding features, incorporating more features into the model does not always improve its performance [44]. Meanwhile, ref. [45] asserts that a lesser number of features affect the performance of a neural network. Finally, and according to [37], removing features from models based on  
Figure 3: Dispersion of values of evaluation metrics. Decision Tree or Random Forest has an enormous impact, but slight in models based on Deep Neural Networks. Some authors propose some recommendations; for instance, splitting data into pieces to send them to compute nodes can make the computational time much lower, which would benefit the handling of social media data [24]. For their part, ref. [19] suggests that the threshold used to separate different states (crash/non-crash) must compensate the values of True Positive Rate and False Positive Rate, and also that the optimal threshold may be found by comparing the performance of different thresholds. Finally, the authors of [5] proposes that the outcomes of a real-time traffic accident prediction model are shown through a variable message sign or transmitted between vehicles using a connected vehicular system. Despite the advantages that simulators offer at present, this mechanism of data generation has not been received as expected. In fact, there is a clear trend in prediction models about using real data instead of simulated ones. From the results, we could remark that only 1 out of 10 models use data generated by simulators. Because traffic accidents are events caused by a group of conditions that are not always the same and take place in space and time and under certain circumstances, it can be suspected that the authors prefer less controlled scenarios than those provided by simulators to generate data. Moreover, it was noted that there are both static and variable data.