## 4 Discussion
Static data, such as most driver data, road infrastructure, points of interest, or satellite images could be used to build a base model. In contrast, data that vary over time, such as traffic accidents, weather conditions, or traffic flow, could be used to adjust the model. The human factor is the leading cause of traffic accidents [49; 50], and the most common human factor (contributing or principal) is inattention while driving because of overloading attention, distraction, or monotonous driving [51]. According to [46], young people are more susceptible than adults to suffer a traffic accident; male drivers are more involved in traffic incidents than female drivers, and female drivers are more susceptible than male drivers to suffering severe injuries. It is clear that the human factor influences and plays an essential role in the occurrence and severity of traffic accidents. This affirmation is confirmed in the Global Status Report on Road Safety. It establishes that factors associated with road user behavior, such as speeding and drink-driving, are two of the key risk factors to be considered and reinforced within the legislation of countries to prevent deaths and injuries due to traffic accidents. Some countries, especially high-income ones, have reduced the number of deaths and injuries by adopting policies for all the key risk factors [1]. Although we have improved much in the prevention of traffic accidents, it is clear that we must now focus on the field of the prediction of traffic accidents. In the context of our research, we could notice that very few models use driver's data, although the human factor is one of the leading causes of traffic accidents. We believe that this may be due to the non-availability of this type of information. Considering that the prediction models are generally fed with real data, the authors have resorted primarily to governmental institutions related to transportation or related areas and secondly to Internet services. The information collected from government platforms is mainly related to traffic accidents, traffic flow, and road infrastructure. The information collected from government platforms is mainly related to traffic accidents, traffic flow, and road infrastructure. Internet services provide information mainly related to weather and light conditions and traffic events. Most Internet services (MapQuest Traffic, Microsoft Bing Map Traffic, or Twitter, among others) provide APIs that can be integrated into the model to establish real-time or deferred information channels. One of the most challenging issues for the traffic accident prediction models is to count on a real-time solution. According to some authors [4; 9; 33; 37], the development of a real-time decision-making tool to avoid traffic accidents is completely viable as soon as shortcomings such as the non-integration of spatial heterogeneity, the incorrect handling of long-scale datasets, the improper handling of unique data properties, the information imbalance, and the lack of related information, are resolved. The correct handling of long-scale datasets requires feature extraction and imbalance correction. First of all, it is not practical to work with a huge amount of raw data, therefore to handle adequately large datasets, it is necessary to extract essential features such as weather, type of environment (for instance, rural highway vs. urban street), road conditions, speed limit, type of traffic, driver data, and type of vehicles [26]. Additionally, the accident-related data are less frequent than the non-accident-related information. Therefore, the datasets are imbalanced, and a predicting model has to be built to correct this situation [32]. It is also essential to consider which characteristics are time-sensitive, time-insensitive, and related to spatial heterogeneity. Time-insensitive data are fully connected, and spatial heterogeneity is a trainable component.