The quantity of goods transported in the transport sector is increasing every year. As a result of the increase, the number of means of transport increases. The most popular sector is road transport, which is also referred to as the most dangerous in terms of safety. The assessment of the traffic situation on the planned route does not take place during its implementation. The consequences of long reaction times on emerging or already occurring incidents affect safety. This phenomenon can also trigger crisis situations in other critical infrastructure sectors. In more serious events, a cascading effect can occur between critical infrastructure elements that could lead to a domino effect. This phenomenon could be likened, for example, to blackout in power engineering. The conclusion of the chapter will include a case scenario as to how a methodological standard for traffic assessment should work on real-time crises.
Part of the book: Introduction to Data Science and Machine Learning
The soft targets are closely related to the risk of attack to the group of people (to the lives). This problem can cause fatal consequences for the population. The current situation on the world reflects the fear of the attack in the soft targets. We can see the fear to lose life at these public places and in all types of access to free buildings. Each of us spends time in the shopping centers or the park every day, and our children spend time in schools where they can be threatened. The characteristics between the soft targets belong to a considerable number of persons at the same time in the same area, and the current state of the security measures is not adequate to the threats yet. The main aim of the software to the assessment of the soft target is to protect the people in the soft targets, minimize the impact to the people (visitors), and help to solve the problem at the moment. The methodology is based on the assessment of the object according to the features (according to the criteria).
Part of the book: Introduction to Data Science and Machine Learning