Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
Repeat victimization has been widely studied from the perspective of environmental criminology for several decades. During this period, criminologists have identified a set of repeat victimization premises that are observed for many crimes; however, it is unknown whether these premises are also valid for cybercrime. In this study we rely on more than 9 million Zone-H data records from 2010 to 2017 to test whether these premises apply for the cybercrime of website defacement. We show that the phenomenon of repeat victimization is also observed in defaced cyber places (i.e. websites). In particular, we found that repeats contributed little to crime rates, that repeats occurred even several years after the original incident, that they were committed disproportionately by prolific offenders, and that few offenders returned to victimize previous targets. The results suggest that some traditional premises of repeat victimization may also be valid for understanding cybercrime events such as website defacement, implying that environmental criminology theories also constitute a useful framework for cybercrime analysis. The implications of these results in terms of criminological theory, cybercrime prevention, and the limitations derived from the use of Zone-H data are discussed
In general, people are poorly protected against cyberthreats, with the main reason being user behaviour. For the study described in this paper, a ques-tionnaire was developed in order to understand how people’s knowledge of and attitude towards both cyberthreats and cyber security controls affect in-tention to adopt cybersecure behaviour. The study divides attitude into a cog-nitive and an affective component. Although only the cognitive component of attitude is usually studied, the results from a questionnaire of 300 respond-ents show that both the affective and cognitive components of attitude have a clearly positive, albeit varying, influence on behavioural intention, with the affective component having an even greater effect on attitude than the cog-nitive aspect. No correlation was found between knowledge and behavioural intention. The results indicate that attitude is an important factor to include when developing behavioural interventions, but also that different kinds of attitude should be addressed differently in interventions.
