When a crime is committed, the task of the regional control rooms is to use the available situational information to identify the possible movements of a fugitive suspect to use in positioning police units. Currently, the methods to do this rely heavily on the intuition and experience of the control room employees and the speed of technology and communication. To reduce this reliance, there is an increasing demand for methods to objectively determine the tasks to undertake in a fugitive escape situation. Two methods to do this are under development which help in determining the location of a fugitive suspect and the optimal positioning of police units to these locations. However both of these methods still require a demarcation of the possible routes that a criminal fugitive will take to be used effectively. Therefore, this study explored the possibility of making likelihood estimations of possible escape routes. Because of a lack of reliable data, alternative methods to determine likelihood of escape routes are needed. A method that could be used is simulation. Simulation of human behaviour is however complex and careful consideration of the assumptions in such a model is needed to be able to have a high level of confidence in the resulting outcome. To do this, it is important that the theoretical background on which behavioural factors influence the criminal fugitive route-choice behaviour is complete and it is known how these factors affect the resulting routes. This is the knowledge gap addressed in this study. To address this knowledge gap, the question of what effect behavioural factors from criminal route choice behaviour have on escape routes will be answered. This is done by determining which main factors influence criminal fugitive route-choice behaviour and how these factors influence the resulting escape routes. The method used to answer these questions is a combination the development of a theoretical background based on a literature review of existing research and expert opinion and a quantitative sensitivity analysis on a simulation model. Because of a lack of research on criminal fugitive route-choice behaviour, it was necessary to use literature from the following research fields to find relevant topics: criminal decision-making, rationality in decision making and route-choice decision-making. From the literature in these fields, it was found that many different personal and crime characteristics exist, but it is unknown how these affect route choice behaviour. Next to this, it was found that rational decision-making cannot be assumed for the criminal situation and that bounded rationality needs to be considered. Lastly, from the route-choice decision-making literature, it was found that many different route-choice factors are relevant. The following list of route-choice behavioural factors was found: obstacle avoidance, risky behaviour, traffic avoidance, route distance and maximum speed, and preference for main or residential roads. For the route choice decision-making modelling methods, the following relevant topics were found: cost benefit calculations, short or long-term goals, emotional state, choice prioritisation and timing. These two lists of factors should be considered when conceptualising criminal fugitive route-choice behaviour. In the conceptualisation phase of this study, it was found that while many different suspect and crime characteristics might affect suspect behaviour, no specific behavioural profiles could be used to conceptualise route-choice behaviour. Therefore it was chosen to conceptualise the behaviour by creating dynamic strategy profiles based on behavioural route-choice factors. From the list of behavioural route choice factors to include in these strategy profiles, it was found that they can be described as either a preference or avoidance of road characteristics. The road characteristics seen to be avoided are cameras, obstacles, one-way roads and high traffic. The preferred road characteristics are a high number of lanes, residential roads, a high maximum speed and short roads. Next, it was found that there is a distinction in decisions based on long or short-term goals, which require either low or full network familiarity. For general route-choice behaviour, the conceptualisation of a route choice as a whole route between an origin and destination location was found to be most appropriate. When considering the rationality of the decisions made for the route choices, it was found that there is too much uncertainty and ambiguity in the considered bounded rationality conceptualisation to use them for aconcrete route-choice conceptualisation. Therefore, alterations to the assumptions of rationality are used to concentualise this. Finally, the emotional state of a fugitive is included in the conceptualisation through the possibility of changing route-choice strategies. This conceptualisation is further used to describe the general criminal fugitive route-choice behaviour in this study. To measure the influence of the behavioural route-choice factors in the conceptualisation, a route cost model was developed. In this model, the cost of a route is calculated using the characteristics of the edges in a road network. Based on this model, an experimental design is defined including a case study and sensitivity analysis to find the quantitative influence of route-choice behaviour on route metrics describing differences in escape routes through route length and overlap. When evaluating the results of the case studies and sensitivity analysis, it was found that the influence of behavioural route-choice factors on routes depends on the origin and destination locations and the distribution of edge characteristics over a road network. Next to this, it was found that there were no behavioural profiles leading to routes with specific characteristics and that in practical application, a broad set of strategies should be included when finding important locations is a road network to use for positioning police units. To do this, a method of using heat maps to find these locations was proposed. This method combined with the route cost model described in this study was found to have high applicability but more research needs to be done on the usability of this method. From the findings of this study, it can be concluded that criminal fugitive route-choice behaviour is complex and that different possible conceptualisations exist to be used for different purposes of studying general route-choice behaviour or specific behavioural factors. This affects the ability to measure the influence of behavioural factors on the resulting routes. Limitations were found on the measurement techniques used in the quantitative method to measure differences in routes which reduced the ability to interpret the resulting influence of behavioural factors on the routes. This showed that to find the influence of behavioural factors on the routes, the results of this study can show that the route-choice factors defined in the conceptualisation affect the routes but that more qualitative research is needed to find how these factors influence the resulting routes. To conclude, the findings of this study add to current research by showcasing the complexity of modelling route-choice decision-making and human behaviour in general and the many considerations that need to be taken when doing so. Next, it shows the difficulty of using quantitative and qualitative methods on the data type of routes to determine relations between factors influencing route-choice behaviour and resulting routes. And lastly, it adds to the current literature by developing an overview of the factors influencing criminal fugitive route-choice behaviour that need to be considered in the simulation of fugitive escape routes.

Censorship and privacy issues have led people to use VPNs when accessing the internet. These VPNs not only try to protect their user but they are also associated with criminality and cyber attacks. Because of this, websites have started to resort to blacklisting the IP addresses that are used by the VPNs, thus blocking both genuine and malicious users. This forces users to sacrifice privacy for accessibility. This paper provides a method on how to measure the amount of blocking that VPN users experience and to be able to determine what type of blocking is occuring. This method is then used in an experiment using a web crawler where nodes from ProtonVPN are used to measure the amount of blocking that occurs while browsing the internet’s most popular websites. This experiment shows that on average 1.12% of the domains perform some type of blocking directed towards the VPN user and that the majority of this blocking consists of a total block, which means that the user is entirely excluded from any use of the website. Next to this it is shown that not all VPN nodes show the same amount of blocking and that there was no large difference in blocking found between days while using the same VPN node. It also shows that the categories which perform the most blocking are Business, Online Shopping and News.