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.

The negatives effects of criminals are a threat to the Dutch society. In 2019, 15 percent of the Dutch citizens are victims of High Impact Crimes (HIC). Policy-makers have been mainly focused on disseminating information to businesses and individuals on how to protect themselves and their properties against crime and violence. However, an increased arrest rate is likely to have the largest impact on reducing the negative effects of HIC. Therefore, this research aims to explore the criminal escape route decision-making, to understand the choices made by criminals during their escape, resulting in a higher arrest rate. Since there is a lack data on escape routes, the modelling approach is used for this research. Therefore, a simulation model is developed to predict the prospective fugitive escape routes. The developed discrete-event model for criminal escape route decision-making within Rotterdam is developed based on both expert knowledge from the Dutch Police and the dual-process theory for criminal’s decision-making developed by van Gelder. Analysis of the interviews held with the Dutch Police shows that, type of crime, location of crime, and time of crime are varied to explore the escape route decision-making of criminals. From the analysis of the model outcomes, we conclude that organized crime criminals prefer the bigger (S and N) roads to escape the spatial area. Whereas local criminals prefer smaller (S and collector) roads to escape the spatial area. Traffic density influences the destinations and road use of organized crime criminals in a more distributed use of smaller roads instead of the bigger S and N roads. The location of crime results in different behavior in terms of destination, road use, and choices. The acquired knowledge of this research can result in catching the criminals faster after the crime, due to improved knowledge on potential decisions a criminal could take. Further research should focus on low-level decision-making to give researchers more detailed insights in criminals escape route behaviour. However, additional research is necessary on how personal and micro-level spatial factors influences the criminal escape route decision-making. Also, further research should focus on developing a real-time simulation model for the Dutch Police to predict possible locations of the criminals during the fugitive escape. Therefore, implementing additional features (e.g., live traffic, traffic lights, and micro-level spatial characteristics) to increase the feasibility of the simulation model is recommended. However, additional research is needed on how the additional features influences the criminal escape route decision-making.

When a crime is committed, catching the offender in the act has several benefits. Among others, it provides evidence of the offender's involvement in the crime, which simplifies the conviction process. However, to capture an offender in the act, the collaboration of multiple police officers is required. When a crime is being reported through the emergency line, it is the responsibility of the police officers in the control room to answer the call. They gather information on the type of crime, the location, a description of the offender, etc. Subsequently, they dispatch police units to the crime scene. The first unit prioritizes reaching the crime scene to ensure the safety of citizens, while the other units are tasked with locating and apprehending the offender. To adequately respond to reported crimes, police officers in the control room and units on the street rely on intuition, experience, and habit. This leads to the development of individual-specific strategies for handling interception scenarios. Reducing the dependency on these individualized approaches by identifying proven and robust strategies could increase the likelihood of successfully capturing a suspect. However, how the police currently save data on fugitive interception scenarios does not allow for such identification. Hence, alternative approaches to overcome this limitation must be found. Therefore, this paper explores whether simulation and game theoretic analysis are suitable methods for determining robust interception strategies for the police, aiming to increase the catch rate in fugitive interception scenarios. Classical game theory is the mathematical theory of interactions among rational decision-makers with opposing interests. It offers valuable insights into the decision-making processes, compromises, and strategies the police and offenders may employ in real-world situations. To analyze the fugitive interception scenario with game theory, it is first simulated with an agent-based model. In this simulation model, the police and offender are individual agents with opposing interests and individual decision-making processes. Before modeling, research is conducted to find the current strategies that the police and offenders can potentially adopt. Given the limited availability of data on fugitive interception scenarios, literature and expert interviews serve as sources for data collection. They provide insights into the behavior and strategies of both agents. Both sources emphasize the nature of the crime as a primary indicator of the offender's escape behavior. Large crimes, such as assassinations or armed robberies, are typically well-planned and characterized by predefined escape routes and rational behavior. During their escape, offenders of large crimes are found to be less susceptible to external factors such as crowd flows or police sightings. On the other hand, smaller crimes are more frequently committed spontaneously and associated with bounded rational behavior. This is depicted by their chaotic and unpredictable escape routes while taking many turns. For the game theoretic analysis, the results of the simulation model are analyzed. The fugitive interception project is regarded as a non-cooperative zero-sum game. The results are presented in a payoff table, in which Nash equilibria are calculated. Nash equilibria are the points at which no player can single-handedly improve their outcome when the other player does not change strategy. The pure-strategy Nash equilibrium resulted from the offender strategy where they started at a central metro station and aimed to transfer to a train network. These routes were frequently identified as the shortest compared to other end goals. Conversely, strategies that focused on getting as far away as possible, as quickly as possible, were found to be the least successful. In determining the success of the police strategy, two factors were found to be crucial. Firstly, strategies where the police conducted surveillance on the metro platforms, as opposed to the station exits, proved significantly more effective. This highlights the importance for the police to strategically position themselves where the offender is most likely to pass, irrespective of assuming it to be the offender's final destination. Secondly, the police's response time served as an indicator for capture success. The quicker the crime is reported, the faster the police can take action to capture the offender, which increases capture chances. Additionally to the game theoretic analysis, the relationship between the model’s output and its sensitivity to changes in input variables is tested. Results showed that variations in input did not lead to significant changes in output. This can be attributed to the deep uncertainty of this model. To address this challenge, the model must be refined, and done with more iterations. In conclusion, by combining simulation and game theory new insights can be found beyond what either method can provide individually. By modeling the dynamic nature of a fugitive interception scenario, the success of the offender and police behaviour can be found. This can help the police during decision-making to adopt more robust strategies while considering the dynamic nature of the environment and strategic interactions with the offender. The study addresses the knowledge gap by simulating offender and police behavior, and analyzing the result with classical game theory. This study has created a simulation model with an intuitively driven agent in a complex dynamic problem. Potential improvements in offender capture chances, with findings informing effective and unbiased police interception strategies. The study aims to contribute to crime reduction and foster increased trust in the Dutch national police. However, before generalizing the results future research must be done to overcome limitations resulting from the simplifications of this simulation model.

The effect of using decision support systems in decision-making processes characterized by time pressure, uncertainty, and dynamism is still very limited and debated among researchers. By utilizing a case study that concerns the employment of a DSS to assist the procedures of intercepting fugitives undertaken by the Dutch National Police, this thesis investigates the difference in the personal efficiency and productivity of the practitioners (the decision time) between the current practices and the model-supported ones. An agent-based model is thus implemented which mirrors the procedures of intercepting fugitives in the two situations. The results highlight that, in contexts of time pressure, uncertainty, and dynamism, the use of the DSS does not always result in faster decision-making processes. This highly depends on how the DSS is implemented, especially in relation to the level of experience of the practitioners with the activities at stake and the level of dynamism and complexity of the decision-making process. Moreover, it is important to guarantee flexibility in the design of the system, and provide the users with the most relevant information to make the decision, so that the time needed to gather and process the information is minimized, while still guaranteeing an appropriate level of accuracy of the information displayed. Finally, the results show that involving the practitioners in the design of the system can improve the decision-making time, since it reduces the discussion between the actors, but is less relevant than the time needed to gather and process the information (which is a very critical aspect in contexts of time pressure), especially for those who work in contact with the system.