C.N. van der Wal
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17 records found
1
Master thesis
(2026)
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J.J. den Nijs, D.C. Duives, Iris Kamphorst, Rob Souren, C.N. van der Wal, Sten Weenink
Large scale music festivals have large crowds, especially at the major shows at the stage areas. It is important that appropriate crowd safety measures are taken and an efficient in- and outflow improves the user experience. At music festivals both the crowd safety and the visitor experience benefit from infrastructural layouts which limit the delay visitors experience when accessing the stage area. Little guidance is available for festival grounds design and no simulation methods are available to assist festival grounds design. This study proposes a commercially available microscopic agent-based model for both ingress and egress scenarios to determine how these kinds of models can be used in infrastructural design. This study starts with a structured literature review about crowd dynamics on festival grounds and then introduces the case study of Defqon.1. The case study and the literature are used to formulate requirements for commercially available microscopic models. Different models are compared using experiments to test for these requirements. A model
is created and the model is then calibrated using extensive data from the case study, structured with an iterative calibration cycle. For the model selection, Crowd:it, MassMotion, Pedestrian Dynamics, Vadere and Viswalk are compared. Vadere and Viswalk are excluded, respectively due to long runtimes and to limited license availability. After the five comparison experiments, the Social Force Model MassMotion is found to be the best suited for this setting due to its computational efficiency, ability to handle stationary agents and realistic pedestrian behaviour. A model is formulated with two experiments: one for the ingress and one for the egress. The model uses a fixed arrival function and a fixed start of egress function. A novel grid cell structure is introduced which divides the stage area into different cells to be able to control the spread of agents over the stage area. Each cell is given a value which consists of two parts: static and dynamic values. The static values are mapped based on the line
of sight, the distance from the stage, the bar adjacency and the remaining ingress effect. The dynamic values are based on the travel time and the density within a grid cell, where the last one is used to limit the inflow into a cell which exceeds its average density. From the calibration, a difference between the ingress and egress mean free flow speed is found. During ingress visitors walk slower compared to egress. Additionally, a decrease in the crowd density between the start of the show and the end of the show is found due
to the crowd spreading out. The model as proposed in this study overestimates the KPIs of the model but is able to show the dynamics of the stage area well, so it can be used for comparing different infrastructural layouts. The model can predict bottlenecks caused by everyone taking the most direct route to their destination, but not other bottlenecks. The calibration has been limited by the available time, but structural problems are identified. Based on the model
it is concluded that better stationary agent behaviour and local dynamic path planning are required to be able to simulate the ingress and egress of stage areas at large scale outdoor music festivals. ...
is created and the model is then calibrated using extensive data from the case study, structured with an iterative calibration cycle. For the model selection, Crowd:it, MassMotion, Pedestrian Dynamics, Vadere and Viswalk are compared. Vadere and Viswalk are excluded, respectively due to long runtimes and to limited license availability. After the five comparison experiments, the Social Force Model MassMotion is found to be the best suited for this setting due to its computational efficiency, ability to handle stationary agents and realistic pedestrian behaviour. A model is formulated with two experiments: one for the ingress and one for the egress. The model uses a fixed arrival function and a fixed start of egress function. A novel grid cell structure is introduced which divides the stage area into different cells to be able to control the spread of agents over the stage area. Each cell is given a value which consists of two parts: static and dynamic values. The static values are mapped based on the line
of sight, the distance from the stage, the bar adjacency and the remaining ingress effect. The dynamic values are based on the travel time and the density within a grid cell, where the last one is used to limit the inflow into a cell which exceeds its average density. From the calibration, a difference between the ingress and egress mean free flow speed is found. During ingress visitors walk slower compared to egress. Additionally, a decrease in the crowd density between the start of the show and the end of the show is found due
to the crowd spreading out. The model as proposed in this study overestimates the KPIs of the model but is able to show the dynamics of the stage area well, so it can be used for comparing different infrastructural layouts. The model can predict bottlenecks caused by everyone taking the most direct route to their destination, but not other bottlenecks. The calibration has been limited by the available time, but structural problems are identified. Based on the model
it is concluded that better stationary agent behaviour and local dynamic path planning are required to be able to simulate the ingress and egress of stage areas at large scale outdoor music festivals. ...
Large scale music festivals have large crowds, especially at the major shows at the stage areas. It is important that appropriate crowd safety measures are taken and an efficient in- and outflow improves the user experience. At music festivals both the crowd safety and the visitor experience benefit from infrastructural layouts which limit the delay visitors experience when accessing the stage area. Little guidance is available for festival grounds design and no simulation methods are available to assist festival grounds design. This study proposes a commercially available microscopic agent-based model for both ingress and egress scenarios to determine how these kinds of models can be used in infrastructural design. This study starts with a structured literature review about crowd dynamics on festival grounds and then introduces the case study of Defqon.1. The case study and the literature are used to formulate requirements for commercially available microscopic models. Different models are compared using experiments to test for these requirements. A model
is created and the model is then calibrated using extensive data from the case study, structured with an iterative calibration cycle. For the model selection, Crowd:it, MassMotion, Pedestrian Dynamics, Vadere and Viswalk are compared. Vadere and Viswalk are excluded, respectively due to long runtimes and to limited license availability. After the five comparison experiments, the Social Force Model MassMotion is found to be the best suited for this setting due to its computational efficiency, ability to handle stationary agents and realistic pedestrian behaviour. A model is formulated with two experiments: one for the ingress and one for the egress. The model uses a fixed arrival function and a fixed start of egress function. A novel grid cell structure is introduced which divides the stage area into different cells to be able to control the spread of agents over the stage area. Each cell is given a value which consists of two parts: static and dynamic values. The static values are mapped based on the line
of sight, the distance from the stage, the bar adjacency and the remaining ingress effect. The dynamic values are based on the travel time and the density within a grid cell, where the last one is used to limit the inflow into a cell which exceeds its average density. From the calibration, a difference between the ingress and egress mean free flow speed is found. During ingress visitors walk slower compared to egress. Additionally, a decrease in the crowd density between the start of the show and the end of the show is found due
to the crowd spreading out. The model as proposed in this study overestimates the KPIs of the model but is able to show the dynamics of the stage area well, so it can be used for comparing different infrastructural layouts. The model can predict bottlenecks caused by everyone taking the most direct route to their destination, but not other bottlenecks. The calibration has been limited by the available time, but structural problems are identified. Based on the model
it is concluded that better stationary agent behaviour and local dynamic path planning are required to be able to simulate the ingress and egress of stage areas at large scale outdoor music festivals.
is created and the model is then calibrated using extensive data from the case study, structured with an iterative calibration cycle. For the model selection, Crowd:it, MassMotion, Pedestrian Dynamics, Vadere and Viswalk are compared. Vadere and Viswalk are excluded, respectively due to long runtimes and to limited license availability. After the five comparison experiments, the Social Force Model MassMotion is found to be the best suited for this setting due to its computational efficiency, ability to handle stationary agents and realistic pedestrian behaviour. A model is formulated with two experiments: one for the ingress and one for the egress. The model uses a fixed arrival function and a fixed start of egress function. A novel grid cell structure is introduced which divides the stage area into different cells to be able to control the spread of agents over the stage area. Each cell is given a value which consists of two parts: static and dynamic values. The static values are mapped based on the line
of sight, the distance from the stage, the bar adjacency and the remaining ingress effect. The dynamic values are based on the travel time and the density within a grid cell, where the last one is used to limit the inflow into a cell which exceeds its average density. From the calibration, a difference between the ingress and egress mean free flow speed is found. During ingress visitors walk slower compared to egress. Additionally, a decrease in the crowd density between the start of the show and the end of the show is found due
to the crowd spreading out. The model as proposed in this study overestimates the KPIs of the model but is able to show the dynamics of the stage area well, so it can be used for comparing different infrastructural layouts. The model can predict bottlenecks caused by everyone taking the most direct route to their destination, but not other bottlenecks. The calibration has been limited by the available time, but structural problems are identified. Based on the model
it is concluded that better stationary agent behaviour and local dynamic path planning are required to be able to simulate the ingress and egress of stage areas at large scale outdoor music festivals.
A Computer Vision–Enriched Discrete Choice Model for Pedestrian Route Choice Preferences in the Netherlands
Integrating Street-Level Visual Characteristics into Pedestrian Route Choice Modelling across Trip-Purpose Contexts
Walking is an important part of urban mobility in the Netherlands, but pedestrian route choice is often simplified in transport models by assuming that pedestrians choose the shortest or fastest route. This may overlook street-level characteristics that influence how people experience a walking route, such as greenery, traffic, pedestrian space, and the presence of other people. These factors may also matter differently depending on why someone is walking. This thesis therefore investigates to what extent visual and non-visual route attributes influence pedestrian route choice preferences in the Netherlands, and how these preferences differ across trip-purpose contexts.
To study this, a stated preference experiment was designed in which respondents repeatedly chose between two walking route alternatives. The alternatives differed in travel time and in the visual appearance of the street, shown through a street-level image. The choice tasks were presented in four contexts: walking to public transport, walking to work or school, walking home, and walking in free time. The collected data were used to estimate three model specifications for each trip purpose: a baseline Multinomial Logit (MNL) model with only travel time, a pixel-share MNL model with predefined visual attributes extracted from the images, and a Computer Vision-Enriched Discrete Choice Model (CV-DCM) that learns visual information directly from the full image.
The results show that travel time remains important, especially for more goal-oriented trips. However, travel time alone does not fully explain the observed choices. Models that include visual information generally perform better than the baseline model, with the CV-DCM showing the strongest predictive performance in most contexts. In the pixel-share MNL, visible greenery has the clearest and most consistent positive effect on route preferences. The qualitative validation of the CV-DCM also suggests that images with higher predicted utility are often greener and more attractive. At the same time, the results show that the role of visual information differs across trip purposes.
The trip-purpose-specific CV-DCM models were also applied in an exploratory pedestrian network analysis in Amsterdam-Zuid. Image utilities were linked to street segments and combined with travel time utility to compare the shortest route with the route selected by each model. The model-selected routes were not always the shortest routes and differed between trip purposes, showing how visual route preferences can be mapped across a pedestrian network.
Overall, this thesis shows that pedestrian route choice is shaped by more than travel time alone. Visual street-level information can improve pedestrian route choice models, but the required level of model complexity depends on the purpose of the analysis. The models in this thesis should be seen as exploratory tools for analysing stated pedestrian route preferences and visual route attractiveness, rather than direct prediction tools for actual pedestrian flows.
...
To study this, a stated preference experiment was designed in which respondents repeatedly chose between two walking route alternatives. The alternatives differed in travel time and in the visual appearance of the street, shown through a street-level image. The choice tasks were presented in four contexts: walking to public transport, walking to work or school, walking home, and walking in free time. The collected data were used to estimate three model specifications for each trip purpose: a baseline Multinomial Logit (MNL) model with only travel time, a pixel-share MNL model with predefined visual attributes extracted from the images, and a Computer Vision-Enriched Discrete Choice Model (CV-DCM) that learns visual information directly from the full image.
The results show that travel time remains important, especially for more goal-oriented trips. However, travel time alone does not fully explain the observed choices. Models that include visual information generally perform better than the baseline model, with the CV-DCM showing the strongest predictive performance in most contexts. In the pixel-share MNL, visible greenery has the clearest and most consistent positive effect on route preferences. The qualitative validation of the CV-DCM also suggests that images with higher predicted utility are often greener and more attractive. At the same time, the results show that the role of visual information differs across trip purposes.
The trip-purpose-specific CV-DCM models were also applied in an exploratory pedestrian network analysis in Amsterdam-Zuid. Image utilities were linked to street segments and combined with travel time utility to compare the shortest route with the route selected by each model. The model-selected routes were not always the shortest routes and differed between trip purposes, showing how visual route preferences can be mapped across a pedestrian network.
Overall, this thesis shows that pedestrian route choice is shaped by more than travel time alone. Visual street-level information can improve pedestrian route choice models, but the required level of model complexity depends on the purpose of the analysis. The models in this thesis should be seen as exploratory tools for analysing stated pedestrian route preferences and visual route attractiveness, rather than direct prediction tools for actual pedestrian flows.
...
Walking is an important part of urban mobility in the Netherlands, but pedestrian route choice is often simplified in transport models by assuming that pedestrians choose the shortest or fastest route. This may overlook street-level characteristics that influence how people experience a walking route, such as greenery, traffic, pedestrian space, and the presence of other people. These factors may also matter differently depending on why someone is walking. This thesis therefore investigates to what extent visual and non-visual route attributes influence pedestrian route choice preferences in the Netherlands, and how these preferences differ across trip-purpose contexts.
To study this, a stated preference experiment was designed in which respondents repeatedly chose between two walking route alternatives. The alternatives differed in travel time and in the visual appearance of the street, shown through a street-level image. The choice tasks were presented in four contexts: walking to public transport, walking to work or school, walking home, and walking in free time. The collected data were used to estimate three model specifications for each trip purpose: a baseline Multinomial Logit (MNL) model with only travel time, a pixel-share MNL model with predefined visual attributes extracted from the images, and a Computer Vision-Enriched Discrete Choice Model (CV-DCM) that learns visual information directly from the full image.
The results show that travel time remains important, especially for more goal-oriented trips. However, travel time alone does not fully explain the observed choices. Models that include visual information generally perform better than the baseline model, with the CV-DCM showing the strongest predictive performance in most contexts. In the pixel-share MNL, visible greenery has the clearest and most consistent positive effect on route preferences. The qualitative validation of the CV-DCM also suggests that images with higher predicted utility are often greener and more attractive. At the same time, the results show that the role of visual information differs across trip purposes.
The trip-purpose-specific CV-DCM models were also applied in an exploratory pedestrian network analysis in Amsterdam-Zuid. Image utilities were linked to street segments and combined with travel time utility to compare the shortest route with the route selected by each model. The model-selected routes were not always the shortest routes and differed between trip purposes, showing how visual route preferences can be mapped across a pedestrian network.
Overall, this thesis shows that pedestrian route choice is shaped by more than travel time alone. Visual street-level information can improve pedestrian route choice models, but the required level of model complexity depends on the purpose of the analysis. The models in this thesis should be seen as exploratory tools for analysing stated pedestrian route preferences and visual route attractiveness, rather than direct prediction tools for actual pedestrian flows.
To study this, a stated preference experiment was designed in which respondents repeatedly chose between two walking route alternatives. The alternatives differed in travel time and in the visual appearance of the street, shown through a street-level image. The choice tasks were presented in four contexts: walking to public transport, walking to work or school, walking home, and walking in free time. The collected data were used to estimate three model specifications for each trip purpose: a baseline Multinomial Logit (MNL) model with only travel time, a pixel-share MNL model with predefined visual attributes extracted from the images, and a Computer Vision-Enriched Discrete Choice Model (CV-DCM) that learns visual information directly from the full image.
The results show that travel time remains important, especially for more goal-oriented trips. However, travel time alone does not fully explain the observed choices. Models that include visual information generally perform better than the baseline model, with the CV-DCM showing the strongest predictive performance in most contexts. In the pixel-share MNL, visible greenery has the clearest and most consistent positive effect on route preferences. The qualitative validation of the CV-DCM also suggests that images with higher predicted utility are often greener and more attractive. At the same time, the results show that the role of visual information differs across trip purposes.
The trip-purpose-specific CV-DCM models were also applied in an exploratory pedestrian network analysis in Amsterdam-Zuid. Image utilities were linked to street segments and combined with travel time utility to compare the shortest route with the route selected by each model. The model-selected routes were not always the shortest routes and differed between trip purposes, showing how visual route preferences can be mapped across a pedestrian network.
Overall, this thesis shows that pedestrian route choice is shaped by more than travel time alone. Visual street-level information can improve pedestrian route choice models, but the required level of model complexity depends on the purpose of the analysis. The models in this thesis should be seen as exploratory tools for analysing stated pedestrian route preferences and visual route attractiveness, rather than direct prediction tools for actual pedestrian flows.
Journey through Crowds!
Modeling passenger distribution on railway platforms in the Netherlands: a discrete choice approach
This study investigates the factors influencing passenger distribution on railway platforms in the Netherlands, using Eindhoven Central Station as a case study. The objective is to quantify how spatial characteristics, environmental conditions, and human behavioral factors jointly shape passenger distribution and to derive actionable recommendations for platform design and management.
A combination of descriptive statistics, spatial analysis, and discrete choice modeling was applied to a high-resolution dataset comprising 142,256 sensor-based observations collected under 49 situational scenarios. The platform was discretized into spatial cells characterized by distance to entrances, information boards, and the track edge, proximity to seating, leaning areas and kiosks, passenger density, lighting conditions, and weather.
Descriptive analyses reveal systematic clustering near entrances and comfort-related facilities, confirming the central role of accessibility and physical support in waiting behavior. Passengers generally avoid track-adjacent areas, while moderate social clustering occurs at intermediate densities. Environmental conditions further influence spatial patterns, with adverse weather and poor lighting reinforcing concentration in sheltered zones.
A multinomial logit model identifies nine statistically significant determinants of waiting location choice. Seating exhibits the strongest positive effect, followed by entrance proximity and leaning facilities, highlighting comfort and accessibility as primary drivers. Safety considerations reduce the attractiveness of areas near the track, although this effect weakens under favorable lighting and weather conditions. Areas near kiosks and information boards are avoided, indicating the disutility associated with congestion and circulation conflicts. The model demonstrates strong predictive performance and reproduces observed passenger distributions with high accuracy.
The findings show that platform waiting behavior reflects structured trade-offs between comfort, safety, accessibility, congestion avoidance, social context, and environmental conditions. Based on these results, design recommendations are proposed, including redistributing seating and leaning facilities, dispersing entrance flows, relocating kiosks and information boards toward circulation corridors, applying adaptive lighting strategies, and maintaining clear safety buffers near the track edge. The study provides a behavioral and empirical foundation for improving comfort, safety, and operational efficiency at Dutch railway stations. ...
A combination of descriptive statistics, spatial analysis, and discrete choice modeling was applied to a high-resolution dataset comprising 142,256 sensor-based observations collected under 49 situational scenarios. The platform was discretized into spatial cells characterized by distance to entrances, information boards, and the track edge, proximity to seating, leaning areas and kiosks, passenger density, lighting conditions, and weather.
Descriptive analyses reveal systematic clustering near entrances and comfort-related facilities, confirming the central role of accessibility and physical support in waiting behavior. Passengers generally avoid track-adjacent areas, while moderate social clustering occurs at intermediate densities. Environmental conditions further influence spatial patterns, with adverse weather and poor lighting reinforcing concentration in sheltered zones.
A multinomial logit model identifies nine statistically significant determinants of waiting location choice. Seating exhibits the strongest positive effect, followed by entrance proximity and leaning facilities, highlighting comfort and accessibility as primary drivers. Safety considerations reduce the attractiveness of areas near the track, although this effect weakens under favorable lighting and weather conditions. Areas near kiosks and information boards are avoided, indicating the disutility associated with congestion and circulation conflicts. The model demonstrates strong predictive performance and reproduces observed passenger distributions with high accuracy.
The findings show that platform waiting behavior reflects structured trade-offs between comfort, safety, accessibility, congestion avoidance, social context, and environmental conditions. Based on these results, design recommendations are proposed, including redistributing seating and leaning facilities, dispersing entrance flows, relocating kiosks and information boards toward circulation corridors, applying adaptive lighting strategies, and maintaining clear safety buffers near the track edge. The study provides a behavioral and empirical foundation for improving comfort, safety, and operational efficiency at Dutch railway stations. ...
This study investigates the factors influencing passenger distribution on railway platforms in the Netherlands, using Eindhoven Central Station as a case study. The objective is to quantify how spatial characteristics, environmental conditions, and human behavioral factors jointly shape passenger distribution and to derive actionable recommendations for platform design and management.
A combination of descriptive statistics, spatial analysis, and discrete choice modeling was applied to a high-resolution dataset comprising 142,256 sensor-based observations collected under 49 situational scenarios. The platform was discretized into spatial cells characterized by distance to entrances, information boards, and the track edge, proximity to seating, leaning areas and kiosks, passenger density, lighting conditions, and weather.
Descriptive analyses reveal systematic clustering near entrances and comfort-related facilities, confirming the central role of accessibility and physical support in waiting behavior. Passengers generally avoid track-adjacent areas, while moderate social clustering occurs at intermediate densities. Environmental conditions further influence spatial patterns, with adverse weather and poor lighting reinforcing concentration in sheltered zones.
A multinomial logit model identifies nine statistically significant determinants of waiting location choice. Seating exhibits the strongest positive effect, followed by entrance proximity and leaning facilities, highlighting comfort and accessibility as primary drivers. Safety considerations reduce the attractiveness of areas near the track, although this effect weakens under favorable lighting and weather conditions. Areas near kiosks and information boards are avoided, indicating the disutility associated with congestion and circulation conflicts. The model demonstrates strong predictive performance and reproduces observed passenger distributions with high accuracy.
The findings show that platform waiting behavior reflects structured trade-offs between comfort, safety, accessibility, congestion avoidance, social context, and environmental conditions. Based on these results, design recommendations are proposed, including redistributing seating and leaning facilities, dispersing entrance flows, relocating kiosks and information boards toward circulation corridors, applying adaptive lighting strategies, and maintaining clear safety buffers near the track edge. The study provides a behavioral and empirical foundation for improving comfort, safety, and operational efficiency at Dutch railway stations.
A combination of descriptive statistics, spatial analysis, and discrete choice modeling was applied to a high-resolution dataset comprising 142,256 sensor-based observations collected under 49 situational scenarios. The platform was discretized into spatial cells characterized by distance to entrances, information boards, and the track edge, proximity to seating, leaning areas and kiosks, passenger density, lighting conditions, and weather.
Descriptive analyses reveal systematic clustering near entrances and comfort-related facilities, confirming the central role of accessibility and physical support in waiting behavior. Passengers generally avoid track-adjacent areas, while moderate social clustering occurs at intermediate densities. Environmental conditions further influence spatial patterns, with adverse weather and poor lighting reinforcing concentration in sheltered zones.
A multinomial logit model identifies nine statistically significant determinants of waiting location choice. Seating exhibits the strongest positive effect, followed by entrance proximity and leaning facilities, highlighting comfort and accessibility as primary drivers. Safety considerations reduce the attractiveness of areas near the track, although this effect weakens under favorable lighting and weather conditions. Areas near kiosks and information boards are avoided, indicating the disutility associated with congestion and circulation conflicts. The model demonstrates strong predictive performance and reproduces observed passenger distributions with high accuracy.
The findings show that platform waiting behavior reflects structured trade-offs between comfort, safety, accessibility, congestion avoidance, social context, and environmental conditions. Based on these results, design recommendations are proposed, including redistributing seating and leaning facilities, dispersing entrance flows, relocating kiosks and information boards toward circulation corridors, applying adaptive lighting strategies, and maintaining clear safety buffers near the track edge. The study provides a behavioral and empirical foundation for improving comfort, safety, and operational efficiency at Dutch railway stations.
Enhancing Business Project Quality in a Complex Environment
Developing a business performance-enhancing adaptive tool using DEME’s Cables Division as a case study
This thesis investigates how project quality can be improved in complex, dynamic environments through the development of an adaptive tool, using DEME Offshore’s cable tender department as a case study. The research highlights challenges such as fragmented knowledge, time pressure, and high staff turnover, which limit the effectiveness of traditional quality approaches like Lean or Six Sigma. A design research methodology, combining literature review and interviews, informed the creation of a Kaizen-based tool structured around a Work Breakdown Structure (WBS). The tool provides 32 project blocks linked to six critical dimensions, Method, Planning, Production, Cost, Qualifications, and Opportunity & Risk Management, offering users structured, accessible knowledge, checklists, and lessons learned. Designed for continuous updates, the tool integrates with existing workflows and promotes user-driven improvement. The study concludes that this approach enhances consistency, knowledge retention, and risk management, with applicability both to DEME and to other project-driven industries.
...
This thesis investigates how project quality can be improved in complex, dynamic environments through the development of an adaptive tool, using DEME Offshore’s cable tender department as a case study. The research highlights challenges such as fragmented knowledge, time pressure, and high staff turnover, which limit the effectiveness of traditional quality approaches like Lean or Six Sigma. A design research methodology, combining literature review and interviews, informed the creation of a Kaizen-based tool structured around a Work Breakdown Structure (WBS). The tool provides 32 project blocks linked to six critical dimensions, Method, Planning, Production, Cost, Qualifications, and Opportunity & Risk Management, offering users structured, accessible knowledge, checklists, and lessons learned. Designed for continuous updates, the tool integrates with existing workflows and promotes user-driven improvement. The study concludes that this approach enhances consistency, knowledge retention, and risk management, with applicability both to DEME and to other project-driven industries.
The hurricane is coming, should I stay or should I go?
An Agent-Based Analysis of Hurricane Evacuation decision-making behaviour
This master’s thesis investigates how policy interventions affect the speed and equity of electric vehicle (EV) adoption in the Netherlands, using an agent-based model (ABM) grounded in behavioural decision-making. The model simulates household-level adoption decisions between 2022 and 2035, drawing on the CODEC framework to reflect attention, enabling conditions, and intention formation. Socio-demographic diversity is represented through spatially clustered household archetypes based on income, education, and infrastructure access. The model is calibrated to reflect Dutch conditions and includes an explicit application to The Hague.
The study explores how different combinations of government interventions, including targeted subsidies, infrastructure investment, awareness campaigns, and zero-emission zones, an shape adoption trajectories across neighbourhoods. A key focus is the trade-off between accelerating overall EV uptake and ensuring an equitable transition across socio-economic contexts. In addition to literature-based policy scenarios, an exploratory modelling approach was used to generate and test a wide range of policy timing combinations under uncertainty.
The findings show that while comprehensive strategies (e.g. combining subsidies, marketing, and zero-emission zone regulation) perform best overall, they deliver only modest gains over simpler, well-timed interventions. Improvements of around 5 percentage points in EV share and moderate reductions in inequality are possible, but come with distinct implementation demands. Simpler strategies, such as infrastructure and marketing alone or early subsidies with infrastructure, often achieve comparable outcomes with less complexity.
Overall, the results highlight the importance of behavioural diversity, timing, and adaptability in policy design. A just and accelerated EV transition is feasible, but not automatic, and requires deliberate, strategically layered interventions. Achieving this requires planning further ahead and having adaptive responses ready for an uncertain future. This thesis contributes to the literature on sustainable mobility transitions by integrating behavioural realism, spatial equity, and exploratory policy design into a unified simulation framework.
...
The study explores how different combinations of government interventions, including targeted subsidies, infrastructure investment, awareness campaigns, and zero-emission zones, an shape adoption trajectories across neighbourhoods. A key focus is the trade-off between accelerating overall EV uptake and ensuring an equitable transition across socio-economic contexts. In addition to literature-based policy scenarios, an exploratory modelling approach was used to generate and test a wide range of policy timing combinations under uncertainty.
The findings show that while comprehensive strategies (e.g. combining subsidies, marketing, and zero-emission zone regulation) perform best overall, they deliver only modest gains over simpler, well-timed interventions. Improvements of around 5 percentage points in EV share and moderate reductions in inequality are possible, but come with distinct implementation demands. Simpler strategies, such as infrastructure and marketing alone or early subsidies with infrastructure, often achieve comparable outcomes with less complexity.
Overall, the results highlight the importance of behavioural diversity, timing, and adaptability in policy design. A just and accelerated EV transition is feasible, but not automatic, and requires deliberate, strategically layered interventions. Achieving this requires planning further ahead and having adaptive responses ready for an uncertain future. This thesis contributes to the literature on sustainable mobility transitions by integrating behavioural realism, spatial equity, and exploratory policy design into a unified simulation framework.
...
This master’s thesis investigates how policy interventions affect the speed and equity of electric vehicle (EV) adoption in the Netherlands, using an agent-based model (ABM) grounded in behavioural decision-making. The model simulates household-level adoption decisions between 2022 and 2035, drawing on the CODEC framework to reflect attention, enabling conditions, and intention formation. Socio-demographic diversity is represented through spatially clustered household archetypes based on income, education, and infrastructure access. The model is calibrated to reflect Dutch conditions and includes an explicit application to The Hague.
The study explores how different combinations of government interventions, including targeted subsidies, infrastructure investment, awareness campaigns, and zero-emission zones, an shape adoption trajectories across neighbourhoods. A key focus is the trade-off between accelerating overall EV uptake and ensuring an equitable transition across socio-economic contexts. In addition to literature-based policy scenarios, an exploratory modelling approach was used to generate and test a wide range of policy timing combinations under uncertainty.
The findings show that while comprehensive strategies (e.g. combining subsidies, marketing, and zero-emission zone regulation) perform best overall, they deliver only modest gains over simpler, well-timed interventions. Improvements of around 5 percentage points in EV share and moderate reductions in inequality are possible, but come with distinct implementation demands. Simpler strategies, such as infrastructure and marketing alone or early subsidies with infrastructure, often achieve comparable outcomes with less complexity.
Overall, the results highlight the importance of behavioural diversity, timing, and adaptability in policy design. A just and accelerated EV transition is feasible, but not automatic, and requires deliberate, strategically layered interventions. Achieving this requires planning further ahead and having adaptive responses ready for an uncertain future. This thesis contributes to the literature on sustainable mobility transitions by integrating behavioural realism, spatial equity, and exploratory policy design into a unified simulation framework.
The study explores how different combinations of government interventions, including targeted subsidies, infrastructure investment, awareness campaigns, and zero-emission zones, an shape adoption trajectories across neighbourhoods. A key focus is the trade-off between accelerating overall EV uptake and ensuring an equitable transition across socio-economic contexts. In addition to literature-based policy scenarios, an exploratory modelling approach was used to generate and test a wide range of policy timing combinations under uncertainty.
The findings show that while comprehensive strategies (e.g. combining subsidies, marketing, and zero-emission zone regulation) perform best overall, they deliver only modest gains over simpler, well-timed interventions. Improvements of around 5 percentage points in EV share and moderate reductions in inequality are possible, but come with distinct implementation demands. Simpler strategies, such as infrastructure and marketing alone or early subsidies with infrastructure, often achieve comparable outcomes with less complexity.
Overall, the results highlight the importance of behavioural diversity, timing, and adaptability in policy design. A just and accelerated EV transition is feasible, but not automatic, and requires deliberate, strategically layered interventions. Achieving this requires planning further ahead and having adaptive responses ready for an uncertain future. This thesis contributes to the literature on sustainable mobility transitions by integrating behavioural realism, spatial equity, and exploratory policy design into a unified simulation framework.
Simulating the delta
Finding the best modeling approach for simulating disaggregated impacts during salinity intrusion in the Vietnamese Mekong Delta
This thesis studied which modeling approach is most suitable to simulate the human behavior of inhabitants in river deltas during environmental changes. The Vietnamese Mekong Delta (VMD) was chosen as the test case. The salinity levels are rising in river deltas, and it is studied how the impacts on local populations can be modeled. System Dynamics (SD), Discrete-Event Simulation (DES), and Agent-Based Modeling (ABM) were compared. There were too few advantages of DES compared to SD and ABM for this case, and therefore, only models were created in ABM and SD.
The ABM offers several advantages, among others, the ability to simulate individual human behavior and emergent behavior. However, there is a risk of overfitting with the current level of data, and the model is more complex to understand.
The SD model runs fast, and the stock-flow structure provides a clear overview of the system. However, the aggregation level of SD also has limitations: individual behavior cannot be modeled.
Due to the strengths and limitations of both techniques, the results of the two models differ considerably. Nevertheless, the ABM model provides a more realistic representation, and it is therefore recommended to the employees at Deltares to develop a model in ABM. This is one of the few studies that developed both an ABM and an SD model using the exact same variables, and it is the first one related to river deltas and farmers. Furthermore, it can be seen as a stepping stone for Deltares to continue its research towards developing a socioeconomic model to simulate the impacts of inhabitants in the VMD. ...
The ABM offers several advantages, among others, the ability to simulate individual human behavior and emergent behavior. However, there is a risk of overfitting with the current level of data, and the model is more complex to understand.
The SD model runs fast, and the stock-flow structure provides a clear overview of the system. However, the aggregation level of SD also has limitations: individual behavior cannot be modeled.
Due to the strengths and limitations of both techniques, the results of the two models differ considerably. Nevertheless, the ABM model provides a more realistic representation, and it is therefore recommended to the employees at Deltares to develop a model in ABM. This is one of the few studies that developed both an ABM and an SD model using the exact same variables, and it is the first one related to river deltas and farmers. Furthermore, it can be seen as a stepping stone for Deltares to continue its research towards developing a socioeconomic model to simulate the impacts of inhabitants in the VMD. ...
This thesis studied which modeling approach is most suitable to simulate the human behavior of inhabitants in river deltas during environmental changes. The Vietnamese Mekong Delta (VMD) was chosen as the test case. The salinity levels are rising in river deltas, and it is studied how the impacts on local populations can be modeled. System Dynamics (SD), Discrete-Event Simulation (DES), and Agent-Based Modeling (ABM) were compared. There were too few advantages of DES compared to SD and ABM for this case, and therefore, only models were created in ABM and SD.
The ABM offers several advantages, among others, the ability to simulate individual human behavior and emergent behavior. However, there is a risk of overfitting with the current level of data, and the model is more complex to understand.
The SD model runs fast, and the stock-flow structure provides a clear overview of the system. However, the aggregation level of SD also has limitations: individual behavior cannot be modeled.
Due to the strengths and limitations of both techniques, the results of the two models differ considerably. Nevertheless, the ABM model provides a more realistic representation, and it is therefore recommended to the employees at Deltares to develop a model in ABM. This is one of the few studies that developed both an ABM and an SD model using the exact same variables, and it is the first one related to river deltas and farmers. Furthermore, it can be seen as a stepping stone for Deltares to continue its research towards developing a socioeconomic model to simulate the impacts of inhabitants in the VMD.
The ABM offers several advantages, among others, the ability to simulate individual human behavior and emergent behavior. However, there is a risk of overfitting with the current level of data, and the model is more complex to understand.
The SD model runs fast, and the stock-flow structure provides a clear overview of the system. However, the aggregation level of SD also has limitations: individual behavior cannot be modeled.
Due to the strengths and limitations of both techniques, the results of the two models differ considerably. Nevertheless, the ABM model provides a more realistic representation, and it is therefore recommended to the employees at Deltares to develop a model in ABM. This is one of the few studies that developed both an ABM and an SD model using the exact same variables, and it is the first one related to river deltas and farmers. Furthermore, it can be seen as a stepping stone for Deltares to continue its research towards developing a socioeconomic model to simulate the impacts of inhabitants in the VMD.
The emergence of social media and recommendation systems has profoundly transformed user interactions with digital content, bringing in both opportunities and ethical challenges. This thesis scrutinizes the online manipulation exerted by RS, which, while enhancing engagement and profitability, can guide user behavior subtly and without their awareness. This definition is based on covert influence, a particular account of manipulation.
A critical examination of existing literature reveals a significant gap: while the conceptual framework for online manipulation is well-discussed, empirical studies providing concrete evidence are scant. This research addresses this deficiency by employing an agent-based model to simulate interactions between users and recommendation systems, aiming to systematically analyze and quantify the effects of covert manipulation on user preferences.
This study contributes to the field by operationalizing the concept of manipulation within a controlled simulation of a book recommendation system, providing a clearer understanding of its mechanisms and effects. This approach not only offers insights into the ethical implications of RS but also aligns with current legislative movements, such as the European Union's Artificial Intelligence Act, aimed at regulating and mitigating harmful manipulative practices by intelligent systems. The findings are intended to guide the design and regulation of RS to ensure they serve the user's interests without compromising ethical standards.
The results show that book recommendation systems can modify user preferences by 9.79\% when prioritizing items covertly, while awareness of the intentions and social influence can diminish the effect of the manipulative algorithm's intention to 4.01\% and 3.64\%. When compared to the 2.58\% change in the case of a non-prioritized RS, the values provide a measurable estimation of the difference between manipulative and non-manipulative book RS for the change of user preferences after interacting with it for some time. ...
A critical examination of existing literature reveals a significant gap: while the conceptual framework for online manipulation is well-discussed, empirical studies providing concrete evidence are scant. This research addresses this deficiency by employing an agent-based model to simulate interactions between users and recommendation systems, aiming to systematically analyze and quantify the effects of covert manipulation on user preferences.
This study contributes to the field by operationalizing the concept of manipulation within a controlled simulation of a book recommendation system, providing a clearer understanding of its mechanisms and effects. This approach not only offers insights into the ethical implications of RS but also aligns with current legislative movements, such as the European Union's Artificial Intelligence Act, aimed at regulating and mitigating harmful manipulative practices by intelligent systems. The findings are intended to guide the design and regulation of RS to ensure they serve the user's interests without compromising ethical standards.
The results show that book recommendation systems can modify user preferences by 9.79\% when prioritizing items covertly, while awareness of the intentions and social influence can diminish the effect of the manipulative algorithm's intention to 4.01\% and 3.64\%. When compared to the 2.58\% change in the case of a non-prioritized RS, the values provide a measurable estimation of the difference between manipulative and non-manipulative book RS for the change of user preferences after interacting with it for some time. ...
The emergence of social media and recommendation systems has profoundly transformed user interactions with digital content, bringing in both opportunities and ethical challenges. This thesis scrutinizes the online manipulation exerted by RS, which, while enhancing engagement and profitability, can guide user behavior subtly and without their awareness. This definition is based on covert influence, a particular account of manipulation.
A critical examination of existing literature reveals a significant gap: while the conceptual framework for online manipulation is well-discussed, empirical studies providing concrete evidence are scant. This research addresses this deficiency by employing an agent-based model to simulate interactions between users and recommendation systems, aiming to systematically analyze and quantify the effects of covert manipulation on user preferences.
This study contributes to the field by operationalizing the concept of manipulation within a controlled simulation of a book recommendation system, providing a clearer understanding of its mechanisms and effects. This approach not only offers insights into the ethical implications of RS but also aligns with current legislative movements, such as the European Union's Artificial Intelligence Act, aimed at regulating and mitigating harmful manipulative practices by intelligent systems. The findings are intended to guide the design and regulation of RS to ensure they serve the user's interests without compromising ethical standards.
The results show that book recommendation systems can modify user preferences by 9.79\% when prioritizing items covertly, while awareness of the intentions and social influence can diminish the effect of the manipulative algorithm's intention to 4.01\% and 3.64\%. When compared to the 2.58\% change in the case of a non-prioritized RS, the values provide a measurable estimation of the difference between manipulative and non-manipulative book RS for the change of user preferences after interacting with it for some time.
A critical examination of existing literature reveals a significant gap: while the conceptual framework for online manipulation is well-discussed, empirical studies providing concrete evidence are scant. This research addresses this deficiency by employing an agent-based model to simulate interactions between users and recommendation systems, aiming to systematically analyze and quantify the effects of covert manipulation on user preferences.
This study contributes to the field by operationalizing the concept of manipulation within a controlled simulation of a book recommendation system, providing a clearer understanding of its mechanisms and effects. This approach not only offers insights into the ethical implications of RS but also aligns with current legislative movements, such as the European Union's Artificial Intelligence Act, aimed at regulating and mitigating harmful manipulative practices by intelligent systems. The findings are intended to guide the design and regulation of RS to ensure they serve the user's interests without compromising ethical standards.
The results show that book recommendation systems can modify user preferences by 9.79\% when prioritizing items covertly, while awareness of the intentions and social influence can diminish the effect of the manipulative algorithm's intention to 4.01\% and 3.64\%. When compared to the 2.58\% change in the case of a non-prioritized RS, the values provide a measurable estimation of the difference between manipulative and non-manipulative book RS for the change of user preferences after interacting with it for some time.
Optimising fugitive interception
A comparative study into the added value of including more realistic traffic conditions in fugitive interception models
In the Netherlands, fewer than half of violent crimes lead to convictions. A key method for increasing convictions is red-handed arrests, which can be enhanced by developing a decision-support system for optimally positioning police units. Existing models often assume an empty city, using maximum road speeds for both fugitives and police, which ignores real-world traffic conditions.
This thesis aims to improve the realism of these models by incorporating traffic effects, such as congestion and delays from traffic lights, into the optimisation of police strategies. The research examines how these traffic conditions impact the escape and interception processes, with the goal of increasing the accuracy of interception strategies and reducing the number of unpunished violent crimes.
The research question addressed is: 'What is the added value of considering realistic traffic conditions in the optimal positioning of police units for fugitive interception?' To answer this, a discrete event simulation model was developed using insights from a literature review and interviews. Simulation modelling was chosen to test complex scenarios without the biases and costs of real-world experiments.
The literature review identified that traffic includes static, semi-static, and dynamic components, such as traffic lights, open bridges, and congestion. It also revealed factors affecting criminal behaviour and route choices under stress, supplemented by interviews with stressed parcel delivery drivers. This knowledge, combined with understanding police behaviour during interceptions, informed the development of the simulation model.
The results showed that incorporating traffic conditions into the simulation model increases the probability of interception. Specifically, when fugitives face delays before reaching highways, the likelihood of interception improves because police units, moving faster due to their priority status, benefit more from traffic delays.
Optimisation of police positions was tested with and without traffic delays, and it was found that positions optimised with realistic traffic conditions were more robust. This was particularly evident in city centre scenarios but not in port dock areas, likely due to the high interception rates in the docks which made traffic impacts harder to assess.
In conclusion, models excluding traffic conditions are less effective in intercepting escape routes compared to those incorporating traffic. Therefore, integrating traffic into optimisation models is crucial for maximising interception probabilities. To mitigate the negative effects of omitting traffic, deploying additional police units is recommended.
The study also found that the impact of traffic on interception timing is more significant than accounting for the suspect's mental state. Traffic affects the timing of interceptions: accurate traffic estimates provide more time for police, while incorrect ones reduce it, affecting interception success. Future research should explore the effects of dynamic traffic conditions, such as varying green times and more differentiated fugitive and police behaviours, to optimise model accuracy while managing computational demands. Recommendations for police include integrating realistic traffic conditions and prioritising traffic lights over congestion, as well as experimenting with different escape speeds to maintain effectiveness in varying scenarios.
...
This thesis aims to improve the realism of these models by incorporating traffic effects, such as congestion and delays from traffic lights, into the optimisation of police strategies. The research examines how these traffic conditions impact the escape and interception processes, with the goal of increasing the accuracy of interception strategies and reducing the number of unpunished violent crimes.
The research question addressed is: 'What is the added value of considering realistic traffic conditions in the optimal positioning of police units for fugitive interception?' To answer this, a discrete event simulation model was developed using insights from a literature review and interviews. Simulation modelling was chosen to test complex scenarios without the biases and costs of real-world experiments.
The literature review identified that traffic includes static, semi-static, and dynamic components, such as traffic lights, open bridges, and congestion. It also revealed factors affecting criminal behaviour and route choices under stress, supplemented by interviews with stressed parcel delivery drivers. This knowledge, combined with understanding police behaviour during interceptions, informed the development of the simulation model.
The results showed that incorporating traffic conditions into the simulation model increases the probability of interception. Specifically, when fugitives face delays before reaching highways, the likelihood of interception improves because police units, moving faster due to their priority status, benefit more from traffic delays.
Optimisation of police positions was tested with and without traffic delays, and it was found that positions optimised with realistic traffic conditions were more robust. This was particularly evident in city centre scenarios but not in port dock areas, likely due to the high interception rates in the docks which made traffic impacts harder to assess.
In conclusion, models excluding traffic conditions are less effective in intercepting escape routes compared to those incorporating traffic. Therefore, integrating traffic into optimisation models is crucial for maximising interception probabilities. To mitigate the negative effects of omitting traffic, deploying additional police units is recommended.
The study also found that the impact of traffic on interception timing is more significant than accounting for the suspect's mental state. Traffic affects the timing of interceptions: accurate traffic estimates provide more time for police, while incorrect ones reduce it, affecting interception success. Future research should explore the effects of dynamic traffic conditions, such as varying green times and more differentiated fugitive and police behaviours, to optimise model accuracy while managing computational demands. Recommendations for police include integrating realistic traffic conditions and prioritising traffic lights over congestion, as well as experimenting with different escape speeds to maintain effectiveness in varying scenarios.
...
In the Netherlands, fewer than half of violent crimes lead to convictions. A key method for increasing convictions is red-handed arrests, which can be enhanced by developing a decision-support system for optimally positioning police units. Existing models often assume an empty city, using maximum road speeds for both fugitives and police, which ignores real-world traffic conditions.
This thesis aims to improve the realism of these models by incorporating traffic effects, such as congestion and delays from traffic lights, into the optimisation of police strategies. The research examines how these traffic conditions impact the escape and interception processes, with the goal of increasing the accuracy of interception strategies and reducing the number of unpunished violent crimes.
The research question addressed is: 'What is the added value of considering realistic traffic conditions in the optimal positioning of police units for fugitive interception?' To answer this, a discrete event simulation model was developed using insights from a literature review and interviews. Simulation modelling was chosen to test complex scenarios without the biases and costs of real-world experiments.
The literature review identified that traffic includes static, semi-static, and dynamic components, such as traffic lights, open bridges, and congestion. It also revealed factors affecting criminal behaviour and route choices under stress, supplemented by interviews with stressed parcel delivery drivers. This knowledge, combined with understanding police behaviour during interceptions, informed the development of the simulation model.
The results showed that incorporating traffic conditions into the simulation model increases the probability of interception. Specifically, when fugitives face delays before reaching highways, the likelihood of interception improves because police units, moving faster due to their priority status, benefit more from traffic delays.
Optimisation of police positions was tested with and without traffic delays, and it was found that positions optimised with realistic traffic conditions were more robust. This was particularly evident in city centre scenarios but not in port dock areas, likely due to the high interception rates in the docks which made traffic impacts harder to assess.
In conclusion, models excluding traffic conditions are less effective in intercepting escape routes compared to those incorporating traffic. Therefore, integrating traffic into optimisation models is crucial for maximising interception probabilities. To mitigate the negative effects of omitting traffic, deploying additional police units is recommended.
The study also found that the impact of traffic on interception timing is more significant than accounting for the suspect's mental state. Traffic affects the timing of interceptions: accurate traffic estimates provide more time for police, while incorrect ones reduce it, affecting interception success. Future research should explore the effects of dynamic traffic conditions, such as varying green times and more differentiated fugitive and police behaviours, to optimise model accuracy while managing computational demands. Recommendations for police include integrating realistic traffic conditions and prioritising traffic lights over congestion, as well as experimenting with different escape speeds to maintain effectiveness in varying scenarios.
This thesis aims to improve the realism of these models by incorporating traffic effects, such as congestion and delays from traffic lights, into the optimisation of police strategies. The research examines how these traffic conditions impact the escape and interception processes, with the goal of increasing the accuracy of interception strategies and reducing the number of unpunished violent crimes.
The research question addressed is: 'What is the added value of considering realistic traffic conditions in the optimal positioning of police units for fugitive interception?' To answer this, a discrete event simulation model was developed using insights from a literature review and interviews. Simulation modelling was chosen to test complex scenarios without the biases and costs of real-world experiments.
The literature review identified that traffic includes static, semi-static, and dynamic components, such as traffic lights, open bridges, and congestion. It also revealed factors affecting criminal behaviour and route choices under stress, supplemented by interviews with stressed parcel delivery drivers. This knowledge, combined with understanding police behaviour during interceptions, informed the development of the simulation model.
The results showed that incorporating traffic conditions into the simulation model increases the probability of interception. Specifically, when fugitives face delays before reaching highways, the likelihood of interception improves because police units, moving faster due to their priority status, benefit more from traffic delays.
Optimisation of police positions was tested with and without traffic delays, and it was found that positions optimised with realistic traffic conditions were more robust. This was particularly evident in city centre scenarios but not in port dock areas, likely due to the high interception rates in the docks which made traffic impacts harder to assess.
In conclusion, models excluding traffic conditions are less effective in intercepting escape routes compared to those incorporating traffic. Therefore, integrating traffic into optimisation models is crucial for maximising interception probabilities. To mitigate the negative effects of omitting traffic, deploying additional police units is recommended.
The study also found that the impact of traffic on interception timing is more significant than accounting for the suspect's mental state. Traffic affects the timing of interceptions: accurate traffic estimates provide more time for police, while incorrect ones reduce it, affecting interception success. Future research should explore the effects of dynamic traffic conditions, such as varying green times and more differentiated fugitive and police behaviours, to optimise model accuracy while managing computational demands. Recommendations for police include integrating realistic traffic conditions and prioritising traffic lights over congestion, as well as experimenting with different escape speeds to maintain effectiveness in varying scenarios.
Exploring potential debris removal strategies during the earthquake response phase
An agent-based modeling approach to improve the post-earthquake emergency response in urban areas
Earthquakes are among the most devastating natural disasters, causing widespread damage, particularly in urban areas. A critical but often overlooked component of post-earthquake emergency response is the role of debris removal in the initial response phase. While debris removal is typically treated as part of long-term recovery, its immediate integration could enhance access to casualties, enabling faster medical care. This study investigates the impact of different debris removal strategies on the effectiveness of emergency response in urban settings using an Agent-Based Model (ABM) developed on the NetLogo platform. The model simulates a virtual city, "Quakecity," with realistic urban features, including road networks, hospitals, building damage, and injured resident distribution.
Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.
Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase. ...
Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.
Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase. ...
Earthquakes are among the most devastating natural disasters, causing widespread damage, particularly in urban areas. A critical but often overlooked component of post-earthquake emergency response is the role of debris removal in the initial response phase. While debris removal is typically treated as part of long-term recovery, its immediate integration could enhance access to casualties, enabling faster medical care. This study investigates the impact of different debris removal strategies on the effectiveness of emergency response in urban settings using an Agent-Based Model (ABM) developed on the NetLogo platform. The model simulates a virtual city, "Quakecity," with realistic urban features, including road networks, hospitals, building damage, and injured resident distribution.
Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.
Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase.
Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.
Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase.
In crowded pedestrian environments, individuals often resort to rotating their bodies as a strategy to avoid collisions. Surprisingly, this rotational behavior, despite its significant implications for crowd capacity, has received relatively little attention in research. This study seeks to fill this gap by diving into the complicated world of pedestrian rotation behavior, particularly in the context of high-density bidirectional and crossing flows.
Drawing upon data gathered from the CrowdLimits experiments, we start the exploration of how various factors impact the rotation behavior of pedestrians. Our investigation covers crowd density, the fundamental movement scenarios (bidirectional and crossing flows), flow ratio, and the influence of disturbances within the crowd under different scenarios.
Our key findings reveal that all these factors play a role in shaping the frequency of rotations within a crowd. However, the extent and precise conditions under which these factors influence this subject demand further in-depth research and exploration.
In essence, this study addresses the fundamental question: How does shoulder rotation behavior vary concerning macroscopic crowd characteristics, including crowd density, flow ratio, and movement patterns like bidirectional and crossing flows? Through this research, we hope to highlight the complex interplay between these factors and the rotational strategies operated by pedestrians, ultimately enhancing our understanding of crowd dynamics. ...
Drawing upon data gathered from the CrowdLimits experiments, we start the exploration of how various factors impact the rotation behavior of pedestrians. Our investigation covers crowd density, the fundamental movement scenarios (bidirectional and crossing flows), flow ratio, and the influence of disturbances within the crowd under different scenarios.
Our key findings reveal that all these factors play a role in shaping the frequency of rotations within a crowd. However, the extent and precise conditions under which these factors influence this subject demand further in-depth research and exploration.
In essence, this study addresses the fundamental question: How does shoulder rotation behavior vary concerning macroscopic crowd characteristics, including crowd density, flow ratio, and movement patterns like bidirectional and crossing flows? Through this research, we hope to highlight the complex interplay between these factors and the rotational strategies operated by pedestrians, ultimately enhancing our understanding of crowd dynamics. ...
In crowded pedestrian environments, individuals often resort to rotating their bodies as a strategy to avoid collisions. Surprisingly, this rotational behavior, despite its significant implications for crowd capacity, has received relatively little attention in research. This study seeks to fill this gap by diving into the complicated world of pedestrian rotation behavior, particularly in the context of high-density bidirectional and crossing flows.
Drawing upon data gathered from the CrowdLimits experiments, we start the exploration of how various factors impact the rotation behavior of pedestrians. Our investigation covers crowd density, the fundamental movement scenarios (bidirectional and crossing flows), flow ratio, and the influence of disturbances within the crowd under different scenarios.
Our key findings reveal that all these factors play a role in shaping the frequency of rotations within a crowd. However, the extent and precise conditions under which these factors influence this subject demand further in-depth research and exploration.
In essence, this study addresses the fundamental question: How does shoulder rotation behavior vary concerning macroscopic crowd characteristics, including crowd density, flow ratio, and movement patterns like bidirectional and crossing flows? Through this research, we hope to highlight the complex interplay between these factors and the rotational strategies operated by pedestrians, ultimately enhancing our understanding of crowd dynamics.
Drawing upon data gathered from the CrowdLimits experiments, we start the exploration of how various factors impact the rotation behavior of pedestrians. Our investigation covers crowd density, the fundamental movement scenarios (bidirectional and crossing flows), flow ratio, and the influence of disturbances within the crowd under different scenarios.
Our key findings reveal that all these factors play a role in shaping the frequency of rotations within a crowd. However, the extent and precise conditions under which these factors influence this subject demand further in-depth research and exploration.
In essence, this study addresses the fundamental question: How does shoulder rotation behavior vary concerning macroscopic crowd characteristics, including crowd density, flow ratio, and movement patterns like bidirectional and crossing flows? Through this research, we hope to highlight the complex interplay between these factors and the rotational strategies operated by pedestrians, ultimately enhancing our understanding of crowd dynamics.
Talk That Talk
The Evaluation and Redesign of a Persuasive Game for Tackling Sexual Violence Among Students in Dutch Universities
Background. Dutch universities struggle to find effective evidence-based intervention tools to reduce the high prevalence of sexual violence among their students. Reducing sexual violence means a safer student climate and thus better inclusion of women and more diversity in higher education. Interesting approaches to consider for cultural change are bystander intervention and an already existing co-designed serious game. This game aims to encourage a dialogue among students but has thus far not been evaluated on its effectiveness. Therefore, the goal of this study was to evaluate how a serious game can promote an intergroup dialogue between female and male students in Dutch universities to encourage bystander intervention in situations of sexually transgressive behaviour. Methods. Literature research and brainstorm sessions with female and male students from Delft University of Technology (TU Delft) were conducted to gain a better understanding of sexual violence among students. These methods were used as part of participatory game design research. Moreover, a quasi-controlled experimental trial was conducted with 64 TU Delft students from different nationalities and study backgrounds to evaluate the serious game on bystander attitudes, sexual violence myth acceptance, the willingness to intervene, and the effectiveness of the intergroup dialogue. Results. The results showed that the serious game session has a significant positive effect on bystander attitudes (i.e. bystander awareness and responsibility) and the willingness to intervene. No significant difference was found in sexual violence myth acceptance. Additionally, the game proved to promote an effective intergroup dialogue between the participants. Conclusion. Serious gaming proved to be effective in encouraging ethical bystander behaviour by promoting an intergroup dialogue. Therefore, universities are recommended to use serious games as an intervention tool to contribute to cultural change. Future studies should include students from other universities and focus on minimising selection bias to research whether serious games can also influence sexual violence myth acceptance.
...
Background. Dutch universities struggle to find effective evidence-based intervention tools to reduce the high prevalence of sexual violence among their students. Reducing sexual violence means a safer student climate and thus better inclusion of women and more diversity in higher education. Interesting approaches to consider for cultural change are bystander intervention and an already existing co-designed serious game. This game aims to encourage a dialogue among students but has thus far not been evaluated on its effectiveness. Therefore, the goal of this study was to evaluate how a serious game can promote an intergroup dialogue between female and male students in Dutch universities to encourage bystander intervention in situations of sexually transgressive behaviour. Methods. Literature research and brainstorm sessions with female and male students from Delft University of Technology (TU Delft) were conducted to gain a better understanding of sexual violence among students. These methods were used as part of participatory game design research. Moreover, a quasi-controlled experimental trial was conducted with 64 TU Delft students from different nationalities and study backgrounds to evaluate the serious game on bystander attitudes, sexual violence myth acceptance, the willingness to intervene, and the effectiveness of the intergroup dialogue. Results. The results showed that the serious game session has a significant positive effect on bystander attitudes (i.e. bystander awareness and responsibility) and the willingness to intervene. No significant difference was found in sexual violence myth acceptance. Additionally, the game proved to promote an effective intergroup dialogue between the participants. Conclusion. Serious gaming proved to be effective in encouraging ethical bystander behaviour by promoting an intergroup dialogue. Therefore, universities are recommended to use serious games as an intervention tool to contribute to cultural change. Future studies should include students from other universities and focus on minimising selection bias to research whether serious games can also influence sexual violence myth acceptance.
Prospective Criminal Escape Routes
An Exploration of Fugitive Escape Route Decision-Making using a Dual-Process Approach
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.
...
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.
Organizational Impact on Project Team Efficiency
How simulations can help create a productive and efficient environment
This research tries to quantitatively identify the relation between team composition, organization structure, communication and team efficiency. The study takes a model-based approach to reach its objective. This research stresses the utility of agent-based modeling as a tool to first replicate the environment of an on-going project in India and then systematically introducing organizational changes into the project team to quantify various impacts.
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This research tries to quantitatively identify the relation between team composition, organization structure, communication and team efficiency. The study takes a model-based approach to reach its objective. This research stresses the utility of agent-based modeling as a tool to first replicate the environment of an on-going project in India and then systematically introducing organizational changes into the project team to quantify various impacts.
The SARS-CoV-2 virus, more commonly known as the coronavirus, is arguably responsible for the biggest global crisis in recent history. In an attempt to effectively deal with this crisis, politicians around the globe have been using simulation models in their policy development. There are multiple types of modelling techniques used for epidemiological transmission modelling (Alsharhan, 2021; Anastassopoulou, Russo, Tsakris, & Siettos, 2020). Each different method used has different advantages and disadvantages related to them. One of the used simulation methods for transmission modelling is the Agent-Based Modelling (ABM) technique. The technique is a bottom-up approach, meaning it focusses on the behaviour of individuals to gather knowledge about the resulting emergent overall system behaviour. This technique specifically excels at developing early epidemic growth profiles, however, to gain this feature it needs to process a large amount of data. This data is not always readily available. Even if it is available, the amount of data that needs to be processed combined with the focus on individual behaviour, requires a lot of computational power for simulations making a robust uncertainty analysis very time consuming. An alternative technique is equation-based modelling, with System Dynamics (SD) being an instance of it with additional benefits regarding communication. This is more of a top-down approach, focussing on the overall mechanics of the systems instead of the behaviour of the individuals in the system. Working with aggregate values for most if not all variables to create increased understanding in the system behaviour under different circumstances. Because this technique works with these aggregate values, there is a less of a computational strain when simulating the model. However, this comes at the costs of being able to generate accurate early epidemic growth profiles, as this technique is not capable of fully incorporating key concepts for transmission models, such as heterogeneity of agents, spatial effects, and stochasticity. These two aforementioned modelling techniques, ABM and SD, have characteristic that lean themselves well to cover for each other’s weaknesses. Utilising a technique that dynamically switches between the two modelling methods depending on the state of the model, could incorporate the strengths both models have, this is called dynamic coarse-graining. These strengths are the accuracy and incorporation of key concepts for the ABM side of the model, combined with the simulation speed of the SD side of the model. This dynamic coarse-graining is still in its infancy, as research related to it is very limited. The goal of this research is to examine what dynamically coarse-graining an ABM model to a SD model would mean for the simulation speed of the model, and whether the results will stay consistent with the more accurate ABM method. The current research that has been performed on this topic has been on behaviourally stable models (Bobashev et al., 2007; Gray and Wotherspoon, 2012), meaning there are no changes to the behavioural mode of the model during the dynamic coarse-graining process. In this research two epidemiological transmission models are analysed. The first model will be a relatively simple model that is similarly incapable of exhibiting behavioural changes during the switching process. This Simple model is used as a test-case, for a more extensive SARS-CoV-2 specific model. This Extensive model will include the option of behavioural change during the switching process itself, meaning both the dynamic switching condition and agents’ behaviour is dependent on disease state. As an added benefit, by comparing the results of the two different models, the gained insights are more generalisable...
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The SARS-CoV-2 virus, more commonly known as the coronavirus, is arguably responsible for the biggest global crisis in recent history. In an attempt to effectively deal with this crisis, politicians around the globe have been using simulation models in their policy development. There are multiple types of modelling techniques used for epidemiological transmission modelling (Alsharhan, 2021; Anastassopoulou, Russo, Tsakris, & Siettos, 2020). Each different method used has different advantages and disadvantages related to them. One of the used simulation methods for transmission modelling is the Agent-Based Modelling (ABM) technique. The technique is a bottom-up approach, meaning it focusses on the behaviour of individuals to gather knowledge about the resulting emergent overall system behaviour. This technique specifically excels at developing early epidemic growth profiles, however, to gain this feature it needs to process a large amount of data. This data is not always readily available. Even if it is available, the amount of data that needs to be processed combined with the focus on individual behaviour, requires a lot of computational power for simulations making a robust uncertainty analysis very time consuming. An alternative technique is equation-based modelling, with System Dynamics (SD) being an instance of it with additional benefits regarding communication. This is more of a top-down approach, focussing on the overall mechanics of the systems instead of the behaviour of the individuals in the system. Working with aggregate values for most if not all variables to create increased understanding in the system behaviour under different circumstances. Because this technique works with these aggregate values, there is a less of a computational strain when simulating the model. However, this comes at the costs of being able to generate accurate early epidemic growth profiles, as this technique is not capable of fully incorporating key concepts for transmission models, such as heterogeneity of agents, spatial effects, and stochasticity. These two aforementioned modelling techniques, ABM and SD, have characteristic that lean themselves well to cover for each other’s weaknesses. Utilising a technique that dynamically switches between the two modelling methods depending on the state of the model, could incorporate the strengths both models have, this is called dynamic coarse-graining. These strengths are the accuracy and incorporation of key concepts for the ABM side of the model, combined with the simulation speed of the SD side of the model. This dynamic coarse-graining is still in its infancy, as research related to it is very limited. The goal of this research is to examine what dynamically coarse-graining an ABM model to a SD model would mean for the simulation speed of the model, and whether the results will stay consistent with the more accurate ABM method. The current research that has been performed on this topic has been on behaviourally stable models (Bobashev et al., 2007; Gray and Wotherspoon, 2012), meaning there are no changes to the behavioural mode of the model during the dynamic coarse-graining process. In this research two epidemiological transmission models are analysed. The first model will be a relatively simple model that is similarly incapable of exhibiting behavioural changes during the switching process. This Simple model is used as a test-case, for a more extensive SARS-CoV-2 specific model. This Extensive model will include the option of behavioural change during the switching process itself, meaning both the dynamic switching condition and agents’ behaviour is dependent on disease state. As an added benefit, by comparing the results of the two different models, the gained insights are more generalisable...
Participatory Serious Game Design for Societal Intervention
Exploring the Effectiveness of Participative Game Design Processes Between Communities and Police in the United States as a means for Reduced Police Bias
Throughout history, complex societal problems have plagued societies with their ever-changing dynamic natures and sheer societal consequences. In efforts to address such complex problems, many actors have turned to participatory methods as a means to incorporate community level knowledge and produce community-oriented solutions while addressing societal concerns. Simultaneously, in the field of serious game design, a large body of research has studied the positive effects of using serious game play for societal intervention. However, fewer efforts have been allocated to exploring the effectiveness of participatory serious game design as a means for societal intervention. In other words, can participatory serious game design act as an effective method for societal intervention?
Such inquiry is the basis of this research, where the complex societal problem of biased US policing serves as the application of this study. A mixed methods approach to this research was implemented to explore how to develop interventions where serious game design concepts are to be created between US communities and police. Through a mixed methods approach involving quantitative elements of inferential and descriptive statistical analysis and qualitative elements of content analysis, sentiment analysis, and micro-interlocutor analysis, group brainstorming data, workshop observation data, and semi-structured group interview data was explored in an effort to understand if and how participatory serious game design can be leveraged effectively as a means to societal intervention in the context of US policing and beyond.
The outcomes of this research are two-fold. First, with regard to improving police-community relations through societal intervention, results from this research indicate that current relations between US police and BIPOC communities, specifically black communities, are in a dire state of mistrust. As such, any attempt at intervention between these two groups will likely be received with caution and scepticism. However, in demonstrating that intervention efforts are long-term oriented and not just “throw away” events, more willing engagement could be facilitated. In addition, this research has also demonstrated that police accountability in intervention efforts can exhibit to communities that such efforts to improve police-community relations are serious. In maintaining police accountability throughout interventions, communities may be more willing to welcome police efforts in engagement and relation building.
Second, in considering participatory game design as an intervention study, results from this study indicate that participatory game design has the potential to be an effective intervention method if it is implemented in a way that appropriately caters to the desired audience. In using participatory game design as a means to societal intervention, participant “buy-in” must be facilitated early on to ensure effective engagement. This study also alludes to the fact that participatory game design could be particularly effective as an intervention method when the topic of intervention is taboo, discomforting, or difficult to talk about, as the game-like nature of such intervention can provide an adequate amount of abstraction from reality that helps facilitate less anxiety-inducing dialogue. In a similar vein, the reality-abstracted game-like nature of participatory game design interventions could also be effective at fostering safe and inclusive spaces where all participants can feel able to engage and contribute to discussion, regardless of their backgrounds. Finally, with regard to using participatory game design as an intervention study, this research has exhibited that in ensuring a diversity of participant backgrounds and perspectives, echo chambers, polarity, and groupthink can be avoided in participatory game design-based interventions. Similarly, more meaningful intervention outcomes and creative problem solving has the potential to arise if a healthy amount of group conflict and pressure is managed appropriately within participatory game design interventions.
To the best of our knowledge, this research represents the first mixed methods study aimed at investigating the effectiveness of participative serious game design as a societal intervention method for biased US policing. Therefore, this study provides several potential scientific contributions to the fields of intervention science and serious game design, and it also has several potential implications in the context of society and public policy. ...
Such inquiry is the basis of this research, where the complex societal problem of biased US policing serves as the application of this study. A mixed methods approach to this research was implemented to explore how to develop interventions where serious game design concepts are to be created between US communities and police. Through a mixed methods approach involving quantitative elements of inferential and descriptive statistical analysis and qualitative elements of content analysis, sentiment analysis, and micro-interlocutor analysis, group brainstorming data, workshop observation data, and semi-structured group interview data was explored in an effort to understand if and how participatory serious game design can be leveraged effectively as a means to societal intervention in the context of US policing and beyond.
The outcomes of this research are two-fold. First, with regard to improving police-community relations through societal intervention, results from this research indicate that current relations between US police and BIPOC communities, specifically black communities, are in a dire state of mistrust. As such, any attempt at intervention between these two groups will likely be received with caution and scepticism. However, in demonstrating that intervention efforts are long-term oriented and not just “throw away” events, more willing engagement could be facilitated. In addition, this research has also demonstrated that police accountability in intervention efforts can exhibit to communities that such efforts to improve police-community relations are serious. In maintaining police accountability throughout interventions, communities may be more willing to welcome police efforts in engagement and relation building.
Second, in considering participatory game design as an intervention study, results from this study indicate that participatory game design has the potential to be an effective intervention method if it is implemented in a way that appropriately caters to the desired audience. In using participatory game design as a means to societal intervention, participant “buy-in” must be facilitated early on to ensure effective engagement. This study also alludes to the fact that participatory game design could be particularly effective as an intervention method when the topic of intervention is taboo, discomforting, or difficult to talk about, as the game-like nature of such intervention can provide an adequate amount of abstraction from reality that helps facilitate less anxiety-inducing dialogue. In a similar vein, the reality-abstracted game-like nature of participatory game design interventions could also be effective at fostering safe and inclusive spaces where all participants can feel able to engage and contribute to discussion, regardless of their backgrounds. Finally, with regard to using participatory game design as an intervention study, this research has exhibited that in ensuring a diversity of participant backgrounds and perspectives, echo chambers, polarity, and groupthink can be avoided in participatory game design-based interventions. Similarly, more meaningful intervention outcomes and creative problem solving has the potential to arise if a healthy amount of group conflict and pressure is managed appropriately within participatory game design interventions.
To the best of our knowledge, this research represents the first mixed methods study aimed at investigating the effectiveness of participative serious game design as a societal intervention method for biased US policing. Therefore, this study provides several potential scientific contributions to the fields of intervention science and serious game design, and it also has several potential implications in the context of society and public policy. ...
Throughout history, complex societal problems have plagued societies with their ever-changing dynamic natures and sheer societal consequences. In efforts to address such complex problems, many actors have turned to participatory methods as a means to incorporate community level knowledge and produce community-oriented solutions while addressing societal concerns. Simultaneously, in the field of serious game design, a large body of research has studied the positive effects of using serious game play for societal intervention. However, fewer efforts have been allocated to exploring the effectiveness of participatory serious game design as a means for societal intervention. In other words, can participatory serious game design act as an effective method for societal intervention?
Such inquiry is the basis of this research, where the complex societal problem of biased US policing serves as the application of this study. A mixed methods approach to this research was implemented to explore how to develop interventions where serious game design concepts are to be created between US communities and police. Through a mixed methods approach involving quantitative elements of inferential and descriptive statistical analysis and qualitative elements of content analysis, sentiment analysis, and micro-interlocutor analysis, group brainstorming data, workshop observation data, and semi-structured group interview data was explored in an effort to understand if and how participatory serious game design can be leveraged effectively as a means to societal intervention in the context of US policing and beyond.
The outcomes of this research are two-fold. First, with regard to improving police-community relations through societal intervention, results from this research indicate that current relations between US police and BIPOC communities, specifically black communities, are in a dire state of mistrust. As such, any attempt at intervention between these two groups will likely be received with caution and scepticism. However, in demonstrating that intervention efforts are long-term oriented and not just “throw away” events, more willing engagement could be facilitated. In addition, this research has also demonstrated that police accountability in intervention efforts can exhibit to communities that such efforts to improve police-community relations are serious. In maintaining police accountability throughout interventions, communities may be more willing to welcome police efforts in engagement and relation building.
Second, in considering participatory game design as an intervention study, results from this study indicate that participatory game design has the potential to be an effective intervention method if it is implemented in a way that appropriately caters to the desired audience. In using participatory game design as a means to societal intervention, participant “buy-in” must be facilitated early on to ensure effective engagement. This study also alludes to the fact that participatory game design could be particularly effective as an intervention method when the topic of intervention is taboo, discomforting, or difficult to talk about, as the game-like nature of such intervention can provide an adequate amount of abstraction from reality that helps facilitate less anxiety-inducing dialogue. In a similar vein, the reality-abstracted game-like nature of participatory game design interventions could also be effective at fostering safe and inclusive spaces where all participants can feel able to engage and contribute to discussion, regardless of their backgrounds. Finally, with regard to using participatory game design as an intervention study, this research has exhibited that in ensuring a diversity of participant backgrounds and perspectives, echo chambers, polarity, and groupthink can be avoided in participatory game design-based interventions. Similarly, more meaningful intervention outcomes and creative problem solving has the potential to arise if a healthy amount of group conflict and pressure is managed appropriately within participatory game design interventions.
To the best of our knowledge, this research represents the first mixed methods study aimed at investigating the effectiveness of participative serious game design as a societal intervention method for biased US policing. Therefore, this study provides several potential scientific contributions to the fields of intervention science and serious game design, and it also has several potential implications in the context of society and public policy.
Such inquiry is the basis of this research, where the complex societal problem of biased US policing serves as the application of this study. A mixed methods approach to this research was implemented to explore how to develop interventions where serious game design concepts are to be created between US communities and police. Through a mixed methods approach involving quantitative elements of inferential and descriptive statistical analysis and qualitative elements of content analysis, sentiment analysis, and micro-interlocutor analysis, group brainstorming data, workshop observation data, and semi-structured group interview data was explored in an effort to understand if and how participatory serious game design can be leveraged effectively as a means to societal intervention in the context of US policing and beyond.
The outcomes of this research are two-fold. First, with regard to improving police-community relations through societal intervention, results from this research indicate that current relations between US police and BIPOC communities, specifically black communities, are in a dire state of mistrust. As such, any attempt at intervention between these two groups will likely be received with caution and scepticism. However, in demonstrating that intervention efforts are long-term oriented and not just “throw away” events, more willing engagement could be facilitated. In addition, this research has also demonstrated that police accountability in intervention efforts can exhibit to communities that such efforts to improve police-community relations are serious. In maintaining police accountability throughout interventions, communities may be more willing to welcome police efforts in engagement and relation building.
Second, in considering participatory game design as an intervention study, results from this study indicate that participatory game design has the potential to be an effective intervention method if it is implemented in a way that appropriately caters to the desired audience. In using participatory game design as a means to societal intervention, participant “buy-in” must be facilitated early on to ensure effective engagement. This study also alludes to the fact that participatory game design could be particularly effective as an intervention method when the topic of intervention is taboo, discomforting, or difficult to talk about, as the game-like nature of such intervention can provide an adequate amount of abstraction from reality that helps facilitate less anxiety-inducing dialogue. In a similar vein, the reality-abstracted game-like nature of participatory game design interventions could also be effective at fostering safe and inclusive spaces where all participants can feel able to engage and contribute to discussion, regardless of their backgrounds. Finally, with regard to using participatory game design as an intervention study, this research has exhibited that in ensuring a diversity of participant backgrounds and perspectives, echo chambers, polarity, and groupthink can be avoided in participatory game design-based interventions. Similarly, more meaningful intervention outcomes and creative problem solving has the potential to arise if a healthy amount of group conflict and pressure is managed appropriately within participatory game design interventions.
To the best of our knowledge, this research represents the first mixed methods study aimed at investigating the effectiveness of participative serious game design as a societal intervention method for biased US policing. Therefore, this study provides several potential scientific contributions to the fields of intervention science and serious game design, and it also has several potential implications in the context of society and public policy.
Enterprise Modelling (EM) is the process of producing models, which in turn can be used to support understanding, analysis, (re)design, reasoning, control and learning about various aspects of an enterprise. Various EM techniques and languages exist, and are often supported by computational tools, in particular simulation. The goal of this thesis is to study the effects and advantages of applying constraint programming (CP) to EM. To the best of my knowledge, no previous study has explicitly combined EM and CP. On the topic of applying CP to EM, this thesis explains where it can be applied, as well as its requirements and advantages. Furthermore, it explains a possible approach where a neural network, trained on a simulation model that represents an enterprise model, is embedded into a constraint program. This approach is supported with experiments, that show typical business objectives can be embedded in a constraint program and find solutions to it in a multi-objective context. The main conclusion is that due to CP being a declarative programming technique, business constraints and goals can be effectively modelled into a constraint program, making the approach understandable and intuitive for business analysts to use. This thesis argues alternative approaches to apply CP to EM can also be realised. Some of these, as well as improvements over the proposed method, are also discussed.
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Enterprise Modelling (EM) is the process of producing models, which in turn can be used to support understanding, analysis, (re)design, reasoning, control and learning about various aspects of an enterprise. Various EM techniques and languages exist, and are often supported by computational tools, in particular simulation. The goal of this thesis is to study the effects and advantages of applying constraint programming (CP) to EM. To the best of my knowledge, no previous study has explicitly combined EM and CP. On the topic of applying CP to EM, this thesis explains where it can be applied, as well as its requirements and advantages. Furthermore, it explains a possible approach where a neural network, trained on a simulation model that represents an enterprise model, is embedded into a constraint program. This approach is supported with experiments, that show typical business objectives can be embedded in a constraint program and find solutions to it in a multi-objective context. The main conclusion is that due to CP being a declarative programming technique, business constraints and goals can be effectively modelled into a constraint program, making the approach understandable and intuitive for business analysts to use. This thesis argues alternative approaches to apply CP to EM can also be realised. Some of these, as well as improvements over the proposed method, are also discussed.