## 2 An Individual-Based Modelling Approach to Traffic Simulation  
The traffic system is characterised by multiple individual actors (drivers) and a street network made up of individual rules such as right of way and speed limits. Given the nature of this system's individual-level components, it is evident that these systems are perfectly poised to be studied using individual-based modelling methods. According to [26], individual-based modelling refers to simulation models that treat individual entities as unique and discrete components with at least one property, for example, age, height, position and these properties change during the life cycle of these entities. Therefore, in this study, vehicles can be thought of as individual heterogeneous entities with their own rules, while the urban street network is the environment in which these vehicle entities are observed from within. The aim is to test various interventions in this simplified world and collect observational data from these entities to assess the impact of these interventions. Agent-Based Modelling (ABM) is a tool that allows the study of emergent behaviour of a system by simulating the actions and interactions of a collection of autonomous agents. It is used in a wide variety of disciplines such as ecology [27], crime [28] and sociology [29]. Implementing simple rules for the agents can lead to the reproduction of complex phenomena observed in the real world. Like all models, an agent-based model simplifies the isolated study of the effect of particular agent behaviour. In light of these advancements, several scholars advocate for contemporary simulation models as better suited in studying the underlying mechanisms of crash occurrence. Furthermore, these methods represent a richer and more detailed set of alternatives than statistical models [30]. Traffic in an urban space is a complex system that includes the environment (a road network with a plethora of features like intersections, traffic lights, roundabouts, hills and weather conditions) and drivers' behaviour as individuals. Urban traffic management has reached utmost importance worldwide as cities battle congestion and its impacts on public health and fossil fuel emissions. Many computational models exist--SUMO [31], AIMSUN [32], ARCHISIM [33] and PARAMICS [34], to name several--which aim to simulate traffic flow and aid the design and layout of urban roads and thereby help to minimise the impact of congestion. However, these models are typically explicitly collision-free; driver behaviour is formulated to prevent collisions. However, some contemporary academic research has focused on various aspects of roadside collisions. The authors in [35] applied data mining techniques on data captured from intersection accidents to support real-time collision detection systems at intersections. A review of near-collision driver behaviour models by [36] found that most research has mainly been interested in the details of control in near-crash and crash-phases and have thus not needed to provide an account of why these states were reached in the first place. Furthermore, the authors in [36] argue that some authors have modelled reactions to collision warnings in various ways [37; 38; 39], while none of the models has addressed the phenomena of behavioural adaptation to long-term system exposure. The model adopted in this study [23] allows for the vehicle's life-cycle to be observed at an individual level while also observing the global patterns that emerge overtime at the street network level. The authors in [36] also found that almost all papers focused on a narrow set of collisions, namely rear-end collisions. Thus, they recommend looking at a more diverse range of pre-crash scenarios to achieve full credibility. The model adopted in this research deploys an environment that can observe multiple vehicle behaviours while applying the laws of physics. A variety of collisions among vehicles can then be observed.