## 2 An Individual-Based Modelling Approach to Traffic Simulation
An example of this is where vehicles tip over if a collision occurs with a heavier, faster-moving vehicle, which ultimately captures a more realistic array of possible collisions and repercussions. Ultimately, collisions occur and contribute to various undesirable circumstances on roads, such as traffic jams and congestion. Furthermore, the rate, type, and severity of these collisions are emergent properties of the system, impacted by driver interaction, driver behaviour, and the environment. Driver behaviour can be observed in many ways; these include surveys, camera footage, police reports, to name but a few. A prominent method within the literature is the Driver Behaviour Questionnaire (DBQ) introduced in 1990 by [40]. This questionnaire consists of 50 items describing various problems and violations while driving, which members of the public can fill out. After surveying 520 drivers, the authors in [40] identified that errors are statistically distinct from violations, indicating that different psychological mechanisms trigger errors and violations. The authors in [41] found that violations were more prevalent among young drivers compared to senior drivers. On the other hand, errors decreased for younger drivers but remained constant with age among older drivers. The differences between attitudes among drivers in rural areas and urban areas reflect the significant difference among collisions in these areas. The authors in [42] identified that urban road network design consisted of higher lengths of road and traffic volume, which in turn increased the collision rates. The authors in [43] add to this by highlighting the strongest predictors of fatality rates due to vehicle collisions as being age and number of residents in the geographical areas. The authors in [44] adopted Naturalistic Driving Study (NDS) data which contains driver, trip and vehicle specific information. These data represent driver behaviour before, during and after the adoption of high-visibility enforcement programs. Furthermore, the study focused specifically on aggressive driving behaviour; these include speeding and tailgating to explore the intensity and duration of these behavioural patterns. The study found that high-visibility enforcement programs are likely to reduce speeding only in some instances. A survey result showed that drivers in rural areas are more likely to drive without a seat belt on or while intoxated with alcohol compared to drivers in urban areas [45]. The author in [46] identified two components that impact traffic risk. These are system risk and risk culture. The latter consists of factorsindependent of the driver, such as vehicle condition, weather and road plans. The former are human factors such as norms, feelings, attitudes and perceptions of risk. Adding to this, the authors in [47] attempted to analyse taxi driver speeding behaviours captured by GPS trajectory data. These data captured the hourly speeding frequency and average speeding severity of each driver. Their study concludes that aggressive driver behaviour among taxi drivers are linked to longer trips, short delivery time, high monetary value, driving at night, and, lastly, forced low-speed limits. Given all of the above, the authors in [43] highlight the impact physical changes to road networks can have by enforcing slower speeds such as road humps, while also indicating that driver behaviour may also be altered indirectly by influencing the public's attitudes and norms which links to the literature on \"self-explaining\" roads [48] mentioned earlier in the Introduction section. Accident reduction is a crucial aim of transport management. It has been hypothesised that higher congestion leads to fewer road fatalities [49] as congestion leads to lower overall speeds, and therefore collisions are less likely to occur.