## 4 Experimental Results
Higher traffic densities also appear to loosely correlate with greater variance in collisions between runs. While collision prevention is a primary goal of traffic management, the prevention of congestion--and its impact on public health and CO2 emissions--is equally crucial [65; 66]. As described in the background, it has been suggested that these goals could conflict [49; 54]. There is no single definition of congestion--several different definitions have been developed for different congestion scenarios [67] or for identifying congestion from the available data [68]. In this study, congestion will be understood both as a decrease in the overall traffic speed in the system and as an increase in the number of vehicles with speeds under 5 mph at a given time. Local traffic density (the number of vehicles in a particular area of the network) will also be considered under the assumption that this correlates with congestion and being of interest in its own right. To compare traffic flow for the different scenarios, the average speed of all vehicles was calculated. The results are shown in Figure 5. Lower adherence to speed limits leads to higher average speed for systems of varying density. However, the average speed of systems with low adherence is impacted more by increasing the density. For example, when increasing the number of vehicles from 50 to 200 for 100% speed limit adherence, the average speed decreases by 3.6%. When increasing the number of vehicles from 50 to 200 for 30% speed limit adherence, the average speed decreases by 13.3%; therefore, as density increases, the average speed of vehicles decreases. While the average speed of traffic is an essential factor for considering the overall efficiency of the system (at least concerning average journey times), it does not communicate the distribution of speeds (for example, some vehicles may enjoy short journey times while others are stuck in congestion) or how these are spatially located. The average agents' speed, the spread of agents' speed, and the percentage of vehicles below 5 mph at the final time step of each scenario are presented in Table 4. The spread of speed, which is the standard deviation of all agents' speeds, increases with lower adherence by a factor of more than 1.6 from 100% adherence to 30% adherence for all traffic densities. Figure 5: Average speed of vehicles against number of vehicles for each adherence scenario, refer to Supplementary Materials for data used. This increase is expected since non-adhering drivers can access a broader range of speeds up to 45 mph while adhering drivers cannot exceed 30 mph. The fraction of vehicles below 5 mph includes vehicles that have collided and cannot move, including vehicles stuck behind these collisions. There is an increase in this fraction for 100 and 200 vehicles as adherence decreases. This increase is not evident for 50 vehicles. The above Table 4 shows that higher traffic densities and lower speed adherence result in a greater fraction of vehicles travelling at very low speeds at any given point in time, even though the average speed is higher. Similarly, low-speed adherence with low traffic densities increases the average speed without increasing the fraction of vehicles at very low speeds. This study is concerned with the spatio-temporal analysis of the whole urban street network. However, Figure 6 shows that local micro-level phenomena can also be observed. We hope to conduct a comprehensive analysis of micro-level interactions between density, congestion and collisions for future studies. The local traffic densities within 30 metres of a collision site, one second before the collision takes place, is shown in Figure 6. Higher global traffic densities result in higher local traffic densities near-collision sites across all adherence levels.