# Human and Environmental Factors Analysis in Traffic Using Agent-Based Simulation
## 4 Simulation Case Study
### Experiments Conducted
This is because driving at high speeds on roads in poor condition can cause technical damage to vehicles. In most instances, there is a considerable speed decrease in the distribution of adults, since people between 30 and 60 years of age are generally the owners of the vehicles and those who are more careful with them. Similar to the analysis of pedestrians, it is observed that as people's age increases, their speed on the road, whether walking or driving, decreases. The speed of Instance 1 is observed to be considerably higher than the others, and this was also the instance with the highest number of violations in the ages of 16 and 30 years, so speeding is related to violations of speed limits. There is an unexpected contrast between pedestrian (Figure 9) and vehicle (Figure 10) speed for each instance. Instance 1 had the lowest pedestrian speed and is shown as the one with the highest vehicle speed, while Instance 4 has the opposite. For crowded areas with higher vehicle speeds, pedestrians are more cautious and this is reflected in these results. Finally, the last traffic performance measure analyzed is the waiting time experienced by vehicles during the simulation, as the principal metric to measure the congestion in the traffic. Figure 11 shows the comparison of the waiting times. Results from uniform pavement distribution are used, for measuring the impact of age on this aspect. The scenario proposed in this paper is shown in Figure 11a. There is an inversely proportional relation between vehicle speed (Figure 10) and waiting time (Figure 11). In this graph, it can be observed for Instance 4, which had the lowest vehicle speed and the highest waiting time; while in Instance 1 the opposite occurs highest vehicle speed and lowest waiting time. In terms of age, the longest waiting time is obtained for adults, who show greater caution compared to young people and better health status compared to the elderly. This caution translates into obedience to signals and therefore longer waiting times at them. The young and the elderly always look lower because of disobedience, distraction or physical impediments, they wait less at the signals or for other vehicles. In the particular case of young people, traveling at a very high speed decreases the possibility of being stopped by a signal. Figure 10: Vehicle average speed by age and pavement conditions. Instance 1 behaves in a particular pattern, where the waiting time increases and the elderly exceeds the other age distributions. This is due to the presence of 5 consecutive signals for the plus traveled line--L St. to take Linea St.--in that instance, which affects the elderly who spend some time reacting to the signals, delaying the acceleration and inevitably will have to stop at the next signal. After executing the simulation with the factors included in this research, we adapted it to imitate other models presented. The drivers-only scenario, Figure 11b, may correspond to models such as [15, 16], where only driver characteristics are evaluated; while the pedestrian-only scenario, Figure 11c, would simulate models such as [14], which only consider pedestrian characteristics. Compared with state-of-the-art scenarios it is evident that pedestrian behavior has a lower influence than driver behavior, since in the simulations with only pedestrians, the waiting times for both age distributions are similar. When there are only drivers, the general trend seen in Figure 11a is maintained. The waiting time on the road can also be due to several waiting factors. Figure 12 shows a distribution of this average waiting time for each of the instances. The main waiting factor is traffic lights, with over half of the time spent in most instances. Then, the time spent waiting at other signals and by pedestrians is similar. These last factors are not less present in the simulations, but the drivers of vehicles consider them of less importance and do not obey the corresponding waiting time. Figure 11: Average waiting time by scenarios: (**a**) Drivers and pedestrian. (**b**) Drivers only.