# Human and Environmental Factors Analysis in Traffic Using Agent-Based Simulation
## 6 Conclusions and Future Work  
Simulation models to analyze traffic dynamics have been widely used to optimize their performance. Our approach combines the flexibility that provides agent-based modeling with the formalism of queuing theory in order to determine the influence of the human factor on traffic. The vehicular flow performance is obtained from the evaluation of the model using a queuing system, which is able to give sense to the time consumed by the vehicles in their transit along the road. This is illustrated in this paper with a case study to analyze the influence of certain aspects such as the age of drivers and pedestrians, proving to be an influential factor in their decisions. In addition, pavement is used as an additional factor, demonstrating that traffic performance is also influenced by environmental factors. The results show that the increase in the age of people on the road slows traffic flow, decreases the number of violations, and generally increases waiting time. In addition, with age also comes an increase in drivers' caution on poorly maintained roads. On the other hand, results were also obtained that were not expected within the design of the experiments and are therefore valuable. The influence of the human factor \"knowledge of the area\", was shown in the analysis of the number of violations, where people with zone expertise are overconfident and commit more infractions. From the experiments, an inverse proportionality relationship between waiting time and vehicle speed was obtained, and also between the average speed of vehicles and pedestrians. These relationships are valuable because they allow for predicting the behavior of one of the factors with prior knowledge of the other. Finally, an expected result is verified: the distribution of waiting time confirmed that the main waiting element is the traffic lights, which is why it is necessary to insist on an adequate configuration of their timing. As part of the forthcoming work, experiments will be applied to other factors present in the simulation, such as gender, distractions on the road or the technical condition of the vehicles. The analysis of the degree of influence of each factor will provide further conclusions and it will be possible to arrive at rules for the configuration process. After evaluating all the factors already included, the simulation model will be extended to new behaviors, such as alcohol consumption, stress or more severe disabilities. The inclusion of new factors can be used to analyze different types of configurations that contribute to better traffic performance, as well as to evaluate the simulation with other existing metrics. Another important feature to further work is the size of the instances so that the simulation allows us to have a more general view of human behavior in regions or entire cities. The results obtained and the new work proposals will help in the conception of a complete traffic control tool. A tool that reproduces the presented simulation model will support signal configuration by evaluating real signaling plans. Therefore, the appropriate configuration for the data provided to the model can be obtained. The following supporting information can be downloaded at: [[https://github.com/amoreno98/simulation-model-data](https://github.com/amoreno98/simulation-model-data)]([https://github.com/amoreno98/simulation-model-data](https://github.com/amoreno98/simulation-model-data)) (accessed on 25 February 2022). Conceptualization, M.M. and J.P.; methodology, M.M. and C.P. ; software, A.C.M. ; validation, A.C.M. ; investigation, A.C.M. and M.M. ; resources, M.M. and J.P.; data curation, A.C.M. ; writing--original draft preparation, A.C.M. ; writing--review and editing, C.P. and J.P.; visualization, A.C.M. ; supervision, C.P. ; project administration, M.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.