This paper presents a novel model framework for complex urban traffic systems based on the interconnection of a dynamical multi-agent system in a macroscopic level. The agents describe all the types of street segments, intersections, sources and sinks of cars, modelling the behavior of the flow of vehicles through them as simple differential equations. These agents include the phenomena of changes in the flow rate due to congestions, traffic signals and the density of the vehicles. Traffic signal changes are obtained by the evolution of Petri Nets, in order to represent a more real behavior. Therefore, a complex network can be constructed by the interconnection of the agents, in continuous time, and the Petri Nets, in a discrete-event behavior, becoming a hybrid and scalable system. In order to analyze the performance of the approach, a real set of streets and intersections in Montevideo City is studied. Also, the approach is compared with a simulation realized in the software TSIS-CORSIM, which contains real data of density of vehicles. The multi-agent system achieves comparable results, taking into account the differences in the level of details respect to TSIS-CORSIM. Thus, the results can represent the most important issues of vehicular traffic with less computational resources.

本文提出了一种新的复杂城市交通系统模型框架，它基于宏观层面的动态多智能体系统的互连。代理描述所有类型的街道、交叉路口、汽车的来源和汇，将通过它们的车辆流动行为建模为简单的微分方程。这些因素包括由于拥堵、交通信号和车辆密度导致的流量变化现象。交通信号的变化是通过 Petri 网的进化获得的，以代表更真实的行为。因此，一个复杂的网络可以通过代理的互连，在连续的时间内构建出来，而 Petri 网，在一个离散的事件行为中，成为一个混合和可扩展的系统。为了分析该方法的性能，研究了蒙得维的亚市的一组真实街道和十字路口。此外，该方法与 TSIS-CORSIM 软件中实现的模拟进行了比较，该软件包含车辆密度的真实数据。考虑到与 TSIS-CORSIM 相关的细节级别的差异，多代理系统实现了可比较的结果。因此，结果可以用较少的计算资源代表最重要的车辆交通问题。