Load Generators for Automatic Simulation of Urban Fleets
[EN] To ensure cities sustainability, we must deal with, among other challenges, traffic congestion, and its associated carbon emissions. We can approach such a problem from two perspectives: the transition to electric vehicles, which implies the need for charging station infrastructure, and the optimization of traffic flow. However, cities are complex systems, so it is helpful to test changes on them in controlled environments like the ones provided by simulators. In our work, we use SimFleet, an agent-based fleet simulator. Nevertheless, SimFleet does not provide tools for easily setting up big experiments, neither to simulate the realistic movement of its agents inside a city. Aiming to solve that, we enhanced SimFleet introducing two fully configurable generators that automatize the creation of experiments. First, the charging stations generator, which allocates a given amount of charging stations following a certain distribution, enabling to simulate how transports would charge and compare distributions. Second, the load generator, which populates the experiment with a given number of agents of a given type, introducing them dynamically in the simulation, and assigns them a movement that can be either random or based on real city data. The generators proved to be useful for comparing different distributions of charging stations as well as different agent behaviors over the same complex setup. ; This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Pasqual Martí and Jaume Jordán are funded by UPV PAID-06-18 project. Jaume Jordán is also funded by grant APOSTD/2018/010 of Generalitat Valenciana - Fondo Social Europeo. ; Martí Gimeno, P.; Jordán, J.; Palanca Cámara, J.; Julian Inglada, VJ. (2020). Load Generators for Automatic Simulation of Urban Fleets. Springer. 394-405. https://doi.org/10.1007/978-3-030-51999-5_33 ; S ; 394 ; 405 ; Campo, C.: Directory facilitator and service discovery agent. FIPA Document Repository (2002) ; Dong, J., Liu, C., Lin, Z.: ...