| Autor: |
Javier Palanca, Pasqual Martí, Vicente Julián, Jaume Jordán |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Neurocomputing. 484:196-210 |
| ISSN: |
0925-2312 |
| DOI: |
10.1016/j.neucom.2021.06.098 |
| Popis: |
Current traffic congestion and the resulting carbon emissions are two of the main problems threatening the sustainability of modern cities. The challenges facing today's cities focus primarily on the optimization of traffic ow and the transition to electric vehicles. The latter aspect implies the need for an adequate deployment of the infrastructure of charging stations. The inherent complexity in today's cities and the difficulty in implementing new policies whose benefits are difficult to measure and predict has led in recent years to consider the enormous potential of simulation tools and in particular of the agent-based simulation (ABS). ABS allows the speciffication of complex models that reect the complexity and dynamism of urban mobility. Current technology in ABS has evolved and matured sufficiently to provide very sophisticated tools but lacking facilities for a exible and realistic generation of input data in the execution of the experiments. In line with this, this paper introduces two configurable generators that automatize the creation of experiments in agent-based simulations. The generators have been developed with the SimFleet simulation tool enhancing the simulation of realistic movements and location of vehicles, passengers and other users of the urban traffic system within a city. The generators proved to be useful for comparing different distributions of locations as well as different agent movement behaviors based on real city data. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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Full Text from ScienceDirect