Abstract
Understanding the impacts of a mobility initiative prior to deployment is a complex task for both urban planners and transport companies. To support this task, Tangramob offers an agent-based simulation framework for assessing the evolution of urban traffic after the introduction of new mobility services. However, Tangramob simulations are computationally expensive due to their iterative nature. Thus, we simplified the Tangramob model into a Timed Rebeca (TRebeca) model and we designed a tool-chain that generates instances of this model starting from the same Tangramob’s inputs. Running TRebeca models allows users to get an idea of how mobility initiatives affect the system performance, in a short time, without resorting to the simulator. To validate this approach, we compared the output of both the simulator and the TRebeca model on a collection of mobility initiatives. Results show a correlation between them, thus demonstrating the usefulness of using TRebeca models for unconventional contexts of application.
The work of the 4th and a part of that of the 5th author are supported by “Self-Adaptive Actors: SEADA” (project nr. 163205-051) from Icelandic Research Fund.
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Castagnari, C., de Berardinis, J., Forcina, G., Jafari, A., Sirjani, M. (2018). Lightweight Preprocessing for Agent-Based Simulation of Smart Mobility Initiatives. In: Cerone, A., Roveri, M. (eds) Software Engineering and Formal Methods. SEFM 2017. Lecture Notes in Computer Science(), vol 10729. Springer, Cham. https://doi.org/10.1007/978-3-319-74781-1_36
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DOI: https://doi.org/10.1007/978-3-319-74781-1_36
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