Transportation systems and related policies are complex and cross-sectoral, covering different socio-economic and management aspects, and involving multiple stakeholders (such as users, operators, and public policymakers). Mobility and accessibility are central to economic and societal well-being, yet the process of doing so may have significant impacts on land use, environment, and public health. Furthermore, the many feedbacks involved occur at varying degrees of spatial, temporal, and socio-demographic granularity and levels of uncertainty. Simulation models are well established in transportation-related operational research and management science, and the alternative approaches of System Dynamics (SD) (Abbas and Bell, 1994; Bivona & Montemaggiore, 2010; Pasaoglu et al., 2016; Shepherd, 2014) and Agent-Based Modelling (ABM) (Davidsson et al., 2005; Maggi & Vallino, 2016; Rossetti & Liu, 2014) not only offer different perspectives to transport planning but also demonstrate to policymakers the importance of understanding cause-and-effect relationships. Further to this, these platforms also offer specialised tools and approaches for hybridisation with other simulation techniques, which aid in the understanding of the whole underlying system, calibration of models, optimisation of policies, and ease of use through simple front-end decision support tools.

Harrison G., Bivona E., & Rossetti R. (2020). Editorial: Special issue on Simulation in Transportation. JOURNAL OF SIMULATION, 14(4), 239-241 [10.1080/17477778.2020.1829514].

Editorial: Special issue on Simulation in Transportation

Bivona E.;
2020

Abstract

Transportation systems and related policies are complex and cross-sectoral, covering different socio-economic and management aspects, and involving multiple stakeholders (such as users, operators, and public policymakers). Mobility and accessibility are central to economic and societal well-being, yet the process of doing so may have significant impacts on land use, environment, and public health. Furthermore, the many feedbacks involved occur at varying degrees of spatial, temporal, and socio-demographic granularity and levels of uncertainty. Simulation models are well established in transportation-related operational research and management science, and the alternative approaches of System Dynamics (SD) (Abbas and Bell, 1994; Bivona & Montemaggiore, 2010; Pasaoglu et al., 2016; Shepherd, 2014) and Agent-Based Modelling (ABM) (Davidsson et al., 2005; Maggi & Vallino, 2016; Rossetti & Liu, 2014) not only offer different perspectives to transport planning but also demonstrate to policymakers the importance of understanding cause-and-effect relationships. Further to this, these platforms also offer specialised tools and approaches for hybridisation with other simulation techniques, which aid in the understanding of the whole underlying system, calibration of models, optimisation of policies, and ease of use through simple front-end decision support tools.
Harrison G., Bivona E., & Rossetti R. (2020). Editorial: Special issue on Simulation in Transportation. JOURNAL OF SIMULATION, 14(4), 239-241 [10.1080/17477778.2020.1829514].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/450468
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