Mobility of people can be configured as an information intensive process resulting from a complex set of factors. Its effective implementation requires the adoption of methods able to leverage on a set of complex and dynamic variables, and mainly on a huge amount of data available. Moving from this assumption, this paper aims to demonstrate that system dynamics could present a useful approach for optimising decision making for people's mobility. The conceptual model is built by using the principles of system dynamics methodology and is based on causal feedback relationships among the various factors related to the different needs of people's mobility. The causal feedback loops and interrelationship among various parameters illustrate the dynamicity and the influence of parameters on one another. The simulation analysis was conducted to dynamically evaluate six scenarios corresponding to the different solutions available for particular segments of demand. Findings highlight that the modelling approaches could guide the city planners to evolve responsive policy interventions for further developing smart mobility of people. Implications for policy makers regard the developing sustainable mobility scenarios based on the analysis of big data from the adoption of digital platforms grounded on the simulation model.
A system dynamic approach for the smart mobility of people: Implications in the age of big data.
Pasquale Del Vecchio;Giustina Secundo;
2019-01-01
Abstract
Mobility of people can be configured as an information intensive process resulting from a complex set of factors. Its effective implementation requires the adoption of methods able to leverage on a set of complex and dynamic variables, and mainly on a huge amount of data available. Moving from this assumption, this paper aims to demonstrate that system dynamics could present a useful approach for optimising decision making for people's mobility. The conceptual model is built by using the principles of system dynamics methodology and is based on causal feedback relationships among the various factors related to the different needs of people's mobility. The causal feedback loops and interrelationship among various parameters illustrate the dynamicity and the influence of parameters on one another. The simulation analysis was conducted to dynamically evaluate six scenarios corresponding to the different solutions available for particular segments of demand. Findings highlight that the modelling approaches could guide the city planners to evolve responsive policy interventions for further developing smart mobility of people. Implications for policy makers regard the developing sustainable mobility scenarios based on the analysis of big data from the adoption of digital platforms grounded on the simulation model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
