|Duration||September 2017 – in progress|
|Key areas||Carsharing; Agent-based modelling.|
As urban environments are growing fast, many major cities suffer from critical congestion levels. Even without considering externalities, such as environmental pollution, the economic loss due to traffic phenomena is immense. As a consequence, both companies and public institutions are showing an increasing interest in new collaborative mobility solutions, such as car sharing and car carpooling systems, to shape the mobility of the future. The key feature of these new services is the advantage of offering a flexible service without the burden of ownership.
As this new emerging trend catches on, tool limitations in quantifying and evaluating the impact of these collaborative mobility solutions in different contexts becomes evident. This project aims at filling this gap, by developing a new Decision Support System (DSS) specifically designed to evaluate different scenarios and to assess different options for achieving higher system performances. Since most of the opportunities of smart mobility are strictly connected to technological innovations for managing and organizing trips, the DSS will provide important insights on a various number of points such as the expected impact of innovative systems.
To achieve this goal, we are going to extend an agent-based simulator to create what-if scenarios based on different characteristics of the territory and the collaborative mobility solutions. The mid-term outcome of this research will therefore consist of a DSS for actual scenario deployed in car- sharing services. In the long-term, the project is addressed to future transportation systems that are not yet available for the general public such as autonomous vehicles. This new agent-based simulator, core of the DSS, will allow private companies and public administrations, to evaluate the most correct sharing service to adopt maximizing the quality of service for the user, the economic benefit for the provider and provide a minimized and quantified societal and environmental impact.