Sustainable Mobility and Robust Transportation (SMART)
Sustainable Mobility and Robust Transportation
Start date: May 1, 2014,
End date: Apr 30, 2018
"Traffic Congestion has several effects on travelers, businesses, agencies, and cities. Congestion costs Europe about 1% of Gross Domestic Product (GDP) every year.Transportation networks are complex and large-scale dynamic systems with uncertainties. Efficient traffic control and management of large-scale transportation networks still remain a challenge both for traffic researchers and practitioners.Our main objective in this project is to develop smart transportation systems for large-scale traffic networks that aim at minimizing network delays and increasing sustainable mobility and accessibility, with the help of feeded data from advanced sensor and information technologies. To achieve this goal, we will develop a new interdisciplinary research approach that combines and fuses research areas developed in different fields in traffic theory, control theory, optimal control, optimization, big data mining and fusing, and estimation theory.The SMART project is unique in several aspects, as it is an interdisciplinary research and it is expected to make advances in the state of the art in different directions: (i) Developing a hierarchical scheme of integrated modeling and control for the intersection and network levels. (ii) Developing efficient control strategies that are robust under uncertainties. (iii) Developing implementable real time control based on feeded information for both levels including the monitoring and estimation procedures. (iv) A real field study implementation.We believe that the new interdisciplinary approach will result in a better understanding of complex and congested cities, and will allow integrating traffic control and management approaches to generate smart cities that are more livable and sustainable. The operational deployment of such smart networks would improve the life quality in Europe by improving traffic conditions in cities, reducing travel times and minimizing delays, and reducing pollution levels."
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