Traffic congestion has become a global issue that has a significant impact on our societys productivity. Its negative effects not only lie in the travel delays and unsafe conditions that it brings to road users, but also many aspects of our lives such as the air we all breathe. Construction and traffic management are typical alternatives for traffic researchers and practitioners to reduce congestion. Traffic management, which intends to make a better use of existing infrastructure, is more economical and environmentally friendly and becoming an increasingly preferred option. Dynamic traffic control proves to be efficient in the management of network traffic flows. This thesis focuses on the development of dynamic traffic control strategies to reduce congestion. Advanced dynamic traffic control strategies using model predictive control (MPC) approaches can considerably reduce traffic congestion. MPC for traffic systems utilizes a traffic model to predict traffic states evolutions based on the current states of the system, and determines the optimal control actions that result in the optimum value of an objective function. This feature enables the controller to take advantage of potentially larger future gains at a current (smaller) cost, so as to avoid myopic control actions...
|Award date||7 Dec 2017|
|Publication status||Published - 2017|
Bibliographical noteTRAIL Thesis Series no. T2017/13, the Netherlands Research School TRAIL
- road traffic
- fast model predictive control