چکیده مقاله
In this paper an advance modeling and control strategy for a real freeway a based on neural networks and model predictive control is proposed Because of the nonlinearity of freeway traffic flow, neural network is a promising method for modeling of this system However, as the freeway length is increased, complexity and dimension of the neural network models will increase It is shown that by using principal component analysis, the network dimension can be decreased while the accuracy of the models will be reasonably preserved Then, the simplified neural network can be used within an MPC framework for ramp metering control within a wider area The approach is then evaluated based on some collected real data from a freeway in USA The simulation results demonstrate that the proposed approach can alleviate traffic congestion and improve efficiency of the freeway
کلیدواژهها
نویسندگان
Azita Dabiri
School of Electrical & Control Engineering, Shiraz University, Shiraz, Iran
Ali Akbar Safavi
School of Electrical & Control Engineering, Shiraz University, Shiraz, Iran
Ali Safavi
School of Electrical & Control Engineering, Shiraz University, Shiraz, Iran
Gabriel Gomes
California PATH, ۱۳۵۷ S ۴۶th Street, Bldg ۴۵۲, Richmond, CA ۹۴۸۰۴ ۴۶۴۸, USA
شیوه ارجاع
Dabiri, Azita and Safavi, Ali Akbar and Safavi, Ali and Gomes, Gabriel,1391,An Advance Modeling and Control Strategy for a Real Freeway,12th International Conference on Traffic and Transportation Engineering,Tehran
ارائهشده در
مجموعه مقالات دوازدهمین کنفرانس بین المللی مهندسی حمل و نقل و ترافیک1 اسفند 1391 · تهران