Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this syste...Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings or tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, a novel method employing a back propagation (BP) neural network is developed to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, the speed of its related road sections can be used to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the proposed method of traffic speed estimation is very effective.展开更多
基金funded by National Key Technology R&D Program of China (No.2006BAG01A03)
文摘Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings or tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, a novel method employing a back propagation (BP) neural network is developed to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, the speed of its related road sections can be used to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the proposed method of traffic speed estimation is very effective.
