Object-oriented Road Traffic Congestion Identification Algorithm Voting Fusion
YU Zhong-xia · Technology and Economy in Areas of Communications · 2008
To satisfy the needs of different objects on the identification rate and false identification rate, this paper chooses a different confidence level for traffic participants or managers on the basis of their fusion demand. Using feed-forward neural networks and probabilistic neural networks to make voting fusion for different objects, the result shows that participants can achieve a high identification rate of traffic, managers achieving a low false identification rate.