Abstract:

To solve the problem of low recognition rate in traditional traffic state recognition algorithms that only consider the differences in individual characteristics of traffic parameters, the concept of swarm intelligence is introduced. A highway traffic state recognition algorithm that considers the differences in individual characteristics of traffic parameters as well as the differences in group characteristics contained in individual parameters is proposed. Because the fuzzy C-means algorithm (FCM) has the disadvantage of slow convergence in the generalization ability of traffic state recognition, the opposition-based learning strategy and whale optimization algorithm (WOA) are adopted. Considering the individual traffic parameters that contain the cluster behavior to enhance the diversity of the initial clustering center population of the traffic state, a swarm intelligence-based highway traffic state recognition algorithm OWF with good global search ability is designed, which incorporates reverse learning along with the WOA and FCM algorithms and overcomes the problem that the FCM recognition algorithm is prone to local optima. Empirical analysis results show that the proposed traffic state recognition algorithm has a good recognition effect, with an accuracy rate of 92%, and the convergence speed is greater than that of the FCM algorithm.

Key words: :highway, traffic state, pattern recognition, WOA, FCM, opposition-based learning