A Multi-Index Fusion Clustering Strategy for Traffic Flow State Identification

This paper attempts to improve the identification accuracy of traffic flow states, and disclose the impacts of different evaluation indices on the identification results. To this end, a multi-index fusion clustering strategy is proposed in this research. Firstly, flow, velocity and occupancy were selected as the evaluation indices. Then, the weights of the three indices were initialized by a group of experts. After that, the objective function of a weight optimization model was set up to maximize the distance between projection centers of samples under different traffic flow states and to minimize the projection variance between samples under the same traffic flow state. The model was solved by the method of Lagrange multipliers, producing the optimal weight combination. Then, the optimal weights were introduced to the fuzzy c-means (FCM) clustering, forming the multi-index fusion clustering method. The results of example analysis show that our method differentiated between traffic flow states more accurately than the original FCM clustering. And the traffic flow identification accuracy improved from 94.0% to 96.6%. This is because the improved method retains most of the original features of the evaluation indices, which further facilitates the accurate clustering of traffic flow states.