A Case Retrieval Strategy for Traffic Congestion Based on Cluster Analysis

Abstract

In order to improve the retrieval efficiency, this paper uses case-based reasoning (CBR) in the retrieval of traffic congestion cases and tries to adopt the strategy of clustering case databases before retrieval so as to narrow the scope of case retrieval. In terms of case clustering, the k-means algorithm, with excellent performance in text clustering, is selected to cluster traffic congestion edge cases. At the same time, considering that there is a certain similarity among the descriptions of traffic congestion, the K-means algorithm is optimized to generate an accurate clustering. Those edge cases are clustered into microcase clusters of traffic congestion and then divided into different traffic congestion categories according to the distance of cluster center. Experimental results show that the clustered case base is divided into several microcase bases, which improves the accuracy and shortens the retrieval time in the process of retrieval and provides a new idea for the retrieval method in the process of case-based reasoning.