Optimization and application of crossroad similarity matching based on the clustering algorithm

The collected crossroad data is clustered through an unsupervised machine learning algorithm to find the internal connections between different traffic intersections. This paper hopes that in the future traffic jams solving process, clustering algorithms can be used for reference to aggregate the similar traffic junction control experience into a same category, to guide relevant technical personnel to quickly find out the cause of the congestion problem in the urban traffic road network, thus formulating a reasonable and feasible traffic jam control plan based on the same category of traffic junction control experience.