Artificial immune K-means grid-density clustering algorithm for real-time monitoring and analysis of urban traffic

A novel clustering algorithm is presented for monitoring and analysing traffic conditions in real-time and automatically. The existing methods concentrate on analysis of traffic flow based on historical information, and they cannot provide timely analysis of traffic conditions. Regarding the vehicles on the roads as data points, a K-means grid-density clustering algorithm is proposed based on an artificial immune network to partition the vehicles data into proper clusters, and marks the densities for monitoring and analysing the traffic conditions. Simulated experimental results show that the proposed algorithm obtains higher efficiency and stability than traditional methods.