Traffic flow matching with clique and triplet cues
This paper addresses a new problem of matching traffic-flow patterns from different scenes. We firstly introduce cliques to measure the topology similarity between traffic flow patterns. Based on the clique information, a matching cost function is formulated to find the optimal flow-pattern matching. In order to avoid wrong matches due to large variations in traffic flow distributions, we further introduce triplets to measure the flow-wise correlation in a scene and include them into the matching cost function. Thus, constraints of traffic flows' relative position can be suitably considered during the flow-pattern matching process. Finally, a random-walk-based graph matching method is also utilized to efficiently solve the matching cost function optimization problem. Experimental results on both simulated flow data and real flow data demonstrate the effectiveness of our approach.