The traditional density peak clustering algorithm not only has high computational complexity,but also does not consider the inherent topology of the road network.Hence,it cannot measure the intrinsic relationship between the various road segments.Aiming at this problem,this paper proposes a travel hotspot road segments discovery based on GDPC algorithm.The GDPC algorithm uses a graph model structure for the traffic road network,then uses each road segment as the basic unit to calculate the local density and the minimum high local density distance.Afterwards,the algorithm draws a decision diagram to find the cluster center,and finally combines the points of interest in the actual area to analyze the potential of the cluster to become a hot spot.With the advantage of the graph-based representation,the GDPC algorithm can not only greatly improve the computational complexity compared with traditional algorithms,but also find hot spots more accurately and reasonably.Experiments on the Chengdu Didi dataset show that the GDPC algorithm is more reasonable,and achieves a significant improvement in computational efficiency.
intelligent transportationtravel hotspotsgraph density peak clusteringhotspots discoveryDiDi dataset
维普
万方数据
