传统的密度峰值聚类算法不仅具有较高的计算复杂度且未考虑路网固有的拓扑结构，无法衡量各路段之间的关联关系。针对这一问题，提出基于图密度峰值聚类算法的出行热点路段发现。该算法将交通路网用图模型结构，然后以各路段为基本单元计算局部密度及高局部密度距离并画出决策图找出聚类中心，最后结合实际区域的兴趣点分析该聚类簇成为热点路段的潜在可能。借助于图模型表达形式的优势，该算法不仅可以大幅度提升算法的计算复杂度，而且可以更加准确合理的找出热点路段。通过在滴滴-成都轨迹数据集上的实验表明，图密度峰值聚类算法具有更高的热点路段发现精度，并且在计算效率上有大幅度提升。

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.