我所殷辰堃老师课题组完成的论文《A Revised Snake Clustering Method for Static Partitioning of Urban Road Networks》于2025-05在《IEEE Data Driven Control and Learning Systems Conference (DDCLS)》正式发表。论文摘要如下：
Abstract: Partitioning of urban road networks is crucial for alleviating traffic congestion in large cities. In this paper, a revised clustering algorithm for road network partitioning is developed based on an existing snake clustering method. In response to the issues present in the existing snake clustering method, the revised snake clustering (RSC) method proposed in this paper tackles these challenges through targeted improvements. To address the significant randomness in the initialization, RSC method selects appropriate key road segments as the deterministic starting state. During computational process, RSC method combines the direction constraint angle with an adaptive threshold to control the expansion direction and termination condition of the snake sequence. Additionally, to overcome the simplicity of the traditional similarity measurement, RSC method comprehensively integrates temporal and spatial factors to constructs a spatiotemporal matrix as a new similarity measurement. From the simulation results of applying the proposed algorithm to a speed dataset collected from a road network in the northeast of Beijing Second Ring Road, the revised snake clustering method outperforms conventional methods, achieving 17.4% higher cluster homogeneity and 17.9% enhanced inter-cluster heterogeneity in each sub-region.
引用信息：Jialuo Han, Xuerui Cai, Ying Zhao, Zhidan Yang, Haoliang Shi, Chenkun Yin. A Revised Snake Clustering Method for Static Partitioning of Urban Road Networks. IEEE Data Driven Control and Learning Systems Conference (DDCLS), 2025-05. DOI: 10.1109/ddcls66240.2025.11065161