基于时空势场修正多处理器城市拥堵并行聚类分析

为提升城市城市道路拥堵检测和治理效率,提出一种基于势场修正多处理器并行聚类的城市道路拥堵时空分析方法。首先,利用GIS四维空间时态数据给出城市道路拥堵的时空模型,并通过设置虚拟数据参数实现时间的低密度采样处理,获得城市道路拥堵路段时空规律性的有效检测；其次,基于势场修正法构建多处理器并行聚类方法,分别设计了距离矩阵、邻域半径和密度函数的并行化多处理计算方法,并实现了并行聚类算法设计,同时给出上述并行计算过程的计算复杂度分析定理；最后,以北京市为试验区,对所提城市道路拥堵分析算法性能进行了验证,实验结果表明,所提方法可实现城市城市道路拥堵情况的快速有效检效检测分析,可为城市道路拥堵管理提供数据支撑。

Potential field modified based multi processor parallel clustering for traffic con-gestion analysis

In order to improve the urban traffic congestion detection and control efficiency, a new method based on potential field modified multi processor parallel clustering for traffic congestion analysis was proposed. Firstly, the temporal data of GIS four-dimensional space was used to construct the time and space model of traffic congestion, and the time of the low density sampling was processed by setting the virtual data parameters, which could effectively detect the space-time regularity of traffic jam; Secondly, a multi processor parallel clustering method are designed based on the potential field modification method, and the parallel multi processing computing method for distance matrix, neighborhood radius and density function was also designed, then the parallel clustering algorithm was realized. At the same time, the computational complexity analysis theorem was presented for the designed parallel computing process; Finally, taking Chengdu City as a test area, the traffic congestion in analyzing the performance of the algorithm is verified. Experimental results show that the method can achieve effective detection for rapid analysis of urban traffic congestion, which could provide data support for traffic congestion management.