基于邻近数据查询算法的街区路网规划仿真

Simulation of Block Road Network Planning Based on Neighboring Data Query Algorithm

城市街区路网的规划主要受到交通密度、主干路间隔的影响,由于交通环境的动态性,对路网的规划也需持续更新,因此通过路网规划获取最优出行路线难度较大。现提出基于邻近数据查询算法的街区路网规划方法。获取Voronoi图对街区路网空间数据集划分后的多个空间单元,并将其存储在路网结构中。基于空间均分法,将街区路网空间区域划分成不同的区域,利用邻近数据查询算法,查询路网目标节点。确定街区各个层次路网的规划拓展等级,建立街区路网拓扑树,通过对拓扑树获取街区路网最优路径节点序列,实现街区路网的规划。实验结果表明,研究方法完成路网规划时其最优路径查询时间、CPU开销以及路网规划耗时指标均优于对比方法,以此验证了提出方法具有更理想的实用性。

The planning of urban block road networks is mainly affected by the traffic density and the spacing of trunk roads.Due to the dynamic nature of the traffic environment,the planning of the road network also needs to be continuously updated,so it is difficult to obtain the optimal travel route through the road network planning.A neighborhood road network planning method based on a neighborhood data query algorithm is proposed.Multiple spatial units are obtained after the Voronoi diagram divides the block road network spatial data set and stores them in the road network structure.Based on the spatial averaging method,the spatial area of the block road network is divided into different areas,and the adjacent data query is used to query the target nodes of the road network.The planning expansion level of the road network at all levels of the block is determined,the topological tree of the block road network is established,and the optimal path node sequence of the block road network is obtained through the topological tree to realize the planning of the block road network.The experimental results show that when the research method completes the road network planning,its optimal path query time,CPU overhead and road network planning time-consuming indicators are better than the comparison method,which verifies that the proposed method has more ideal practicality.