摘要
早期公交场站规划仅考虑了功能需求,与周边交通组织规划脱节,造成城市现有公交场站基本存在时空布局不均衡、建设滞后等问题,制约了公共交通发展,故有必要考虑定量分析场站供需分布特征,为公交场站布局优化提供决策依据。基于栅格数据模型,利用改进的加权核密度估计法分别建立场站需求及供应的概率密度估计模型,然后基于多层栅格数据叠合分析方法将场站需求及供应的栅格层进行叠加分析,构建解析场站供需分布特征的栅格估计模型。最后,选取深圳市为应用案例,利用构建的供需分布模型对深圳市的公交场站供需分布特征进行定量、可视化分析,展现了深圳市公交场站覆盖区域及分配情况,验证了模型的有效性及适用性。
The bus station planning in the early only considered the functional requirements,which was out of touch with the surrounding traffic organization planning in early stage.Therefore,the existing bus stations in the city basically face problems such as unbalanced space-time layout and lagging construction,which restrict the development of public transportation.It is necessary to consider the quantitative analysis of the distribution characteristics of supply and demand of bus stations,so as to provide decision-making basis for the layout optimization of bus stations.Based on the grid data model,the improved weighted kernel density estimation method is used to establish the probability density estimation models of station demand and supply,respectively.Then,based on the multi-layer grid data superposition analysis method,the grid layer of station demand and supply is superimposed and analyzed,and the grid estimation model that analyzes the distribution characteristics of station supply and demand is constructed.Finally,taking Shenzhen as an application case,the constructed supply and demand distribution model is used to quantitatively and visually analyze the supply and demand distribution characteristics of public transport stations in Shenzhen,showing the coverage area and distribution of public transport stations in Shenzhen,and verifying the effectiveness and applicability of the model.
作者
谭英嘉
况雪
TAN Yingjia;KUANG Xue(Shenzhen General Integrated Transportation and Municipal Engineering Design & Research Institute Co. ,Ltd. ,Shenzhen Guangdong 518000,China)
出处
《科技和产业》
2022年第4期271-278,共8页
Science Technology and Industry
关键词
公交场站
供需分布特征
栅格数据模型
加权核密度估计法
多层栅格数据叠合分析
bus station
distribution characteristics of supply and demand
grid data model
weighted kernel density estimation method
multilayer raster data overlay analysis