摘要
针对城市路网关键节点识别与管控问题中传统方法欠考虑结构、流量等多元因素影响,并引入了过多权重参数主观性太强等问题,引入了节点的空间流量度和网络结构流量熵两个新概念,构建了有向含权路网模型,提出了基于空间流量度的城市关键节点识别方法。以北京市四环内路网为例,计算了路网度分布以及速度分布,对比了不同方法的有效性。研究结果表明:方法能够同时识别出流量较大和结构重要的节点;此外,模型参数α增加和β降低有利于提高网络连通性能,但α增加和β降低不利于流量的均衡分布;文中方法和结果可为城市交通规划、设计和管理提供参考,对于缓解拥堵、提高鲁棒性具有重要意义。
Aiming at the identification and control of key nodes of urban road network,the traditional method lacked the consideration of the influence of multiple factors such as structure and flow and introduced too many weighted parameters,which was too subjective.Therefore,two new concepts such as the node spatial-flow degree and the network structural flow entropy were introduced to establish a directed weighted road network model.And the identification method of urban key nodes based on the spatial-flow degree was proposed.Taking the road network of the Fourth Ring Road in Beijing as an example,the road network degree distribution and speed distribution were calculated,and the effectiveness of different methods was compared.The research results show that the proposed method can simultaneously identify nodes with large traffic flow and important structure.Moreover,increasingαand decreasingβis beneficial for improving the network connectivity performance,however,it is not conducive to the balanced distribution of traffic.The proposed method and results can shed light on the urban traffic planning,design and management,which is of great significance to alleviate congestion and improve robustness.
作者
张诚
汪成银
陈志伟
张正向
张子磊
ZHANG Cheng;WANG Chengyin;CHEN Zhiwei;ZHANG Zhengxiang;ZHANG Zilei(Administration Office of Henan Provincial Department of Transportation,Zhengzhou 450000,Henan,China;School of Reliability and System Engineering,Beihang University,Beijing 100191,China;Zhengzhou Haiwei Electronic Technology Co.,Ltd.,Zhengzhou 450000,Henan,China)
出处
《重庆交通大学学报（自然科学版）》
CAS
CSCD
北大核心
2021年第6期28-35,共8页
Journal of Chongqing Jiaotong University(Natural Science)
基金
国家自然科学基金项目(72001213)
陕西省自然科学基础研究计划项目(2021JQ-369)。
关键词
交通工程
城市路网
关键节点识别
空间流量度
轨迹大数据
traffic engineering
urban road network
identification of key nodes
spatial-flow degree
trajectory big data
