摘要
成渝地区双城经济圈作为我国综合立体交通网主骨架的“四极”之一,近年发展水平逐年提升,但仍面临着网络连通性不足、抗脆弱性较差等问题,亟需开展网络优化研究。运用复杂网络理论开展综合立体交通网络构建与拓扑特征分析,基于图神经网络开展节点强化优化方法研究,并以四川省综合立体交通网络为例开展了仿真分析。研究结果表明,通过图神经网络节点强化方法对网络进行多轮迭代优化后,网络关键节点的接近中心性提升了20%,中介中心性提升了40%,网络节点最短路径显著缩短,聚类系数分布更加均衡。优化过程以节点邻域信息聚合与动态权重调整为约束条件,在提升网络连通性与抗脆弱性的同时,验证了该方法在省域交通网络优化中的有效性。
The Chengdu-Chongqing economic circle,one of the“four poles”in China’s comprehensive three-dimensional transportation network backbone,has experienced progressive development yet faced challenges like insufficient network connectivity and poor robustness,necessitating network optimization.By using complex network theory,the construction and topological feature analysis of the comprehensive three-dimensional transportation network were conducted.A node reinforcement optimization method based on graph neural networks was developed,and simulation analysis was carried out by taking the comprehensive three-dimensional transportation network in Sichuan Province as an example.The results indicate a 20%improvement in closeness centrality and a 40%increase in betweenness centrality for critical network nodes after multi-iteration optimization by using the proposed method.The shortest path length of network nodes decreases significantly,while clustering coefficient distribution becomes more balanced.The optimization process employs neighborhood information aggregation of nodes and dynamic weight adjustment as constraints,demonstrating the method’s effectiveness in provincial transportation network optimization via enhancing network connectivity and robustness.
作者
王兆川
李舒霞
蒋军
罗斌文
WANG Zhaochuan;LI Shuxia;JIANG Jun;LUO Binwen(Transportation Policy Research Office,Institute of Transportation Development Strategy&Planning of Sichuan Province,Chengdu 620866,Sichuan,China;College of Traffic&Transportation,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《铁道运输与经济》
北大核心
2025年第5期96-107,共12页
Railway Transport and Economy
基金
四川省青年科技创新研究团队项目(2023JBKY05)
四川省科技厅项目(2023JDR0061)
重庆市教育委员会人文社会科学研究项目(23SKGH141)
重庆市研究生联合培养基地课题(JDLHPYJD2022002)。
关键词
综合立体交通
图神经网络
节点强化
网络优化
网络连通性
Comprehensive Three-Dimensional Transportation
Graph Neural Network
Node Enhancement
Network Optimization
Network Connectivity
