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基于网络拓扑的子网络OD需求估计

孙超 程琳 栾鑫 涂强 马捷

东南大学学报(自然科学版)2017,Vol.47Issue(6):1248-1252,5.
东南大学学报(自然科学版)2017,Vol.47Issue(6):1248-1252,5.DOI:10.3969/j.issn.1001-0505.2017.06.026

基于网络拓扑的子网络OD需求估计

Subnetwork origin-destination matrix estimation considering network topology

孙超 1程琳 1栾鑫 1涂强 1马捷1

作者信息

  • 1. 东南大学交通学院,南京210096
  • 折叠

摘要

Abstract

To design and evaluate the local transportation road network,the subnetwork origin-destination (OD) demand is estimated using the method of topology analysis.The nodes on the boundary of the subnetwork and inside the subnetwork are analyzed based on the subnetwork topology structure.The nodes on the boundary of the subnetwork,which connect with the outside of the subnetwork,generate or attract traffic flows;and the demands of the nodes inside the subnetwork are the same as those in the original full network.Then,the subnetwork OD matrix estimation model considering the subnetwork topology is established.This new model considers the maximum entropy and elasticity of OD demands in the objective function,and uses the OD demands as the constraints.The original problem is divided into solving the OD demand and road impedance,respectively.The heuristic iterative algorithm is designed to solve the established model,and the convex combination algorithm is developed to calculate the OD demand.The Sioux Falls network is used to illustrate the essential idea of the proposed model and the applicability of the proposed solution algorithm.The results show that the proposed model with elastic demand is superior to the OD estimation model with fixed demand in the terms of reliability and computational accuracy.The designed algorithm can rapidly convergence to the required accuracy,and the proposed model is an effective approach for simplifying the full network.

关键词

OD矩阵估计/子网络分析/拓扑结构/弹性需求/启发式迭代算法

Key words

origin-destination matrix estimation/subnetwork analysis/topology structure/elastic demand/heuristic iterative algorithm

分类

交通工程

引用本文复制引用

孙超,程琳,栾鑫,涂强,马捷..基于网络拓扑的子网络OD需求估计[J].东南大学学报(自然科学版),2017,47(6):1248-1252,5.

基金项目

国家自然科学基金资助项目(51578150,51378119)、东南大学优秀博士论文基金资助项目(YBJJ1679)、江苏省研究生创新基金资助项目(KYLX15_0150)、国家留学基金委资助项目. (51578150,51378119)

东南大学学报(自然科学版)

OA北大核心CSCDCSTPCD

1001-0505

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