[1]孙超,程琳,栾鑫,等.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.[doi:10.3969/j.issn.1001-0505.2017.06.026] 　Sun Chao,Cheng Lin,Luan Xin,et al.Subnetwork origin-destination matrix estimation considering network topology[J].Journal of Southeast University (Natural Science Edition),2017,47(6):1248-1252.[doi:10.3969/j.issn.1001-0505.2017.06.026] 点击复制 基于网络拓扑的子网络OD需求估计() 分享到： var jiathis_config = { data_track_clickback: true };

47

2017年第6期

1248-1252

2017-11-20

文章信息/Info

Title:
Subnetwork origin-destination matrix estimation considering network topology

Author(s):
School of Transportation, Southeast University, Nanjing 210096, China

Keywords:

U491
DOI:
10.3969/j.issn.1001-0505.2017.06.026

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.

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