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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 9814909, 11 pages
Research Article

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

1College of Civil and Transportation Engineering, Hohai University, 1 Xikang Rd, Nanjing, Jiangsu 210098, China
2School of Transportation, Southeast University, 35 Jinxianghe Rd, Nanjing, Jiangsu 210096, China

Correspondence should be addressed to Muqing Du

Received 28 April 2017; Accepted 17 July 2017; Published 10 September 2017

Academic Editor: Dongjoo Park

Copyright © 2017 Muqing Du et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As more and more cities in worldwide are facing the problems of traffic jam, governments have been concerned about how to design transportation networks with adequate capacity to accommodate travel demands. To evaluate the capacity of a transportation system, the prescribed origin and destination (O-D) matrix for existing travel demand has been noticed to have a significant effect on the results of network capacity models. However, the exact data of the existing O-D demand are usually hard to be obtained in practice. Considering the fluctuation of the real travel demand in transportation networks, the existing travel demand is represented as uncertain parameters which are defined within a bounded set. Thus, a robust reserve network capacity (RRNC) model using min–max optimization is formulated based on the demand uncertainty. An effective heuristic approach utilizing cutting plane method and sensitivity analysis is proposed for the solution of the RRNC problem. Computational experiments and simulations are implemented to demonstrate the validity and performance of the proposed robust model. According to simulation experiments, it is showed that the link flow pattern from the robust solutions to network capacity problems can reveal the probability of high congestion for each link.