Robust Evaluation for Transportation Network Capacity under Demand Uncertainty
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| Publicado no: | Journal of Advanced Transportation vol. 2017 (2017) |
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| Autor principal: | |
| Outros Autores: | , , |
| Publicado em: |
John Wiley & Sons, Inc.
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| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1155/2017/9814909 |2 doi | |
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| 045 | 2 | |b d20170101 |b d20171231 | |
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| 100 | 1 | |a Du, Muqing |u College of Civil and Transportation Engineering, Hohai University, 1 Xikang Rd, Nanjing, Jiangsu 210098, China | |
| 245 | 1 | |a Robust Evaluation for Transportation Network Capacity under Demand Uncertainty | |
| 260 | |b John Wiley & Sons, Inc. |c 2017 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Sensitivity analysis | ||
| 653 | |a Optimization | ||
| 653 | |a Transportation networks | ||
| 653 | |a Traffic assignment | ||
| 653 | |a Computer applications | ||
| 653 | |a Parameter uncertainty | ||
| 653 | |a Travel | ||
| 653 | |a Probability distribution | ||
| 653 | |a Traffic congestion | ||
| 653 | |a Robustness | ||
| 653 | |a Computer simulation | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Flow pattern | ||
| 653 | |a Route choice | ||
| 653 | |a Network management systems | ||
| 653 | |a Travel demand | ||
| 653 | |a Transportation systems | ||
| 653 | |a Traffic jams | ||
| 653 | |a Equilibrium | ||
| 653 | |a Transportation planning | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Jiang, Xiaowei |u School of Transportation, Southeast University, 35 Jinxianghe Rd, Nanjing, Jiangsu 210096, China | |
| 700 | 1 | |a Cheng, Lin |u School of Transportation, Southeast University, 35 Jinxianghe Rd, Nanjing, Jiangsu 210096, China | |
| 700 | 1 | |a Zheng, Changjiang |u College of Civil and Transportation Engineering, Hohai University, 1 Xikang Rd, Nanjing, Jiangsu 210098, China | |
| 773 | 0 | |t Journal of Advanced Transportation |g vol. 2017 (2017) | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2407643077/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2407643077/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2407643077/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |