Robust Evaluation for Transportation Network Capacity under Demand Uncertainty
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/9814909 |
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| author | Muqing Du Xiaowei Jiang Lin Cheng Changjiang Zheng |
| author_facet | Muqing Du Xiaowei Jiang Lin Cheng Changjiang Zheng |
| author_sort | Muqing Du |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-68c3e380441e43dc99b4e2a48f17ff1b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-68c3e380441e43dc99b4e2a48f17ff1b2025-02-03T01:33:17ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/98149099814909Robust Evaluation for Transportation Network Capacity under Demand UncertaintyMuqing Du0Xiaowei Jiang1Lin Cheng2Changjiang Zheng3College of Civil and Transportation Engineering, Hohai University, 1 Xikang Rd, Nanjing, Jiangsu 210098, ChinaSchool of Transportation, Southeast University, 35 Jinxianghe Rd, Nanjing, Jiangsu 210096, ChinaSchool of Transportation, Southeast University, 35 Jinxianghe Rd, Nanjing, Jiangsu 210096, ChinaCollege of Civil and Transportation Engineering, Hohai University, 1 Xikang Rd, Nanjing, Jiangsu 210098, ChinaAs 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.http://dx.doi.org/10.1155/2017/9814909 |
| spellingShingle | Muqing Du Xiaowei Jiang Lin Cheng Changjiang Zheng Robust Evaluation for Transportation Network Capacity under Demand Uncertainty Journal of Advanced Transportation |
| title | Robust Evaluation for Transportation Network Capacity under Demand Uncertainty |
| title_full | Robust Evaluation for Transportation Network Capacity under Demand Uncertainty |
| title_fullStr | Robust Evaluation for Transportation Network Capacity under Demand Uncertainty |
| title_full_unstemmed | Robust Evaluation for Transportation Network Capacity under Demand Uncertainty |
| title_short | Robust Evaluation for Transportation Network Capacity under Demand Uncertainty |
| title_sort | robust evaluation for transportation network capacity under demand uncertainty |
| url | http://dx.doi.org/10.1155/2017/9814909 |
| work_keys_str_mv | AT muqingdu robustevaluationfortransportationnetworkcapacityunderdemanduncertainty AT xiaoweijiang robustevaluationfortransportationnetworkcapacityunderdemanduncertainty AT lincheng robustevaluationfortransportationnetworkcapacityunderdemanduncertainty AT changjiangzheng robustevaluationfortransportationnetworkcapacityunderdemanduncertainty |