Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data
Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-desti...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5597130 |
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| author | Baichuan Mo Zhenliang Ma Haris N. Koutsopoulos Jinhua Zhao |
| author_facet | Baichuan Mo Zhenliang Ma Haris N. Koutsopoulos Jinhua Zhao |
| author_sort | Baichuan Mo |
| collection | DOAJ |
| description | Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-destination flows, include passenger path choices and train capacity. Train capacity, which has often been overlooked in the literature, is an important input that exhibits a lot of variabilities. The paper proposes a simulation-based optimization (SBO) framework to simultaneously calibrate path choices and train capacity for urban rail systems using AFC and AVL data. The calibration is formulated as an optimization problem with a black-box objective function. Seven algorithms from four branches of SBO solving methods are evaluated. The algorithms are evaluated using an experimental design that includes five scenarios, representing different degrees of path choice randomness and crowding sensitivity. Data from the Hong Kong Mass Transit Railway (MTR) system is used as a case study. The data is used to generate synthetic observations used as “ground truth.” The results show that the response surface methods (particularly constrained optimization using response surfaces) have consistently good performance under all scenarios. The proposed approach drives large-scale simulation applications for monitoring and planning. |
| format | Article |
| id | doaj-art-b3a7c0bc419b4dbfbee4067de8c1783a |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b3a7c0bc419b4dbfbee4067de8c1783a2025-02-03T00:58:56ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55971305597130Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement DataBaichuan Mo0Zhenliang Ma1Haris N. Koutsopoulos2Jinhua Zhao3Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Civil Engineering, Monash University, Melbourne, VIC 3800, AustraliaDepartment of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USADepartment of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA 20139, USATransit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-destination flows, include passenger path choices and train capacity. Train capacity, which has often been overlooked in the literature, is an important input that exhibits a lot of variabilities. The paper proposes a simulation-based optimization (SBO) framework to simultaneously calibrate path choices and train capacity for urban rail systems using AFC and AVL data. The calibration is formulated as an optimization problem with a black-box objective function. Seven algorithms from four branches of SBO solving methods are evaluated. The algorithms are evaluated using an experimental design that includes five scenarios, representing different degrees of path choice randomness and crowding sensitivity. Data from the Hong Kong Mass Transit Railway (MTR) system is used as a case study. The data is used to generate synthetic observations used as “ground truth.” The results show that the response surface methods (particularly constrained optimization using response surfaces) have consistently good performance under all scenarios. The proposed approach drives large-scale simulation applications for monitoring and planning.http://dx.doi.org/10.1155/2021/5597130 |
| spellingShingle | Baichuan Mo Zhenliang Ma Haris N. Koutsopoulos Jinhua Zhao Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data Journal of Advanced Transportation |
| title | Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data |
| title_full | Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data |
| title_fullStr | Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data |
| title_full_unstemmed | Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data |
| title_short | Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data |
| title_sort | calibrating path choices and train capacities for urban rail transit simulation models using smart card and train movement data |
| url | http://dx.doi.org/10.1155/2021/5597130 |
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