Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty
The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/16/3493 |
| _version_ | 1797584914437111808 |
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| author | Pruk Sasithong Amir Parnianifard Nitinun Sinpan Suvit Poomrittigul Muhammad Saadi Lunchakorn Wuttisittikulkij |
| author_facet | Pruk Sasithong Amir Parnianifard Nitinun Sinpan Suvit Poomrittigul Muhammad Saadi Lunchakorn Wuttisittikulkij |
| author_sort | Pruk Sasithong |
| collection | DOAJ |
| description | The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals. |
| first_indexed | 2024-03-10T23:59:28Z |
| format | Article |
| id | doaj.art-a6ea86a854a54dd6ae5bd7b9b3114747 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:59:28Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a6ea86a854a54dd6ae5bd7b9b31147472023-11-19T00:54:28ZengMDPI AGElectronics2079-92922023-08-011216349310.3390/electronics12163493Simulation-Based Headway Optimization for the Bangkok Airport Railway System under UncertaintyPruk Sasithong0Amir Parnianifard1Nitinun Sinpan2Suvit Poomrittigul3Muhammad Saadi4Lunchakorn Wuttisittikulkij5Wireless Communication Ecosystem Research Unit, Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, ThailandWireless Communication Ecosystem Research Unit, Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, ThailandWireless Communication Ecosystem Research Unit, Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, ThailandSchool of Information Technology, King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok 10520, ThailandDepartment of Electrical Engineering, University of Central Punjab, Lahore 54590, PakistanWireless Communication Ecosystem Research Unit, Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, ThailandThe ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals.https://www.mdpi.com/2079-9292/12/16/3493simulation-based modelrailway systemtimetable schedulinguncertaintyparticle swarm optimization |
| spellingShingle | Pruk Sasithong Amir Parnianifard Nitinun Sinpan Suvit Poomrittigul Muhammad Saadi Lunchakorn Wuttisittikulkij Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty Electronics simulation-based model railway system timetable scheduling uncertainty particle swarm optimization |
| title | Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty |
| title_full | Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty |
| title_fullStr | Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty |
| title_full_unstemmed | Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty |
| title_short | Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty |
| title_sort | simulation based headway optimization for the bangkok airport railway system under uncertainty |
| topic | simulation-based model railway system timetable scheduling uncertainty particle swarm optimization |
| url | https://www.mdpi.com/2079-9292/12/16/3493 |
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