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Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System
https://orcid.org/https://orcid.org/0000-0002-3497-2209
In this paper, we propose a timetable optimizer (TO) consisting of the following parts: 1) Demand Forecasting Module 2) Train Optimization Module 3) Timetable Generator Module. TO is a specialized system for planning the train timetable which designed to help solve the problem of determining the number of trains that are suitable and scheduling the train suitably according to the number of trains designated by the system. TO integrates the passenger data from the latest round trip or historical information and forecasts a number of passengers of high-speed trains based on historical data in order to find the optimal number of trains by using a mixed integer programing model. Lastly, the TO system can applies the number of trains to calculate the appropriate train schedule so that the planning cycle is complete. A case study of high-speed railway system in Thailand with 36 trains to satisfy demand of 35,000 passengers/ each direction is conducted. The results show that the proposed system can quickly generate a timetable having an optimal number of train with suitable time interval according to a demand forecasting.
Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System
https://orcid.org/https://orcid.org/0000-0002-3497-2209
In this paper, we propose a timetable optimizer (TO) consisting of the following parts: 1) Demand Forecasting Module 2) Train Optimization Module 3) Timetable Generator Module. TO is a specialized system for planning the train timetable which designed to help solve the problem of determining the number of trains that are suitable and scheduling the train suitably according to the number of trains designated by the system. TO integrates the passenger data from the latest round trip or historical information and forecasts a number of passengers of high-speed trains based on historical data in order to find the optimal number of trains by using a mixed integer programing model. Lastly, the TO system can applies the number of trains to calculate the appropriate train schedule so that the planning cycle is complete. A case study of high-speed railway system in Thailand with 36 trains to satisfy demand of 35,000 passengers/ each direction is conducted. The results show that the proposed system can quickly generate a timetable having an optimal number of train with suitable time interval according to a demand forecasting.
Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System
https://orcid.org/https://orcid.org/0000-0002-3497-2209
In this paper, we propose a timetable optimizer (TO) consisting of the following parts: 1) Demand Forecasting Module 2) Train Optimization Module 3) Timetable Generator Module. TO is a specialized system for planning the train timetable which designed to help solve the problem of determining the number of trains that are suitable and scheduling the train suitably according to the number of trains designated by the system. TO integrates the passenger data from the latest round trip or historical information and forecasts a number of passengers of high-speed trains based on historical data in order to find the optimal number of trains by using a mixed integer programing model. Lastly, the TO system can applies the number of trains to calculate the appropriate train schedule so that the planning cycle is complete. A case study of high-speed railway system in Thailand with 36 trains to satisfy demand of 35,000 passengers/ each direction is conducted. The results show that the proposed system can quickly generate a timetable having an optimal number of train with suitable time interval according to a demand forecasting.
Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System
KSCE J Civ Eng
Khwanpruk, Somkiat (author) / U-tapao, Chalida (author) / Khwanpruk, Kankanit (author) / Laokhongthavorn, Laemthong (author) / Suwannatrai, Arkom (author) / Moryadee, Seksun (author)
KSCE Journal of Civil Engineering ; 25 ; 3502-3510
2021-09-01
9 pages
Article (Journal)
Electronic Resource
English
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