Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System

Khwanpruk, Somkiat; U-tapao, Chalida; Khwanpruk, Kankanit; Laokhongthavorn, Laemthong; Suwannatrai, Arkom; Moryadee, Seksun

doi:10.1007/s12205-021-1710-5

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Given a disturbed railway operations or timetable, a real-time train dispatching and rescheduling solutions are suggested by Ariano et al [9] and [10] who designed a dispatching system of decision support system for traffic controllers. Khwanpruk et al [11] also proposed a timetable optimizer (TO) consisting of; Demand Forecasting Module, Train Optimization Module and Timetable Generator Module. whereas [12] developed a discrete-event simulation model that employs a variable neighborhood search algorithm to maintain the service level under infrastructure elements' unavailability.…”

Section: Related Workmentioning

confidence: 99%

Optimized Train Dispatching and Rescheduling During a Disruption in a Bottleneck Section

Byegon

Bisrat

Gashaw

2022

Preprint

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Railway transportation is nowadays becoming one of the most preferred mode of transport due to its safety, capacity and reliability; the capital cost for the construction of the railway infrastructure is however very high and is characterized by high rigidity as the track layout is fixed; therefore there is need to optimally use the available infrastructure. Minor delays arising from a simple disruptions or even a single train failure can have massive impacts in terms of overall delays for subsequent trains using the track facility if not solved amicably. Disruptions can be attributed to power outages, mechanical failures, derailments, accidents or even environmental factors. In a case of multiple uncertain perturbations happening in a busy complex railway network, where there are many trains requesting to use the available track resources concurrently, there will be massive delays which has a lot of negative operational and economic implications as well as passengers’ dissatisfaction. A mathematical model that is; a mixed-integer linear programming formulation is modelled to minimize total time delays in case of a set of multiple disruptions occurring on a busy track section i.e. bottleneck section, the model is formulated with consideration of sets of constraints factoring in feasible routes and safety margins and other operational dynamics such as dwell times to achieve optimal use of the available infrastructure. A number of numerical experiments based on arbitrary data and real network data are carried out to verify the effectiveness and efficiency of the proposed model. Performance of the designed model is evaluated and results are validated, the results obtained shows that the model offers an efficient rescheduled trains operation plan during disruptions, furthermore the performance of Fmincon solver and genetic algorithms (GA) are compared and their robustness evaluated, GA shows better performance during multiple disruption scenario.

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Section: Related Workmentioning

confidence: 99%

Optimized Train Dispatching and Rescheduling During a Disruption in a Bottleneck Section

Byegon

Bisrat

Gashaw

2022

Preprint

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“…Consequently, the train schedule must be organized in such a way with reduced passenger travel times to minimize the possibility of contagion risks. As the travel time and passenger demand have been the primary concerns in establishing potential train schedules in many studies ( Rajabighamchi et al, 2019 ; Boroun et al, 2020 ; Huang et al, 2021 ; Khwanpruk et al, 2021 ; Wang et al, 2021 ; Zhang et al, 2021 ; Khathawatcharakun and Limsawasd, 2022 ), the sensitive circumstances of a pandemic require greater attention to maintaining the level of system functionality and minimizing the exposure to the risk of contact. This complex issue needs the development of a decision-support tool to suggest an effective train schedule that can concurrently reduce the unserved passengers and minimize passenger travel times during the sensitive pandemic case and its aftermath.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the train service efficiency has been concurrently considered to assure the demand and reduce unsatisfied passenger numbers. For example, Khwanpruk et al (2021) constructed a timetable optimizer to determine the optimal number of trains and the time interval to effectively suit passenger demand. Wang et al (2020) proposed an optimization model to minimize the passenger waiting time and the number of unserved passengers.…”

Section: Introductionmentioning

confidence: 99%

Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak

et al. 2022

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Optimization of train timetables in high-speed railway corridors considering passenger departure time and seat-class preferences

Huang

Yuzhao

Zhang

et al. 2022

Transportation Letters

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Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System

Cited by 4 publications

References 18 publications

Optimized Train Dispatching and Rescheduling During a Disruption in a Bottleneck Section

Optimized Train Dispatching and Rescheduling During a Disruption in a Bottleneck Section

Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak

Optimization of train timetables in high-speed railway corridors considering passenger departure time and seat-class preferences

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