Mass Transit Timetable Modeling and Evaluation

Authors

  • กานต์นภัส ช้ำเกตุ
  • วเรศรา วีระวัฒน์

Keywords:

Mass transit, Short-turn operation, Simulation

Abstract

The mass transit system is supported by Thai Government which expands its in Bangkok Metropolitan Region for serving the growing urban areas. This research focused on the south extension of the metro green line, with total distances of about 35 kilometers and 31 stations (Mochit station to National Housing station). This research studied operation pattern by Communication Based Train Control systems (CBTC) with an application of a simulation method which considered infrastructure, turn back point, rolling stock, and headway (service) that all affect the performance of service. Simulation results showed that a short turn operation solved passenger unbalances that occur between the existing line and the extension line. However, “knock on delay” under the short turn operations, was possible with the increasing of a headway, so the operator of the mass transit should consider turn back point management to reduce the impact of the knock on delay. Hence, in case of double passenger unbalance, the operators should apply 2 short turnings.

References

[1] Hansen, I.A. and J. Pachl, Railway Timetabling&Operation. 2008.
[2] Wang, Y., B. Ning, T. van den Boom, and B. De Schutter, Background: Train Operations and Scheduling, in Optimal Trajectory Planning and Train Scheduling for Urban Rail Transit Systems. 2016, Springer International Publishing: Cham. p. 7-21.
[3] Deckoff, A.A., The Short Turn as a Real-time Transit Operating Strategy, in Department of Civil Engineering. 1990, Massachusetts Institute of Technology: United States.
[4] Canca, D., E. Barrena, G. Laporte, and F.A. Ortega, A short-turning policy for the management of demand disruptions in rapid transit systems. Annals of Operations Research, 2014: p. 1-22.
[5] Dell, S.W.O., Optimal control strategies for a rail transit line, in Department of Electrical Engineering and Computer Science. 1997, Massachusetts Institute of Technology: United States.
[6] Canca, D., E. Barrena, A. Zarzo, F. Ortega, and E. Algaba, Optimal Train Reallocation Strategies under Service Disruptions. Procedia - Social and Behavioral Sciences, 2012. 54: p. 402-413.

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Published

2020-06-22

How to Cite

[1]
ช้ำเกตุ ก. . and วีระวัฒน์ ว. ., “Mass Transit Timetable Modeling and Evaluation ”, Eng. & Technol. Horiz., vol. 34, no. 3, pp. 50–57, Jun. 2020.

Issue

Section

Research Articles