Dwell Time Modelling and Optimized Simulations for Crowded Rail Transit Lines Based on Train Capacity

  • Zhibin Jiang
  • Chao Xie Tongji University
  • Tingting Ji Tongji University
  • Xiaolei Zou Tongji University
Keywords: dwell time, train capacity, train delay, timetable simulation, rail transit, passenger volume,

Abstract

Understanding the nature of rail transit dwell time has potential benefits for both the users and the operators. Crowded passenger trains cause longer dwell times and may prevent some passengers from boarding the first available train that arrives. Actual dwell time and the process of passenger alighting and boarding are interdependent through the sequence of train stops and propagated delays. A comprehensive and feasible dwell time simulation model was developed and optimized to address the problems associated with scheduled timetables. The paper introduces the factors that affect dwell time in urban rail transit systems, including train headway, the process and number of passengers alighting and boarding the train, and the inability of train doors to properly close the first time because of overcrowded vehicles. Finally, based on a time-driven micro-simulation system, Shanghai rail transit Line 8 is used as an example to quantify the feasibility of scheduled dwell times for different stations, directions of travel and time periods, and a proposed dwell time during peak hours in several crowded stations is presented according to the simulation results.

References

Kittelson & Associates, Inc, Parsons Brinckerhoff, et al. Transit Capacity and Quality of Service Manual, Third Edition, TCRP Report 165. Washington, DC: Transportation Research Board; 2013.

Vuchic V. Urban transit operations, planning and economics. Reston: American Society of Civil Engineers; 2005.

Lin T, Wilson NH. Dwell time relationships for light rail systems. Transport Res Rec. 1992(1361):287-95.

Lam WHK, Cheung C, Lam CF. A study of crowding effects at the Hong Kong light rail transit stations. Transportation Research Part A: Policy and Practice. 1999;33:401-15.

Wiggenraad PBL. Alighting and boarding times of passengers at Dutch railway stations. Delft: TRAIL Research School; 2001.

Harris NG, Anderson RJ. An international comparison of urban rail boarding and alighting rates. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 2007;221(4):521-6.

Tirachini A. Bus dwell time: the effect of different fare collection systems, bus floor level and age of passengers. Transportmetrica A: Transport Science. 2013;9(1):28-49.

Meng Q, Qu X. Bus dwell time estimation at bus bays: A probabilistic approach. Transportation Research Part C: Emerging Technologies. 2013;36:61-71.

Aashtiani HZ, Iravani H. Application of dwell time functions in transit assignment model. Transportation Research Record: Journal of the Transportation Research Board. 2002;1817:88-92.

Zhang Q, Han B, Li D. Modeling and simulation of passenger alighting and boarding movement in Beijing metro stations. Transportation Research Part C: Emerging Technologies. 2008;16:635-49.

Jiang Z, Li F, Xu R, Gao P. A simulation model for estimating train and passenger delays in large-scale rail transit networks. Journal of Central South University. 2012;19:3603-13.

Grube P, Núñez F, Cipriano A. An event-driven simulator for multi-line metro systems and its application to Santiago de Chile metropolitan rail network. Simulation Modelling Practice and Theory. 2011;19:393-405.

Hadas Y, Ceder A. Optimal coordination of public-transit vehicles using operational tactics examined by simulation. Transportation Research Part C: Emerging Technologies. 2010;18:879-95.

Kanai S, Shiina K, Harada S, Tomii N. An optimal delay management algorithm from passengers’ viewpoints considering the whole railway network. Journal of Rail Transport Planning & Management. 2011;1:25-37.

Carey M, Carville S. Testing schedule performance and reliability for train stations. J Oper Res Soc. 2000;51:666-82.

Heimburger DE, Herzenberg AY, Wilson NHM. Using simple simulation models in operational analysis of rail transit lines: Case study of Boston’s Red Line. Transport Res Rec. 1999:21-30.

Lam WHK, Cheung CY, Poon YF. A study of train dwelling time at the Hong Kong mass transit railway system. J Adv Transport. 1998;32:285-95.

Yu B, Yao JB, Yang ZZ. An improved headway-based holding strategy for bus transit. Transport Plan Techn. 2010;33:329-41.

Nash A, Huerlimann D. Railroad simulation using OpenTrack. Computers in railways IX. 2004;15:45-54.

Published
2015-04-13
How to Cite
1.
Jiang Z, Xie C, Ji T, Zou X. Dwell Time Modelling and Optimized Simulations for Crowded Rail Transit Lines Based on Train Capacity. Promet [Internet]. 2015Apr.13 [cited 2024Nov.21];27(2):125-3. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/1487
Section
Articles