Comprehensive analysis of the influence of door width on the passenger flow time on Korean urban railways

Abstract

This paper deals with the effect of door width on the passenger flow time on Korean urban railways using analysis of covariance (ANCOVA) and regression analysis. All regression coefficients are statistically significant in both analyses. The ANCOVA results show that increasing the door width can be quite useful in reducing passenger flow time. Regression analysis, which controls for other independent variables, also shows a clear relationship between door width and passenger flow time. In an additional regression model, we consider the interaction between door width and the number of boarding passengers. This paper also compares flow times estimated through two different methods: laboratory experiments and live railway observation, and finds no significant difference between the two approaches.

Keywords

Railway station scheduling train driving railway transport urban transport

Get full access to this article

View all access options for this article.

References

Harris

Anderson

. An international comparison of urban rail boarding and alighting rates. Proc IMechE, Part F: J Rail Rapid Transit 2007; 221: 521–526.

Parkinson

Fisher

. Transit cooperative research program: rail transit capacity. Report 13, Washington DC: National Academy Press, 1996.

Heinz W. Passenger service times on trains: theory, measurements and models. Licentiate Thesis, Royal Institute of Technology, Sweden, 2003.

Harris NG, Graham DJ, Anderson RJ, et al. The impact of urban rail boarding and alighting factors. In: The 93rd annual meeting of the Transportation Research Board, 2014.

Wiggenradd PBL. Alighting and boarding times of passengers at Dutch railway stations. Report, TRAIL Research School, Delft University of Technology, Holland, 2001.

Jang

Shin

. A statistical study on doorway flow-time for designing doors of Ui LRT. J Korean Soc Railway 2013; 16: 144–150 (in Korean).

Rowe I and Tyler N. High density boarding and alighting: a psycho-physical experiment. Arup J 2010: 26–27.

Douglas N. Modeling train and station demand and capacity. Final report for Transport for NSW, Australia, 2012, pp.47–48.

Harris

. Train boarding and alighting rates at high passenger loads. J Adv Transport 2005; 40(3): 249–263.

10.

Puong A. Dwell time model and analysis for the MBTA Red Line. Working paper, DSPace@MIT, Massachusetts Institute of Technology, 2000.

11.

Lam

WHK

Cheung

Lam

. A study of crowding effects at the Hong Kong light rail transit stations. Transport Res Part A 1999; 33: 401–415.

12.

Hermant LFL. Human movement behavior in South African railway stations: implications for design. In: The 30th South African transport conference, 2011, pp.520–533.

13.

Jong JC and Chang EF. Investigation and estimation of train dwell time for timetable planning. In: The ninth World congress on railway research, 2011.

14.

Jiang Z, Ji T and Li F. Rail transit dwell time modelling and delay simulation in variant passenger flow. In: In: The 92nd annual meeting of the Transportation Research Board, 2013.

15.

Daamen

Lee

Wiggenraad

. Boarding and alighting experiments: overview of setup and performance and some preliminary results. J Transport Res Board 2008; 2042: 71–81.