journal article·2022-06-12Open access

Passenger Volumes and Dwell Times for Commuter Trains: A Case Study Using Automatic Passenger Count Data in Stockholm

Ruben Alaric Kuipers, Carl-William Palmqvist

Applied Sciences· Volume 12· Issue 12

Abstract

This study investigates the relationship between passenger volumes and the frequency and size of dwell times for commuter trains. We use data collected by automatic passenger count systems in Stockholm and show that dwell times often take longer than scheduled. To understand the effect of passenger volumes on delays, we use the frequencies of delays for different passenger volumes and delay sizes. We find that these frequencies differ significantly from a frequency distribution independent of passenger volumes, indicating that passenger volumes have an effect on the frequency of dwell time delays. Neglecting passenger volumes underestimates the frequency of delays in most cases, especially for smaller delays. Although the frequency of dwell time delays increases as passenger volumes increase, the same is not necessarily true for their size. The relationship between passenger volumes and delay sizes is thus non-linear, and it depends on scheduled dwell times. We conclude that small increases in dwell times can result in sharp increases in on-time performance but that only increasing dwell times is not sufficient. Measures to speed up the boarding and alighting process are also necessary.
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Research context

OpenAlex topics: Transportation Planning and Optimization · Railway Systems and Energy Efficiency · Urban Transport and Accessibility

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Affiliations

Department of Technology and Society, Lund University, P.O. Box 118, 22100 Lund, Sweden

K2 Swedish Knowledge Centre for Public Transport, Bruksgatan 8, 22236 Lund, Sweden

Funding

K2, The Swedish Knowledge Centre for Public Transport

References (36)

Show source-provided references
1. Harris, N., Graham, D.J., Anderson, R.J., and Li, H. (2014, January 12–16). The Impact of Urban Rail Boarding and Alighting Factors. Proceedings of the Transportation Research Board 93rd Annual Meeting, Washington, DC, USA.
2. Vieira, A.P., Christofoletti, L.M., and Vilela, P.R.S. (2018, January 18–20). Analyzing Railway Capacity Using a Planning Tool. Proceedings of the 2018 Joint Rail Conference, Pittsburgh, PA, USA.10.1115/JRC2018-6160 ↗
3. Optimal allocation of buffer times to increase train schedule robustness10.1016/j.ejor.2016.05.013 ↗
4. Palmqvist, C.-W. (2019). Delays and Timetabling for Passenger Trains. [Ph.D. Thesis, Faculty of Engineering, Technology and Society, Transport and Roads, Lund University]. Available online: http://portal.research.lu.se/ws/files/70626078/Carl_William_Palmqvist_web.pdf.
5. Travel-time reliability impacts on railway passenger demand: A revealed preference analysis10.1016/j.jtrangeo.2010.11.009 ↗
6. Brons, M.R.E., and Rietveld, P. (2008). Rail Mode, Access Mode and Station Choice: The Impact of Travel Time Unreliability, VU University. Research Report for the Project TRANSUMO BTK.
7. The valuation of reliability for personal travel10.1016/S1366-5545(00)00011-9 ↗
8. Jiang, S., Persson, C., and Brundell-Freij, K. (2019, January 17–20). Evaluation of travel time reliability using ‘revealed preference’ data & Bayesian posterior analysis. Proceedings of the 8th International Conference on Railway Operations Modelling and Analysis (ICROMA), Norrköping, Sweden.
9. Li, D., Goverde, R.M.P., Daamen, W., and He, H. (2014, January 8–11). Train dwell time distributions at short stop stations. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.10.1109/ITSC.2014.6958076 ↗
10. Investigating the Impact of Dwell Time on the Reliability of Urban Light Rail Operations10.1007/s40864-020-00128-1 ↗
11. Train boarding and alighting rates at high passenger loads10.1002/atr.5670400302 ↗
12. Brons, M., and Rietveld, P. (2007). Betrouwbaarheid en Klanttevredenheid in de Ov-Keten: Een Statische Analyse, Vrije Universiteit Amsterdam.
13. Gysin, K. (2018). An Investigation of the Influences on Train Dwell Time. [Master’s Thesis, Swiss Federal Institute of Technology, ETH].
14. Exploring the Effect of Boarding and Alighting Ratio on Passengers’ Behaviour at Metro Stations by Laboratory Experiments10.1155/2019/6530897 ↗
15. Goverde, R.M.P. (2005). Punctuality of Railway Operations and Timetable Stability Analysis, Netherlands TRAIL Research School.
16. The passenger’s influence on dwell times at station platforms: A literature review10.1080/01441647.2021.1887960 ↗
17. A Comprehensive Analysis of Passenger Alighting and Boarding Rates10.1007/s40864-021-00161-8 ↗
18. Effect of passengers’ flows on regularity of metro services: Case studies of Rome lines A and B10.2495/TDI-V2-N1-1-10 ↗
19. Puong, A. (2020, April 28). Dwell Time Model and Analysis for the MBTA Red Line. Mass. Inst. Technol. Res. Memo, Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.583.7667&rep=rep1&type=pdf.10.1038/s41598-020-75683-5 ↗
20. Explaining dwell time delays with passenger counts for some commuter trains in Stockholm and Tokyo
21. Development of a dwell time calculation model for timetable planning10.2495/CR080511 ↗
22. Wiggenraad, P.B.L. (2001). Alighting and Boarding Times of Passengers at Dutch Railway Stations, Netherlands TRAIL Research School.
23. The spatial and temporal variation in passenger service rate and its impact on train dwell time: A time-series clustering approach using dynamic time warping10.1080/15568318.2018.1432731 ↗
24. Railway Network Timetabling and Dynamic Traffic Management
25. Storstockholms Lokaltrafik (2021, February 28). Fakta om SL och länet 2016. Available online: https://www.sll.se/globalassets/2.-kollektivtrafik/fakta-om-sl-och-lanet/sl_och_lanet_2016.pdf.
26. Olsson, N., Halse, A.H., Hegglund, P.M., Killi, M., van der Kooij, R., Landmark, A., Seim, A., Sørensen, A.Ø., Økland, A., and Østli, V. (2015). Punktlighet i Jernbanen—Hvert Sekund Teller, SINTEF Akademisk Forlag.
27. Type I Error Rate Comparisons of Post Hoc Procedures for I j Chi-Square Tables10.1177/00131640021970871 ↗
28. Visual Graphical Analysis: A Technique to Investigate Symptom Trajectories Over Time10.1097/01.NNR.0000270029.82736.5a ↗
29. Keim, D.A., Mansmann, F., Schneidewind, J., and Ziegler, H. (2006, January 5–7). Challenges in Visual Data Analysis. Proceedings of the Tenth International Conference on Information Visualisation (IV’06), London, UK.
30. Visualisation of Train Punctuality—Illustrations and Cases10.1016/j.trpro.2017.12.092 ↗
31. Graphical Data Analysis10.1146/annurev.ps.32.020181.001203 ↗
32. Data driven improvements in public transport: The Dutch example10.1007/s12469-015-0114-7 ↗
33. Line Length versus Operational Reliability: Network Design Dilemma in Urban Public Transportation10.3141/2112-13 ↗
34. McDonald, J.H. (2022, May 25). The Handbook of Biological Statistics. Available online: http://www.biostathandbook.com/.
35. The large sample size fallacy10.1111/j.1471-6712.2012.01052.x ↗
36. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Erlbaum Associates. [2nd ed.].