Visualisation of Train Punctuality – Illustrations and Cases

Landmark, Andreas D.; Seim, Andreas Amdahl; Olsson, Nils O.E.

doi:10.1016/j.trpro.2017.12.092

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Another large improvement process, including better maintenance work and winter management, has been developed for the Nordland Line since 2014 in order to make the railway more robust against climate challenges [39]. The research on visualization of punctuality data [40] contributes to the identification of such actions. There are also larger investments in new tracks, especially in the southern part of the line which has the most traffic.…”

Section: Discussionmentioning

confidence: 99%

Investigating the effect of weather on punctuality of Norwegian railways: a case study of the Nordland Line

Zakeri

Olsson

2018

J. Mod. Transport.

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The overall punctuality of the railway system is of particular importance for customers in order to use trains as a means of public transport. Investigating the drivers of low punctuality of trains is an essential step for improving their punctuality. Severe weather conditions are assumed to be one of the drivers of delays and low punctuality of the transportation system. This paper addresses the extent to which the weather factors such as temperature, snow, precipitation and wind influence the punctuality of trains on the Norwegian railways. The data for the study are collected from the passenger trains on the Nordland Line. The study period is 10 years, from the beginning of 2007 to the end of 2016. Data are analysed on a daily and weekly basis, and correlation and regression analyses are used to perform the statistical analysis. The results show that extreme cold weather during the winters is a crucial influencing factor contributing to delays and low punctuality. We also found that the weather variable that best explains variations in punctuality of passenger trains on this line is snow depth.

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Section: Discussionmentioning

confidence: 99%

Investigating the effect of weather on punctuality of Norwegian railways: a case study of the Nordland Line

Zakeri

Olsson

2018

J. Mod. Transport.

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“…According to [30], quoting [31], train punctuality is "related to trains running according to schedule", while [32] refers to punctuality as a "numerical measurement (. .…”

Section: Delays and Punctuality: Background And Related Workmentioning

confidence: 99%

Delay Indices for Train Punctuality

Denti

Burroni

2023

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Indicators of expected quality of service in public contracts are often based on some kind of “punctuality”, usually defined in terms of the percentage of trains arriving at the final destination (and/or at intermediate significant stops) within a given delay. Passengers, however, tend to use the word “punctuality” with a more general meaning, mostly as a synonym for expected delay at their own destination, and especially in case of commuters are much less tolerant of even smaller delays than train operators would normally allow. In particular, measuring the delay only at the final destination is perceived as largely inadequate, leading to underestimation of the actual percentage of late trains, and in turn undermining passengers’ trust in official performance statistics. In this paper, we take the passengers’ perspective, introducing a family of delay indices called D-indices aimed at capturing the overall performance of a train “as a whole”, taking into account both the delays at the sampling points and the mutual location and order of such sampling points. In this paper, all indicators have the physical dimension of time in order to be easily replaceable to other delay measures. We first present typical approaches and definitions of punctuality in the literature, then introduce D-indices while exploring their features, pros and cons, and relevant properties. We validate and discuss our approach by comparing this model with existing approaches both theoretically and by comparison with selected datasets consisting of about one hundred trains transcribed over the last three years.

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

Kuipers

Palmqvist

2022

Applied Sciences

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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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Visualisation of Train Punctuality – Illustrations and Cases

Cited by 3 publications

References 8 publications

Investigating the effect of weather on punctuality of Norwegian railways: a case study of the Nordland Line

Investigating the effect of weather on punctuality of Norwegian railways: a case study of the Nordland Line

Delay Indices for Train Punctuality

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

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