Delays for passenger trains on a regional railway line in southern Sweden

Abstract

The aim of this article is to study how selected variables influence delays in train traffic. Data has been collected on train movements, timetables, weather and capacity utilization on a highly utilized single-track railway line in southern Sweden during 2014. based on this dataset, we have analysed how different factors affect delays in passenger traffic. We measure delays in a novel way, as deviations from the scheduled duration for each line section and station stop, not as deviations from a published or operational timetable, and this allows us to identify when and where the delays first occur. average delays were much larger at station stops. The most significant factor affecting delays was the scheduled duration time at station stops and the existence of margins on line sections. If trains arrive to a line section or station stop slightly delayed they speed up the activity, otherwise they are typically delayed. The influence of weather was less significant and somewhat contradictory: snow and cold temperatures increase delays on line sections but reduce them at station stops, while precipitation made no difference. Capacity utilization seems to have a negative correlation with delays, but we have too little variation in the levels to be confident. all studied variables, except for precipitation, have impacts that are statistically significant to a very high degree of confidence, using both t-tests and regression analysis. The results of this study have important practical implications for timetable construction; for instance we estimate that a reallocation of scheduled time at stations could reduce delays by as much as 80%.

Effects of rural bus stops on travel time and reliability

“…The time-point segment analysis is based on a method previously developed for analysing delays in rail traffic (Palmqvist et al 2017), using a combination of t-tests, plots for different explanatory variables against average bus travel times, and multivariate regression models. First, Welch's t-test was used to gain an initial understanding, depicting statistical significance and effect size of the explanatory variables on bus travel time between time points.…”

“…To be able to compare the impact of the bus stops with other factors, and to control the influence of these factors on the focal relationship, variables concerning day type, time of day, delay status, weather conditions, and passenger activity were included in the analyses. The selection of explanatory variables is based on previous studies on travel time variability (El-Geneidy et al 2011;Palmqvist et al 2017), combined with data availability considerations. Infrastructure variables were not included, due to the small number of time-point segments and, consequently, a too small variability of the infrastructure conditions.…”

Evaluating Sustainability of Mass Rapid Transit Stations in Dubai

“…Many researchers have demonstrated that punctuality is of paramount importance to rail user satisfaction [69][70][71]. Punctuality is considered to be one of the most relevant sustainability indicators in railway operations [72][73][74].…”

The use of recovery time in timetables: rail passengers’ preferences and valuation relative to travel time and delays

“…The over-arching advice on how to calculate recovery time is published by the International Union of Railways (UIC 2000). However, it is ultimately down to individual railway administrations and operators to calculate guidance on their own recovery margins (Palmqvist et al 2017) and hence these differ between and within countries, with different route densities, rolling stock and route lengths all exerting an influence. We might expect the significance of recovery time to vary across countries if only because what is deemed to be acceptable lateness, termed a delay threshold, itself varies across countries (Li et al 2010).…”