Towards a robust traffic timetable for the Swedish Southern Mainline

Abstract

We identify the on-time performance as a key to evaluate a railway timetable's robustness to disturbances and evaluate the on-time performance for two single services on the Swedish Southern Mainline for the autumn period 2011. We analyse the punctuality by studying how the performance develops en route. Typically the time spent in the stations is underestimated, which partly is compensated for by time margins along the line, giving rise to a sawtooth formed delay muster with an increasing trend. The standard deviation in the delay reports seems to be a good indicator for the precision in the traffic. In this material it is almost linearly increasing with a good minute per hour scheduled running time. Two attempts are made to change the timetable for a better performance. By using socio-economic values a customer-oriented description of the current timetable is calculated as a trade-off between high punctuality and short travel time. This timetable does not affect any other traffic. Minor effects to other traffic would be the result if the existing margins are re-distributed to better match the demand en route in some sense. As long as no more time supplement is added, we will, however, not improve on the punctuality to the final destination. For future research is left, how the decrease in precision can be accounted for already in the timetable construction.

Application of Data Clustering to Railway Delay Pattern Recognition

“…Similarly, Peterson [4] studies the on-time performance along the path of specific train services, using the rolling average delay of the last three timing points. Such on-time performance is plotted for all the repetitions of a specific train service over a time period and compared to the average, standard deviation, and 75th percentile.…”

“…The study represents delay data similarly to Peterson [4], Andersson et al [19,21], and Schittenhelm and Richter [1], plotting the recorded delay over individual repetitions of the same service path, and adds the plot of relevant delay percentiles over the stations. The shape of the percentile-based delay profiles highlights recurrent patterns in the deviation from schedule.…”

“…New applications of these techniques would support the analysis of the realized development of delays along the path of individual train delays. As shown by statistical analysis and visual search for patterns presented by Schittenhelm and Richter [1,18], Peterson [4], Andersson et al [19,21], and van Oort et al [22], this type of data contains a great deal of information yet to be explored, which would provide insight into the effectiveness of running time supplements and into the presence of structural issues that generate delay in transport operation. In this paper we present a clustering technique to identify recurrent delay patterns among train services, based on readily available data, which leaves room for inference on the factors that generate specific delay patterns.…”

“…The increasing availability of automated data sources is offering new ways to analyze operations, providing deeper insight and more reliable information. Railway management is very accepting of these new possibilities, and considerable effort is made by operators and institutions to use operations analysis in feedback loops for improving the timetabling process [1][2][3][4]. A better understanding of the development of delays in railways, and in transportation in general, provides the opportunity to improve the processes and identify the factors affecting reliability.…”

Assessment of Robustness in Railway Traffic Timetables

“…It appears like the timetable might be insufficient when it comes to handling delays. The robustness study by Peterson (2012) gives further aspects of the en-route punctuality.…”