Cyclic Timetable Improvement With Train Traffic Data Analysis
Free (open access)
341 - 352
The Swiss Federal Railways (SBB) plans and operates one of the densest railway networks in the world. Analysis of train traffic data shows that overall punctuality has steadily increased since 2003. One challenge presented by a cyclic timetable is that the departure and arrival times for a given connection always remain the same during the day. In order to increase traffic density further while maintaining high punctuality, it is increasingly important to identify and manage delay fluctuations. The first part of this paper will be a short discussion about the problematic of a cyclic timetable in regard to the daily and seasonal variations. The second part of this paper will focus on setting a framework for the analysis of train traffic data by defining three dimensions: train, time and place. These three dimensions might at first seem obvious but are very powerful if used systematically. Based on these three dimensions, we will show some examples of different groupings of train traffic data used at SBB to identify potential punctuality problems. The third part presents a practical analysis of the seasonal delay fluctuations for the entire Swiss train network. In order to analyse the yearly fluctuation, we define a common punctuality measure, then compute ten years of train traffic data (about 14 million train arrivals). We will show that despite the fact that in these ten years the timetable changed different times, the seasonal fluctuation remain similar. Different questions such as the influence of winter, number of passengers or technical problems are discussed. Finally, the need for further data and other analysis to better understand the shown delay fluctuations will be addressed. Keywords: cyclic timetable, punctuality, data analysis, train traffic, statistic.
cyclic timetable, punctuality, data analysis, train traffic, statistic.