Identification of causes of unpunctuality in autumn

A statistical model assesses drivers for long-distance punctuality. From this, the most important causes can be systematically identified.

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Overview of punctuality drivers

Approach applicable to different plans and timetable years

Challenge

  • The autumn months have lower long-distance punctuality than other months, so the company's punctuality targets are missed
  • The causes of this unpunctuality are unclear, so that measures to control it are insufficient
  • The analysis is intended to identify possible unknown drivers in 2019 and subsequent years at an early stage

Solution

  • Potential factors influencing long-distance punctuality are compiled in a driver tree
  • The necessary data to quantify these factors are collected and linked in a large data set
  • A linear model with regularisation identifies the most important punctuality drivers, quantifies their punctuality effect and their effect in autumn

Result

  • Overview of relevant punctuality drivers available in the form of a driver tree
  • This generic approach works with statistical models and is thus applicable to different timetable years and train types with little effort

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