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.
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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