Data analysis in long-distance traffic and identification of the causes of unpunctuality
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
Are you interested in your own use cases?
Challenge
An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.
Solution
There will be a interactive and Flexible application, including of different maps with two different views implemented.
Result
Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.