Abstract
The existing Big Data of transport flows and railway operations can be mined through advanced statistical analysis and machine learning methods in order to describe and predict well the train speed, punctuality, track capacity and energy consumption. The accurate modelling of the real spatial and temporal distribution of line and network transport, traffic and performance stimulates a faster construction and implementation of robust and resilient timetables, as well as the development of efficient decision support tools for real-time rescheduling of train schedules. In combination with advanced train control and safety systems even (semi-) automatic piloting of trains on main and regional railway lines will become feasible in near future.
| Translated title of the contribution | Closing the loop between data mining and fast decision support for intelligent train scheduling and traffic control |
|---|---|
| Original language | Chinese |
| Pages (from-to) | 18-30 |
| Number of pages | 13 |
| Journal | Journal of Beijing Jiaotong University. (Natural Science Edition) |
| Volume | 43 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2019 |
Keywords
- Big railway data
- Intelligent train rescheduling
- Robust timetabling
- Statistical learning
- Train control
