Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

A high-speed railway network dataset from train operation records and weather data

High-speed train operation data are reliable and rich resources in data-driven research. However, the data released by railway companies are poorly organized and not comprehensive enough to be applied directly and effectively. A public high-speed railway network dataset suitable for research is still lacking. To support the research in large-scale complex network, complex dynamic system and intelligent transportation, we develop a high-speed railway network dataset, containing the train operation data in different directions from October 8, 2019 to January 27, 2020, the train delay data of the railway stations, the junction stations data, and the mileage data of adjacent stations. In the dataset, weather, temperature, wind power and major holidays are considered as factors affecting train operation. Potential research values of the dataset include but are not limited to complex dynamic system pattern mining, community detection and discovery, and train delay analysis. Besides, the dataset can be used to solve various railway operation and management problems, such as passenger service network improvement, train real-time dispatching and intelligent driving assistance.

Measurement(s)	train operation data of China high-speed railway network • locations of railway stations • weather and temperature and wind power
Technology Type(s)	web scraping with python • Geographic Information System
Factor Type(s)	high-speed train departure delay time • high-speed train arrival delay time • various external factors affecting train operation • bad weather exposure time of railway stations • railway station community size
Sample Characteristic - Organism	high-speed train
Sample Characteristic - Environment	railway system
Sample Characteristic - Location	China