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

Area of Science:

• Transportation Science
• Network Science
• Data Science

Background:

• High-speed train operation data are valuable but often disorganized and incomplete for research.
• A lack of a standardized, public high-speed railway network dataset hinders data-driven studies.
• Existing datasets are insufficient for complex network analysis, dynamic systems, and intelligent transportation research.

Purpose of the Study:

• To develop a comprehensive, public high-speed railway network dataset for research.
• To facilitate studies in large-scale complex networks, complex dynamic systems, and intelligent transportation.
• To provide a resource for analyzing train operations, delays, and network characteristics.

Main Methods:

• Collected and organized train operation data (Oct 2019 - Jan 2020).
• Integrated train delay data, junction station information, and adjacent station mileage.
• Incorporated external factors like weather, temperature, wind power, and holidays.

Main Results:

• A structured high-speed railway network dataset is created.
• The dataset includes diverse operational and contextual information.
• Factors influencing train operations are systematically included.

Conclusions:

• The developed dataset supports advanced research in complex dynamic systems and network analysis.
• It offers potential for improving railway operations, such as delay analysis and dispatching.
• This resource enables advancements in intelligent transportation systems and passenger service networks.