Subway Carriage Congestion Monitoring Dataset

The current transportation industry faces the issue of urban public transportation congestion and poor passenger experience, especially during peak times when the congestion level of subway carriages directly affects the passenger travel experience. The current monitoring methods mostly rely on manual checks, which are inefficient and prone to errors. This dataset aims to assist in the automated identification and analysis of carriage congestion through high-quality image data, enhancing the efficiency and accuracy of public transportation management. The data collection is conducted using high-resolution cameras capturing real-time images inside subway carriages to ensure coverage of passenger situations across different times and environments. To ensure data quality, we implemented multiple rounds of labeling and consistency checks, with the labeling results confirmed through expert review. The data is stored in JPEG format and organized based on time and location classifications, making subsequent querying and analysis more efficient.