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Train capacity utilization (TCU), usually represented by passenger load factor (PLF), is a critical measure of effectiveness for rail operation. In literature, efforts are usually made to improve capacity utilization by optimizing rail operation and management strategies. Comparably little attention is paid to analyzing the factors that affect TCU and to understanding the behavioral patterns behind it. This paper applies exploratory data mining techniques to a 3-month long real world train operation data of the Beijing-Shanghai High-Speed Railway. Principal component analysis (PCA) is conducted to find the principal components that can efficiently represent the collected data. Clustering techniques are then applied to understand the unique characteristics that affect PLF and the travel pattern. The findings can be further used to guide train operation planning and facilitate better decision-making.

Analyzing Capacity Utilization and Travel Patterns of Chinese High-Speed Trains: An Exploratory Data Mining Approach