The railway is an indispensable means of transportation for people living in urban areas in Japan. However, unexpected accidents or disasters disturb the train operation. People usually check the operation status of trains on the official websites or Twitter of each railway company. However, it is still unclear whether such information is provided in realtime, when it is updated and which station is severely affected. Therefore, we tackle a real-world application of transportation big data using 8 months' data collected by smart cards for public transportation in Keikyu Line operating in Tokyo and Kanagawa Prefectures. We propose a method to detect the anomaly state by using the number of train users every 10 minutes in major 9 stations in Keikyu Line. In the method, outlier detections by interquartile range, interval estimation and Hotelling's theory are utilized to detect anomaly points. As the results, our proposal detects anomaly state better than the official announcement by Twitter on some points in terms of realtimeness, update frequency and geographic detail.