In recent years, statistical surveys have been used for understanding actual traffic situations. However, it is difficult to discern travel or routes between different facilities within a narrow area, such as the sphere of influence of a train station, on a real-time basis. One type of big traffic data, a Wi-Fi packet sensor, is capable of measuring mobile terminals with effective Wi-Fi within a sphere of about 200m at all times. However, since the Wi-Fi packet sensor does not provide an

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... ive survey, it is necessary to make compensations. It can be considered possible to identify the traffic flow within a station's sphere of influence by combining the data measured by a Wi-Fi packet sensor with big traffic data containing complementary information. The purpose of this study is to devise a method to estimate the traffic flow within the sphere of influence of a railway station by combining multiple types of big traffic data.