NONLINEAR SUPERVISED LEARNING TO ANALYZE TRADE AREA POPULATION AROUND STATIONS FROM GEOSPATIAL INFORMATION AND IC COMMUTTER PASS DATA

Abstract

When estimating demand for new stations, railroad companies estimate the station coverage area that is a type of trade area where stations are used by people around the station. Conventionally, the station coverage area is estimated using statistical data, but this method does not consider spatial anisotropy and results in significant errors. Recently, with the advent of IC commuter pass services, large-scale spatial data has become available, and we expect that accurate station coverage can be estimated with finer spatial granularity. In this study, we define station coverage based on the number of IC commuter pass holders per zip code, and formulate station coverage estimation as a predicting problem. We propose a method for estimating the station coverage area by supervised learning using geospatial information such as the time required to get from a zip code to a nearby station and the geographic relationship.