[3G3-OS-15a] 移動系列のデータマイニングと機械学習(1/2)

[3G3-OS-15a-04] Anisotropic estimation of station market areas by supervised learning using geospatial information and IC commuter pass data

〇Yohei Kodama¹, Yuki Akeyama¹, Yusuke Miyazaki¹, Koh Takeuchi² (1. West Japan Railway Company, 2. Kyoto University)

Keywords:geospatial information, data mining, transportation

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. Experimental results show that our proposed method reduces the estimation errors.