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2026.10.21
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JR East and LocationMind Estimate Railway Passengers’ Transfer Routes Using GPS Mobility Data – Joint research results published in JR EAST Technical Review No.76 –

JR East and LocationMind Estimate Passenger Transfer Routes Using GPS Mobility Data

LocationMind has developed and applied a method to estimate railway passengers' transfer routes by combining GPS mobility data with train location data, as part of joint research with East Japan Railway Company (JR East), one of Japan's largest railway operators. The results have been published in JR East's technical journal, JR EAST Technical Review No.76, under the title "Empirical Analysis of Railway Transfer Routes Using Mobile GPS Data."

 

Background

Ticket gate data from IC cards show where passengers enter and exit the railway network, but not which lines they take or where they transfer once inside the gates. This is a particular challenge in areas with many parallel lines, where passengers can choose among several routes.

 

Research Overview

Covering the corridor between Omiya and Ueno/Ikebukuro in the Tokyo metropolitan area (Keihin-Tohoku Line, Ueno-Tokyo Line, Shonan-Shinjuku Line and Saikyo Line), the study matched GPS trajectories with train positions in both time and space to estimate which train each passenger boarded. This made it possible to quantify transfers between lines inside the ticket gates at major interchange stations such as Akabane. The estimates showed trends consistent with ticket gate data and onboard load data. The GPS mobility data used in this study was processed so that individuals cannot be identified.

 

Roles
JR East Planning and leading the research; validation using railway data
LocationMind Developing the algorithm to identify boarded trains and transfer stations from GPS mobility data; analysis
GeoTechnologies Providing the GPS mobility data

 

Outlook

The method is expected to support analysis of passenger flows within stations, timetable planning and station improvement projects.
LocationMind will continue to contribute to more advanced transportation and urban planning through mobility data analysis.

 

Contact

inquiry@locationmind.com