Mapping the Spatial Distribution Patterns of Personal Time Spent Based on Trip Purpose

Abstract

Understanding the spatial distribution patterns of the time spent by people based on their trip purpose and other social characteristics is important for sustainable urban transport planning, public facility management, socio-economic development, and other types of policy planning. Although personal trip survey data includes travel behavior and other social characteristics, many are lacking in detail regarding the spatial distribution patterns of individual movements based on time spent, typically due to privacy issues and difficulties in converting non-spatial survey data into a spatial format. In this article, geospatially-enabled personal trip data (Geospatial Big Data), converted from traditional paper-based survey data, are subjected to a spatial data mining process in order to examine the detailed spatial distribution patterns of time spent by the public based on various trip purposes and other social characteristics, using the Tokyo metropolitan area as a case study.