Spatial Movement Pattern Analysis in Public Transportation Networks in Seoul

Recently, many studies have tried to find travel patterns to understand citizen’s movement behaviours and improve transportation services based on the big data in public transportation systems. This research introduces a method of discovering and evaluating spatial movement patterns from smart card transaction data of multi-modal transportation networks such as subway and bus. The transaction data contains the origin and destination subway stations (or bus stops), boarding and alighting times, traveller’s type, and so on. To abstract the location data of origin and destination, the GeoHash coding system is adopted, in this paper, which is a hierarchical geo-coding scheme based on rectangle grid. The proposed method discovers the spatial movement patterns, which express frequent movement behaviours between two zones on the map. The zone is a set of adjacent grids on the map which have similar movement characteristics. The extracted movement patterns between zones are evaluated by three measures from different viewpoints. Smart card transaction data of subway and bus networks in Seoul was used to illustrate the spatial movement pattern analysis and the result was visualized on the Google Map. It is expected that this kind of movement pattern discovery can be exploited for wide applications such as transportation network planning and location-based target marketing.