Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

Abstract

Analyzing public movement in transportation networks in a city is significant in understanding the life of citizen and making improved city plans for the future. This study focuses on investigating the flow orientation of major activity regions based on smart card transit data. The flow orientation based on the real movements such as transit data can provide the easiest way of understanding public movement in the complicated transportation networks. First, high inflow regions (HIRs) are identified from transit data for morning and evening peak hours. The morning and evening HIRs are used to represent major activity regions for major daytime activities and residential areas, respectively. Second, the directional orientation of flow is then derived through the directional inflow vectors of the HIRs to show the bias in directional orientation and compare flow orientation among major activity regions. Finally, clustering analysis for HIRs is applied to capture the main patterns of flow orientations in the city and visualize the patterns on the map. The proposed methodology was illustrated with smart card transit data of bus and subway transportation networks in Seoul, Korea. Some remarkable patterns in the distribution of movements and orientations were found inside the city. The proposed methodology is useful since it unfolds the complexity and makes it easy to understand the main movement patterns in terms of flow orientation.

User Activity and Trip Recognition using Spatial Positioning System Data by Integrating the Geohash and GIS Approaches

“…Geohash is a public domain geocoding system that encodes a geographic location into a short string of letters and digits ( 62 ). It maintains a hierarchical spatial data structure that subdivides space into buckets of grid shapes by using latitude and longitude points ( 53 ). In this research, we clustered the GPS points based on the Geohash approach.…”

“…In recent times, the Geohash technique was used in different sectors including spatial modeling studies for business ( 49 ), mobile sensing ( 50 ), spatial query ( 51 ), and spatiotemporal mapping ( 52 ). The Geohash method was used in the study of Singh et al for the purpose of investigating the flow orientation of major activity regions in South Korea ( 53 ). In addition, the Geohash method was used in the study of Oh et al ( 54 ), which is close to the idea of Singh’s research, to assess spatial movement patterns of smart card transaction data for multi-modal transportation networks.…”

Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data

“…In recent years, various positive and passive human footprints have been organized to uncover human movement patterns within the city [13][14][15][16][17]. Travel surveys and geotagged social media data (geotagged tweets or social media check-in data) are typically used to characterize tourist flow [18], detect events [19], model traffic demands [20][21][22], and explore human mobility patterns [23].…”