Abstract: As cities develop and expand, the demand for automobiles and the frequency of travel have increased, leading to various social issues such as traffic congestion and rising air pollution that degrade the quality of life for urban residents. To address these problems, there is an ongoing transition from a car-oriented transportation system to a human-centered green transportation system. Public transit and shared bicycles are representative modes of human-centered transportation, and recently, there has been growing interest in the movement of people using these alternatives to automobiles within cities (Haraguchi et al., 2022). This study aims to examine the spatial patterns of bus movement—a primary mode of public transit—and shared bicycle movement using BiFlowLISA (Bivariate Flow Local Indicators of Spatial Association; BFI) (Tao and Thill, 2020). While many previous studies have independently analyzed the patterns of public transit or shared bicycle trips, BiFlowLISA offers the advantage of categorizing the mutual spatial patterns of two types of flows, allowing for a simultaneous examination of the spatial correlation and competitive relationships between bus and shared bicycle movements. First, Origin-Destination (OD) data for both buses and shared bicycles are collected and pre-processed to ensure they share the same spatial units for origin and destination information. Subsequently, the study explores shared bicycle ODs adjacent to bus ODs and calculates BFI values based on trip volume to define spatial flow patterns relative to buses as HH (High-High), HL (High-Low), LH (Low-High), and LL (Low-Low). Similarly, adjacent bus ODs are explored based on shared bicycle ODs to define spatial flow patterns relative to shared bicycles using BFI values. By visualizing the regional differences in bus and shared bicycle flows—considering the spatial distribution, distance, and regional characteristics of the ODs across a total of eight patterns—this research examines the regional distribution of complementary and competitive relationships between the two modes. Examining the mutual relationships and differences in spatial patterns between buses and shared bicycles has the potential to serve as foundational data for promoting sustainable, human-centered transportation systems. Furthermore, by strengthening the connectivity between public transit and shared bicycles, it may help alleviate urban problems caused by automobile-oriented systems.

Recommended citation: Hwang, T., Kim, K., Lee, Y., Cho, S., Lee, S., Hong, H., & Hwang, C. S. (2023, June). BiFlowLISA를 이용한 버스와 공유자전거 OD의 공간적 패턴 탐색 (Exploring spatial patterns of bus and shared bike OD using BiFlowLISA) [Paper presentation]. Proceedings of the Korean Geographical Society Conference Republic of Korea, 154-155.