Impact of Shared Bicycle Spatial Patterns During Public Health Emergencies

A Case Study in the Core Area of Beijing

Bibliographic Data

ID22033686
AuthorsWen Zheng (0000-0001-9318-5806, Beijing University of Civil Engineering and Architecture), Zheng Wen (0000-0002-9404-8800, Beijing University of Civil Engineering and Architecture), Lujin Hu (0000-0003-1465-7006, Beijing University of Civil Engineering and Architecture, corresponding author), Jing Hu (0009-0007-8446-0862, Ministry of Natural Resources)
Year2025
Volume14
Issue2
Pages92
Publication date2025-02-19
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi14020092
OpenAlexW4407743487
LanguageEN
Citations received1
References cited46

During public health emergencies, studying the travel characteristics and influencing factors of shared bicycles during different time periods on weekdays can provide valuable insights for urban transportation planning and offer recommendations for bike-sharing systems (BSS) affected by such events. Utilizing bike-sharing data, this study initiated the analysis by scrutinizing the spatial flow patterns in the core area of Beijing, employing network indicators within the framework of complex network theory. Subsequently, influencing factors associated with bike-sharing trips were pinpointed using the exponential random graph model (ERGM). Using COVID-19 as an example, it examines the impact of public health emergencies on bike-sharing during multiple time periods. Supported by the network analysis method, our findings revealed that the majority of travel activities occurred between adjacent areas. Throughout weekdays, a consistent level of travel activity was observed, exhibiting distinct patterns during daytime and nighttime. The period from 4:00 to 8:00 emerged as the peak time, characterized by heightened traffic and temperature changes. Morning commuting extended until 8:00–12:00, followed by a transition period from 12:00–16:00. The most active travel time, encompassing various purposes, was identified as 16:00–20:00. Additionally, the presence of hospitals and train stations amplified travel within the pandemic-affected area. Finally, variants of ERGMs were employed to assess the influence of finance, shopping, dining, education, transportation, roads, and COVID-19 on bike-sharing activities. The road network emerged as the most critical factor, exhibiting a significant negative impact. Conversely, COVID-19 had the most pronounced positive influence, with transportation stops and educational institutions also contributing significantly in a positive manner. This research provides valuable transportation planning insights for addressing public health emergencies and promotes the effective utilization of bike-sharing systems

Archaeology · Beijing · China · Geography · Political science · Public health · Telecommunications · Air Quality and Health Impacts · COVID-19 epidemiological studies · Engineering · Medicine · Nursing · Urban Transport and Accessibility

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