Exploring Influential Factors of Free-Floating Bike-Sharing Usage Frequency before and after COVID-19

In order to better understand the impact of COVID-19 on the free-floating bike-sharing (FFBS) system and the potential role of FFBS played in the pandemic period, this study explores the impact mechanism of travel frequency of FFBS users before and after the pandemic. Using the online questionnaire collected in Nanjing, China, we first analyze the changes of travel frequency, travel distance, and travel duration in these two periods. Then, two ordered logit models are applied to explore the contributing factors of the weekly trip frequency of FFBS users before and after COVID-19. The results show that: (1) While the overall travel duration and travel distance of FFBS users decreased after the pandemic, the trip frequency of FFBS users increased as the travel duration increased. (2) Since COVID-19, attitude perception variables of the comfort level and the low travel price have had significantly positive impacts on the weekly trip frequency of FFBS users. (3) Respondents who use FFBS as a substitution for public transport are more likely to travel frequently in a week after the outbreak of COVID-19. (4) The travel time in off-peak hours of working days, weekends, and holidays has a significantly positive correlation with the trip frequency of FFBS users. Finally, several relevant policy recommendations and management strategies are proposed for the operation and development of FFBS during the similar disruptive public health crisis.

Impacts of COVID-19 on Bike-Share Usage: The case of Daejeon, Korea.

After COVID-19 began spreading through fecal-oral routes, crowded cities introduced social distancing policies. Mobility patterns in urban also changed because of the pandemic and the policies to reduce the infection of it. This study investigates the impact of COVID-19 and related policies such as social-distancing by comparing bike-share demand in Daejeon, Korea. By using big data analytics and data visualization, the study measures differences in bike-sharing demand between 2018-19, before the pandemic, and 2020-21, during the pandemic. According to results, (1) bike-share users tend to travel long distances and cycle more than before the pandemic, (2) bike users choose cycling not for commuting but for transportation during the pandemic, and (3) the pandemic has broadened the spatial borders bike-usages. These results provide meaningful implications for urban planners and policymakers by identifying differences in the ways people use public bikes during the pandemic era.

Changes in public bike usage after the COVID-19 outbreak: A survey of Seoul public bike sharing users.

When the COVID-19 pandemic swept across the world, people tended to seek more individualized and viable transportation modes, such as a bicycle. In this study, we examined the factors influencing changes in public bike sharing (PBS) in Seoul, to assess this trend post-pandemic. We conducted an online survey of 1,590 Seoul PBS users between July 30 and August 7, 2020. Using a difference-in-differences analysis, we found that participants who were affected by the pandemic used PBS 44.6 h more than unaffected individuals throughout the year. In addition, we used a multinomial logistic regression analysis to identify the factors affecting changes in PBS usage. In this analysis, the discrete dependent variables of increased, unchanged, and decreased were considered, representing the changes in PBS usage after the COVID-19 outbreak. Results revealed that PBS usage increased among female participants during weekday trips such as commuting to work and when there were perceived health benefits of using PBS. Conversely, PBS usage tended to decrease when the weekday trip purpose was for leisure or working out. Our findings offer insight into PBS user behaviors within the context of the COVID-19 pandemic and present policy implications to revitalize PBS usage.

Evolvement patterns of usage in a medium-sized bike-sharing system during the COVID-19 pandemic.

The global outbreak of COVID-19 has fundamentally reshaped human mobility. Compared to other modes of transportation, how spatiotemporal patterns of urban bike-sharing have evolved since the outbreak is yet to be fully understood, especially for bike-sharing systems operating on a smaller scale. Taking Pittsburgh as a case study, we examined the changes in spatiotemporal dynamics of shared bike usage from 2019 to 2021. By distinguishing between weekday and weekend usage, we found different temporal patterns between trip volume and duration, and distinct spatial patterns of within- and between-region rides with respect to naturally separated regions. Overall, the results illustrate the resilience and the vital role of bike-sharing during the pandemic, consistent with previous observations on bike-sharing systems of a larger scale. Our study contributes to a comprehensive understanding of bike-sharing that calls for more research on smaller-scale systems under disruptive events such as the pandemic, which can greatly inform decision-makers from smaller sized cities and enable future studies to compare across different urban regions or modes of transportation.

Investigation on changes in the usage patterns of Seoul Bike usage patterns owing to COVID-19 according to pass type.

Human mobility has been significantly impacted by varying degrees of social distancing and stay-at-home directives that have been implemented in many countries to prevent the spread of COVID-19; this effect was observed regardless of the mode of transportation. Several studies have indicated that bike-sharing is a relatively safe option in terms of COVID-19 infection, and more resilient than public transportation. However, previous studies on the effects of COVID-19 on bike-sharing, rarely considered the type of pass in their investigation of the pandemic-induced changes in usage patterns of shared bikes. To overcome this limitation, this study used trip records obtained from Seoul Bike to investigate the changes in usage patterns of shared bikes during the COVID-19 pandemic. The spatiotemporal usage patterns were characterized in this study based on the type of pass. Additionally, using t-tests and k-means clustering, we discovered significant factors that influenced changes in one-day pass usage rates and temporal usage patterns at the station level. Finally, we constructed spatial regression models to estimate changes in bike rentals caused by COVID-19 based on pass type. The findings provided a comprehensive understanding of how bike-sharing usage varies depending on pass type, which is closely related to shared bikes trip purposes.

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study.

The COVID-19 pandemic has remarkably impacted urban mobility. All non-essential movements were restricted in Valencia (Spain) to contain the virus. Thus, the transport usage patterns of Valencia's bike-sharing system (BSS) users changed during this emergency situation. The primary objective of this study was to analyse the behaviour patterns of BSS users in Valencia before and after the COVID-19 outbreak, specifically those who maintained or changed their transport routines. A within-subjects comparison design was developed using a group of BSS users before and after the onset of the pandemic. Data mining techniques were used on a sample of 4355 regular users and 25 variables were calculated to classify users by self-organising maps analysis. The results show a significant reduction (40 %) in BSS movements after the outbreak during the entire post-outbreak year. There was some recovery during the rest of 2020; however, this has yet to reach the pre-pandemic levels, with variations observed based on the activities performed in different areas of the city. Of the users, 63 % changed their BSS use patterns after the onset of the pandemic (LEAVE group), while 37 % maintained their patterns (REMAIN group). The user profile of the REMAIN group was characterised by a general reduction of approximately 35 % of journeys during 2020, with a slight increase in morning movements compared to those made in the evening. These users also presented an equivalent number of cycling days to those of the previous year, reduced the number of connections and increased the network's density and the travelling speed. These results can be useful in estimating the percentage of people who do not vary their usual behaviour during emergencies. Finally, several policy implications are outlined based on the findings.

Exploring year-to-year changes in station-based bike sharing commuter behaviors with smart card data

Station-based bike sharing (SBBS) not only provides commuters with direct "door-to-door" trips, but also plays a vital role in addressing the "first/last mile" challenges for public transportation system. However, there is a lack of research into portraying year-to-year changes in SBBS commuter behaviors. With five-year (from 2016 to 2020) SBBS smart card data collected in Nanjing, China, a longitudinal analysis is performed in this study to trace yearly dynamics of commuter behaviors at an individual level. We identify two sorts of SBBS commuters (i.e., SBBS-alone and SBBS-metro commuters) based on users' spatial-temporal travel regularities. The paper finds that (i) the number of SBBS users presented a considerable fluctuation trend over a five-year span, while the proportion of SBBS commuters stabilized at an equilibrium level;(ii) the COVID-19 outbreak accelerated the decline in the proportion of female and young SBBS commuters;(iii) most SBBS commuters were recorded for only one year out of five, while the share of commuters who used SBBS for four years or more is tiny, < 5%;(iv) the trip duration of SBBS-alone commuters was significantly longer than that of SBBS-metro commuters, and both showed some increase during the COVID-19 pandemic;(v) the number of non-loop trip chains was dramatically higher than that of loop trip chains, which is more prominent among SBBS-metro commuters. Our findings could provide valuable insights into the behavioral dynamics of SBBS commuters and offer recommendations on how policy makers and transportation planners could respond to these precipitate changes. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Impact of COVID-19 on the Belfast Bike Sharing Scheme

The COVID-19 pandemic brought the connectivity and mobility of Northern Irish residents to a halt forcing the bike hiring service Belfast Bikes to cease operations. After reintroducing the service in Summer 2020, the usage of the bike hiring service was severely impacted. This paper investigates the usage of Belfast Bikes pre-pandemic and during the pandemic. Research includes how consumer trends for Belfast Bikes customers changed due to the pandemic and predictive modelling is used to predict whether customers hire a Belfast Bike for direct travel from stations or for an indirect, leisurely trip. We will conclude how the connectivity and mobility of Belfast residents have changed due to the pandemic and provide recommendations for Belfast Bikes and Belfast City to recover after the pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Geospatial Network Analysis and Origin-Destination Clustering of Bike-Sharing Activities during the COVID-19 Pandemic

Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work-home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings.

Effects of a bike sharing system and COVID-19 on low-carbon traffic modal shift and emission reduction

The rise of dockless bike sharing and the development of bike-only roads have facilitated a low-carbon transition in transportation. In this direction, Beijing opened the first bike-only road in 2019. Shortly after opening this bike road, the city encountered the COVID-19 pandemic. In this study, we evaluate the impacts of the first bike-only road and COVID-19 on the travel modal shift of the public and quantify the corresponding changes in carbon emissions. Using proposed integrated choice and latent variable models, we find that respondents who were previously low-carbon travelers were more likely to choose low-carbon traffic options and more sensitive to perceived instrumental usefulness of the bike sharing system than were high-carbon respondents. The bike lanes significantly increased the probability for all respondents to switch their travel mode to bike sharing. The COVID-19 pandemic significantly inhibited the high-carbon group from switching to bike sharing for both long- and short-distance trips, while the pandemic significantly promoted the low-carbon group to choose bike sharing for short-distance trips. However, there was no significant effect on this group by the pandemic for long-distance trips. Furthermore, we find that bike sharing can reduce carbon emissions by 41.55% and 33.43% for short- and long-distance trips, respectively. The overall emission reduction effect of bike sharing increased from 28.29% to 47.21% when the bike lane was introduced;whereas the COVID-19 pandemic decreased bike sharing emission reduction from 43.74% before to 29.87% during the pandemic. Our research reveals that the bike sharing system plays both an alternative role to other traffic modes for short-distance traffic and a complementary role to public transport for long-distance traffic. © 2022 Elsevier Ltd

Travel patterns of free-floating e-bike-sharing users before and during COVID-19 pandemic.

Free-floating micro-mobility as a mobility solution is becoming increasingly popular in cities. In this study, the travel patterns of free-floating electric bike-sharing service (FFEBSS) users before and during the COVID-19 pandemic were explored using big data and data mining. Existing real-time data studies provide a limited understanding of trip patterns and the characteristics of each user. Interpretations concerning the occurrence of life-changing events such as the COVID-19 pandemic are important. This study aimed to understand each user over 13 months comprising multiple time frames of market trends, seasonal change, and the COVID-19 pandemic outbreak. Multiple features were extracted from each user to explain the hidden data characteristics, and a data mining method was employed for clustering and evaluating user similarities with the extracted features. The results showed that FFEBSS users demonstrated a moderately stable travel pattern despite the COVID-19 pandemic, indicating the possibility of micro-mobilities being well adoptedas our future urban transportation.

The effects of COVID-19 on female and male bike sharing users: Insights from Lisbon's GIRA.

Women are among the groups most affected by the pandemic as they are more likely to be dependent on public transport (PT), which was heavily restricted during COVID-19. Thus, there is a need to consider transport alternatives such as bike sharing that can ensure their mobility needs. By conducting a survey to the bike sharing system (BSS) of Lisbon, we explored differences in travel behaviour and attitudes between female and male users before and during COVID-19. We found men to have higher bike ownership rates, a higher modal share of personal bicycle regarding commuting, and more likely to use their own bikes if BSS was unavailable. Conversely, women more frequently combined BSS with PT and were more likely to use PT if BSS was unavailable. Moreover, while men were using BSS more frequently than women pre-pandemic, during COVID-19 women are using BSS as frequently as men. Our research provides evidence on the potential role of BSS as a transport alternative during pandemics, inducing women to take up cycling who otherwise would not cycle, therefore, potentially decreasing the current cycling gender gap. Findings suggest that introducing family/friend discounts and promoting BSS for exercising may increase the share of female cyclists.

The strengths and weaknesses of bike sharing as an alternative mode during disruptive public health crisis: A qualitative analysis on the users' motivations during COVID-19.

The coronavirus pandemic may provoke an increase on our overreliance on private car usage due to a permanent loss of confidence on public transport (PT), threatening current decarbonization efforts of the transport sector. Thus, alternative modes like bike sharing systems (BSS) must be considered. In this study, through conducting 16 semi-structured interviews and by employing thematic analysis, we explore the users' perceptions of using Lisbon's BSS during this pandemic. Our findings show that the observed decrease on BSS usage during the COVID-19 lockdowns was mostly due to mandatory teleworking than to a perceived infection risk. Even during the height of the pandemic, users still turned to BSS to fulfil their essential trip needs. Users considered bike sharing to have a lower infection risk comparatively to PT, with some users joining BSS during the pandemic to specifically avoid using PT. Furthermore, users associate riding a shared bicycle with a pleasant activity that reduces their travel times and costs, while also providing health and environmental benefits. Consequently, bike sharing contributes to the resilience of transport systems by providing its users with a transport alternative perceived to have a low infection risk, ensuring their mobility needs during disruptive events. Findings from this research provide evidence that support policies, such as, expanding BSS coverage areas, optimizing rebalancing operations, introducing shared e-bikes, and implementing segregated cycling lanes alongside BSS. These policies may be particularly effective at increasing the competitiveness of BSS as an alternative mode during disruptive public health crises and beyond.

Can Shared Mobility Compensate for Public Transport Disruptions? The Case of Milan’s Bike Sharing System During the COVID-19 Pandemic

The COVID-19 pandemic poses an unprecedented challenge for public transport systems. The capacity of transport systems has been significantly reduced because of the social distancing measures. Therefore, new avenues to increase the resilience of public urban mobility need to be explored. In this work, we investigate the integration of bike sharing and public transport systems to compensate for limited public transport capacity under the disruptive impacts of the COVID-19 pandemic. As a first step, we develop a data analysis model to integrate the demand of the two underlying systems. Next, we build an optimization model for the design and operation of hybrid mixed-fleet bike sharing systems. We analyze the case of the subway and public bike sharing systems in Milan to assess this approach. We find that the bike sharing system (in its current state) can only compensate for a minor share of the public transport capacity, as the needs in fleet and station capacity are very high. However, the resilience of public urban mobility further increases when new design concepts for the bike sharing system are considered. An extension to a hybrid free-floating bike and docked e-bike system doubled the covered demand of the system. An extension of the station capacity of about 37% yields an additional increase of the covered demand by 6.5%-7.5%. On the other hand, such a hybrid mixed-fleet bike sharing system requires many stations and a relatively large fleet to provide the required mobility capacity, even at low demand requirements.

A Demand-Centric Repositioning Strategy for Bike-Sharing Systems.

Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station's capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system.

Minimizing fleet size and improving vehicle allocation of shared mobility under future uncertainty: A case study of bike sharing

As a rapidly expanding type of shared mobility, bike sharing is facing severe challenges of bike over-supply and demand fluctuation in many Chinese cities. In this paper, a large-scale method is developed to determine the minimum fleet size under future demand uncertainty, which is applied in a case study with millions of bike sharing trips in Nanjing. The findings show that if future uncertainty is not considered, more than 12% of trip demands may not be satisfied. Nevertheless, the proposed algorithm for minimizing fleet size based on historical trip data is effective in handling future uncertainty. For a bike sharing system, supplying 14.5% of the original fleet could be sufficient to meet 96.8% of trip demands. Meanwhile, the results suggest a unified platform that integrates multiple companies can significantly reduce the total fleet size by 44.6%. Moreover, in view of the Coronavirus Disease 2019 (COVID-19) pandemic, this paper proposes a contact delay policy that maintains a suitable usage interval, which results in increased bike amount requirements. These findings provide useful insights for improving resource efficiency and operational services in shared mobility applications.

Impact of COVID-19 on the Belfast Bike Sharing Scheme

The COVID-19 pandemic brought the connectivity and mobility of Northern Irish residents to a halt forcing the bike hiring service Belfast Bikes to cease operations. After reintroducing the service in Summer 2020, the usage of the bike hiring service was severely impacted. This paper investigates the usage of Belfast Bikes pre-pandemic and during the pandemic. Research includes how consumer trends for Belfast Bikes customers changed due to the pandemic and predictive modelling is used to predict whether customers hire a Belfast Bike for direct travel from stations or for an indirect, leisurely trip. We will conclude how the connectivity and mobility of Belfast residents have changed due to the pandemic and provide recommendations for Belfast Bikes and Belfast City to recover after the pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Long Term Time Series Prediction of Bike Sharing Trips: A Cast Study of Budapest City

Bike-sharing services provide easy access to environmentally-friendly mobility reducing congestion in urban areas. Increasing demand requires more service planning based on the behavior of bike-sharing users. The Time Series models Seasonal Auto-Regressive Integrated Moving Average, Artificial Neural Network, and Exponential Smoothing have been investigated to reveal bike-sharing use for five years. Results show that weekends are attracting more trips. Summer is the most season influencing more demand. The model is predicted within a seasonal trend with a three-day lag. Compared to the Exponential Smoothing Model, SARIMA and ANN provide better predictions. Similarities are obtained in the periods of COVID-19 and after that, in the lags and highest days having bike-sharing trips. This study helps decision-makers in forecasting bike-sharing trips. © 2022 IEEE.

A long-term perspective on the COVID-19: The bike sharing system resilience under the epidemic environment.

Introduction: The sudden COVID-19 pandemic poses a fresh and tough challenge to bike sharing systems (BSS). With this epidemic as a shock event, this paper aspires to shed light on the phenomenon of changing demand and usage regularity in New York City's BSS under the epidemic environment, spanning a period of 18 months. Methods: Technically, BSS's normal performance and the timely responses to the outbreak could be conceptualized as having four different stages. One provides a comparative analysis of bike sharing spatial-temporal mobility patterns and connectivity of the bike sharing usage network, before and during the public health crisis with a macroscopic perspective. Also, a multivariate investigation of user and trip characteristics on BSS is conducted to uncover the difference in the frequency of outdoor and sojourn time between various user communities. Results: Due to the impact of the outbreak, BSS registered severe ridership drops, yet it quickly recovered to the pre-pandemic levels within months. The decline of bike sharing usage was felt throughout all the areas during the outbreak. However, there were places where BSS ridership actually increased, particularly in the areas near supermarkets, parks and hospitals. The less densely connected network of the bike sharing usage has also resulted in a reduction in users' destination heterogeneity. This study also finds evidence of the significant gender, age and cycling pattern gaps in response to potential risk. Conclusions: Investigating the dynamics of bike sharing usage will help to comprehend how the serious pandemic caused by COVID-19 impacts people's daily mobility. Practically, this work hopes to provide insights into adapting this unprecedented pandemic so as to respond to similar events in the future.

Impacts of COVID-19 on bike-sharing usages in Seoul, South Korea.

The COVID-19 pandemic and social distancing restrictions have had a significant impact on urban mobility. As micro mobility offers less contact with other people, docked or dockless e-scooters and bike-sharing have emerged as alternative urban mobility solutions. However, little empirical research has been conducted to investigate how COVID-19 might affect micro mobility usage, especially in a major Asian city. This research aims to study how COVID-19 and other related factors have affected bike-sharing ridership in Seoul, South Korea. Using detailed urban telecommunication data, this study explored the spatial-temporal patterns of a docked bike-sharing system in Seoul. Stepwise negative binomial panel regressions were conducted to find out how COVID-19 and various built environments might affect bike-sharing ridership in the city. Our results showed that open space areas and green infrastructure had statistically significant positive impacts on bike-sharing usage. Compared to registered population factors, real-time telecommunication floating population had a significant positive relationship with both bike trip count and trip duration. The model showed that telecommunication floating population has a significant positive impact on bike-sharing trip counts and trip duration. These findings could offer useful guidelines for emerging shared mobility planning during and after the COVID-19 pandemic.