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This study explores the relationship between bike-sharing and public transit trips before and after COVID-19 lockdowns, focusing on Columbus, Ohio. Using CoGo Bike Share trip data together with Automatic Passenger Counter (APC) data from the Central Ohio Transit Authority (COTA), we first identify bike-share trips and public transit ridership patterns during this period and classify bike-share trips as substitutive and complementary to public transit trips. We then employ binary logit models to analyze the determinants of substitutive trips. Some key findings are as follows. First, the COVID-19 outbreak caused decreases in bus ridership while increases in bike-sharing trips. Second, bike-sharing may compete with public transit for short distance trips. Third, bike-sharing trips are likely to substitute public transit within more intensive and congested public transit networks after controlling for various other factors. Fourth, trips made in different neighborhoods and land use reveal significant relationships with modal types. Lastly, lockdowns due to the COVID-19 pandemic led to substitutive trips substantially, particularly on weekdays. [ABSTRACT FROM AUTHOR]

One important factor in determining whether commuters will use public transport is spatial accessibility rooted in the first-mile problem. This study explores commuter behaviour in terms of how they utilize bike-sharing to manage the first-mile accessibility of a public transportation station. Historical data from Taipei Metro smart cards were analyzed using RFM (recency, frequency, and monetary) segmentation models to identify commuter segments. This study reveals two significant findings: comprehensive spatiotemporal characteristics and homogeneous behavioural patterns are derived from clustering algorithms. The city's penetration pricing strategy for bike-sharing motivates modal splitting transfer between bike-sharing and transit (MSTBT). In addition, we observed a supplementary and utilitarian relationship between bike-sharing and the metro. A convenient transportation network improves first-mile accessibility, thus the frequency of MSTBT usage is a key metric for measuring engagement. The findings provide a useful reference for urban planners promoting the design and development of sustainable transportation systems. [ABSTRACT FROM AUTHOR]

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing's six central districts. Subsequent to this, a comprehensive analysis of the network's structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities. [ABSTRACT FROM AUTHOR]

In recent decades, shared mobility has gained prominence as a sustainable alternative in transport, yet a comprehensive understanding of its effects on travel behaviour remains limited. This paper provides a narrative review of quantitative empirical studies, focusing on car-sharing and bike-sharing, and revisits the magnitude of the effects on four indicators: public transport use, active transport use, auto dependence, and auto ownership. Both cross-sectional and longitudinal perspectives are considered, examining variances in trip characteristics. Shared mobility users tend to rely less on private vehicles and increase cycling, with varying effects on transit use and walking. Car-sharing typically replaces private vehicles for non-commuting trips, while bike-sharing mainly competes with rather than complements public transport, especially for shorter commutes. The longitudinal effects of shared mobility appear more limited than those observed in cross-sectional analyses, indicating that shared mobility can potentially lead to a positive trend in travel mode shifts over time, albeit slowly. Additionally, this study highlights differences in shared mobility outcomes between Australia and other global contexts, exploring potential reasons for these discrepancies. Integrating shared mobility and other transport paradigms requires long-term strategies to shape travel behaviour towards multimodality, offering a continuum of choices covering most daily trips without private vehicles. [ABSTRACT FROM AUTHOR]

Bike-sharing services provide easy access to environmentally-friendly mobility reducing congestion in urban areas. Increasing demand requires highly service planning methods based on bike-sharing user behavior. Negative Binomial, Poisson Regression, and Time Series models were elaborated considering the weather to reveal the differences between the members, occasional users, and visitor bike-sharing user groups. The negative Binomial approach is found to be superior to Poisson. Weather effects were varied in their influence on bike-sharing user classifications. In general, good weather conditions lead to more usage of bike-sharing. Weekends attract more occasional users and visitors than weekdays. In time series models, the seasonal trend of bike-sharing trips conducted by members was predicted without weather impact. According to the comparison, Random Forest performed better than SARIMA when the number of observations was low. Visitors are more influenced by temperature, wind and type of day. Occasional users are more subjected to precipitation. For members, it is found that the temperature, type of day are the most significant factors. The least factors for all are varied as well: precipitation for visitors, humidity for occasional users, precipitation and wind for members. The results help decision-makers predict the daily usage of bike-sharing for various user groups. [ABSTRACT FROM AUTHOR]

The purpose of this research is to investigate the effect of land use, built environment and public transportation facilities’ locations on destinations of bike-sharing trips in an urban setting. Several methods have been applied to determine the relationship between predicting variables and trip destinations, such as ordinary least squares regression, spatial regression and geographically weighted regression. Additionally, a comparison between the proposed models, count models and random forest has been conducted. The data were collected in Budapest, Hungary. It has been found that touristic points of interest, and healthcare and educational points have a positive impact on bike-sharing destinations. Public transportation stops for buses, trains and trams attract bike-sharing users, which has a potential for the bike-and-ride system. Land use has different effects on bike-sharing trip destinations; mostly as a circular shape variation within the urban structure of the city, such as residential, industrial, commercial and educational zones. Other variables, such as road length and water areas, form as constraints to bike-sharing trip destinations. Geographically weighted and spatial regression performs better than count models and random forest. This study helps decision-makers in predicting the origin-destination matrix of bike-sharing trips based on the transportation network and land use.

The focus of this research is to assess the impact of COVID-19 outbreak in 2020 on the Capital Bikeshare (CaBi) ridership in Washington, D.C. area compared to 2019. Correlation analysis was conducted and mixed-effects negative binomial (MENB) models were developed to assess CaBi ridership by the type of CaBi user, as the available bike-sharing trip data followed a time-series cross-section panel data structure and the variables (season, stay-at-home order and lift, and weekday) have time-specific effects. The correlation coefficients between CaBi variables, COVID-19 variables, and characteristics of D.C.’s population profile and their statistical significance are sensitive to the year (2019 or 2020), the season of the year (Winter, Spring, Summer, or Fall), and the type of CaBi user (member or casual user). The number of stations between pickups and drop-offs, and the dummy variables representing the before and after stay-at-home order and lift were found significant in the MENB models. There was no significant difference in travel time of CaBi casual users and members during the outbreak. Riding for leisure or commuting has been potentially limited with the stay-at-home order. The findings have practical implications and help bike-sharing operators to increase their resilience during unexpected situations such as the pandemic.

Dockless bike-sharing systems have grown rapidly in China in recent years. A large number of shared bikes have been put into use, which results in a serious problem of uncollected faulty bikes. However, since the exact number of faulty shared bikes cannot be known a priori, a reverse logistic network must be designed strategically to reduce the total costs of the collection process. This paper studies a robust reverse logistic network design problem of shared bikes that integrates the locations of recycling centres and the planning of collection routes in uncertain collection demand situations. A two-stage robust location routing model is presented to capture the uncertainty of collection demands, and then a set-partition reformulation is presented to address the computationally intractable model. We develop a column-and-constraint-generation-based algorithm combining a tailored column generation algorithm to efficiently solve the proposed model. Numerical experiments and parameter analyses, which are based on real data from bike-sharing companies, are conducted to evaluate the performance of the proposed algorithm. A case study is presented to further analyse the spatial features of the reverse logistic network of shared bikes. Benefits of applying robust optimization are demonstrated experimentally in terms of total cost under uncertain collection demand. • A robust reverse logistic network design problem in bike sharing is studied. • A column-and-constraint-generation based algorithm is presented. • A case study demonstrates the benefits of applying robust optimization. [ABSTRACT FROM AUTHOR]

Development of cycling infrastructure is becoming a global trend in urban policies. High congestion, mitigating smog and CO2 reduction have led to the re-birth of cycling as a significant mode of urban mobility. The article aims to present the response of medium-sized Polish cities (ranging from 100.000 to 200.000 residents) to these challenges, which are among the most important problems to solve within the next decades. The study analyses the cycling policy of Polish cities, showing the dynamic increase in kilometres of cycle paths constructed (often built alongside street renovation projects), as well as the boom in bike-sharing programmes. Shortcomings such as the lack of consistency in realisation and varied implementation in different cities and culturally ingrained preference for cars, related to historical experiences of post-communist cities, are also outlined. The Polish approach is the cautious policy of carrots without sticks, in which solutions facilitating bicycle travel are not followed up by deterrents against using cars. As a result, there are no political conflicts and opposition regarding this issue, but the numbers of cyclists (although increasing) is still lower than in the world's leading bicycle cities. [ABSTRACT FROM AUTHOR]

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This meta-analytic review explores the association between the built environment (BE) and the integrated usage of rail transport and bike-sharing (RTBS). The RTBS refers to the combined utilization of both modes (rail and bike-sharing) of transportation within a single journey or trip, allowing individuals to seamlessly switch between rail transit and bicycles for enhanced accessibility and convenience. The study aims to identify the specific BE factors and sub-factors that have a significant impact on RTBS, the direction of this influence, and the most consistently linked BE variable to RTBS. The findings reveal that land use, population and employment density, intersections, branch roads, number of public transportation (PT) stops and stations, PT located in suburban areas, bikeways, and distance to the central business district (CBD) significantly impact RTBS, while housing density does not. Among these BE variables, distance to the CBD is the most consistently significant predictor of RTBS. The study also indicates that employment density has a more significant impact on the integrated usage of dockless bike-sharing and rail transport compared to docked bike-sharing systems. The study identifies high heterogeneity in all BE-RTBS relationships and further investigates the possible causes of these discrepancies. The study did not find evidence of publication bias affecting the meta-analysis results. This study extends the existing literature by providing a more comprehensive and nuanced understanding of the role of BE in shaping RTBS, which is useful for policymakers and practitioners in the transportation sector.

This research looks at how spatial factors of public transportation influence the use of bike-sharing in an urban context. Based on a grid-cell-based method, ordinary least square regression and geographically weighted regression (GWR) are developed to reveal the link between the spatial distribution of bus, tram, rail stops, and bike-sharing trips. The public transportation coverage in Budapest City is rated as excellent, with all parts of the city covered within a 5 km radius. We find that areas with a high proportion of public transportation stops have a significantly higher number of bike-sharing trips. Bike-sharing trips are concentrated near regional railway stations, the central business district, and surrounding zones. The connection between bike-sharing and trams/rails appears to be stronger than the connection between bike-sharing and buses. According to the findings, nearly one-third of public transportation stops have accessible bike docks within 125 m walking distance. In GWR analysis, the coefficients of bus stops are increasing towards the center of the city, while the coefficients of tram/rail stops are decreasing. Finally, by examining the priority zones for establishing more bike-sharing facilities, it is discovered that the eastern side of the city requires more development than the western side because it has a high number of bike-sharing trips but no adequate facilities near public transportation facilities.

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.

As a low-carbon and environmentally friendly travel mode, bike-sharing (BS) has developed rapidly in many cities at home and abroad. However, existing studies have paid little attention to the relationship between urban morphology factors and BS ridership, and overlooked the nonlinearity of the relationship. This study analyzes the spatio-temporal characteristics of BS trips based on Boston's BS system data. We use generalized additive mixed model (GAMM) to capture the nonlinear relationship between BS ridership and built environment characteristics. The results show that: (1) BS ridership has a spatial pattern of attenuation from the downtown ar‐ ea to the periphery area. BS travel distances are concentrated at 3km, with a limit of 6km. (2) Temperature, weekday, and weather have significant effects on BS ridership. (3) BS ridership is linearly and positively correlated with total population, road density and metro accessibility, and has a nonlinear relationship with street orientation entropy and average road length. Finally, we provide corresponding policy implications for China based on the empirical findings. [ABSTRACT FROM AUTHOR]

• The combination of the metro and new travel methods closes the accessibility disparity of urban commercial services. • Short-distance travel exhibits a greater increase in urban commercial service equity. • Metro continues to play a key role in the urban transportation modes. The integration of shared mobility services, such as bike-sharing and ride-hailing services, with metro systems enhances the effectiveness and convenience of urban mobility. Various transportation modes significantly impact the accessibility and equity of commercial travel for the purpose of shopping, entertainment, tourism, and catering, due to the diversity of options, destinations, and origins. This study, based on the primary urban regions of Chengdu in 2023, employs the Gaussian two-step floating catchment area model, Gini Index, and Lorenz curve to examine the effects of shared mobility on the accessibility pattern and spatial equity of commercial services. It shows that the bike-sharing-metro travel mode enhances overall commercial accessibility, whereas the ride-hailing-metro travel mode has a substantial impact on accessibility distribution. Additionally, the commercial accessibility distribution pertains to the arrangement of commercial establishments in short-distance travel, whereas it exhibits a central hub and peripheral locations for long-distance travel. While the equity of commercial services improves effectively, metro transportation remains situated at the core of the transportation mode. The creation of the metro transit-oriented development should thoroughly assess the impact of shared mobility on the station area and prioritize the equilibrium between urban commercial and residential sectors. [ABSTRACT FROM AUTHOR]

Bike-sharing, as an open and intricate system, encompass a vast and diverse array of data, and are often affected by various time-varying and uncertain factors. Consequently, employing scientific and appropriate rebalancing in the age of big data is pivotal for the system's sustained and healthy development. This paper takes the dockless bike-sharing system as the research object, considers regional heterogeneity and time-varying demand uncertainty, and proposes a rebalancing strategy that integrates initial inventory determination and remaining inventory uncertainty. Firstly, this paper considers using of Poisson distribution chi-square tests to assess the borrowing and returning behaviors within a zone, selects a unit time tailored to the zone's unique circumstances to estimate the parameter rate, and independently establishes a non-stationary Markov chain for each zone to determine the initial expected inventory under dynamic zonal demand conditions. Using big data from bike-sharing operations in Nanjing for empirical validation, the results indicate that borrowing and returning behavior in most zones follows a Poisson distribution, and that zones with higher volumes of traffic have fewer bikes initially deployed. Secondly, we address the uncertainty of demand-driven residual inventory and spatial correlations, formulating a robust two-stage optimization model aimed at minimizing the worst-case scenario. We then transform this model into a computationally tractable form using polyhedral uncertainty sets. By analyzing the model structure and mathematical properties, we develop a column-and-constraint generation algorithm for customizing a two-stage robust optimization model based on residual inventory, and compare it with other traditional algorithms. The numerical experimental results show that the proposed model and algorithm have significant advantages in terms of solution accuracy and efficiency, and can play a role in real-world problems. Finally, the paper discusses the impact of various parameters in the model on the solution, yielding results consistent with our expectations. • Choose suitable time window interval for different zones based on zonal heterogeneity. • Create non-stationary Markov chains to set initial inventory based on user satisfaction. • Two-stage Robust optimization tackles residual inventory uncertainty in big data. • Develop an efficient G&CG algorithm for optimizing remaining inventory uncertainty. [ABSTRACT FROM AUTHOR]

The COVID-19 has led to significant changes in urban travel behaviors, with commuting being one of the most affected travel modes. Commuting cycling by bike-sharing systems (BSS) is regarded as a new transportation mode that is low-carbon and low-cost. However, its dynamic changes and spatiotemporal characteristics in different periods of COVID-19 still lack exploration. Therefore, this study adopts machine learning methods to identify commuter bike-sharing activities and develops a combined analysis method to analyze commuting cycling data via temporal, spatial, and spatiotemporal aggregation. Finally, we select the bike-sharing data in New York City from periods before, during, and after COVID-19 to conduct experiments. It has been found that commuting cycling experienced a "decrease-rebound" trend at the macroscopic level under the pandemic impact. However, at the micro level, urban mobility driven by this travel mode failed to fully recover, as evidenced by significant changes in spatial and temporal mobility patterns. The findings shall not only help traffic operators and managers discover the BSS commuting patterns but also reveal the pandemic impact on the travel behavior of urban residents, promoting the development of intelligent services for urban emergency management and traffic management. [ABSTRACT FROM AUTHOR]

The rapid growth of dockless bike-sharing services (DBSs) in recent years has provided a new potential technological solution to the critical last mile problem of urban mobility and is thus considered a complement to public transit. However, there is little causal evidence of such technology-enhanced DBSs complementing public transit. To address this research gap, this paper empirically investigates the complementary effect of DBSs on public transit using a quasi-natural experiment on the opening of Subway Line 3 in Xiamen, China. Our difference-in-differences estimation results show that the number of DBS trips near newly opened stations increased by 84.7 % on average after the subway opening, representing 40.4 % of all bike-sharing trips within the 100-m buffer zone of the subway station entrance. The results of the spatiotemporal heterogeneity analysis revealed that such complementary effects were concentrated within 2 km of rail transit stations, especially during peak commuting hours and in areas with low bus accessibility and high population density. Moreover, the number of street intersections negatively affected the complementary effect of DBS on the subway when the connection distance was short (i.e., less than 800 m). These findings can inform city governments on the effective use of DBSs to solve the critical last mile problem of public transit and to improve urban mobility, while also illuminating how service providers can optimize capacity allocation and vehicle rebalancing decisions. [ABSTRACT FROM AUTHOR]

The integration of urban rail transit and land use has been adopted as a crucial approach to fostering compact development in cities. Proximity to rail transit stations can increase the probability of land use changes, while few studies have analyzed the spatial heterogeneity of the impact of rail transit on land use changes. This study proposes a distance-decay function to delineate the spatial heterogeneity of metro station accessibility using bike-sharing data and examines the impact of metro stations accessibility on land use changes. Jiading New Town in Shanghai, China, is selected as our study case. By utilizing a non-linear distance-decay function to delineate metro station accessibility as a driving factor for training neural networks in a vector-based cellular automata model, an improvement in simulation accuracy is achieved compared to the model using a linear distance-decay function. This study could help establish more efficient strategies for promoting integrated development of rail transit and land use. The significance of this study lies in the generality of the optimized land use vector-based cellular automata model considering the spatial heterogeneity of metro station accessibility, which could also be applied to other situations, such as considering the accessibility to bus stops and road networks. • We measure metro station accessibility by using a non-linear distance-decay function calibrated from dockless bike-sharing trip data. • We adopt a metro station accessibility integrated vector-based cellular automata model to simulate land use changes at the land parcel level. • The vector-based CA model considering non-linear accessibility provides a higher simulation accuracy than that considering linear accessibility. [ABSTRACT FROM AUTHOR]

Exiting research has noted the significance of sharing economy business model, while insufficient attention has been paid to understand the user's sustainable usage behavior in it. To narrow the research gap and enrich literature, based on the bike-sharing context, this study explores user's sustainable usage behavior in the bike-sharing economy business model. On the basis of Push-Pull-Mooring (PPM) model and the survey data collected from 1029 shared bike users in China, we find that user's sustainable intention of using shared bike can be influenced by push, pull and mooring factors. Specifically, push (e.g., social norm and government and enterprise regulation) and pull (e.g., moral norm and perceived behavioral control) factors significantly promote sustainable usage intention, while mooring factor (e.g., inertia) significantly inhibit it. Meanwhile, mooring factor also weakens the positive influence of push and pull factor on sustainable usage intention. Furthermore, this research further reveals that there is a disparity between sustainable intention and sustainable behavior of using shared bike and uncovers that the "intention-behavior" discrepancy can be bridged by providing punitive measures. Additionally, this research uncovers that user's gender, age and monthly income do not significantly promote sustainable usage intention and behavior, user's educational level significantly promotes sustainable usage intention while not for sustainable usage behavior, and the frequency of using shared bike significantly promotes both sustainable usage intention and behavior. This research provides recommendations for government and enterprise to promote sustainable usage behavior in the bike-sharing context. [ABSTRACT FROM AUTHOR]

In recent years, the bike-sharing industry has experienced rapid development as a business model but still faces several challenges. Prior research has predominantly focused on bicycle service quality and traffic safety from the perspective of companies. This article takes a consumer-centered approach and incorporates security as a crucial perceptual factor into the technology acceptance model. Drawing on the consumer perceived value theory, we employ app perceived value and bicycle perceived value as mediating variables to construct a model of consumers' behavioral intentions to use bike-sharing. The study finds that app perceived value and bicycle perceived value are strong predictors of consumers' willingness to use bike-sharing. At the same time, app perceived value also indirectly affects their perceived behavioral control of using bike-sharing through the mediating role of bicycle perceived value. Furthermore, the perceived usefulness, ease of use, and security of the app are found to positively impact app perceived value, which in turn increases the preference for bike-sharing of consumers. In conclusion, these findings provide practical management recommendations for bike-sharing companies to balance and enhance app services and the quality of the bicycle, thus enabling the ride-hailing ecosystem to generate more sustainable urban transport solutions. [ABSTRACT FROM AUTHOR]

The post-coronavirus era is a long-lasting challenge, but also a window of opportunities to strengthen the role of bike-sharing in molding a resilient transportation system. However, few studies have attempted to holistically review the recent literature in the last four years. This study aims to identify emerging topics regarding the impact of COVID-19 on bike-sharing by systematically critiquing research published between 2020 and 2023. The review was complemented by a case study in New York (USA) to address a research gap identified by the review. Scientometric analysis was applied to demonstrate research frontiers. We constructed and visualized network maps that depict information on the citations and terms of the documents that frequently appear in the title, keywords, and abstract of each reviewed document. Next, a clustering-based procedure was developed to infer and compare the major trip purposes in different pandemic periods for the case study. The results reveal that emerging topics include infection risks, active travel, and modal substitution that were less discussed before the pandemic. The results of the case study show that both residential and work trips had experienced significant growth before and after the outbreak, while residential trips saw a slightly higher increase in terms of the number of commuters. Additionally, residential trips can partly replace work trips in most Manhattan communities during the pandemic, implying that people may become more dependent on bike-sharing when working from home is prioritized. This research provides new perspectives on the pandemic's impact on bike-sharing for researchers. Practitioners may use the tools to better understand how the pandemic may drive changes in travel behaviors and plan accordingly. • Emerging topics include infection risks, active travel, and modal substitution. • Residential and work trips experienced significant growth pre- and post-outbreak. • Residential trips saw a higher increase in number of communities than work trips. • Residential trips may partly substitute work trips in Manhattan communities. • People became more dependent on bike-sharing during the pandemic. [ABSTRACT FROM AUTHOR]

• Bike-sharing studies in medium-sized cities are underrepresented. • Milton Keynes, a car-centric UK city is used as a case study. • Numbers of bike rentals by season, month and hour are compared. • The connection between public transport and shared bikes is investigated. • Evidence for the utilitarian use of bike sharing is presented. Emerging modes of shared mobility like bike-sharing may significantly contribute to increasing the share of sustainable travel in medium- and small-sized cities. However, research exploring the bike-sharing usage determinants specifically for them has been severely underrepresented in the literature. To address this gap, we conducted an analysis of bike-sharing rentals for over a one-year period in Milton Keynes, a medium-sized city of 288,000 residents. Being the largest of the planned settlements built under the UK government "New Towns" programme, Milton Keynes despite having the infrastructure to offer opportunities for modal shift has been repeatedly characterised as a car-centric city. The paper examines temporal variation by comparing the average number of bike rentals in different seasons and months. Analysis of peak rentals and travel directions suggests that throughout the year bikes are mainly used to travel to work. By contrast, recreational travel appears to happen mostly during the summer months. Regression analysis shows that the number of public transport stops, offices and schools are associated with a higher number of bike rentals, supporting the view that utilitarian travel is the main usage pattern. Our research also highlights the bike-sharing's potential to be adopted as a first/last-mile option that will complement and support public transit. [ABSTRACT FROM AUTHOR]

• BSC trips were identified using large-scale bike-sharing trajectory data. • Spatial quality factors influencing BSC trips were extracted from street-view images. • Spatial heterogeneity of factors influencing BSC trips was validated through the nonlinear analysis. • Explored how interactions among built environment factors affect BSC trips. Bike-sharing has become an effective solution to address the "last mile" challenge in public transit. Existing studies insufficiently consider architectural environmental factors from a design perspective in impacting bike-sharing-to-subway connection(BSC) behavior, and often relying on subjective assessments to explain the spatial heterogeneity of the built environment on BSC travel. To investigate the impact mechanisms of the built environment on BSC behaviors, this study used weekly data on bike-sharing travel trajectories from April 7 to April 14, 2021, in Shenzhen, China, as the research sample. By integrating large-scale urban point of interest(POI), street-view images and road-network data from Shenzhen, this study examines the mechanisms by which the built environment influences BSC travel. This study initially applied linear regression and multiscale geographically weighted regression (MGWR) to analyze the spatial heterogeneity of built environment impacts on BSC travel. Then, XGBoost's nonlinear relationships were used to explain and validate the MGWR results. This study reveals that functional diversity is the most significant factor influencing BSC travel. At subway stations where it is difficult to enhance functional diversity, improvements in road connectivity, vegetation, and enclosure can enhance BSC commuting. These findings are crucial for decreasing the reliance on motor vehicles and fostering sustainable urban growth. [ABSTRACT FROM AUTHOR]

The bike-sharing system has advanced urban transportation by solving "the last mile problem," enabling riders to better connect to public transit. There has been a paucity of knowledge, however, regarding the relationship between bike-sharing and public transit. In this article, we solicit one year of bike trip data comprising approximately 17 million trips from Citi Bike, the largest dock-based bike-sharing system in New York City. Then, we derive six bike usage clusters based on three clustering variables: the start trips, end trips, and station empty status. Finally, we propose three relationships between bike-sharing and public transit: competition, integration, and complementation. The result demonstrates that bike-sharing can largely compete with public transit in New York City. A significant portion of bike-sharing trips are more time-intensive than their public transit alternatives. The article concludes that this competition exists due to riders' preferences for lower costs and flexibility over savings in travel time, which helps to improve transportation equity for socioeconomically disadvantaged populations. Thus, in New York City, bike-sharing primarily fulfills the need for low-cost and flexible travel rather than solving "the last mile problem." This revelation provides new insights into the roles of bike-sharing in urban transportation. [ABSTRACT FROM AUTHOR]

The focus of this research is to assess the impact of COVID-19 outbreak in 2020 on the Capital Bikeshare (CaBi) ridership in Washington, D.C. area compared to 2019. Correlation analysis was conducted and mixed-effects negative binomial (MENB) models were developed to assess CaBi ridership by the type of CaBi user, as the available bike-sharing trip data followed a time-series cross-section panel data structure and the variables (season, stay-at-home order and lift, and weekday) have time-specific effects. The correlation coefficients between CaBi variables, COVID-19 variables, and characteristics of D.C.'s population profile and their statistical significance are sensitive to the year (2019 or 2020), the season of the year (Winter, Spring, Summer, or Fall), and the type of CaBi user (member or casual user). The number of stations between pickups and drop-offs, and the dummy variables representing the before and after stay-at-home order and lift were found significant in the MENB models. There was no significant difference in travel time of CaBi casual users and members during the outbreak. Riding for leisure or commuting has been potentially limited with the stay-at-home order. The findings have practical implications and help bike-sharing operators to increase their resilience during unexpected situations such as the pandemic. [ABSTRACT FROM AUTHOR]

Fitting and modeling spatial-temporal processes are essential research topics in transportation studies. Recently, due to the analytically tractable formulation and good fitting accuracy, the Gaussian Process (GP) is becoming increasingly preferable in fitting transportation processes. However, conventional GPs are inapplicable for large-scale problems due to computational issues. Moreover, they dismiss physics laws when conducting multi-output fitting tasks. This paper proposes a physics regularized multi-output grid Gaussian Process Model (PRMGGP) model for fast and multi-output fitting of large-scale spatial-temporal processes in transportation systems. The PRMGGP model adopts a grid input structure to capture inherent spatial-temporal correlations in the fitting process, takes advantage of the Kronecker algebra to notably accelerate the computation speed, and utilizes a shadow GP to incorporate physics laws of the process. Model training and predictive algorithms are developed coordinately and are tested via synthetic datasets. Furthermore, we apply the proposed model and other widely used machine learning models to fit the numbers of pickups, returns, and idle bikes of a large-scale bike-sharing system based on Citi Bike data from New York City. The results demonstrate the computational efficiency, interpretable results, and the prediction accuracy of the PRMGGP model, which can be a promising methodology for modeling multi-output processes in transportation systems. [ABSTRACT FROM AUTHOR]

Bike-sharing is recognized as a fast and efficient transport solution in cities, with zero emissions, convenience, speed of movement for short distances and beneficial effects on users' health. In recent years, especially during the COVID-19 pandemic, its use has been rather heterogeneous across countries and significant differences have emerged from empirical studies. However, there has been no systematic review of the empirical studies focused on the impacts that the COVID-19 pandemic has had on the use of bike-sharing. This article aims to review all of the papers that have been published on this topic since the outbreak of the pandemic. In this analysis, we consider several elements, such as the impacts of the pandemic on actual and potential ridership, on the demand for bike-sharing compared to other public transport means, and on commuting and non-commuting trips. We also focus on the factors that have had an impact on the change in bike-sharing usage and the related policy and operational implications that have emerged from the literature. Finally, we highlight the gaps in the literature that require further investigation. [ABSTRACT FROM AUTHOR]

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing’s six central districts. Subsequent to this, a comprehensive analysis of the network’s structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities.

This study aims to explain what is and why the operational challenge of bike-sharing companies exits and how it can be overcome for bike-sharing platform companies to achieve the expected outcomes. This study finds that operational challenges exist for these companies because the reciprocity norm is not obeyed by some customers, who immorally damage or displace the bikes; the government, who does not provide subsidies; and investors, who pursue their own agenda; or because there is a lack of coordination between the company's online and offline operations arising from a lack of technology and expertise. To overcome the operational challenges of bike-sharing platforms, the study identifies two antecedents (i.e. social capital and technology application) that facilitate the operations process of bike-sharing, and in turn affect operational efficiency. Moreover, three types of social exchange (i.e. exchange between the platform and its customers, government and capital organizations) are identified as moderators of the relationship between bike-sharing operations process and operational efficiency. The proposed theoretical model provides an overall framework to assist bike-sharing platform companies to design and operate their business so that they can overcome operational challenges and achieve expected outcomes. [ABSTRACT FROM AUTHOR]

Bicycling has grown significantly in the past ten years. In some regions, the implementation of large-scale bike-sharing systems and improved cycling infrastructure are two of the factors enabling this growth. An increase in non-motorized modes of transportation makes our cities more human, decreases pollution, traffic, and improves quality of life. In many cities around the world, urban planners and policymakers are looking at cycling as a sustainable way of improving urban mobility. Although bike-sharing systems generate abundant data about their users' travel habits, most cities still rely on traditional tools and methods for planning and policy-making. Recent technological advances enable the collection and analysis of large amounts of data about urban mobility, which can serve as a solid basis for evidence-based policy-making. In this paper, we introduce a novel analytical method that can be used to process millions of bike-sharing trips and analyze bike-sharing mobility, abstracting relevant mobility flows across specific urban areas. Backed by a visualization platform, this method provides a comprehensive set of analytical tools to support public authorities in making data-driven policy and planning decisions. This paper illustrates the use of the method with a case study of the Greater Boston bike-sharing system and, as a result, presents new findings about that particular system. Finally, an assessment with expert users showed that this method and tool were considered very useful, relatively easy to use and that they intend to adopt the tool in the near future. [ABSTRACT FROM AUTHOR]

This paper proposes an accurate short-term prediction model of bike-sharing demand with the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a low-carbon, green and healthy way of transportation. However, the explosive growth and free-form development of bike-sharing has also brought about a series of problems in the area of urban governance, creating a new opportunity and challenge in the use of a large amount of historical data for regional bike-sharing traffic flow predictions. In this study, we built an accurate short-term prediction model of bike-sharing demand with the bike-sharing dataset from 2015 to 2017 in London. First, we conducted a multidimensional bike-sharing travel characteristics analysis based on explanatory variables such as weather, temperature, and humidity. This will help us to understand the travel characteristics of local people, will facilitate traffic management and, to a certain extent, improve traffic congestion. Then, the explanatory variables that help predict the demand for bike-sharing were obtained using the Granger causality with the entropy theory-based MIC method to verify each other. The Multivariate Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model were integrated to build the prediction model, and this is abbreviated as the TCN-GRU model. The fitted coefficient of determination R2 and explainable variance score (EVar) of the dataset reached 98.42% and 98.49%, respectively. Meanwhile, the mean absolute error (MAE) and root mean square error (RMSE) were at least 1.98% and 2.4% lower than those in other models. The results show that the TCN-GRU model has strong efficiency and robustness. The model can be used to make short-term accurate predictions of bike-sharing demand in the region, so as to provide decision support for intelligent dispatching and urban traffic safety improvement, which will help to promote the development of green and low-carbon mobility in the future. [ABSTRACT FROM AUTHOR]

This research looks at how spatial factors of public transportation influence the use of bike-sharing in an urban context. Based on a grid-cell-based method, ordinary least square regression and geographically weighted regression (GWR) are developed to reveal the link between the spatial distribution of bus, tram, rail stops, and bike-sharing trips. The public transportation coverage in Budapest City is rated as excellent, with all parts of the city covered within a 5 km radius. We find that areas with a high proportion of public transportation stops have a significantly higher number of bike-sharing trips. Bike-sharing trips are concentrated near regional railway stations, the central business district, and surrounding zones. The connection between bike-sharing and trams/rails appears to be stronger than the connection between bike-sharing and buses. According to the findings, nearly one-third of public transportation stops have accessible bike docks within 125 m walking distance. In GWR analysis, the coefficients of bus stops are increasing towards the center of the city, while the coefficients of tram/rail stops are decreasing. Finally, by examining the priority zones for establishing more bike-sharing facilities, it is discovered that the eastern side of the city requires more development than the western side because it has a high number of bike-sharing trips but no adequate facilities near public transportation facilities. [ABSTRACT FROM AUTHOR]

• The integration of bike-sharing with urban rail transport is examined. • There is a nonlinear relationship between the built environment and bike-and-ride usage. • Bicycle infrastructure shows more salient nonlinear effects. • Built environment effects differ between the morning and evening peak hours. Bike-sharing offers a convenient feeder mode for connecting to public transport, which helps to address the last-mile problem. However, few studies have examined the nuanced relationship between the built environment and the integration of free-floating bike-sharing (FFBS) with urban rail transport (URT). Drawing on weekly records of 3.12 million trips of the FFBS system in Nanjing, China, we examined the nonlinear effects of the built environment on FFBS-URT integrated use. A quantile regression method is utilised to estimate the relationships for morning and evening peaks, respectively. The results demonstrate the existence of the nonlinearity of the relationships. The effects of the built environment show variations in the significance levels and magnitudes of coefficients, depending on the quantiles. For example, the length of minor roads in station areas is strongly related to the integrated use at low quantile stations, whereas this effect is not statistically significant at medium and high quantiles. We also find that bicycle infrastructure displays more salient nonlinear effects than land-use variables and external transport facilities. In addition, temporal differences in the relationships between the built environment and the integrated use are also unveiled. Our findings help to inform dedicated and effective built environment interventions which support the planning of seamless connections between bike-sharing and urban rail transport. [ABSTRACT FROM AUTHOR]

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Currently, many countries are faced with many problems regarding the environment and the destruction to the planet’s natural resources. In the long run, this will have a devastating effect on the well-being of mankind. As technology advances, and cities become over-populated, the damaging effects of air pollution, noise pollution, carbon monoxide from machinery and overcrowding could very well destroy the planet if countries don’t take measures to minimise this damage, and focus on boosting biodiversity and fighting climate change, thereby making the city a healthier place for everyone. Many countries have implemented the bike sharing system to solve some of the problems caused by mechanised transportation. In this paper, an IoT-based bike sharing docked system is proposed for the city of Erbil. This proposal has looked at expected challenges and solutions.

The growth of bike-sharing systems worldwide has led to an increased need for accurate bike-sharing flow prediction at the station level. However, previous studies mostly overlooked both substantial heterogeneity and strong correlations between the returned and rented bike flows among bike stations. This study proposes the T wo- S tream S tation-level B ike-sharing F low P rediction (TS-SBFP) framework that incorporates single-stream feature extraction, cross-stream feature interaction, global information embedding, and two-stream feature fusion for station-level bike-sharing flow prediction. In this framework, inflow and outflow data are separated into two streams and input to the prediction model respectively. First, multiscale gated convolution is applied in both streams to extract abundant temporal correlations. Second, two-stream graph attention is applied to capture rich local spatial correlations between the inflows and outflows among bike stations. Furthermore, spatiotemporal fusion network is designed to effectively and efficiently fuse the two-stream features. Additionally, to identify the predictive contributions of bike stations, global station-aware attention is designed to acquire the importance of each bike station using the squeeze-and-excitation mechanism. Finally, experiments conducted on three real-world datasets demonstrate the effectiveness of the proposed approach. In addition, two-stream attention visualization and analysis verify that the proposed approach can adaptively learn spatiotemporal interaction patterns. [ABSTRACT FROM AUTHOR]

Micromobility service systems have recently appeared in urban areas worldwide. Although e-bike and e-scooter services have been operating for some time now, their characteristics have only recently been analyzed in more detail. In particular, the influence on the existing transportation services is not well understood. This study proposes a framework to gather data, infer micromobility trips, deduce their characteristics, and assess their relation to a public transportation network. We validate our approach by comparing it to similar approaches in the literature and applying it to data of over a year from the city of Aachen. We find hints at the recreational role of e-scooters and a larger commuting role for e-bikes. We show that micromobility services in particular are used in situations where public transportation is not a viable alternative, hence often complementing the available services, and competing with public transportation in other areas. This ambivalent relationship between micromobility and public transportation emphasizes the need for appropriate regulations and policies to ensure the sustainability of micromobility services. [ABSTRACT FROM AUTHOR]

Worldwide bike-sharing systems are growing in popularity as an alternative, environmentally friendly mode of transportation. As cities seek to further develop bike-sharing programmes, it is important to consider how systems should expand to simultaneously address existing inequalities in accessibility, and best serve demand. In this paper, we determine ideal locations for future bike-sharing stations in Glasgow, Scotland, by integrating demand modelling with accessibility considerations. We began by analysing the spatio-temporal trends of bike-sharing usage, and assessed the spatial equity of access to stations in Glasgow. To identify important determinants of bike-sharing demand, we ran an ordinary least squares regression model using bike sharing trip data from Nextbike Glasgow. We then quantifiably measured the level of spatial accessibility to stations by applying the two-step floating catchment area (2SFCA) methodology and ran a GIS weighted overlay analysis using the significant determinants of station demand. Lastly, we combined the demand and accessibility results to determine where new stations should be located using a maximum covering location problem (MCLP) that maximized the population served. Our results show that distance from transit stations, distance from downtown, employment rates, and nearby cycling lanes are significant factors affecting station-level demand. Furthermore, levels of spatial access were found to be highest primarily in the centre and eastern neighbourhood of Glasgow. These findings aided in determining areas to prioritize for future station locations, and our methodology can easily be applied to other bike-share programmes with adjustments according to varying aims for system expansion. [ABSTRACT FROM AUTHOR]

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Competitiveness is an important factor for the sustainable mobility of an integrated multimodal system. In this study, we explore and answer the question concerning what makes bike-sharing trips more competitive than bus trips. The empirical analysis is conducted using transit smartcard and bike-sharing GPS data collected in Seoul, and logistic regression models were developed to understand the factors contributing to the bike-sharing being more competitive than buses. The findings demonstrate that bike-sharing is not only a complementary mode but could also be competing with buses at certain extents. The results indicate that bike-sharing trips can be more competitive than bus trips with longer detours or when the speed of the bus is reduced, such as peak-time periods and short trips. Travel time of bike-sharing is more reliable than bus's during peak times, while the bike lanes contribute to keeping bike-sharing more competitive even during off-peak times. [ABSTRACT FROM AUTHOR]