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Urban transportation has changed dramatically in the recent years through the large scale implementation of shared micro-mobility services, especially bike sharing systems (BSS) and electric scooter sharing (ESS). The COVID-19 pandemic brought further changes and uncertainty to this turbulent business environment. In 2020, the major BSS operator in Poland filed for bankruptcy claiming that its problems arise from the COVID-19 pandemic. Market reports and media speculated that BSS business, despite being publicly financed, and considered to be a of part of public transportation system, could be unsustainable in face of the competition from ESS. We used Z‑score analysis to investigate if bike sharing systems operators' problems began before or during the COVID-19 pandemic and large scale development of ESS. Our study focuses on the Nextbike company, which held a dominant stake in the Polish BSS market. It also covers two other major Polish operators and includes a German operator for comparative analysis. To complement the quantitative findings from z‑score analysis, we have also interviewed representatives of major stakeholders, which deepened our understanding of BSS problems. The results of our research indicate that although the market was affected by the COVID-19 pandemic and growing scooter-sharing competition, the problems of the largest BSS operator were specific to this company, and the entire market was not under the risk of failure. [ABSTRACT FROM AUTHOR]

Increasing urban traffic congestion and environmental pollution have led to the embrace of bike-sharing for its low-carbon convenience. This study enhances the operational efficiency and environmental benefits of bike-sharing systems by optimizing electronic fences (e-fences). Using bike-sharing order data from Shenzhen, China, a data-driven multi-objective optimization approach is proposed to design the sustainable dynamic capacity of e-fences. A dynamic planning model, solved with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), adjusts e-fence capacities to match fluctuating user demand, optimizing resource utilization. The results show that an initial placement of 20 bicycles per e-fence provided a balance between cost efficiency and user convenience, with the enterprise cost being approximately 76,000 CNY and an extra walking distance for users of 15.1 m. The optimal number of e-fence sites was determined to be 40 based on the solution algorithm constructed in the study. These sites are strategically located in high-demand areas, such as residential zones, commercial districts, educational institutions, subway stations, and parks. This strategic placement enhances urban mobility and reduces disorderly parking. [ABSTRACT FROM AUTHOR]

Conceptualizations of scaling sustainable practices tend to focus on the uptake of new technologies and practices, whereas the scaling or maintaining of convival practices with a long history has received less attention. In this article, we explore mainstreaming of cycling along three contemporary paths: the spread of e-bikes, bike sharing, and bike kitchens. We develop an analytical framework for assessing mainstreaming of sustainable practices and its impacts on conviviality – exploring which new groups are reached and new forms of usage opened up, as well as the impacts on accessibility, adaptability, and socio-ecological relations. Our analysis shows that the spread of e-bikes and bike sharing are associated with a certain loss of conviviality, while bike kitchens explicitly aim to maintain conviviality. The mainstreaming paths also widen and deepen the practice and notion of cycling. We conclude by reflecting on policy implications and how strategies for more convivial mainstreaming can be organized.

Shared mobility solutions such as bike sharing services play a key role to reduce greenhouse gas emissions in urban areas. In this paper, we present an approach to model station-based bike sharing in the multi-modal agent-based travel demand model mobiTopp. We compare different implementations of how agents choose their bike pick-up and drop-off stations. In addition to two variations of distance minimization, we also present a gravity approach to represent the reliability of a system. By also comparing different behavioral attitudes of the agents towards walking, a total of six scenarios were implemented and tested. The presented approach allows to easily test scenarios with a varying number of bikes and stations. We apply our algorithm to a model for the city of Hamburg, Germany, where the mobility behavior of a total of 1.9 million agents is modeled. Our simulations show plausible results. The average distances, utilization shares of each station, and other parameters match with values from the actual service. While the different strategies result in significantly different access times, and provide further new valuable insights and options for parameterization, differences in resulting demand are small. Overall, this model provides new methods to simulate bike sharing in travel demand models, thus helps to simulate an important mode of transport of the future. [ABSTRACT FROM AUTHOR]

Bike sharing systems have become a sustainable alternative to motorized private transport in urban areas. However, users often face high costs and availability issues due to the operational effort required to redistribute bicycles between stations. For addressing those issues, the AuRa (Autonomes Rad, Eng. Autonomous Bicycle) project introduces a new mobility offer in terms of an on-demand, shared-use, self-driving cargo bikes service (OSABS) that enables automated redistribution. Within the project, we develop different order management and rebalancing strategies and validate them using simulation models. One prerequisite for this is sound demand scenarios. However, due to the novelty of OSABS, there is currently no information about its utilization. Consequently, the objective of this study was to develop an approach for defining OSABS demand scenarios in a temporally and spatially disaggregated manner as an input for simulation models. Therefore, we first derived city-wide usage potentials of OSABS from a survey on mobility needs. We then spatially and temporally disaggregated the determined usage likelihood using travel demand matrices and usage patterns from a conventional bike-sharing system, respectively. Finally, we performed cluster analyses on the resulting annual demand to summarize sections of the yearly profile into representative units and thus reduce the simulation effort. As we applied this approach as a case study to the city of Magdeburg, Germany, we could show that our methodology enables the determination of reasonable OSABS demand scenarios from scratch. Furthermore, we were able to show that annual usage patterns of (conventional) bike sharing systems can be modeled by using demand data for only eight representative weeks. [ABSTRACT FROM AUTHOR]

The planning process for bike sharing systems is often complex, involving multiple stakeholders and several considerations: finding hotspots in the potential demand, and dimensioning the system, requires an intimate knowledge of urban mobility patterns and specific local features of the city. The significant costs associated with dynamic rebalancing of bike sharing systems, i.e. with moving bikes across the city to correct the demand imbalance and ensure that they are available where and when they are needed, make correct planning even more critical for the economic viability of the system. In this work, we consider urban environment data from multiple sources and different cities in Europe and the United States to design an automated planning pipeline to place stations in an area with no direct knowledge of the demand. The first step in the planning is to build models of activity patterns and correlate them with features of the urban environment such as land use and mass transit availability; these statistical models can then be used to expand an existing network or even create an entirely new one in a different city. A use case in New York City shows that our system can effectively plan a bike sharing system expansion, providing a valuable first step for the planning process and allowing system designers to identify gaps in existing systems and the locations of potential demand hotspots. [ABSTRACT FROM AUTHOR]

Background: Conventional bike sharing systems are frequently adding electric bicycles. A major question now arises: Does the bike sharing system have a sufficient number of ebikes available, and are there customers who prefer to use an ebike even though none are available? Methods: Trip data from three different bike sharing systems (Indego in Philadelphia, Santander Cycles in London, and Metro in Los Angeles and Austin) have been used in this study. To determine if an ebike was available at the station when a customer departed, an algorithm was created. Using only those trips that departed while an ebike was available, a random forest classifier and deep neural network classifier were used to predict whether the trip was completed with an ebike or not. These models were used to predict the potential demand for ebikes at times when no ebikes were available. Results: For the system with the highest prediction accuracy, Santander Cycles in London, between 21% and 27% of the trips were predicted to have used an ebike if one had been available. The most important features were temperature, distance, wind speed, and altitude difference. Conclusion: The prediction methods can help bike sharing operators to estimate the current demand for ebikes. [ABSTRACT FROM AUTHOR]

The implementation of bike-sharing systems (BSSs) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSSs based on stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety-related aspects on the mode shift, through which behavioural insights are derived. For example, car users are found to be twice as sensitive to the cost of BSSs than to that of car. Similarly, they are highly sensitive to BSS travel time. Based on the behavioural findings, policy measures are suggested under the following categories: (i) finance, (ii) regulation, (iii) infrastructure, (iv) campaigns and (v) customer targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach, as the latter is slowly gaining prominence in the field of transport. For the comparison, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur when using mathematically more powerful models, such as the random forest classifier.

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 and 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.

(1) Background: This work analyzes socioeconomic equity in bike-sharing systems. Specifically, we study the effect of income on bike use in an innovative way by analyzing the frequency of bike routes connecting areas with different mean incomes. (2) Methods: We use Social Network Analysis tools to estimate the probability of connection between two stations depending on income and controlling for other predictors. The method was applied to a bike-sharing system located in the city of Las Palmas de Gran Canaria, Spain. (3) Results: Stations located in lower-income neighborhoods have a lower probability of generating routes, and stations located in higher-income areas are more likely to be connected to each other. (4) Conclusions: The frequency of bike routes is more influenced by income than other socioeconomic characteristics of the area, such as commercial and leisure use. Since socioeconomic inequities are corroborated by the work, policies for lower-income users should be promoted. [ABSTRACT FROM AUTHOR]

Bike sharing offers a usable form of feeder transportation for connecting to public transportation and effectively meets unmet travel demands, alleviating the pressure on public transportation systems by diverting urban commuters. To advance the comprehension of how the built environment shapes the relationship between bike-sharing systems and public transport modes, we implement a categorization framework that divides bike-sharing data into three distinct patterns: competition, integration, and complementation, based on their coordination with public transportation. The SLM model is employed to investigate the complex correlations between the relationship patterns and four key groups of environmental factors encompassing land use, transportation systems, urban design, and social economy. We find a strong correlation between four groups of environmental factors and three relationship patterns. Furthermore, the built environment variables exhibit significant variations across the three patterns. Users in the competitive mode prefer the flexibility of shared bikes and place a higher value on the sightseeing and leisure benefits. Instead, users in the integration and complementation modes tend to prefer shared bikes to supplement unmet travel demand and place a higher value on commuting benefits. These findings can benefit urban planners seeking to encourage greater diversity in transportation modes and incentivize more commuting. [ABSTRACT FROM AUTHOR]

In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality of service at best and a loss of customers and system failure at worst. This paper focuses on using machine learning (ML) classifiers, most notably random forest and gradient tree boosting, for estimating the inventory level from available features including historical data. However, while similar approaches adopted in the context of bike sharing assume the data to be well-balanced, this assumption is not met in the case of the inventory problem. Indeed, as the demand for bike sharing is sparse, datasets become biased toward low demand values, and systematic errors emerge. Thus, we propose to include a new iterative resampling procedure in the classification problem to deal with imbalanced datasets. The proposed model, tested on the real-world data of the Citi Bike operator in New York, allows to (i) provide upper-bound and lower-bound values for the bike-sharing inventory problem, accurately predicting both predominant and rare demand values; (ii) capture the main features that characterize the different demand classes; and (iii) work in a day-to-day framework. Finally, successful bike-sharing systems grow rapidly, opening new stations every year. In addition to changes in the mobility demand, an additional problem is that we cannot use historical information to predict inventory levels for new stations. Therefore, we test the capability of our model to predict inventory levels when historical data is not available, with a specific focus on stations that were not available for training. [ABSTRACT FROM AUTHOR]

Urban mobility has evolved rapidly in recent years with the diversification of mobility innovations. Our analysis focuses on the reception, expressed through early media coverage, of shared and self-service mobility services in Montreal. Using seven case studies (Communauto, Bixi, Auto-mobile, Car2Go, electric Bixi and bicycles ( Jump) and self-service scooters without anchoring (Lime and Bird)), we seek to understand how the press depicts these innovations. For each case, a three-year time window starting one year before its introduction is identified. Three major daily newspapers in Montreal are used to form our corpus of articles (n=179), published between 1997 and 2021. This analysis also informs the dynamics of integration of an innovation in the sociotechnical sector of daily mobility. Three periods emerge from the coverage. 1) An initial and largely descriptive treatment of innovations on a case-bycase basis, precedes 2) a comparative treatment of cases, which echoes a political vision in favor of the complementarity of transport modes, despite integration complexities. The positive perceptions of the new modes give way to 3) a more critical approach to emerging dockless systems that, while being part of the same movement, produce a variety of distinct issues. The local origin of an innovation and the motorized nature of the innovation seem to positively influence its reception and its integration into the city. [ABSTRACT FROM AUTHOR]

The launch of large-scale bike sharing in China has effectively met the demand for low-carbon short-distance travel, but it has also led to the emergence of a large number of abandoned bikes, which is called the "bike-sharing siege". In order to achieve the sustainable development of the bike-sharing industry, this paper discusses how to encourage bike-sharing companies to recycle and reuse abandoned bikes and improve the efficiency of local government financial resources based on the promotion of the EPR system. We apply the evolutionary game to investigate the interaction mechanism of complex behaviors between local governments and bike-sharing companies under two subsidy policies: a fixed recycling subsidy and regressive recycling subsidy. The results show that both recycling subsidy policies have diminishing marginal effects. In addition to incentives, local governments need to unify bike-sharing parking, establish bike-sharing monitoring platforms, and reasonably allocate bike parking spaces to ensure the quality of the urban environment. Under effective regulation, by implementing a regressive recycling subsidy policy and setting a rational amount for the recycling subsidy, local governments can effectively promote the EPR system, strengthen the environmental responsibility of companies, and avoid the "free-rider phenomenon". Based on the current situation in China, this research provides references for each local government to formulate management policies from the perspective of subsidy policy. [ABSTRACT FROM AUTHOR]

Supply chain responsiveness and big data analytics (BDA) have garnered considerable interest in academia and among practitioners. BDA helps researchers understand the current challenges in data management, including the high volume, velocity, and variety of data. This study is concerned with improving the responsiveness of supply chain networks to bike-sharing systems (BSS), which exhibit BDA characteristics. To address the challenges of forecasting bike usage and accordingly optimizing repair shop operations, we analyze multi-factor BSS data (Data from Washington D.C. BSS available to public), wherein attributes, such as weather conditions, registration, humidity, date, and time, are present. We use machine learning algorithms, such as neural networks, decision-tree-based regression, K-nearest neighbor, support vectors, and ensemble random forest, to predict bike usage and repair. This work contests the results and demonstrates the effectiveness of combining machine learning with supply chain network design. Supply chain networks model bike repairs by means of capacity extensions, which entails a nonlinear problem. In this study, we utilize a gradient search to solve a nonlinear supply chain network model. By enabling capacity extension, bike repair shops within the BSS exhibit a promising 50 % reduction in lead repair time. Furthermore, a 25 % overall throughput increase in BSS is achieved. Ultimately, this study demonstrates the importance of operational flexibility in responding to big data challenges.

Based on cited literature, it can be observed that the bike road system is an important part of Smart Mobility. The amount of bicycle roads can be seen as one of the technical-infrastructure indicators used to measure the potential of the Smart Mobility system. Taking this into account the analysis of the development of bike roads in particular provinces of Poland, which is conducted in this paper is a contribution to the knowledge about Smart Cities in the area of Smart Mobility. This publication uses data on the operation of bicycle roads in Poland from 2012 to 2021 for analysis. It uses data collected by the Polish Central Statistical Office. The analysis covered the last 10 years. The latest data that is currently available in terms of complete statistics are from 2021, and it was decided to end the conducted analysis this year. The data for all Polish 16 provinces were analyzed in the paper. The main important finding of the paper is finding the correlation between the GDP (Gross Domestic Product) and the length of bicycle roads in Poland. Also, the very important finding of the paper is connected with the analysis of geographical patterns of bicycle roads in Poland. The analysis of the correlations between the variables allows us to conclude that more is invested in bicycle paths in the provinces located in northwestern Poland than would result from the wealth of the provinces as measured by GDP per capita. In particular, this is the case in Greater Poland, Pomerania, and Kuyavia-Pomerania. By contrast, in central and southwestern Poland, investments in bicycle paths in individual provinces are smaller than their wealth level would suggest. This situation is particularly the case in Opole Province, Lesser Poland, Lower Silesia, Subcarpatia, and Holy Cross. For these provinces in particular, it should be recommended to accelerate the construction of new bicycle roads, which will have a positive impact on the quality of life of residents in smart cities as well as ecology. Analysis of the functioning of bicycle roads as an element of a Smart City on the example of Poland Provinces.

Abstract The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system.

As an important component of bike-sharing (BS) users, the travel behavior of college students and office workers is important to the promotion of BS within Chinese cities. To explore the influencing factors for the behavioral intentions of BS, this paper makes a different analysis between the two groups. Based on the theory of planned behavior, and using environmental awareness as an extended variable, a BS travel intention model was developed. A total of 676 valid questionnaires were collected and analyzed from college students and office workers in Zhengzhou. The results indicate that attitude, subjective norms, perceived behavior control, and environmental awareness have a positive impact on the behavioral intentions of BS. However, the influence degree of each variable is different between the two groups. Perceived behavior control, including travel time, travel cost, and cycling difficulty, has the greatest impact on BS behavioral intention for college students. Meanwhile, subjective norms, including policy and media publicity, has the most significant impact on BS behavioral intention for office workers. The impact of environmental awareness on college students' use of BS is higher than that of office workers. We also found that undergraduates use BS more frequently than postgraduates. The findings provide the clear influence factors on behavioral intentions of BS between college students and office workers, that can help policy optimization in terms of bike-sharing systems, giving some suggestion for an approach devoted to deepen the individual-context interactions. [ABSTRACT FROM AUTHOR]

The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system. [ABSTRACT FROM AUTHOR]

Bike sharing systems have become a sustainable alternative to motorized private transport in urban areas. However, users often face high costs and availability issues due to the operational effort required to redistribute bicycles between stations. For addressing those issues, the AuRa (Autonomes Rad, Eng. Autonomous Bicycle) project introduces a new mobility offer in terms of an on-demand, shared-use, self-driving cargo bikes service (OSABS) that enables automated redistribution. Within the project, we develop different order management and rebalancing strategies and validate them using simulation models. One prerequisite for this is sound demand scenarios. However, due to the novelty of OSABS, there is currently no information about its utilization. Consequently, the objective of this study was to develop an approach for defining OSABS demand scenarios in a temporally and spatially disaggregated manner as an input for simulation models. Therefore, we first derived city-wide usage potentials of OSABS from a survey on mobility needs. We then spatially and temporally disaggregated the determined usage likelihood using travel demand matrices and usage patterns from a conventional bike-sharing system, respectively. Finally, we performed cluster analyses on the resulting annual demand to summarize sections of the yearly profile into representative units and thus reduce the simulation effort. As we applied this approach as a case study to the city of Magdeburg, Germany, we could show that our methodology enables the determination of reasonable OSABS demand scenarios from scratch. Furthermore, we were able to show that annual usage patterns of (conventional) bike sharing systems can be modeled by using demand data for only eight representative weeks.

The Mobility on Demand (MOD) Sandbox Demonstration Program provides a venue through which integrated MOD concepts and solutions – supported through local partnerships – are demonstrated in real-world settings. For each of the 11 MOD Sandbox Demonstration projects, the MOD Sandbox Independent Evaluation includes an analysis of project impacts from performance measures provided by the project partners, as well as an assessment of the business models used. This report constitutes the Evaluation Plan for the Chicago Transit Authority (CTA) Integrated Fare Systems -From Transit Fare to Bike Share Sandbox project. It includes the following chapters: project overview; evaluation approach and process; evaluation schedule and management; and data collection & analysis plan.

The influence of the built environment on dockless bike-sharing (DBS) trips connecting to urban metro stations has always been a significant problem for planners. However, the evidence for correlations between microscale built-environment factors and DBS-metro transfer trips remains inconclusive. To address this, a framework, augmented by big data, is formulated to analyze the correlation of built environment with DBS–metro transfer trips from the macroscopic and microscopic views, considering Beijing as a case study. The trip density and cycling speed are calculated based on 11,120,676 pieces of DBS data and then used to represent the characteristic of DBS-metro transfer trips in a multiple linear regression model. Furthermore, a novel method is proposed to determine the built-environment sampling area around a station by its corresponding DBS travel distances. Accordingly, 6 microscale built-environment factors are extracted from street-view images using deep learning and integrated into the analysis model, together with 14 macroscale built-environment factors and 8 potential influencing factors of socioeconomic attributes and metro station attributes. The results reveal the significant positive influence of greenery and presence of barriers on trip density and cycling speed. Additionally, presence of streetlights is found to be negatively correlated with both trip density and cycling speed. Presence of signals is also found to have an influence on DBS-metro transfer trips, but it only negatively impacts trip density.

We develop new techniques involving group symmetries and complex analysis to obtain exact solutions for the transition probabilities of the M/M/1/k queueing process. These methods are based on the underlying Markovian structure of these random processes and do not involve any generating functions, Laplace transforms, or advanced special functions. Our techniques exploit the intrinsic group symmetries for both the state spaces and the matrix generators of the Markov processes related to the M/M/1/k queue. These results complement and extend the previous transient solutions given by Takács (Introduction to the theory of queues. University texts in the mathematical sciences, Oxford University Press, New York, 1962). Much of the inspiration for this work comes from viewing this queueing process as a fundamental Markovian model for the dynamics of a bike sharing station. The exact transient analysis for a related stopped version of this process can be used to address fundamental decision-making issues for managing bike-sharing services. [ABSTRACT FROM AUTHOR]

Harbin, China, has a large population density and a large number of motor vehicles. To alleviate traffic congestion, based on the survey data of bike-sharing riders in the new and old urban areas of Harbin in May 2022, this paper uses an impact-asymmetric analysis and gradient enhancement decision tree to analyse the asymmetric relationship between bike-sharing travel environment elements and cyclists' satisfaction, and the optimisation strategy for the bike-sharing riding environment was obtained so that more residents can choose to ride. This research shows that the infrastructure of the motorway in the old urban area had the greatest impact on the overall satisfaction, while the travel quality of the shared bikes in the new urban area had the greatest impact on the overall satisfaction. In addition, due to the differences in urban environments and satisfaction, planning directions are different when satisfying cyclists in the new and old urban areas. The old urban area should emphasise cycling comfort and road coherence to provide a good travel environment; however, the new urban area should focus on the operation of shared bikes to meet the needs of cyclists. Therefore, future research should formulate refined improvement strategies for different regions. [ABSTRACT FROM AUTHOR]

• A demand prediction model for docked bike-sharing is proposed using deep learning approach. • Dynamic effects of dockless bike-sharing on the demand for dock-based scheme are incorporated. • There are spillover effects for the influence area of dockless bike-sharing on the demand. • Effect of dockless bike-sharing on the demand for dock-based scheme can magnify over time. • Influences on the demands on weekends are more significant than that on weekdays. To evaluate the dynamic effects of the dockless bike-sharing scheme on the demand of the London Cycle Hire (LCH) scheme at the station level, a novel bicycle demand prediction model is proposed using the deep learning approach, based on the transaction records at 645 docking stations of LCH in the period between July 2017 and March 2018. First, an intervention response module (IRM) is established to model the time-series trends of bicycle demands at individual LCH docking stations, with and without the dockless bike-sharing scheme. Then, the Graph Neural Networks (GNN) predictors are adopted to predict the demand for LCH, incorporating the learned effects from IRM. Results indicate that the proposed bicycle demand prediction model can achieve promising prediction performances, with higher R-squared (R2), lower Root Mean Squared Errors (RMSE) and lower Mean Absolute Errors (MAE), compared to conventional prediction models. More importantly, the proposed model can recognize the dynamic effects of the dockless bike-sharing scheme on the demand for LCH. For instance, there are possible spillover effects for the influence area of dockless bike-sharing scheme, especially for the neighboring areas that have well-integrated bicycle facilities (e.g., cycle lanes). In addition, the effect of dockless bike sharing on the demand for LCH can magnify over time. Moreover, influences on the demands on weekends are more remarkable than that on weekdays. Findings should improve the understanding on the interdependency between the demands of dockless and docked bike-sharing systems. This should shed light to the optimal management strategy for the docked bike-sharing system that can maximize the operational efficiency and cost-effectiveness. [ABSTRACT FROM AUTHOR]

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. • BSS usage should be considered and promoted during disruptive public health crises. • Bike sharing offers a transport alternative perceived to have a low infection risk. • Even during lockdowns, users still resorted to BSS to fulfil their mobility needs. • Although most BSS users own a car, they still prefer BSS in several of their trips. [ABSTRACT FROM AUTHOR]

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Shared mobility services, such as bike sharing, have gained immense popularity and emerged as an integral part of sustainable urban mobility solutions. The planning of such systems requires forecasting the potential origin–destination (OD) flows between mobility sites (e.g., bike sharing stations) within the proposed network. Existing methods primarily focus on mobility flows between large regions, and do not generalize well to detailed planning applications due to the high spatial resolution required, with increased uncertainty and data sparsity. This study proposes a zero-inflated negative binomial graph neural network (ZINB-GNN) to generate sparse OD flows while capturing complex spatial dependencies. To reflect sparsity, OD flows are modeled as following ZINB distributions parameterized via feed-forward networks. To capture spatial dependencies, localized graphs are constructed to represent proximity between OD pairs, with spatial features encoded using GNNs. ZINB-GNN is validated through a case study of the bike sharing system in New York City. The results verify its prowess in both prediction accuracy and uncertainty quantification under real-world network expansion scenarios. We also demonstrate its interpretability by revealing important factors affecting OD flows. These findings can directly inform the planning of bike sharing systems, and the methodology may be adapted for other shared mobility systems. • A graph deep learning model is proposed for origin–destination (OD) flow generation. • Zero-inflated negative binomial distributions are adopted to capture data sparsity. • Graph neural networks are used to model spatial dependencies between OD flows. • The model is validated using data from the bike sharing system in New York City. • The results demonstrate improved prediction accuracy and uncertainty quantification. [ABSTRACT FROM AUTHOR]

This study aims to investigate the public attention and attitude towards shared bikes (SBs) in China. More than 721,000 posts collected from Sina Weibo (one of the most popular social media platforms) from January 2017 to August 2023 have been analyzed utilizing an analytical framework comprising statistical and temporal analysis, sentiment analysis, and topic analysis methods. The results reveal a consistent upward trend in public interest since 2018, with high engagement from the media, education, and institutional sectors, and greater interest from women and younger Weibo users. Economically developed regions with supportive policies and flat terrain, such as Beijing, Guangdong, and Shanghai, exhibit a higher level of public attention. Sentiment analysis indicates an overall positive public attitude, primarily driven by discounted monthly passes, but there are negative sentiments arising from improper parking management practices. The results are expected to be helpful for improving the SB system and informing relevant policy-making. [ABSTRACT FROM AUTHOR]

In recent years, the truck-based broken bike recycling mode (i.e., dedicated trucks deal with all transportation activities of broken bikes) in bike sharing systems (BSSs) has led to some problems in China, such as BSS operators have no willingness to collect broken bikes from a bike station in time. As a result, many BSSs are in bad condition due to an increase in the number of broken bikes. To solve this problem, many cities in China have started to introduce a new broken bike recycling mode, where some citizens (i.e., sanitation workers, freelancers, volunteers, etc.) are incentivized to participate in recycling activities. To deal with this new recycling mode, a recycling routing problem of broken bikes with incentives in BSSs is proposed, where the recycling operation is performed by citizen participants and a fleet of homogeneous trucks. Participants are incentivized to relocate broken bikes scattered in a BSS to transshipment stations in exchange for monetary rewards, and then these broken bikes are transported from transshipment stations to the depot by trucks. The problem is formulated as a mixed-integer linear programming model to minimize the total operation cost, and an efficient modified tabu search is developed to solve the proposed problem. Experimental results reveal the proposed broken bike recycling mode is beneficial for BSS operators to reduce the operation cost. Experimental results also show the proposed modified tabu search can generate high-quality solutions in short computing times.