Bike Sharing Research Articles

Assessing the effects of dockless bicycle-sharing on docked bicycle-sharing usage: Evidence from Xi’an, China

Dockless bicycle-sharing system substantially impacts the present docked bicycle-sharing usage in the urban transportation system. This study explores such effects using usage data of docked bicycle-sharing systems in Xi’an before and after launching the dockless bicycle-sharing system. The influence period is divided into the no-influence stage, transition stage, and stabilization stage. The hotspot analysis method is employed to identify the most influenced areas. The geographically weighted regression model is then revised to determine the response of socioeconomic factors considering the launches of the dockless bicycle-sharing system. The results reveal that the critical impact of the dockless bicycle-sharing system on the docked system. Specifically, the weekly usage share of docked bicycle-sharing declined ranged from 0.51 % to 41.4 % in transition stage. Furthermore, population density, job density, per capita income, and per capita GDP are identified as the key contributing factors based on spatial analysis, where for each 1 % increase in the four indicators, docked bicycle-sharing system’s usage decreases by 1.014 %, 1.182 %, 2.021 %, and 0.904 %, respectively.

Improving Bike Share Programs on College Campuses in Southeastern US: A Case Study

Improving Bike Share Programs on College Campuses in Southeastern US: A Case Study

Towards a System Dynamics Framework for Human–Machine Learning Decisions: A Case Study of New York Citi Bike

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

Bike sharing systems have been widely deployed in urban cities for first- and last-mile transportation. However, because of the geographical and temporal imbalance of bike demand, bikes need to be reallocated system-wide among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. Two technical challenges remain in optimizing the static bike rebalancing operations. One challenge is to accurately predict bike pickup and dropoff demand at each station, considering demand substitution effects and subsequently determining the optimal rebalancing quantity for each station. The other is to efficiently optimize the routing of multiple rebalancing vehicles for large-scale bike sharing systems, considering outlier stations with rebalancing quantities exceeding vehicle capacity. To this end, we propose an end-to-end solution to tackle the aforesaid challenges. Specifically, we first develop deep learning-based predictors that capture the time dependencies of station-level demand, the impact of weather conditions, and the demand substitution effect by nearby stations. Based on the demand rate, a sequential simulation-based demand loss estimator is developed to find the optimal rebalancing quantities that lead to the minimum expected demand loss. Then, a mixed integer linear programming model is formulated to optimize the routing problem of rebalancing vehicles. To address the computational challenge, we propose a data-driven decomposition algorithm to support a multivehicle multivisit rebalancing strategy by decomposing the multivehicle routing problem into smaller and tractable single-vehicle routing problems, which can be solved in parallel. Finally, extensive numerical experiments using real-world data from New York City Citi Bike demonstrate the accuracy of the proposed bike demand predictors, the impact of demand substitution, and the efficiency of the data-driven optimization framework. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201222] and the Hong Kong Research Grants Council [Grants CityU 21500220 and CityU 11504322]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0182 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0182 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Analyzing the micro-mobility patterns of shared dockless bike and e-bike systems through multi-scale complex networks

Analyzing the micro-mobility patterns of shared dockless bike and e-bike systems through multi-scale complex networks

Boosting Winter Green Travel: Prioritizing Built Environment Enhancements for Shared Bike Users Accessing Public Transit in the First/Last Mile Using Machine Learning and Grounded Theory

Shared bikes are widely used in Chinese cities as a green and healthy solution to address the First/Last Mile issue in public transit access. However, usage declines in cold regions during winter due to harsh weather conditions. While climate factors cannot be changed, enhancing the built environment can promote green travel even in winter. This study uses data from Shenyang, China, to investigate how built environment attributes impact the travel satisfaction of shared bike users who utilize bikes as a First/Last Mile solution to access public transit in winter cities. By employing machine learning algorithms combined with Asymmetric Impact-Performance Analysis (AIPA) and grounded theory, we systematically identify the key attributes and rank them based on their asymmetric impact and urgency of improvement. The analysis revealed 19 key attributes, 17 of which are related to the built environment, underscoring its profound influence on travel satisfaction. Notably, factors such as the profile design of cycling paths and safety facilities along routes were identified as high priorities for improvement due to their significant potential to enhance satisfaction. Meanwhile, features like barrier-free access along paths and street greenery offer substantial opportunities for improvement with more modest efforts. Our research provides critical insights into the nuanced relationship between built environment features and travel satisfaction for First/Last Mile shared bike users. By highlighting priority improvements, we offer urban planners and policymakers a framework for creating livable, sustainable environments that support green travel even in harsh winter conditions.

Estimating Motives for Renting Shared Bicycles Using Multinomial Logistic Regression: A Case Study from the Slovak Republic

This research aims to estimate the purposes for which bike-share users utilize shared bicycles from bike-sharing companies using selected predictors through multinomial logistic regression in Slovakia. The study seeks to provide a novel perspective on alternative transportation by addressing a gap in existing research, which has not previously focused on modeling the specific purposes of bicycle use. The final sample comprises 162 bike-share users out of more than 300 respondents. The results show that social status, bike-sharing company, and average distance are statistically significant input variables. Finally, we find that using shared bikes for recreation is more typical for employees with a traveled distance of up to 3 km from one of the two bike-sharing companies compared to the reference group (commuting to school). The paper contributes to better planning and management of bike-sharing systems.

A Study on Bike Share Analysis

This study explores the role of bike-sharing programs within urban mobility and the potential impacts of autonomous vehicles (AVs) on cyclists' safety perceptions and behaviour. The research focuses on analysing factors that influence the adoption of bike-sharing services, including safety perceptions, AV technology familiarity, and demographic characteristics. Using tools like MS Excel and statistical models such as multiple regression and logistic regression, the study examines patterns in bike-sharing usage, public attitudes toward AVs, and the implications of AV testing on public streets. Findings suggest that factors like age, perceived safety, and AV familiarity significantly influence bike-sharing behaviour. The study recommends enhanced infrastructure, integration with public transit, and data-driven insights to improve bike-sharing systems and promote safer interactions with AVs

Partnering with Communities to Understand Social Determinants of Health (SDoH) Impacts on Access to Shared Micromobility

This study explored the facilitators and barriers of community bike share use in a mid-sized city with high incidence of poverty and racial diversity using a community-based participatory action research (CBPAR) photovoice framework with the Stanford Our Voice (OV) Discovery Tool digital application. Community members participated in one of three community citizen science walks with follow up focus groups facilitated by osteopathic medical student researcher to address “What makes it easy or hard to ride a bike using the bike share?” Twenty-seven diverse community members partnered with four osteopathic medical students exploring vulnerable individuals’ lived experiences, beliefs/understanding of the Social Determinants of Health (SDoH) and access to the bike share program. A total of 322 photos and narrative comments from citizen science walk audits developed deductive themes and follow up focus groups informed inductive themes. Themes addressed challenges to access, maintenance, safety in bike transit, comfort, and environment that create barriers to use and increase inequities for lower income and historically underrepresented communities. The use of OV provided photograph, narrative, and geocoded photo location. This novel approach served as an effective tool for community action with city decision makers. The narrative research identified the impact of the barriers, and the photographs and geocoding provided clear descriptions for locations to prioritize change by adding street signs for access and safety, fixing road safety issues or bike maintenance concerns. It actively engaged the community with the city to drive discussions and plans for change in repair systems and infrastructure that also addressed equity and acknowledged the SDoH supporting residents in lower income or historically underrepresented communities. Citizen science engaged community voices, supporting change in city policies and transportation initiatives to support the sustainability of the bike share program.

A Spatial-Temporal Aggregated Graph Neural Network for Docked Bike-Sharing Demand Forecasting

Predicting the number of rented and returned bikes at each station is crucial for operators to proactively manage shared bike relocation. Although existing research has proposed spatial-temporal prediction models that significantly advance traffic prediction, these models often neglect the unique characteristics of shared bike systems (BSS). Spatially, the entire bike-sharing system (BSS) experiences peak activity during morning and evening rush hours, whereas, during other periods, activity is localized to local stations, with some recording no rides, highlighting the need to distinguish between global and local spatial information across different times. Temporally, the historical riding records for each station exhibit non-stationary patterns, necessitating the analysis of both global trends and local fluctuations. Existing Graph Neural Network (GNN) approaches to predicting shared bike demand primarily capture static spatial-temporal data and fail to account for the dynamic nature of bike flows. Moreover, these studies focus on global spatial-temporal information without considering local nuances, making it challenging to capture spatiotemporal dynamics in fluctuating BSS. To address these challenges, we introduce the Spatial-Temporal Aggregated Graph Neural Network (STAGNN). Our model first constructs a dynamic adjacent matrix to describe the evolving connections between stations, followed by local and global information layers to capture spatial-temporal information from large-scale shared bike networks accurately. Our methodology has been validated through experiments on four real-world datasets, comparing it against benchmark models to demonstrate superior prediction accuracy. Additionally, we conduct extended experiments on four datasets during the morning and evening rush hours, and the results also affirm the efficacy of the STAGNN in enhancing prediction performance.

Enhancing bikeshare planning and operation: Strategic use of web-based public feedback

Bikeshare system operators have leveraged online platforms to gather user feedback on station experiences and service quality. This crowdsourced data presents a valuable opportunity to enhance bikeshare planning and operations. However, planners have limited methods to analyze those qualitative data given the sparse nature of this data, which is insufficient for training comprehensive machine learning models. Addressing this challenge, our study employs a novel approach combining spatial analysis and Factor Analysis of Mixed Data (FAMD) to delve into bikeshare users’ perceptions and expectations. Focusing on Chicago’s bikeshare system, we utilize its limited online comments to demonstrate the robustness of our algorithm and its applicability to both large and small datasets. Our findings reveal a geographic pattern in feedback: negative comments on bike rebalancing, station locations, and facilities are concentrated in the city center, while dissatisfaction with the cycling environment is consistent across both urban and peripheral areas. Moreover, we discovered that the demographic and employment characteristics of areas surrounding bikeshare stations significantly influence positive feedback, overshadowing the impact of station design and local infrastructure. Overall, this study offers a quantitative framework for leveraging limited crowdsourced feedback effectively, providing strategic insights for refining bikeshare planning and operational decisions.

Perceived value, service quality and behavioral intentions towards bike-sharing services: Using an extended technology acceptance model

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.

Stawanie się miejskim rowerzystą. Biografie mobilności dojeżdżających do pracy rowerem

This article presents the results of semi-structured interviews with eight individuals whose primary or significant mode of transportation in the city is the bicycle. The purpose of the analysis was to describe the trajectory of becoming an urban cyclist. During the interview, three stages of bicycle use emerged: childhood, high school, and college and work. These stages were somewhat more clearly separated by interviewees, who were also able to distinguish periods of cycling and noncycling. At the same time, the beginning of a new stage may represent one of the key moments when survey participants may give up cycling (especially the start of high school) or take up the activity anew (e.g., children entering a period of greater independence). In addition, the start of cycling for transportation purposes may be easier to think about for those accustomed to physical activity, and especially for those cycling for recreational or sporting purposes. The city’s bike-sharing system can be a significant factor in initiating the practice of cycling for people who did not own bicycles and/or have not used them for many years.

Does the Bike-Sharing Platform Share Its Own Private Information or Not in a Reverse Supply Chain?

Bike sharing facilitates the public’s daily lives and possesses substantial market demand, yet its overwhelming presence has triggered some environmental problems. To enhance resource utilization efficiency, we examine the decision-making strategies within a reverse supply chain considering the private information of the bike-sharing platform. Using Stackelberg games, two models are formulated in order to investigate whether the platform should share its own private information when the used shared bikes are recycled. A supply chain without private information is provided as a benchmark for comparison, and we obtain the optimal equilibrium strategies of the benchmark and the proposed model. Furthermore, how the optimal strategies change with the variation in parameters and the impacts of the private information on the optimal strategies are discussed. The results show that information asymmetry could lead to a reduction in supply chain efficiency. Thus, a two-part pricing contract is introduced to incentivize the platform to share information and coordinate the supply chain, which can effectively improve the performance of the integral supply chain and implement a “win-win” strategy for the third-party remanufacturer and the platform.

CDO-TCN-BiGRU: An Optimized Hybrid Deep Learning Model for Shared Bicycles Demand Forecasting

<div>Accurate prediction of the demand for shared bicycles is not only conducive to the operation of relevant enterprises, but also conducive to improving the image of the city, facilitating people’s travel, and solving the balance between supply and demand of bicycles in the region. To precisely predict the demand of shared bicycles, a model combining temporal convolution network (TCN) and bidirectional gating recurrent unit (BiGRU) model is proposed, and the Chernobyl disaster optimizer (CDO) is used to optimize its hyperparameters. It has the ability of TCN to extract sequence features and gated recurrent unit (GRU) to mine time series data and combine the characteristics of CDO with fast convergence and high global search ability, so as to reduce the influence of model hyperparameters. This article selects the shared bicycles travel data in Washington, analyzes its multi-characteristics, and trains it as the input characteristics of the model. In the experiments, we performed comparison study and ablation study. The results show that the prediction error of the proposed model is less than other comparative models. Therefore, CDO-TCN-BiGRU model has the characteristics of high prediction precision and good stability.</div>

Analysis of Behaviour Pattern of Bicycle-sharing Users Based on Complex Network: a Case Study of Beijing Downtown Area

Abstract. The introduction of the Bicycle-sharing System (BSS) has provided great convenience for residents for short travel in city. However, the parking lot planning and bicycle logistics distribution remain suboptimal, adversely affecting traffic order. Studying the spatial and temporal usage patterns can be helpful for better management of BSS. In this paper, we propose an analytical framework for detecting the bicycle-sharing pattern based on the community detection algorithms from complex networks to study the spatial-temporal patterns of usage.Firstly we identify the potential demand for bicycle among urban residents with a dataset from the BSS in Beijing downtown area. Then we construct a linkage network for modelling the BSS by applying community detection algorithms to identify regions of both high and low connectivity. Following this, based on the division of sub-areas, three indicators (graph density, cross-area travel demand, and confidence ellipses) are established for characterizing these sub-areas. Our findings include: (1) The usage pattern of the BSS within the downtown area consistently exhibits relatively stable clustering phenomena over time. (2) The usage pattern is related to the urban spatial structure, with significant differences between weekdays and weekends. (3) Residents tend to complete their cycling within the current sub-areas. (4) Generally, smaller sub-areas tend to have denser bicycle travel behaviour. These insights are vital for improving BSS parking lot planning and logistics distribution.

Spatio-temporal dynamics and recovery of commuting activities via bike-sharing around COVID-19: A case study of New York

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.

Exploring Spatiotemporal Interaction Patterns of Dockless Shared Bicycle Networks: A Case Study of Shenzhen, China

Abstract. The explosive development of shared bicycles has changed the way people travel. In parallel with the urbanization process, shared bicycles have emerged as a significant mode of transportation, especially for the first and last mile transit. With the aim of obtaining a comprehensive understanding of the spatiotemporal patterns underlying shared bicycle usage, this study takes Shenzhen, China as a case study. Drawing on the principles and techniques of complex network analysis, the research adopts a spatial grid framework, treating each grid as a node, the flow of shared bicycles as an edge, and the flow frequency as the corresponding weight. Subsequently, the spatiotemporal interaction networks of different periods of working days and weekends are constructed respectively, based on which the topological characteristics are analysed. The results show that the spatiotemporal interaction networks exhibit high regional connectivity and node interdependence, suggesting the presence of a small-world phenomenon.

Cargo Bike Sharing in Urban Areas: A Comparative Perspective on Different Types of Operators

Cargo Bike Sharing in Urban Areas: A Comparative Perspective on Different Types of Operators

A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks

Shared bikes, as an eco-friendly transport mode, facilitate short commutes for urban dwellers and help alleviate traffic. However, the prevalent station-based strategy for bike placements often overlooks urban zones, cycling patterns, and more, resulting in underutilized bikes. To address this, we introduce the Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model, leveraging multi-source data and Spatio-Temporal Graph Convolutional Networks (STGCN). This model predicts spatial user demand for bikes between stations by constructing a spatial demand graph, accounting for geographical influences. For precision, ST-BDP integrates an attention-based graph convolutional network for station demand graph’s temporal-spatial features, and a sequential convolutional network for multi-source data (e.g., weather, time). In real dataset, experimental results show that ST-BDP has excellent performance with mean absolute error (MAE) = 1.62, mean absolute percentage error (MAPE) = 15.82%, symmetric mean absolute percentage error (SMAPE) = 16.14%, and root mean square error (RMSE) = 2.36, outperforming the baseline techniques. This highlights its predictive accuracy and potential to guide future bike-sharing policies.