Bike Sharing Research Articles

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.

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.

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.

Micro-Sharing Mobility for Sustainable Cities: Bike or Scooter Sharing?

Micro-Sharing Mobility for Sustainable Cities: Bike or Scooter Sharing?

Assessment of weather-driven travel behavior on a small-scale docked bike-sharing system usage

Bike-sharing systems play a crucial role in encouraging sustainable transportation, and understanding their usage characteristics is essential for enhancing their contribution to urban mobility. This research seeks to investigate how weather conditions impact the utilization of a small-scale docked bike-sharing system. The study employed Generalized Linear Mixed Effects (GLME) models to analyze interactive events, using categorized weather parameters to represent various weather conditions. Several models were developed to comprehensively understand distinct travel behaviors and identify significant weather variables affecting the frequency of bike trips for transportation and leisure purposes. The findings reveal that rain had a significant deterrent effect on leisure cycling, particularly on weekdays. Cold and hot weather conditions exhibited a more pronounced impact on weekday bike trips, while weekend bike trips appeared to be less influenced by weather variables. The fall season was found to be the least favorable for leisure trips, while winter was determined to be the most unfavorable for transportation trips. Furthermore, hot days in the summer season negatively impacted bike usage only on weekdays. These insights have important implications for the development of a more resilient bike-sharing system, particularly in small-scale contexts. They provide valuable recommendations for tailored strategies to mitigate the impact of adverse weather conditions, thereby fostering an increase in usage of bike-sharing systems for both leisure and transportation purposes.

Dynamic Investigations of Shared Bicycle Operators’ Competition Based on Profit Maximization

With the rise of the sharing economy, shared bicycles have become an important component of urban transportation. This paper explores the nonlinear dual oligopoly system for the Cournot model in the bike-sharing market; both operators have maximized profits as their competitive goals. The analysis of pivotal factors influencing passenger preferences, including pricing discounts and comfort levels, is meticulously depicted by a bifurcation diagram. A new chaotic attractor—the shared bicycle attractor—is discovered. The research results indicate that larger discounts and adjustment speeds can cause the system to be in a chaotic state, which is not conducive to the long-term development of operators, although discounts can indeed attract more passengers to a certain extent. On the other hand, the increase in the marginal cost of comfort loss can also make it difficult for enterprises to operate, which requires continuous technological innovation to improve the comfort of cycling.

Effects of meteorology on bike-sharing: Cases of 13 cities using non-linear analyses

Bike-sharing system (BSS) provides bicycle rental services for short-term use. The association of BSS with meteorological factors has not been studied sufficiently using nonlinear analysis. To bridge these gaps, this study explored the impact of meteorological conditions on bike-sharing system (BSS) usage in 13 cities in Europe and North America, using a combination of nonlinear analyses to avoid the limitations of linear analyses used traditionally. Specifically, self-organizing map (SOM) and decision tree analyses were used. The SOM results revealed six meteorological profiles (clusters). Kruskal–Wallis test was applied to determine the main effect of the cluster on the input variables. The decision tree indicated temperature (34.4 %), followed by clusters (23.3 %), and cities (17.5 %) as the most influential factors on BSS usage. These findings demonstrated the need to generate nonlinear analyses to understand the dynamic interaction of meteorological variables and BSS usage, providing insights for developing sustainable urban transportation policies.

Exploring the complex relationship between metro and shared bikes in the built environment: Competition, connection, and complementation

Merging the flexibility of bike-sharing systems with the high capacity of metro transit significantly enhances both connectivity and efficiency in urban transportation, promoting eco-friendly travel options and supporting sustainable urban development. Current studies primarily examine how these two transportation modes work together to enhance urban travel efficiency and convenience. However, there is still a lack of discussion on the spatial heterogeneity of the competitive and complementary relationships between two modes across different built environments. This study selects Shenzhen as a case study and employs a data-driven approach to explore the relationships between bike-sharing and the metro system in practical application, including competition, connection, and complementation. The OPGD model is deployed to assess how the built environment influences these dynamics. The results reveal that bike-sharing typically complements the metro system, with longer ride durations occurring mainly in the urban core areas. Conversely, competitive interactions between these two modes are less frequent and associated with shorter rides, typically occurring in locales with a high density of metro stations. Educational, service, and residential factors are the main influences on people's choice of the "bike-sharing + metro" travel mode. The built environment exerts a greater impact on competitive relationships and less on complementary ones.

Association between bike-sharing systems and the blood pressure of local citizens: a cross-sectional study in China

IntroductionGlobally, hypertension stands as the foremost preventable risk factor for cardiovascular disease and premature death. However, scalable approach to lowering blood pressure (BP) at the population level remains lacking. We...

Exploring the relationship between built environment and bike-sharing demand: Does the trip length matter?

ABSTRACT Over the past decade, bike-sharing has garnered significant attention in both research and practice. As a manpower-driven transportation mode, the usage of bikes seems more sensitive to trip length, since one could take a shared bike to a destination where is too far to walk, or choose it for simply replacing walking when going to a nearby place. This paper identifies a threshold of bike-sharing trip lengths from bike-sharing trace data, and employs the Semiparametric Geographically Weighted Poisson Regression model to investigate the relationship between built environment and bike-sharing demand with different lengths by considering the heterogeneity in the relationship. Results show that built environment has heterogeneous effects on the bike-sharing demand in urban areas, and the effects differ across groups with trip lengths. The findings contribute to understanding the relationships between built environment and bike-sharing demand, and providing support for the placements and dispatching of shared bikes.

A dynamic electric fence planning framework for dockless bike-sharing systems based on inventory prediction

While dockless bike-sharing systems are growing in popularity due to their convenience, indiscriminate parking creates disorder in cities. To address this problem, this paper proposes a novel prediction-based approach for planning dynamic electric fences. These fences can adapt to dynamic usage patterns and efficiently guide parking behavior. First, graded clustering and spatial point integration algorithms are used to determine the locations of electric fence candidates. Second, initial parking and scheduling simulations are developed to calculate the inventory of each candidate. Finally, a spatiotemporal graph neural network is utilized to predict inventory and generate real-time deployment plans for electric fences. Case studies are conducted in two regions in Shanghai, China. Compared to a non-electric fence scenario, extensive experiments show that our framework can satisfy 98.6% of the parking demand and reduce spatial entropy by 15% within a reasonable walking distance. The results contribute to improving urban orderliness and promoting the sustainability of bike-sharing systems.

Microcirculation Bus Routes Design and Coordinated Schedules Considering the Impact of Shared Bicycles

Microcirculation Bus Routes Design and Coordinated Schedules Considering the Impact of Shared Bicycles

Characterizing Temporal Patterns of Intra-Urban Human Mobility in Bike-Sharing through Trip Analysis: A Case Study of Shanghai, China

Human mobility, encompassing the movement of individuals and/or groups across space and time, significantly impacts various aspects of society, with intra-urban mobility being a major research focus of scholars in diverse disciplines. Bike-sharing systems have become an alternatives in cities for achieving more sustainable transportation. Hence, bike-sharing-related data are considered an important data source to study intra-urban human mobility. To better understand human mobility in cities, it is essential to characterize the typical patterns involved in intra-urban human mobility. This paper mainly focuses on characterizing the temporal patterns of intra-urban human mobility on bike-sharing based on the trip information of the acquired bike-sharing data. To achieve this, on the one hand, we adopted an exploratory data analysis (EDA) method to describe the temporal patterns by performing exploratory analyses of bike-sharing trips. On the other hand, we used the continuous triangular model (CTM) to conduct multi-temporal-scale analysis of bike-sharing trips for further explorations of the temporal patterns where necessary. The data of bike-sharing trips in Shanghai, China, were adopted as the dataset for the case study. Generally, the study was conducted at two different levels: the trip level and the bike level. Specifically, at each level, the explorations were conducted from varying perspectives. According to the analyses, numerous meaningful temporal patterns were discovered, and several distinctive findings were acquired. The results of this study show the effectiveness of the EDA and CTM methods in characterizing temporal patterns of intra-urban human mobility, based on which potentially insightful information and suggestions can be provided to assist related actions.

Factors related to the intention of choosing shared E-scooters for metro transfer: A survey study integrating weather perception into satisfaction evaluation from Changsha.

Shared E-scooter (SE) travel is a low-carbon transportation method that can be further enhanced by integrating with metro systems. This study aims to quantify the impact of the built environment, attitude preferences, weather perception, and other factors on the evaluation and intention to use the "SE-metro transfer" travel mode, as well as how to efficiently and concisely measure and model these effects. Empirical analysis was conducted using questionnaire data from Changsha, China, with 683 participants surveyed. Three satisfaction models were established and compared based on the Technology Acceptance Model (TAM), and an optimal M2 model was expanded to incorporate users' subjective perceptions of weather, proposing a method to simplify questionnaire length. The study found that well-designed vehicles and infrastructure, along with necessary supporting facilities, play important roles in enhancing SE usage. However, there are still many areas for optimization in Changsha's SE policies. Despite the advantages of SE in terrain and physical fitness, which have significantly expanded and changed their user base compared to traditional shared bicycles, there is still much potential to adapt to the middle-aged and older user groups. The results of this study can provide valuable insights for professionals and government officials in designing systems, constructing infrastructure, and formulating policies.