Bike Sharing Research Articles

A Cluster-Then-Route Framework for Bike Rebalancing in Free-Floating Bike-Sharing Systems

Bike-sharing systems suffer from the problem of imbalances in bicycle inventory between areas. In this paper, we investigate the rebalancing problem as it applies to free-floating bike-sharing systems in which the bicycles can be rented and returned almost anywhere. To solve the rebalancing problem efficiently, we propose a framework that includes (1) rebalancing nodes at which requirements for the redistribution (pickup or delivery) of bicycles are determined, (2) “self-balanced” clusters of rebalancing nodes, and (3) bicycle redistribution by service vehicles within each cluster. We propose a multi-period synchronous rebalancing method in which a rebalancing period is divided into several sub-periods. Based on the anticipated redistribution demand at each node in each sub-period, the service vehicle relocates bicycles between nodes. This method improves the efficiency of the system and minimizes rebalancing costs over the entire rebalancing period, rather than for a single sub-period. The proposed framework is tested based on data from the Mobike (Meituan) free-floating bike-sharing system. The test results demonstrate the effectiveness of the proposed methodologies and show that multi-period synchronous rebalancing is superior to single-period rebalancing.

Unveiling the roles of public bike systems: From leisure to multimodal transportation

As bike-sharing systems gain popularity, understanding their roles in urban transportation, such as the first/last mile problem and the replacement of public transit, is crucial to comprehensively promote urban transport systems. Due to the Internet of Things (IoT) technique, copious data from public bike trips worldwide are available to urban researchers. In this study, we examine how public bikes function not just as a means of transportation, but also for leisure. The leisure function was ignored in previous empirical studies using data mining techniques, an oversight that could skew results in places like Seoul where leisure trips constitute a significant portion of the trips. We investigate when, where, and to what degree bike-sharing serves as a leisure tool and substitutes for, integrates with, or complements the public transit system. We propose an integrated data-mining approach to identify the above four categories of public bike trips. Our method combines multiple sources of real-world data, the distributed density-based spatial clustering of applications with noise (DBSCAN), and spatial feature engineering. The results reveal that the distribution of each category shows substantial spatial–temporal heterogeneity. Longitudinal analysis reveals that the use of public bicycles as a transportation mode primarily contributes to the growth observed from 2018 to 2021, compared to use for leisure. The determining factors and non-linear functions of each type of bike trip were interpreted by explainable machine learning methods, Shapley additive explanations (SHAP), and partial dependence plot (PDP). We discovered that lower slopes, higher residential densities, and more commercial neighborhoods consistently attract users across all categories. Additionally, rivers and green areas attract more leisure trips, while areas with denser transit routes see more substitution trips and fewer complementary trips. By understanding the characteristics, spatial–temporal distribution, and determining factors of public bike trips across these four categories, city planners and operators can tailor services to meet the diverse needs of citizens, fostering a more convenient, sustainable, and accessible urban transport system.

Walk & Bike Society in KMUTT THAILAND

KMUTT committed to be Green and sustainable university for SDGs 2030 since 2018 and committed to be a part of society that is a major force in promotion and implementation on walk and bike society within the university and expand to communities surrounded. To support walk & bike society in campus, walk & bike master plan has been designed with bike lane, pedestrian cover way, bicycle share services, bicycle repair center and “Zombike” activities which all students and staff make their own bicycle for free by using bicycle accessories from old bicycle in campus. KMUTT Walk & Bike Society policy has been launched since 2018 with bike riding activities which help to support sustainable transportation goal comply to SDG 3: Good Health and Well-being; SDG 7: Affordable and Clean Energy; SDG 11: Sustainable cities and communities and SDG13: Climate Action. The results from 5 years implementation (2018-2022) showed 75% of students and staff walking daily which can make 20,250,000 Km per 5 years in our campus. Bike activities showed daily use and 228 bicycle riding activities in total of 2,008,995 Km distance, initiated by 650 Green heart‘s students and 48,720 people from students, staff and communities participated on these activities. The results showed these walking and riding activities help to reduce carbon emission 3,188 tCO2 and reduce Diesel usage 1.59 million L. KMUTT walk & bike society help to initiate KMUTT sustainable leadership, make Green Heart ‘s students and change agents for helping communities and society expand the sustainable mobility to make healthy and low carbon society, and sustainability for all

Factors influencing bike share intentions of users in Indian cities: a structural equation modelling approach

ABSTRACT Public bike sharing is an increasingly popular mode of transportation in India, and understanding the factors influencing the intention to use bike share is crucial. This study employs a comprehensive approach by integrating the theory of planned behaviour (TPB), theory of interpersonal behaviour (TIB), and the technology acceptance model (TAM). Additionally, variables such as policy support, habits, external factors, and perceived bicycling conditions are also considered. A sample of 450 respondents from Delhi, the capital city of India, was collected to assess their intention to use bike sharing. The study employed Structural Equation Modelling, to examine the relationships among variables. The findings demonstrate that the research model effectively explains residents’ intention to use bike share. Factors such as perceived ease of use, behavioural attitude, subjective norms, and habits have direct and positive effects on the intention to use bike share. Notably, perceived ease of use, behavioural attitude, and policy support have the most substantial impact on the intention to use bike share. Conversely, perceived bicycling conditions and external factors exert negative effects on bike share usage intentions. This study provides valuable insights into the acceptance of bike sharing and offers a strategic direction for promoting bike sharing in Indian cities.

Spatio-temporal Analysis of Precipitation Effects on Bicycle-Sharing Systems with Tensor Approach

Spatio-temporal Analysis of Precipitation Effects on Bicycle-Sharing Systems with Tensor Approach

A data-driven agent-based simulation of the public bicycle-sharing system in Sejong city

In this study, we propose a data-driven agent-based model for simulating Eoulling, the public bicycle-sharing systems (PBSSs) in Sejong City, the administrative capital of South Korea. Most existing models for PBSSs based on top-down approaches have limitations in reflecting Eoulling users’ behavioral characteristics and analyzing their convenience. Unlike these, the proposed model is based on a bottom-up approach of agent-based simulation. We model each user as an agent to capture their bicycle rental and return behaviors, and analyze user convenience through interactions with bicycle station agent models. To improve model fidelity, multiple parameters for determining agent behaviors are extracted from the actual operations data of Eoulling, along with the population and geographic information of Sejong City. The validation results showed that the proposed model accurately describes the behavioral characteristics. We provide a workable solution addressing multiple concerns with Eoulling by evaluating its utilization and user convenience in virtual scenarios via model simulations.

Examining market segmentation to increase bike-share use and enhance equity: The case of the greater Sacramento region

As bike-share systems proliferate across the US, a deeper understanding of current bike-share users could enable an expansion of these services and their benefits to a larger population. To grow bike-share demand sustainably and equitably, cities must understand how different segments of the population use this service. Understanding the different segments within a market enable operators to develop strategies that are tailored toward the needs of each segment. This study uses data from bike-share user surveys in the Sacramento region to perform market segmentation based on perceptions of the bike-share service, mode use patterns, and bike-share use. We focus on the market segments with more limited means of transportation: low-income individuals, zero-car households, students, and transit and car-share users. The results show that individuals with low incomes, those without cars, and students use the service frequently for commuting and a variety of non-commuting purposes. Bike-share is generally adopted by all mode user groups but is used at a higher rate by the super multimodal and active multimodal groups. The occasional users of the bike-share service are mainly those with higher incomes and individuals who have access to a personal car. The segments consisting of non- and infrequent-personal bike users use the bike-share service at a greater rate for different purposes than regular bicyclists, suggesting that bike-share may act as a lever for increasing bike travel for some users. Results from data mining suggest that bike-share operators should target low-income and zero-car owners for new recruitment as these groups are more likely to use bike-share frequently. The results provide insights that may be helpful to cities as they consider strategies to increase bike-share demand in a way that enhances social equity.

Dual Stream Meta Learning for Road Surface Classification and Riding Event Detection on Shared Bikes

Dual Stream Meta Learning for Road Surface Classification and Riding Event Detection on Shared Bikes

A Rolling-Horizon Strategy for Dynamic Rebalancing of Free-Floating Bike-Sharing Systems

A Rolling-Horizon Strategy for Dynamic Rebalancing of Free-Floating Bike-Sharing Systems

Association of bike-sharing service with gentrification in a transit-rich city: A catalyst or an outcome?

This research aims to clarify the relationships between bike infrastructure changes and the gentrification processes by considering the pre-existing mass rapid transit (MRT) system and using gentrifiable neighborhood data from 2011 to 2017 in Taipei, Taiwan. Empirical evidence suggests that gentrifying neighborhoods accorded further accessibility to bike-sharing service (BSS), and increasing MRT service accessibility raised gentrification and BSS accessibility. However, the evidence does not support the hypothesis that equipping bike infrastructure catalyzes gentrification. Instead, an MRT system has a more significant effect on gentrification than bike infrastructure in a city where BSS acts as a last-mile mode of public transport. The results of this research not only clarify relationships between bike infrastructure and gentrification but also offer policy directions to develop inclusive bike-friendly cities.

How Many Are Too Many? Analyzing Dockless Bike-Sharing Systems with a Parsimonious Model

Using a parsimonious model, this paper analyzes a dockless bike-sharing (DLB) service that competes with walking and a generic motorized mode. The DLB operator chooses a fleet size and a fare schedule that dictate the level of service (LOS) as measured by the access time or the walking time taken to reach the nearest bike location. The market equilibrium is formulated as a solution to a nonlinear equation system over which three counterfactual design problems are defined to maximize (i) profit, (ii) ridership, or (iii) social welfare. The model is calibrated with empirical data collected in Chengdu, China, and all three counterfactual designs are tested against the status quo. We show the LOS of a DLB system is subject to rapidly diminishing returns to the investment on the fleet. Thus, under the monopoly setting considered herein, the current fleet cap set by Chengdu can be cut by up to three quarters even when the DLB operator aims to maximize ridership. This indicates the city’s fleet cap decision might have been misguided by the prevailing conditions of a competitive yet highly inefficient market. For a regulator seeking to influence the DLB operator for social good, the choice of policy instruments depends on the operator’s objective. When the operator focuses on profit, limiting fare is much more effective than limiting fleet size. If, instead, it aims to grow market share, then setting a limit on fleet size becomes a dominant strategy. We also show, both analytically and numerically, that the ability to achieve a stable LOS with a low rebalancing frequency is critical to profitability. A lower rebalancing frequency always rewards users with cheaper fares and better LOS even for a profit-maximizing operator. Funding: This research was partially supported by the U.S. National Science Foundation [Grant CMMI 1922665] and the National Natural Science Foundation of China [Grant 71971044]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0304 .

Spatial equity of micromobility systems: A comparison of shared E-scooters and docked bikeshare in Washington DC

Many cities around the world have introduced dockless micromobility services and witnessed their rapid growth. Shared dockless e-scooters can expand mobility options for underserved neighborhoods and population groups, but they may also exacerbate existing transportation inequalities. So far, our understanding of the equity implications of dockless e-scooter services is limited. To address this research gap, this study develops an analytical framework to compare the equity performance of dockless e-scooter and docked bikeshare systems in Washington DC with a set of measures including availability, accessibility, usage, and idle time. Results reveal that dockless e-scooter services increase accessibility to shared micromobility options in disadvantaged neighborhoods but widen the access gaps across neighborhoods. Compared to bikeshare, shared e-scooters provide a higher level of spatial accessibility overall due to greater supply; however, the greater supply of shared e-scooters largely leads to longer average idle time rather than a greater number of trips. Finally, it appears that the bikeshare system's equity program is more effective than the equity programs provided by e-scooter operators in promoting usage in low-income neighborhoods. These findings suggest that increasing vehicle supply alone would probably not lead to higher micromobility use in disadvantaged neighborhoods. Instead, policymakers should combine a variety of strategies such as promoting the enrollment of equity programs and reducing access barriers (e.g., smartphone and banking requirements) to micromobility services.

Analyzing the Spatial Interaction Characteristics of Urban Area Shared Bicycle Systems: A Case Study of Beijing’s Central Area

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing’s urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development.

Identification of homogenous zones using riding speed time-series to reveal the spatial structure of the urban pace

Traffic speed is essential for the measurement of urban pace. The riding speed metric is one of the most effective ways to identify the underlying dynamic patterns and structures of urban pace. However, few studies have explored the patterns of urban pace reflected by riding speed differentiation of public shared bicycle groups. To this end, this study first infers the riding speeds under unknown path conditions based on the origin–destination of large sample riding records in Suzhou, China. Second, an identification model of homogenous zones for urban pace is established by neighboring station search and time-series similarity-based neighboring station merger. Finally, the fluctuation and deviation indexes of the fitted rhythm curves are adopted to measure the heterogeneity degree of the urban pace in each homogenous zone. Results show that the urban paces based on riding speed have significant spatial differentiation characteristics in the city, and the generated homogeneous zones show a certain stable structure in urban space. The old town has the fastest urban pace and is at a consistently high level, the new district has a faster urban pace and a double-peak pattern, the suburb area has a moderate urban pace and shows a single-peak pattern, while the peripheral area has the slowest urban pace with no significant pace spikes. Meanwhile, the waveform of the urban pace in different zones have a delay of approximately one hour. This study proposes an effective approach to reflect the spatiotemporal homogeneity and heterogeneity of urban pace through the dynamic traffic flow elements, which can be universally applied to other cities and is helpful for planning more dynamic urban functional areas and detecting life stress of urban residents.

Can dockless and docked bike-sharing substitute each other? Evidence from Nanjing, China

Bike-sharing systems are promoted worldwide due to their low carbon environmental and social benefits. However, the substitution relationship between dockless and docked bike-sharing systems is barely studied. This paper uses an online survey in Nanjing, China to reveal the factors influencing the substitution relationship between dockless and docked bike-sharing systems using a hybrid choice model. Results show that male prefer to substitute dockless bike-sharing with docked bike-sharing. It is more likely that dockless bike-sharing provides substitution services for docked bike-sharing in the scenario of transferring with bus/subway. Satisfaction to service quality of both bike-sharing systems influence the substitution relationship between the two bike-sharing systems. This paper offers policy implications to improve service level and promote coordinated services of the two bike-sharing systems.

Exploring the association between travel demand changes and the built environment during the COVID-19 pandemic

This study compared the extent to which COVID-19 impacted travel demand of bike-sharing and taxi in New York City, and further explored how the changes in travel demand were associated with the built environment through four typical regression models, namely, least squares (OLS) regression, geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models. In particular, this study looked at two phases: the lockdown phase (during which travel demand decreased dramatically) and initial recovery phase (during which travel demand started to increase). The results suggested that 1) GTWR performed better than the other three model types; 2) shared bike ridership rebounded much more significantly during the recovery phase than taxi ridership; 3) Commercial Point of Interest (POI) was positively associated with the change of ridership in both lockdown and recovery phases.

The elasticity and efficiency of carbon reduction strategies in transportation

ABSTRACT Transportation significantly contributes to carbon emissions, prompting the need for effective mitigation policies. This study addresses the knowledge gaps in assessing the effectiveness of transport carbon policies and offers the lack of a holistic comparative overview. The study used a model composed of a mixed-effects meta-regression and carbon elasticity to investigate policies, like shared bikes, mobility hubs, low emission zones, congestion pricing, electric vehicles, and hydrogen vehicles. This model included seven control variables: year, GDP, implementation costs, geographic scale, environmental benefits, and transport share of energy consumption and carbon emissions. Mobility hubs and electric vehicles ranked are top effective policies with carbon elasticities of 3.73 and 3.72, effect sizes of 127.47 and 86.73, and confidence intervals of [65.55, 107.93] and [106.17, 148.78], respectively. Followed by the low emission zone of 16.3 carbon elasticity, proving its cost-effectiveness, effect size of 10.16, and a confidence interval of [−2.48, 22.80]. Congestion pricing, despite having the highest effect size of 873.39, its confidence interval [−354.01, 2100.80] is wide, indicating the uncertainty of this effect. Shared bikes and hydrogen vehicles ranked lowest, suggesting a need for deeper life cycle-based analysis. Although this model displayed high accuracy, the findings’ interpretation should consider the inherent data limitations.

A Demand Truncation and Migration Poisson Model for Real Demand Inference in Free-Floating Bike-Sharing System

The free-floating bike-sharing system enables users to pick up bikes from everywhere. However, the spatial-temporal imbalance of bikes becomes severer in such systems. Consequently, bikes are sometimes not available at desired location and time, and the pick-up demand of users is truncated. On demand truncation, the bike user would either give up using the bike or migrate to the nearby to pick up the bike. Thus, the demand of bikes observed from smart locks does not equate with the real demand. However, capturing the real demand is crucial for system planning and operation. Therefore, a statistical model, namely the demand truncation and migration Poisson (DTMP) model, is proposed to analyze the real demand. It is able to jointly model the real demand of FFBS and the migration behavior of users. In particular, we analyzed the demand truncation and migration processes to establish the relationship between the expected value of observed demand and that of real demand. Then, the real demand of using bikes was assumed to be related to the influential factors and the demand function was formulated. Subsequently, the likelihood for each observation was established. The performance of the proposed model was evaluated through field-testing data. The results revealed that the proposed DTMP model is superior to the baseline Poisson model from both the fitness and accuracy perspectives. Finally, the spatial-temporal distribution of the real demand and unmet demand of bikes in typical hours are presented. The results can guide the efficient management of the bike-sharing system.

Study on Community Detection Method for Morning and Evening Peak Shared Bicycle Trips in Urban Areas: A Case Study of Six Districts in Beijing

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

How do dockless bikesharing services affect public transit and taxi use? Evidence from 36 central cities in China

Despite the rapid growth in the popularity of technology-enabled dockless bikesharing services, comprehensive research on their causal effects on other modes of transportation is still scarce. To fill this gap, this study exploits the staggered entry of Ofo and Mobike into 36 Chinese central cities as a quasi-natural experiment using a difference-in-differences approach to examine the overall impact of dockless bikesharing services on bus, rail, and taxi use. The results show that the entry of dockless bikesharing services significantly reduced city bus ridership by 5.1% and taxi passenger miles by 6.4%, but had no significant impact on rail ridership overall. Further heterogeneity analysis shows that a higher proportion of BRT vehicles can attenuate the negative impact of bikesharing services on bus ridership, while bikesharing seems to promote rail transit use in cities with low population densities and limited rail transit networks. Moreover, the reduction effect of bikesharing services on taxi passenger miles was driven by a decrease in average trip distance, with the unintended consequence of total taxi deadheading increased by 9.7%. This study provides important and valuable references for city governments to better utilize bikesharing services to develop efficient and sustainable urban transportation systems.