Bikesharing Operators Research Articles

Park smart or face the music: Understanding users’ orderly parking behavior of dockless shared bikes from the perspective of deterrence theory

The rapid growth of dockless bike-sharing has revolutionized urban transportation. However, the disorderly parking of dockless shared bikes has emerged as a critical challenge, significantly hindering the benefits of this system by causing obstructions, safety hazards, and public nuisance. This study aims to investigate users’ orderly parking behavior of dockless shared bikes through the lens of deterrence theory. Our findings reveal that punishment severity, punishment certainty, personal norm, and descriptive norm have significant positive effects on users’ attitudes towards orderly parking, which in turn positively influences their orderly parking behavior. This study contributes to the literature by extending the application of deterrence theory to the context of dockless bike-sharing and providing empirical evidence on the role of deterrence factors in shaping users’ orderly parking behavior. Our findings offer valuable insights for bike-sharing operators and policymakers to develop effective strategies.

Prediction of bike-sharing station demand using explainable artificial intelligence

Bike-sharing systems have grown in popularity in metropolitan areas, providing a handy and environmentally friendly transportation choice for commuters and visitors alike. As demand for bike-sharing programs grows, efficient capacity planning becomes critical to ensuring good user experience and system sustainability in terms of demand. The random forest model was used in this study to predict bike-sharing station demand and is considered a strong ensemble learning approach that can successfully capture complicated nonlinear correlations and interactions between input variables. This study employed data from the Smart Location Database (SLD) to test the model accuracy in estimating station demand and used a form of explainable artificial intelligence (XAI) function to further understand machine learning (ML) prediction outcomes owing to the blackbox tendencies of ML models. Vehicle Miles of Travel (VMT) and Greenhouse Gas (GHG) emissions were the most important features in predicting docking station demand individually but not holistically based on the datasets. The percentage of zero-car households, gross residential density, road network density, aggregate frequency of transit service, and gross activity density were found to have a moderate influence on the prediction model. Further, there may be a better prediction model generating sensible results for every type of explanatory variable, but their contributions are minimum to the prediction outcome. By measuring each feature's contribution to demand prediction in feature engineering, bike-sharing operators can acquire a better understanding of the bike-sharing station capacity and forecast future demands during planning. At the same time, ML models will need further assessment before a holistic conclusion.

Causes of transportation inequality: The case of bike sharing in the U.S.

The bike-sharing system, as an innovation to solve the first-and-last-mile transportation problem, tends to be disproportionally used much less by lower income users. In this paper, we focus on testing two causes of this inequality: availability of stations and affordability, using a difference-in-difference framework with various treatments. We discovered that stations are more readily available in higher-income areas. However, increasing station availability will not reduce the usage inequality. Instead, we find that equity programs that increase the affordability of low-income users can reduce the usage gap between high and low-income groups. The major findings of this paper suggest that to create a more inclusive and equal urban transportation ecosystem, urban planners, policymakers, and bike-sharing operators need to allocate more resources to support affordability.

Spatiotemporal analysis of bike-share demand using DTW-based clustering and predictive analytics

This paper investigates bike-share activities and explores their relationships with neighborhood features, advancing our current knowledge for integrating cycle facilities into urban space to support first/last-mile mobility. To identify distinct demand patterns, bike stations are clustered based on time-series ridership. Measuring the similarity between time-series data in the transportation field should take into account the influence of phase difference because similar demands happening in the morning and in the afternoon should be considered dissimilar. This study uses a weighted Dynamic Time Warping to address this issue by assigning a larger weight to data points with a larger time difference, enabling a more realistic measure to compare ridership. Using bike-share trip data from Citi Bike, we identified eight station clusters, each of which exhibits unique temporal activities. We further integrated the locations of various points of interest to explore the profiles of the clusters. Bike stations that are closer to public transport, commercial buildings and food service establishments are generally more popular, suggesting that incorporating spatial contexts can develop a richer understanding of bike-share usage. In addition, this study goes beyond descriptive analytics by investigating the role of neighborhood features in predicting cluster memberships of bike stations. Our results show that non-tree-based models, such as Support Vector Machines and K-Nearest Neighbors, outperform tree-based models. This study provides valuable insights for both urban planners and bike-share operators. When assessing the potential of new bike-share infrastructure, urban planners can deploy our models to identify the cluster-specific demand pattern based on neighborhood features. Bike-share operators can also utilize our findings to identify neighborhoods that require strategic supply of bikes and parking space that may vary within a day.

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.

Repositioning shared bikes under low-carbon policies

Low-carbon policies implemented by the Chinese government have a significant impact on third-party logistics, particularly for bike-sharing operators. This study investigated the static bike repositioning problem under low-carbon policies. Repositioning shared bikes under low-carbon policies such as emission cap, carbon trading, carbon offset, and carbon tax requires strict emission limitations and complicated routing plans. Therefore, a mixed-integer linear programming (MILP) model was formulated for the integrated planning of vehicle routes and bike allocation, aiming to minimize the repositioning cost and total imbalance penalty. An improved variable neighborhood search (IVNS) is proposed to solve this problem. The algorithm includes a route generation strategy heuristic and an improved neighborhood search operator for optimizing the route design, and a greedy heuristic for tackling loading and unloading instructions along the route. Numerical experiments show that (i) the proposed IVNS algorithm outperforms the GUROBI solver and existing method in striking a balance between solution quality and computational time; and (ii) the carbon trading policy is more flexible, not only effectively reducing emissions but also encouraging the further development of the bike-sharing industry.

Learning from Imbalanced Datasets: The Bike-Sharing Inventory Problem Using Sparse Information

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.

Photovoltaic-battery powered bike share stations are not necessarily energy self-sufficient

Many cities use solar photovoltaic (PV) panels to power off-grid bike share stations to provide sustainable transportation. When a station's energy demand for operating the station’s kiosk and docks exceeds the PV panel's supply, the battery can be depleted, requiring manual battery replacement to avoid service disruption. However, existing research on siting, modeling, or assessing the environmental impacts of bike share stations has not accounted for spatially variable solar PV potential and energy usage. This study addresses this gap to analyze the performances of PV-battery systems for bike share stations and evaluates strategies to improve their energy independence, sustainability, and system reliability. Using Chicago’s Divvy bike share system as a case study, we simulated the battery charging, discharging, and replacements, and measured their performances with three metrics: energy self-sufficiency level, energy payback time (EPBT), and station downtime. We found that PV energy alone is insufficient to meet all energy needs of bike share stations, with the average energy self-sufficiency levels being 56%. Additionally, relying solely on battery replacement during rebalancing operations was insufficient to maintain stations' operational status. On average, bike share operators need to manually replace a station’s batteries 72 times a year. Furthermore, the current PV panels for most stations are sized too small. Increasing panel sizes could reduce most stations' downtime and increase their self-sufficiency without significantly affecting EPBT. The study highlights the need for bike share system modeling and operation planning to account for battery replacement needs and demonstrates the importance to evaluate smaller yet large-scale street-level PV applications. The modeling framework presented in this study can also be extended to examine other smaller PV-battery systems similar to bike stations.

Modelling the effects of metro and bike-sharing cooperation: Cost-sharing mode vs information-sharing mode

Bike-sharing provides a key role in offering a solution to the first- and last-mile problem, and thus extends the service catchment areas of public transport and facilitates low-carbon travel in urban transport systems. Despite the fact that it has become popular for metro and bike-sharing operators to cooperate to improve their services in recent years, the effect of cooperation modes has largely been neglected in the literature. We thus develop a metro-lead Stackelberg game model to investigate the strategic interaction and potential integration between a bike-sharing operator and a metro operator under different cooperation modes; i.e., the cost-sharing mode (CS mode) and the information-sharing mode (IS mode). We analytically and numerically examine the impact of the CS and IS modes on the participants’ pricing and effort decisions. This paper highlights the value of a carefully designed cooperation mode on benefiting operators (profits) and society (demand for green travel). Specifically, the IS mode enables both operators to gain a profit increment, leading to a win-win situation from the profit point of view. This superior performance of the IS mode remains robust, regardless of whether the bike-sharing operator is fairness-neutral or fairness-concerned. The CS mode benefits the metro operator yet hurts the profitability of the bike-sharing operator. Interestingly, the CS mechanism can effectively promote the demand for connecting travel, which might explain the adoption of the CS mode by bike-sharing operators in practice. That is, the increased demand created by the CS mode caters to bike-sharing operators whose primary goal is to capture markets, commonly for new entrants. Essentially, the IS mode also has a positive effect on increasing demand, and the dominance of these two cooperation modes regarding the promotion of demand is sensitive to the cost efficiency of each operator. Additionally, we find that a greater degree of fairness concern weakens (strengthens) the superiority of the IS (CS) mode in both demand promotion and profit improvement. Our results provide novel insights into how cooperation mechanisms impact the integrated operation of metro and bike-sharing systems.

Exploring the Spatially Heterogeneous Effects of the Built Environment on Bike-Sharing Usage during the COVID-19 Pandemic

Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly.

Can an Incentive-Based approach to rebalancing a Dock-less Bike-share system Work? Evidence from Sacramento, California

• Incentivizing users to walk farther is a way of rebalancing a bike-share fleet. • The impact of incentives on willingness-to-walk (WTW) to get a bike was examined. • Half of bike-share users would use a bike located 8.9-minutes away without an incentive. • WTW for incentives at origins and destinations were 3.8 min/US$ and 4.2 min/US$. • Results suggest the potential effectiveness of incentives as a rebalancing policy. Bike-share services will produce more limited benefits if users cannot find bikes when and where they need them. Bike-share operators must thus have process for “rebalancing” the bikes within the system to ensure that they are available where demanded. A potentially cost-effective strategy for rebalancing bikes is to offer incentives of some sort to users to walk farther to get a bike (origin-based incentive) or bring a bike to the undersupplied area (destination-based incentive). This paper aims to examine bike-share users’ willingness-to-walk to pick up a bike or drop off a bike at some distance from their origins or destinations if rewarded and to identify characteristics influencing willingness-to-walk. We use data from a survey of dock-less e-bike-share users conducted in the Sacramento region. The analysis shows that half of the respondents use bike-share if the available bike is located 8.9 min away. Our estimates of willingness-to-walk farther than the mean distance for incentives at origins and destinations were 3.8 min and 4.2 min per dollar, respectively. Our results give operators and policy makers insights into the potential effectiveness of incentives as a strategy for spatially rebalancing bike-share fleets.

A Demand-Centric Repositioning Strategy for Bike-Sharing Systems.

Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station’s capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system.

A before-after impact assessment of COVID-19 outbreak on bike-sharing ridership in Washington, DC

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

Increasing Bike-Sharing Users' Willingness to Pay - A Study of China Based on Perceived Value Theory and Structural Equation Model.

Bike sharing, as an innovative travel mode featured by mobile internet and sharing, offers a new transport mode for short trips and has a huge positive impact on urban transportation and environmental protection. However, bike-sharing operators face some operational challenges, especially in sustainable development and profitability. Studies show that the customers’ willingness to pay is a key factor affecting bike-sharing companies’ operating conditions. Based on the theories of perceived value, this study conducts an empirical analysis of factors that affect bike-sharing users’ willingness to pay for bike-sharing through measurement scales, user surveys, and structural equation models. We designed a five-point Likert-type scale containing 11 latent variables affecting willingness to pay and a total of 34 measurement items. We investigate bike-sharing users in China’s first and second-tier cities, with a total of 502 participants. The results show that perceived value, payment awareness, trust, and environmental awareness constitute key factors that directly affect bike-sharing users’ willingness to pay. And perceived usefulness, perceived ease-of-use, perceived cost, and perceived risk indirectly affect bike-sharing users’ willingness to pay. However, we found no significant effects of perceived entertainment on perceived value or word of mouth on willingness to pay. Our results are expected to provide theoretical and practical implications for bike-sharing programs.

Bike sharing rebalancing problem with variable demand

This paper investigates an extended bike-sharing rebalancing problem called the bike-sharing rebalancing problem with variable demand (BRPVD), which considers the influence of the number of bikes distributed by the operator on users’ demand. The solutions to the BRPVD aim at maximizing the profit of bike-sharing operators by making routing plans of transport vehicles and determining the target number of bikes at each station after redistribution operation. The problem is modeled as a mixed-integer non-linear programming model and is linearized into a mixed-inter linear programming model. The model is tested in cases adopting real Beijing Mobike data, revealing that the rebalancing behavior significantly impacts users’ demand, hence guiding the operator.

The interplay between dockless bikeshare and bus for small-size cities in the US: A case study of Ithaca

As another mode of shared transportation, bikeshare can substitute or complement public transit. Prior studies mainly relied on self-reported survey data or aggregated station-level data from docked bikeshare systems, and their conclusions and implications were focused on large cities. It is largely unknown how and to what extent a dockless bikeshare system complements or substitutes public transit, especially in small cities. This study was set to measure the interplay between Lime dockless bikeshare and bus service in Ithaca, NY – a typical small-size college town – and its environs. By joining about 3.42 million records of bus stop data and 102 thousand Lime bikeshare trip data from 2019, two types of Bikeshare-Bus-Linkage (BBL) trips were identified, namely (1) the first-mile trip where a user rides a Lime to board a bus, and (2) the last-mile trip where a user bikes to their destination after alighting a bus. BBL trips were identified using a spatiotemporal proximity framework based on two important parameters: the catchment radius and the time window between a bus stop event and a Lime trip. Different values were tested with a sensitivity analysis, and the parameters were finally set at 100 ft. and 5 min. As such, 3026 BBL trips were identified, which was 3% of total Lime ridership or 0.1% of total bus ridership. Our findings indicated that Lime provided useful first- and last-mile transfers to bus service for commuters. The complementary effect was particularly strong in the urban core and with transit development and employment land use areas. Moreover, in the morning peak, there were more first-mile trips from residential areas to bus stops in the urban core, while in the evening peak more last-mile trips started from bus stops in the urban core to residential areas. Based on the unique first-mile and last-mile trip patterns identified, policy implications and recommendations for bikeshare operators, local government, transit agencies, and transportation policymakers were discussed to better integrate bikeshare and public transit.

Stochastic bi-objective optimisation formulation for bike-sharing system fleet deployment

Bike-sharing schemes (BSSs) have gained popularity as an alternative mode of transportation to solve the first/last mile problem. However, the bike-share market is subject to an oversupply problem with a low fleet utilisation rate in many countries. The aim of this paper is to tackle the BSS optimisation problem under stochastic demand scenarios by determining the shared bicycle fleet size and a fleet deployment strategy, where the interests of multiple stakeholders are taken into consideration at the same time. A stochastic multi-period bi-objective optimisation model is formulated to maximise the profit of bike-share operators and minimise the unmet demand, controlling the bike fleet utilisation rate. The problem is solved by an augmented ε-constraint method (Augmecon), which generates a set of non-dominated solutions. The numerical test is conducted with real data from Citi Bike to evaluate the validity of the model formulation and the effectiveness of the solution algorithm. The effect of fleet utilisation rate on BSS is analysed, where the trade-offs among earned profit, service level and fleet utilisation rate are addressed.

News media influence on public trust in bike-sharing operators in Singapore

PurposeThe overnight introduction of tens of thousands of dockless bike-share bicycles in Singapore with its indiscriminate parking drew the attention of the media, which generated extensive news reports on the activities carried out by bike-sharing operators. Given the meteoric rise and fall of the industry, this study examines the influence of agenda-setting of news reporting on the public’s perception of the industry and the impact on the firms’ corporate reputation.Design/methodology/approachUtilizing the Reputation Quotient Index, the study content analyzed 147 textual data of online reports which were crawled over two years between 2017 and 2018 from six mainstream news organizations.FindingsOur findings showed that the news reports carried more negative frames in the headlines and body content. It also found that only five out of six dimensions of the Index were emphasized with varying degrees of importance, indicating that the corporate reputation as determined by the media reports did not collectively represent the operators’ past actions and results with valued outcomes.Practical implicationsPractical implications discussed included the need to integrate corporate strategies into public relations programs and the importance of engaging the media to demonstrate congruence between business objectives and positive social impact on society.Originality/valueAlthough the study limited its data collection only to online media reports, it is one of the few research to provide empirical evidence concerning the media’s influence on the public’s perceptions and reputation of the nascent bike-sharing industry.

Adaptive Bike Share: Expanding Bike Share to People with Disabilities and Older Adults

Bike share systems are expanding efforts to be more equitable and accessible to everyone by offering adaptive bicycle options to people who might otherwise be unable to ride. These systems tend to range from the inclusion of electric bikes and standard trikes into the existing systems to offering a more full range of adaptive bicycle options for use at rental location. Surveys of residents living in several low-income communities of color ( n = 1,885) are used to explore the potential need for adaptive bike share options in urban locations. A national survey of cities and bike share operators ( n = 70) is used to document the prevalence and basic models of adaptive bike share programming currently in place. Interviews conducted with bike share representatives in select cities with adaptive bike share programs provide context and details on how specific programs operate. Finally, interviews with adaptive bike share participants ( n = 5) in Portland, Oregon, help to illuminate users’ experiences, including the perceived value and potential improvements for adaptive bike share. This opportunistic combination of data sources indicated that there is an underserved market of people who do not feel they can use existing bike share systems because of some type of physical limitation, but that reaching and serving those people presents substantial hurdles. Current bike share systems are slowly exploring the right way to include accessible options but are challenged by cost, resources, bicycle types, program implementation, and infrastructure.

From Mobike to no bike in Greater Manchester: Using the capabilities approach to explore Europe's first wave of dockless bike share

Globally, bike share schemes are an element of a rapidly changing urban transport landscape. Whilst many docked schemes are now embedded in cities around the world, the recent explosion of dockless systems provides an opportunity to evaluate claims that this form of shared mobility has the potential to alleviate common barriers to cycling, relieve congestion, boost low carbon travel, get people active, and reduce social exclusion. Drawing on a mixed methods study of 2270 online survey respondents and 27 interviews, all living in, working in or visiting Greater Manchester during a trial of dockless bike share, we explore the ways in which the technological, spatial and practical configuration of bike share schemes relate to a city's infrastructure and existing cycling practices. We question assertions that bike share provision necessarily results in increased rates of cycling and enhanced social inclusion.By using a capabilities approach and utlilising the concept of ‘conversion factors’ to describe the differing capacities or opportunities that people have to convert resources at their disposal into ‘capabilities’ or ‘functionings’, we show how the practice of bike sharing can influence a population's propensity to cycle, as well as how bike share interacts with established barriers to cycling. We find that many established barriers to cycling remain relevant, especially environmental factors, and that bike share creates its own additional challenges.We conclude that bike share operators must recognise the role of personal and social conversion factors more explicitly and be sensitive to the social and physical geography of cities, rather than assuming that a ‘one size fits all’ approach is adequate. To do this they should engage more closely with existing bodies, including transport authorities and local authorities, in co-creating bike share systems. Using the capabilities approach enables us to identify ways in which it could be made relevant and accessible to a more diverse population.