Dockless Bike-sharing System Research Articles

Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems

Dockless bikesharing (DBS) has been considered as a solution to the first and last mile problem of metro connectivity. Leveraging data covering all DBS programs in Shanghai, China, this study evaluated bike-and-ride (BnR) activities in DBS-metro systems via four metrics: BnR trip count, BnR rate, shared-bike utilization rate, and catchment size (85th percentile transfer distance). A set of generalized additive models considering marginal nonlinear interactions was fitted to examine associations between the four metrics and external environment, including land use, socio-demographics, roadway designs, transportation facilities, metro station features, and DBS operator features. Different buffer sizes measured by network distance were tested to check model robustness and find optimal buffers. Results showed that: 1) metro stations near the city center exhibited greater BnR trip count, higher BnR rate, lower shared-bike utilization rate, and smaller catchment size; 2) proportion of public and residential land suggested positive relationships with BnR trip count but lose their significances after offsetting metro ridership; 3) numbers of colleges, shopping malls, and carsharing stations presented positive relationships with both BnR trip count and BnR rate; 4) land use mix was significantly positively associated with BnR trip count only when buffer size was larger than 1.5 km; 5) regions with higher population density went from less BnR activities in the city center to more BnR activities in the suburbs; 6) Large DBS operators outperformed small ones in BnR trip count but not in bike utilization rate. Taken together, this study uncovers a spatially disproportionate and supply-demand unbalanced distribution of DBS resources, which could attenuate the efficiency and attractiveness of using DBS to BnR. DBS operators and local governments should evaluate DBS systems from multiple perspectives to avoid an oversupplied and over-competing market.

Mining dockless bikeshare data for insights into cyclist behavior and preferences: Evidence from the Boston region

• Studied cyclists’ behavior using en route GPS records from a dockless bikeshare system. Commercial areas have excess origins, while residential areas have excess destinations. • Cyclists often use sidewalks, other footpaths , and parking lot drives. • 36% of trips involve contraflow on a one-way segment. • Due to underdeveloped bike network, only 7% of trips are 100% on low-stress links. Emerging micromobility services provide not only new transportation options, but also valuable sources of data for researchers and planners seeking to understand traveler behavior and preferences and to improve transportation networks. Dockless systems for shared bicycles and electric scooters have recently begun operating in many American cities, providing data not only on trip start and end, but also on the route followed. In this study, we analyze data from more than 110,000 dockless bikeshare trips in Boston’s suburbs. About 15% of trip ends were within 100 m of a transit station, indicating that bikeshare serves many more functions than access to transit. More than 40% ended in a town or village center, suggesting that bikeshare may support the local economy. From examining GPS traces, it was clear that – contrary to the assumptions of common bike routing algorithms – bike riders often use sidewalks, other footways and soft-surfaced paths, parking lots, and driveways, and 36% ride the wrong way on one-way streets. An examination of the streets with high contraflow volumes indicates two common profiles. One is streets whose travel channel is so narrow that either the bike or vehicle has to pull into a parking lane to let the other pass; those streets typically have low traffic speeds and very low traffic volumes. The other is streets whose travel channel is wide enough for a bike and car to pass without either yielding to the other; they typically have greater vehicular speed and volume, but still only one lane. After classifying streets by level of traffic stress, we find that only 7 percent of trips use exclusively low-stress links, and that about 40 percent of bike-miles are ridden on low-stress links. These low percentages are consistent with an underdeveloped bike network that forces riders to use through streets for all except ultra-local trips; it may also reflect a prevalence of sidewalk riding on major arterials.

How Many Are Too Many? Analyzing Dockless Bikesharing Systems with a Parsimonious Model

Using a parsimonious model, this paper analyzes a dockless bikesharing (DLB) system in a city, where the DLB service competes with walking and a generic motorized mode. The DLB system operator chooses a fleet size and a fare schedule that dictate the utility of its service. The access time to the service---a crucial component in the utility---is modeled as a function of the number of unique bike locations, which is estimated from the number of idle bikes through an empirical relationship. The market equilibrium is formulated as a solution to a nonlinear equation system, over which three design problems are formulated to maximize (i) profit; (ii) ridership; and (iii) social welfare. We calibrate the model against empirical data collected in Chengdu, China and test three counterfactual strategies, each for one of the three design problems. We find: (i) the current fleet limit set by Chengdu should be cut by roughly two thirds in order to avoid severe oversupply and waste; (ii) maximizing ridership keeps the fare at an affordable level to maintain revenue neutrality, while raising ridership and social welfare close to system optimum; (iii) for a regulator seeking to influence the DLB operator for social good, the choice of policy instruments depends on the operator's objective. When it focuses on profit, limiting price is a much more effective policy than limiting fleet size. If, instead, the operator aims to grow its market share, then setting a limit on fleet size becomes a dominant strategy.

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.

The role of objective and perceived built environments in affecting dockless bike-sharing as a feeder mode choice of metro commuting

• Built environment effects on bike-sharing as a feeder mode of metro is examined. • Low agreement exists between objective and perceived measures of built environment. • Perceived built environment directly affects the usage of dockless bike-sharing. • Perception mediates the effects of metro accessibility, bus and bike availability. • Effects of built environment are moderated by gender, age, income, and location. The rapid growth of dockless bike-sharing (DBS) systems has attracted increased academic attention in the solutions to first- and last-mile problems. However, only a few studies have examined how the synergy between DBS and metro transit is affected by objective and perceived measures of built environment collectively. This study intends to fill this research gap by focusing on the effects of objective and perceived measures of built environment on DBS–metro integrated use for commuting trips. Results reveal that low agreement between the two measures of built environment and that the perceived measure is more likely to be directly associated with DBS–metro integration than the objective measure. Different built environment attributes may affect DBS–metro integration by unique paths. Moreover, individual characteristics (i.e., gender, age, and income) and location factor moderate the association between the built environment and DBS–metro integration. Particularly, built environment attributes related to transportation service are easier to be moderated than land use and cycling condition attributes. We conclude that the understanding of and interventions for the built environment as objectively measured are necessary but not sufficient for DBS–metro integration. Promoting the perception of the built environment among different population groups is also important for interventions.

System Dynamics Modeling for a Public–Private Partnership Program to Promote Bicycle–Metro Integration Based on Evolutionary Game

A marriage between dockless bike-sharing systems and rail transit presents new opportunities for sustainable transportation in Chinese cities. However, how to promote the bicycle–metro integration mode remains largely unstudied. This paper designs a public–private partnership program to promote bicycle–metro integration. We consider the cooperation between bike-sharing companies and rail transit companies to improve both services and attract long-distance travelers to choose the bicycle–metro integration mode, with government subsidies. To analyze the proportion of each population participating in this public–private partnership program, we establish an evolutionary game model considering bike-sharing companies, rail transit companies, and long-distance travelers, and obtain eight scenarios of equilibriums and corresponding stable conditions. To prove the evolutionary game analysis, we construct a system dynamics simulation model and confirm that the public–private partnership project can be achieved in reality. We discuss key parameters that affect the final stable state through sensitivity analysis. The results demonstrate that by reasonably adjusting the values of parameters, each equilibrium can be changed into an optimal evolutionary stable strategy. This study can provide useful policy implications and operational recommendations for government agencies, bike-sharing companies, and transit authorities to promote bicycle–metro integration.

Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai

Distance decay is a vital aspect for modeling spatial interactions of human movements and an indispensable input for land use planning and travel demand prediction models. Although many studies have investigated the usage demand of bike-sharing systems in an area, research investigating the distance decay patterns of using dockless bike-sharing systems (DLBS) from a spatially heterogeneous perspective based on large-scale datasets is lacking. This study firstly utilizes massive transaction record data from DLBS in Shanghai of China and online map navigator Application Programming Interface to empirically estimate the distance decay patterns of using DLBS and reveal the spatial heterogeneity in distance decay of using DLBS across different urban contexts. Afterward, this study examines the mechanism of spatial heterogeneity in distance decay, leveraging multiple data resources including Point of Interest (POI) data, demographic data, and road network data. The associations among the distance decay of using DLBS with built environment factors are investigated by multiple linear regression. Results indicate that factors such as population density, land use entropy, branch road density, and metro station density are significantly related to larger distance decay of using DLBS, while factors such as commercial land use ratio, industrial land use ratio, and motorway density are significantly linked to smaller distance decay in Shanghai. Lastly, we further employ an adaptative geographically weighted regression to investigate the spatial divergences of the influences of built environment factors on distance decay. Results reveal notably distinct and even inverse influences of a built environment factor on the distance decay of using DLBS in different urban contexts. The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.

Where are public bikes? The decline of dockless bike-sharing supply in Singapore and its resulting impact on ridership activities

Active mobility modes have become a significant alternative mode of transport within all major urban cities. This could be partly attributed to the rise and fall of dockless bike-sharing services in the last three years which significantly changed the transport ecosystem in these cities. However, the various complex relationships between the supply of bike-sharing services, the generated demand as well as the cycling dynamics and behavior of subscribers is not well understood. In this article, we examine how the changes in bike supply impacted the cycling activities of a dockless bike-sharing system in Singapore. We employed a clustering algorithm and modelled the network community detection tool into a three-month cycling GPS dataset and compared the spatiotemporal pattern and network structures of biking journeys for two major time periods (1) January 2019 and (2) March 2019 between which a significant number of bike-sharing companies exited the industry. This resulted in a drastic drop in the number of available shared bikes for the latter period. Our results show that prior to the decline of the bike supply, we identified three types of bike journeys that shared similar temporal patterns, indicating different riders, namely casual, moderate, or enthusiastic users. Over 90 percentage of enthusiastic riders disappeared after the supply of public bikes decreased. Our network analysis further showed that nine out of ten observed communities either contracted or collapsed after the supply downsize. We therefore hope that urban and transportation planners and scientists would be able to use these findings to further understand the dynamics of supply demand of bike-sharing systems and help improve planning considerations in the future.

Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros

Dockless bike-sharing is emerging as a convenient transfer mode for metros. The riding distances of bike-sharing to or from metro stations are defined as transfer distances between dockless bike-sharing systems and metros, which determine the service coverages of metro stations. However, the transfer distances have rarely been studied and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variations of transfer distances between dockless bike-sharing systems and metros. First, a catchment method was proposed to identify bike-sharing transfer trips. Then, the Mobike trip data, metro smartcard data, and built environment data in Shanghai were utilized to calculate the transfer distances and travel-related and built environment variables. Next, a multicollinearity test, stepwise regression, and spatial autocorrelation test were conducted to select the best explanatory variables. Finally, a geographically weighted regression model was adopted to examine the spatially varying relationships between the 85th percentile transfer distances and selected explanatory variables at different metro stations. The results show that the transfer distances are correlated with the daily metro ridership, daily bike-sharing ridership, population density, parking lot density, footway density, percentage of tourism attraction, distance from CBD, and bus stop density around metro stations. Besides, the effects of the explanatory variables on transfer distances vary across space. Generally, most variables have greater effects on transfer distances in the city suburbs. This study can help governments and operators expand the service coverage of metro stations and facilitate the integration of dockless bike-sharing and metros.

A text mining approach to elicit public perception of bike-sharing systems

Bike-sharing has globally emerged as an alternative travel mode for trips that are longer to walk but shorter to drive. Previous studies have used either the actual ridership data or survey responses from users to understand the public perception about bike-sharing systems. Where the actual ridership data is hard to obtain, survey-based studies limit respondents’ ability to express their views by relying on structured questionnaires (e.g., Likert scale). To this end, we contribute with the first application of a text network approach by analyzing the open-ended text responses from over 700 Seattle residents regarding their perceptions about the characteristics of docked and dockless bike-sharing systems. The text network approach enables the analysis of the open-ended text responses by creating a network based on the frequency and co-occurrence of keywords in a sentence. Our empirical analysis shows that Seattle residents appreciate dockless bike-sharing systems for their flexibility. However, they are unhappy about the blocked sidewalks due to parked bikes and less usage of helmets. Additionally, the text network’s sparsity indicates that respondents have a variety of negative perceptions regarding docked bike-sharing systems, and therefore, improving these systems is challenging. The method also allows us to explore the heterogeneity in user groups’ perceptions based on their bike-sharing experiences. Considering the consistency of our findings with those obtained using econometric approaches, we suggest that a hybrid approach can leverage the advantages of both econometric and text-based analysis. The approach would lead into reliable policy recommendations in the context of new services and technologies.

Patterns and predictors of dockless bikeshare trip generation and duration in Boston’s suburbs

The introduction and proliferation of privately-operated dockless bikeshare systems across North America has caught many public planning agencies, who seek evidence to recognize the extent of dockless bikeshare adoption in their communities and its impact on existing transportation systems, by surprise. In this study, we investigate systemwide travel patterns during the first 18 months of a dockless bikeshare program in the Greater Boston region. Specifically, by identifying neighborhood-level predictors of dockless bike access and usage, this study offers insights into the spatial equity-related impacts of this promising active mobility option in Boston’s suburbs, which have limited access to the region’s decade-old public dock-based bikeshare system. Utilizing spatiotemporal route-level data provided by the sole dockless system operator to model bikeshare trip generation and duration, this study finds that neighborhoods with a higher share of renter-occupied housing and historically disadvantaged populations had less access to dockless bikes while also exhibiting higher rates of bike usage. We conclude that this undesirable finding may be addressed by implementing safeguard policies such as an equitable dockless bikeshare rebalancing scheme.

Modeling the impact of dock-less bike-sharing system on outpatient trips

Better transportation and medical service systems contribute to urban sustainable development. Dock-less bike-sharing system (DLBS) is an innovative, sustainable, and flexible travel mode that has been a worldwide spread. This paper addresses a pioneer research aims to explore the characteristics of DLBS usage in outpatient trips and its impacts on travelers’ mode choice behavior. The data sources include the survey at Beijing Friendship Hospital and the trip data from a DLBS company. The research results show that DLBS has evidently decreased the private bike usage by over one third and become a replacement. It is also an important feeder mode for public transport service to the medical service. Among 1,348 DLBS usage in outpatient trips, more than 40 % trip transactions are connecting the hospital and public transport stations. Over 70 % travelers ride DLBS only in a single trip while having other activity purpose after leaving the hospital. Multinomial logit (MNL) model is used to investigate the factors that influence the DLBS daily usage. Education and online payment have positive effects. This study can be extended to DLBS usage in other trips with similar characteristics such as appointment-based, short-duration, and multiple activities.

Carbon emission effect of the dock-less bike-sharing system in Beijing from the perspective of life cycle assessment

As the government has begun to promote the sharing economy and green travel, shared bikes have wide spread in many cities in China. Bike sharing system (BSS) facilitates people's travel, relieves traffic jams, and mitigates carbon emissions. In this research, a basic method is proposed to measure the carbon emission mitigation of dock-less BSS from the perspective of life cycle analysis (LCA). The method is applied in a typical city of China (i.e., the city of Beijing). The carbon emissions of six models of shared bikes in Beijing are calculated in this paper. The results indicate that: (a) the use process of dock-less BSS has mitigated carbon emissions in the central district of Beijing (9.23 Mt CO$_{\text{2-eq}}$ in 2016 and 9.26 Mt CO$_{\text{2-eq}}$ in 2017); (b) in consideration of all life cycle processes of BSS, the carbon emissions actually has risen with the best scenario of 10% substitute rate (i.e., 33.2 Mt CO$_{\text{2-eq}}$ in 2016 and 80.9 Mt CO$_{\text{2-eq}}$ in 2017); (c) the carbon mitigation effect of BSS is closely related to the processes of production, maintenance and rebalancing. Finally, to promote the effect of carbon mitigation, it is necessary to control the number of shared bikes and increase their utilization rate.

High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data

Dockless bike-sharing systems (DLBS) have gained much popularity due to their environmentally friendly features. This study puts forward a distinctive framework for assessing the environmental influences of DLBS in high resolution based on DLBS transaction data. The proposed framework firstly estimates the transport mode substituted by DLBS for each recorded bike-sharing trip by utilizing the route planning techniques of online maps and a well-calibrated discrete choice model. Afterward, greenhouse gases (GHG) emission reductions in every recorded DLBS trip are quantified using Life Cycle Analysis. The proposed framework is applied to an empirical dataset from Shanghai, China. The empirical results reveal that the substitution rates of DLBS to different transport modes have substantial spatiotemporal variances and depend strongly on travel contexts, highlighting the necessity of analyzing the environmental impacts of DLBS at the trip level. Moreover, each DLBS trip is estimated to save an average 80.77 g CO2-eq GHG emissions versus than the situations without DLBS in Shanghai. The annual reduced GHG emissions from DLBS are estimated to be 117 kt CO2-eq, which is substantial and equals to the yearly GHG emissions of over 25,000 typical gasoline passenger vehicles. Additionally, the associations among built environments and GHG emission reductions from DLBS are quantitatively investigated to shed light on the spatial variances in the environmental impacts of DLBS. The results can efficiently support the benefit-cost analysis, planning, and management of DLBS.

Tidal phenomenon of the dockless bike-sharing system and its causes: the case of Beijing

Dockless bike-sharing system, as a flexible and eco-friendly solution to improve urban public transportation, has rapidly expanded in many cities around the world. The higher flexibility of the dockless bike-sharing system produces more significant tidal phenomenon that leads to serious traffic problems. However, as a new travel mode, the spatiotemporal characteristics of tidal phenomenon of the dockless bike-sharing system is unknown. This study proposed a method to quantify tidal traffic patterns of shared bikes in Beijing, the capital and megacity of China, and then applied multinomial logit model to reveal main causes of these patterns. Five traffic patterns were found on weekdays, among which three patterns display extreme convergence and divergence states during morning and evening rush hours. Only three patterns exist on weekends and the tidal traffic phenomenon becomes less intensive but lasts longer. Population is the most decisive factor, which determines the density of total traffic flow. Subsequently, resident-employment ratio controls the direction of commute flows thus causing tidal traffic on weekdays, while land use diversity and factors related to leisure activities are more influential on weekends. With the knowledge of tidal phenomenon of dockless bike-sharing usage, some operational strategies were suggested, such as optimizing the stock of the shared bikes in different time and locations, which will benefit bike-sharing enterprises and the local administrators to mitigate problems caused by tidal traffic and promote the usage and efficiency of dockless bike-sharing system.

Modeling and Optimization in Resource Sharing Systems: Application to Bike-Sharing with Unequal Demands

The imbalanced distribution of shared bikes in the dockless bike-sharing system (a typical example of the resource-sharing system), which may lead to potential customer churn and lost profit, gradually becomes a vital problem for bike-sharing firms and their users. To resolve the problem, we first formulate the bike-sharing system as a Markovian queueing network with higher-demand nodes and lower-demand nodes, which can provide steady-state probabilities of having a certain number of bikes at one node. A model reduction method is then designed to reduce the complexity of the proposed model. Subsequently, we adopt an operator-based relocation strategy to optimize the reduced network. The objective of the optimization model is to maximize the total profit and act as a decision-making tool for operators to determine the optimal relocation frequency. The results reveal that it is possible for most of the shared bikes to gather at one low-demand node eventually in the long run under the influence of the various arrival rates at different nodes. However, the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands, especially when the size of the network and the number of bikes in the system are large. It may cause a significant loss for operators, to which they should pay attention. Meanwhile, different estimated values of parameters related with revenue and cost affect the optimization results differently.

Gravity model in dockless bike-sharing systems within cities.

Due to previous technical challenges with the collection of data on riding behaviors, there have only been a few studies focusing on patterns and regularities of biking traffic, which are crucial to understand to help achieve a greener and more sustainable future urban development. Recently, with the booming of the sharing economy, and the development of the Internet of Things (IoT) and mobile payment technology, dockless bike-sharing systems that record information for every trip provide us with a unique opportunity to study the patterns of biking traffic within cities. We first reveal a spatial scaling relation between the cumulative volume of riding activities and the corresponding distance to the city center, and a power law distribution on the volume of biking flows between fine-grained locations in both Beijing and Shanghai. We validate the effectiveness of the general gravity model on predicting biking traffic at fine spatial resolutions, where population-related parameters are less than unity, indicating that smaller populations are relatively more important per capita in generating biking traffic. We then further study the impacts of spatial scale on the gravity model and reveal that the distance-related parameter grows in a similar way as population-related parameters when the spatial scale of the locations increases. In addition, the flow patterns of some special locations (sources and sinks) that cannot be fully explained by the gravity model are studied.

Estimating the Potential for Shared Autonomous Scooters

Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities.

Predicting Travel Demand of a Docked Bikesharing System Based on LSGC-LSTM Networks

The sustainable development of docked bikesharing systems has gained focus again owing to several problems in dockless bikesharing systems, including wanton destruction, theft, illegal parking, loss, and bankruptcy. The prediction of pickup/return demands is a critical issue for the sustainable operation of docked bikesharing systems. We propose a novel local spectral graph convolution (LSGC)- long short-term memory (LSTM) to predict pickup/return demands based on multi-source data. We apply LSGC to indicate the spatial dependency according to the geographic information system data that provide the location of stations, and we apply LSTM to demonstrate the temporal dependency based on the time-series data that represent pickup/return demands for public bikes. The LSGC-LSTM and six baseline models are trained with multi-source data of a month from a docked bikesharing system. The baseline models consist of a recurrent neural network, a LSTM, a gated recurrent unit, a graph attention LSTM network, an adaptive graph convolutional recurrent network, and a dynamic graph convolutional neural network. Results indicate that the LSGC-LSTM obtains a higher prediction accuracy and a higher efficiency than the baseline models.

Dynamic Incentive Schemes for Managing Dockless Bike-Sharing Systems

Dynamic Incentive Schemes for Managing Dockless Bike-Sharing Systems