Dockless Bike-sharing Service Research Articles

Connecting the Last Mile: The Impact of Dockless Bike-sharing on Public Transportation

In this study, we examine the impact of a new mobile-based, dockless bike-sharing service on public transportation usage. This new bike-sharing model removes the constraint of having fixed stations and gives users full flexibility on where to pick up and return bikes. This innovative feature of dockless bike sharing potentially disrupts the current norms of how people commute. The dockless shared bikes offer easy connections between destinations and public transportation stations. They can potentially promote public transportation, by improving its flexibility and outreach. To examine this impact, we collaborate with one of the largest dockless bike-sharing companies in China and collect unique daily-station-level panel data of shared-bike rides and subway traffic. Our findings indicate that a 1% increase in shared-bike rides leads to an increase of 0.35% in subway traffic. Further analyses show that this positive effect is stronger when people need to travel a longer distance to reach subway stations. These results suggest one potential underlying mechanism for the positive relationship we observe, that is, dockless shared bikes alleviate the “last-mile problem” for public transportation, making it a more appealing mode of transportation, compared with alternatives. Overall, we find that dockless shared bikes, in contrast to ridesharing or traditional bike sharing, act as a complement, rather than a substitute, for public transportation. Dockless shared bikes present a greener way of commuting, with significant environmental and societal impacts.

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.

Analysis of links between dockless bikeshare and metro trips in Beijing

In this study, we examine the factors associated with the integrated use of dockless bikesharing (DBS) and metros. Special attention is paid to the nonlinear effects of these factors, using machine learning; in this case, a random forest model and accumulated local effects (ALE) plots. We measure the integrated use of DBS and metros based on DBS data and metro smart card data in Beijing. Explanatory variables include metro station characteristics, road infrastructure, public transportation services, land use, and urban density, as well as meteorological variables. We find that metro ridership and DBS density around service areas are the most crucial factors for promoting cycling access/ egress near metro stations. After reaching the thresholds of 6,000 trips for metro ridership and 200 bikes/ per km2 for DBS density, notable joint effects are evident between the two variables, which are negative in egress trips but positive in access trips. Bicycle parking and bus transfer distance and their connectivity to metro stations are important for integrated use. We find that buses will be less competitive when exceeding 25 lines per station. Population density has a nonlinear effect, with a positive association below 9,000 persons/ km2 and a decreasing effect above this level. The land-use mix index was also found to be negatively associated with integrated use, and bicycle lanes are not associated with integrated use. Our results demonstrate that public transit service planning should consider the constraints of resources and public space, and we provide recommendations for governments and stakeholders to reallocate DBS and other public transit services around metro stations.

The last mile matters: Impact of dockless bike-sharing services on traffic congestion

The last mile matters: Impact of dockless bike-sharing services on traffic congestion

How do technology-enabled bike-sharing services improve urban air pollution? Empirical evidence from China

Technology-enabled bike-sharing services are increasingly being regarded as effective means of reducing air pollution. However, their actual impact on urban air pollution, especially the factors that might amplify these environmental benefits, is still not well understood. Taking advantage of Ofo and Mobike's staggered entry into 98 major Chinese cities as a quasi-natural experiment, this study empirically examines the impact of technology-enabled dockless bike-sharing services on air pollution and its heterogeneity using a difference-in-differences approach. The results show that there is significant heterogeneity in the impact of the entry of bike-sharing services on urban air pollution, while the overall effect is not significant. Specifically, the reduction effect of dockless bike-sharing service entry on air pollution is greater in cities with larger urban populations, longer average commute times, and higher public transit use, and the increase in traffic congestion further amplifies such effects. This study serves as the first empirical analysis of the impact of bike-sharing services on air pollution, providing important and valuable references for policymakers to better utilize these services to improve air quality and develop sustainable urban transport systems.

Using the Extended Acceptance Model to Understand Continuance Intention of Dockless Bike-Sharing.

Despite the fact that dockless bike-sharing (DBS) usage first experienced explosive growth, its continuous usage rate remains low. The ultimate success of a DBS service is more dependent on its continued usage rather than its initial adoption. Following the extended technology acceptance model (TAM), this study aims to explore factors that influence the continuance intention of DBS users. The framework of research was validated using a sample of 369 DBS users in China. The results show that perceived usefulness and perceived ease of use positively influence a user’s intention to continue using DBS. Both descriptive social norms and injunctive social norms are positively related to the continuance intention of DBS users. Moreover, environmental concern significantly affects the continuance intention of a user indirectly via perceived usefulness and perceived ease of use. Furthermore, the extended TAM has stronger prediction ability than the original TAM in the context of DBS services.

The impact of shared mobility services on housing values near subway stations

With the rapid development of smart phones and mobile internets, shared mobility services have become increasingly popular among travellers, and may influence housing values through their effects on people’s demand on subway services. This paper makes one of the first attempts to investigate the aggregate impact of different shared mobility services on housing values near subway stations, by conducting an empirical analysis on a dataset of apartment resale records in Beijing. We find that, the on-demand ride service offered by transportation network companies (like Didi) is more of a substitute than a complement to subway, leading to a reduction in house price in the vicinity of subway stations. In contrast, dockless bikesharing service brings about a greater complementary effect than substitution effect on subway usage, resulting in house price appreciation in the proximity of subway station. The effects are found to be heterogenous at different temporal and spatial scales.

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.

Evaluation of Potential Contribution of Dockless Bike-sharing Service to Sustainable and Efficient Urban Mobility in China

China has recently experienced the rapid development of a new generation of bike-sharing service, namely dockless bike-sharing service (DBS). Though previous studies indicated that bike-sharing is associated with various social, environmental, and economic benefits, unintended adverse outcomes of DBS that constitute public nuisance also have been reported. Thus, it is necessary to assess the DBS effects on urban transport sustainability for strategic decisions on its further developments. This study is focused on the evaluation of environment-, public- and individual user-interest related effects of DBS, in terms of efficient resource utilization by sharing, Green House Gas (GHG) emission reduction, urban transport efficiency enhancement, public space occupation and individual cost-savings. The assessment models of DBS's resource demand and GHG emission, user transport time and cost, road and roadside parking space demand allocated in the functional unit of transporting one passenger for one kilometre are developed. Economic value models of the reduced resources demand, GHG emissions, transport time, and the occupied public space are then proposed for scaling DBS's overall benefits. A scenario model is developed to describe the DBS scheme and the benefits analysis's social-economic backgrounds. The case study of Shanghai, the largest DBS market nationwide, is presented to enrich the knowledge about DBS schemes in China, their potential contribution to urban transport sustainability, and implications for public administration, operation management and user behaviour guidance. The proposed assessment models can be applied to studies on DBS schemes in other China contexts to provide some comparisons and a more comprehensive understanding of its effects.

Why individuals choose dockless bike sharing services?

This study investigates dockless bike sharing service (DBS) users’ behavior; particularly, why individuals choose DBS. A latent segmentation-based logit (LSL) model is developed using data from a DBS user survey conducted in Kelowna, Canada. The model is developed considering the following reasons for choosing DBS: cheapest option, fastest option, exercise purposes, recreational purposes, parking constraint, and unavailability of other modes or other reasons. The LSL model captures unobserved heterogeneity by assigning individuals into discrete latent segments. The model is estimated for two segments. Results suggest that segment 1 can be identified to include older, lower-income, frequent bike rider, females; whereas, segment 2 includes higher income, younger, non-frequent bike user, males. The parameter estimation results suggest that built environment attributes such as bike index, land use diversity index, transit accessibility, density of destinations, and length of bike infrastructure might influence the choice of DBS. The model confirms significant heterogeneity across the segments. Individuals residing in mixed land use areas with longer active transportation infrastructure are more likely to use DBS for recreational purposes in segment 1. In contrast, higher-income individuals in segment 2 show a negative relationship. The elasticity effects suggest that transit accessibility, length of bike lanes and cycle tracks, dwelling density, and vehicle ownership reveal substantial impact in segment 1. In contrast, bike index, land use diversity index, and personal vehicle ownership reveal significant impact in segment 2. This study offers important behavioral insights; specifically, the heterogeneity addressed in this research needs to be accommodated within the policy-making for efficient operation and expansion of DBS.

Modeling Destination Choice Behavior of the Dockless Bike Sharing Service Users

This study investigates trip-level destination choice behavior of users of the dockless bike sharing service (DBS). A random parameter latent segmentation-based logit (RPLSL) model is developed utilizing the DBS users’ trip itinerary data for Kelowna, Canada. The RPLSL model captures multi-dimensional heterogeneity such as inter-segment and intra-segment heterogeneity. The model is developed at a micro-spatial resolution which is defined as the bicycle analysis zone. One of the key features of this study is to test the interdependencies between the origin and destination of a trip using their built environment attributes. Model results suggest that segment 1 is more likely to include trips originating from the urban neighborhoods; whereas, segment 2 includes trips originating from the suburban neighborhoods. The parameter estimation results reveal that DBS trips are more likely to be destined to locations with longer length of cycle tracks, higher employment density, and that are closer to the Central Business District and bus stops (i.e., within 500 m). The model confirms multi-layer heterogeneity. For instance, trips originating from the urban areas in segment 1 are more likely to be destined to destinations within 500 m of the designated bike return sites (i.e., havens). In contrast, shorter trips originating in suburban areas in segment 2 show a negative relationship. Interestingly, a bike-friendly environment might increase the attractiveness of destinations closer to havens, even for the trips originating in suburban areas. The findings of this study will assist in developing policies and infrastructure investment decision making at the destination locations to promote DBS usage.

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

The success of metro systems depends on effective multimodal solutions that bridge the first-and-last-mile gaps. Both dockless bike sharing (DBS) and taxis are important feeder modes for metros, which provide on-demand travel options with high flexibility and accessibility. Based on one-week trip data of DBS and taxis during a concurrent period in Beijing, China, the paper aims to compare the temporal-spatial distribution of two modes as first-and-last-mile connectors and find out the socio-demographic and built-environment factors that impact their usage. K-means clustering is implemented to visualize the spatial distribution of DBS and taxis around metro stations, and the spatial lag model incorporating spatial autocorrelations of variables is developed. The results show that people prefer to use DBS as a substitutable mode for bus services to serve first-mile interchange in the morning. Also, less economically developed areas with a high density of branches and fewer signalized intersections are more favored by DBS users, whereas people in the central areas with high housing price and developed arterial road network tend to take a taxi, especially during evening peak period. The study can offer the policy guidance to improve DBS services, and several recommendations are suggested to ensure the sustainable development of DBS.

Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories

ABSTRACT In this paper, we present a data-driven framework to support exploratory spatial, temporal, and statistical analysis of intra-urban human mobility. We leveraged a new mobility data source, the dockless bike-sharing service Mobike, to quantify short-trip transportation patterns in Shanghai, China, the world’s largest bike-share city. A data-driven framework was established to integrate multiple data sources, including transportation network data (roads, bikes, and public transit), road characteristics, and urban land use, to achieve a detailed, accurate analysis of cycling patterns at both the individual and group levels. The results provide a comprehensive view of mobility patterns in the use of shared-ride bicycles, including: (1) the temporal and spatiotemporal distribution of shared-bike usage and how this varies according to different land use; (2) the statistical distribution of Mobike trips, which are primarily short-distance; and (3) the travel behavior and road factors that influence Mobike users’ route choice. The findings offer valuable insights for city planners regarding infrastructure development, for shared-ride bike companies to offer better bike rebalancing strategies to meet user demand, and for the promotion of this new green transportation mode to alleviate traffic congestion and enhance public health.

Spatio-Temporal Usage Patterns of Dockless Bike-Sharing Service Linking to a Metro Station: A Case Study in Shanghai, China

The dockless bike-sharing (DLBS) system serves as a link between metro stations and travelers’ destinations (or originations). This paper aims to uncover spatio-temporal usage patterns of dockless bike-sharing service linking to metro stations for supporting scientific planning and management of the dockless bike-sharing system. A powerful visualization tool was used to analyze the differences in usage patterns in workdays and weekends. The travel distance distributions of using dockless bike-sharing near metro stations were investigated to shed light on the service area of the dockless bike-sharing system. Agglomerative hierarchical clustering was applied to analyze differences in usage patterns of metro stations located in different areas. The results show that the usage patterns of dockless bike-sharing on weekends are different from those on workdays. The average travel distance using the dockless bike-sharing system at weekends is significantly larger than that of workdays. The travel distance distribution could be nicely fitted by the Fréchet distribution of the Generalized Extreme Value (GEV) distribution family. The usage characteristics of shared bikes are correlated with land use and population density around metro stations. No matter in urban or suburban areas, there is a great demand for bike-sharing in densely populated areas with intensive land development, such as university towns in suburban areas. This study improves the understandings regarding the usage patterns of the DLBS system serving as a link between the final destinations (or originations) and metro stations. The results can be helpful to the operation and demand management of DLBS.

Dockless bike-sharing systems: what are the implications?

ABSTRACTThe emergence of dockless bike-sharing services has revolutionised bike-sharing markets in recent years, and the dramatic growth of shared bike fleets in China, as well as their rapid expansion throughout the world, exceeds prior expectations. An understanding of the impacts of these new dockless bike-sharing systems is of vital importance for system operations, transportation and urban planning research. This paper provides a first overview of the emerging literature on implications of dockless bike-sharing systems for users' travel behaviour, user experience, and relevant social impacts of dockless bike-sharing systems. Our review suggests that the dockless design of bike-sharing systems significantly improves users' experiences at the end of their bike trips. Individuals can instantly switch to a dockless shared bike without the responsibility of returning it back to a designated dock. Additionally, the high flexibility and efficiency of dockless bike-sharing often makes the bike-sharing systems' integration with public transit even tighter than that of traditional public bikes, providing an efficient option for first/last-mile trips. The GPS tracking device embedded in each dockless shared bike enables the unprecedented collection of large-scale riding trajectory data, which allow scholars to analyse people's travel behaviour in new ways. Although many studies have investigated travel satisfaction amongst cyclists, there is a lack of knowledge of the satisfaction with bikeshare trips, including both station-based and dockless bikeshare systems. The availability and usage rates of dockless bike-sharing systems implies that they may seriously impact on individuals' subjective well-being by influencing their satisfaction with their travel experiences, health and social participation, which requires further exploration. The impact of dockless bike-sharing on users' access to services and social activities and the related decreases in social exclusion are also relevant issues about which knowledge is lacking. With the increases in popularity of dockless shared bikes in some cities, issues related to the equity and access and the implications for social exclusion and inequality are also raised.

A Smart Solution to Rush-Hour Traffic Congestion: Effects of Dockless Bike-Sharing Entry on Ride-Sharing

Problem definition: Dockless bike-sharing users use a mobile app to rent a bike located throughout a city, lock the bike at their destination, and then make it available to another user. We examine whether dockless bike-sharing systems can offer a technology-enabled solution to traffic congestion problems by providing a cost-effective and sustainable alternative mode of transportation. Academic/practical relevance: Urban planners, policymakers, transportation engineers, and scholars have sought innovative and sustainable solutions to the rush-hour traffic congestion problem. Methodology: We exploit a unique natural experiment setting in which the first dockless bike-sharing service was launched in Chengdu, China, in November 2016. We employ a difference-in-differences framework and utilize detailed ride-sharing order information from DiDi Chuxing (DiDi) to measure the demand for car travel. Results: We find that the entry of dockless bike-sharing decreases the demand for car travel during rush hour. For example, the number of ride-sharing pick-ups decreases by 12 percent during rush hour after the entry of dockless bike-sharing. Moreover, more congested locations witness a greater decline in ride-sharing orders during rush hour. We also find that locations with access to public transit experience a greater decline in car travel demand, which provides empirical evidence that dockless bike-sharing helps solve the first-mile/last mile problem during rush hour. Furthermore, we find a greater reduction in car travel demand during rush hour for locations with a high level of mobility and locations that are closer to the city center. Managerial implications: Overall, the dockless bike-sharing system, which combines information technologies with a sharing economy business model, reduces car travel demand during rush hour and provides a smart solution to the rush-hour traffic congestion problem.

Biking islands in cities: An analysis combining bike trajectory and percolation theory

Bike trajectories generated by the dockless bike-sharing service provides a great opportunity to explore users' travel behavior within the shared mobility transportation ecosystem. This paper proposes a new concept, namely biking islands, defined as geographical areas of interest with a high concentration of bike usage. Leveraging high-resolution trajectory data, biking islands are identified via percolation theory because of its suitability in describing the formation of clusters and critical road segments that have significant influence on biking behavior in an urban context. We showcase our methodology using the bike trajectory dataset provided by a market-leading company and use Shanghai, China as the study area. Results reveal a hierarchical structure of biking islands. With the increase of threshold, the biking islands start to shrink and split into various smaller ones. Larger biking islands are usually located in the central urban area of Shanghai and the Huangpu River acts as a natural barrier that impedes biking continuity across the region. Besides, the formation of biking islands is highly influenced by the surrounding land uses. The proposed concept and methods are not only helpful to understand travel behavior of cyclists and urban structures used for cycling, but also has the potential to support relevant urban and transportation planning, such as identifying designated non-motorized areas for cycling and biking facilities and pinpointing critical road segments that can improve the cycling efficiency of the entire network. Biking islands could be designed as designated areas for cycling where sufficient bike facilities are provided, and/or motorized transport modes are restricted or even prohibited, so as to ensure the convenience and safety of cyclists and support the development of bike-friendly cities.

The Effect of Dockless Bike-Sharing on Public Transportation: An Empirical Study

We examine the impact of a new mobile-based, dockless bike-sharing service on public transportation. In contrast to traditional rental bikes that are parked at fixed stations, the dockless bikes can be picked up and returned at literally anywhere. This dockless feature of the shared bikes likely provides a solution to the last mile problem, potentially making it a complement to public transportation. Assembling a unique panel data of shared-bike rides and subway traffic, we estimate the relationship between shared-bike ridership and public transportation. Our results show that increases in shared-bike rides lead to increases in subway traffic. This positive effect is stronger for peak hours during weekdays and non-peak hours during weekends. We argue this effect is most likely driven by shared bikes promoting public transportation use and substituting for private cars (substitution effect) and also stimulating new travel (stimulating effect). Overall, we find that dockless shared bikes, in contrast to most of the other sharing economy phenomenon, acts as a complement rather than a substitute for public transportation. In addition, increased use of dockless shared bikes has a positive societal impact, leading to less urban congestion and better environmental protection.

Electric fence planning for dockless bike-sharing services

A new generation of bike-sharing services is emerging in China. With this service, bikes can be unlocked and paid by using a smartphone and then picked up and left anywhere at users' convenience. The unprecedented development of dockless bike-sharing services results in considerable socioeconomic and environmental benefits but also creates new urban issues. One of the most severe issues is users’ inappropriate parking behaviour. To solve this problem, electric fence (or geo-fence) policy and technology have been introduced in China to guide users to park bikes in designated zones. In this paper, we first propose a methodological framework to support electric fence planning for dockless bike-sharing services. We then apply our framework in a case study of Shanghai using a big dataset of bike trips. Results show that when the number of planned electric fences is 7,500, our electric fence plan can cover 91.8% of total parking demand. In addition, our plan can ensure that at least 95.8% of all bikes can be docked at one of planned electric fences and can help efficiently and accurately determine suitable locations for setting up planned electric fences.

Understanding the usage of dockless bike sharing in Singapore

ABSTRACTA new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.