Understanding the usage of dockless bike sharing in Singapore

A 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.     

Enhancing equitable service level: Which can address better,dockless or dock-based Bikeshare systems?

Dockless bikeshare systems show potential for replacing traditional dock-based systems, primarily by offering greater flexibility for bike returns. However, many cities in the US currently regulate the maximum number of bikes a dockless system can deploy due to bicycle management issues. Despite inventory management challenges, dockless systems offer two main advantages over dock-based systems: a lower (sometimes zero) membership fee, and being free-range (or, at least free-range within designated service areas). Moreover, these two advantages may help to solve existing access barriers for disadvantaged populations. To date, much of the research on micro-mobility options has focused on addressing equity issues in dock-based systems. We have limited knowledge of the extent to which dockless systems can help mitigate barriers to bikeshare for disadvantaged populations. Using San Francisco as a case study, because the city has both dock-based and dockless systems running concurrently, we quantify bikeshare service levels for communities of concern (CoCs) by analyzing the spatial distribution of service areas, available bikes and bike idle times, trip data, and rebalancing among dock-based and dockless systems. We find that dockless systems can provide greater availability of bikes for CoCs than for other communities, attracting more trip demand in these communities because of a larger service area and frequent bike rebalancing practices. More importantly, we notice that the existence of electric bikes helps mitigate the bikeshare usage gap between CoCs and other tracts. Our results provide policy insights to local municipalities on how to properly regulate dockless bikeshare systems to improve equity.     

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai,China

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R² = 0.774) than the OLS regression model (Adjusted R² = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.     

How could the station-based bike sharing system and the free-floating bike sharing system be coordinated?

The station-based bike sharing system (SBBSS) and the free-floating bike sharing system (FFBSS) have been adopted on a large scale in China. However, the overlap between the services provided by these two systems often makes bike sharing inefficient. By comparing the factors that affect the usage of the two systems, this paper aims to propose appropriate strategies to promote their coordinated development. Using data collected in Nanjing, a predictive model is built to determine which system is more suitable at a given location. The influences of infrastructure, demand distribution, and land use attributes at the station level are examined via the support vector machine (SVM) approach. Our results show that the SBBSS tends to be favored in areas where there is a high concentration of travel demand, and close proximity to metro stations and commercial properties, whereas locations with a higher density of major roads and residential properties are associated with more frequent use of the FFBSS. With regard to the methods used, a comparison of several machine learning approaches shows that the SVM has the best predictive performance. Our findings could be used to help policy makers and transportation planners to optimize the deployment and redistribution of docked and dockless bikes.     

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.     

Docked vs. dockless equity: Comparing three micromobility service geographies

Docked bike-share programs have proliferated worldwide, but studies find that the distribution of docked stations is geographically unequal. New dockless systems offer more flexibility compared to docked systems, but it remains unclear if dockless systems can address existing geographic inequities. This study examines all 32 US cities with both docked and dockless micromobility (bikeshare and e-scooter) programs and develops three service geography indicators to compare the geographic equity of docked versus dockless systems. We first use Lorenz curves and Gini indices to examine the overall spatial distribution of micromobility; we then use logistic and Tobit regressions to investigate how service geography corresponds to neighborhood characteristics. Results show that the distribution of docked systems is extremely unequal, and that dockless systems greatly reduce geographical inequalities relative to docked. Low-density areas and neighborhoods with low median household incomes, smaller shares of young people, and fewer zero-car households have limited micromobility service. Docked services are less prevalent in communities of color, and the implementation of dockless systems yields mixed outcomes for racial equity. Importantly, designated service areas do not always translate into available micromobility vehicles. Policymakers should use program design and performance metrics to address the mismatch between designated and actual service geographies and to ensure that micromobility services benefit marginalized communities.     

Investigating the effects of a shared bike for tourism use on the tourist experience and its consequences

ABSTRACT

As public bike-sharing systems have been widely set up in cities around the world, a shared bike for tourism use can enhance tourists’ experience at destinations and lead to tourists’ post-visit evaluations. Thus, this research explores the attributional effects of the value of using shared bikes (namely, instrumental value and affective value) on tourists’ emotional experience (namely, hedonics and perceived uniqueness) as well as on satisfaction and destination loyalty. We obtain a sample of 302 tourists using shared bikes as transport modes during their visits to examine the proposed relationship model. Results show that both instrumental and affective values of shared bike use positively relate to hedonics and perceived uniqueness experiences. While both hedonics and perceived uniqueness have positive effects on satisfaction, only perceived uniqueness shows a positive effect on destination loyalty. The findings of multi-group analyses indicate that the direct effects of the tourism experience on destination loyalty are significantly positive for the low-motivation group but not for the high-motivation group. Empirical implications and recommendations for future research are also discussed herein.     

Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan,China

Many countries have implemented public bike systems to promote sustainable public transportation. Despite the rapid development of such systems, few studies have investigated how built environment factors affect the use of public bikes at station level using trip data, taking account of the spatial correlation between nearby stations. Built environment factors are strongly associated with travel demand and play an important role in the success of public bike systems. Using trip data from Zhongshan's public bike system, this paper employed a multiple linear regression model to examine the influence of built environment variables on trip demand as well as on the ratio of demand to supply (D/S) at bike stations. It also considered the spatial correlations of PBS usage between nearby stations, using the spatial weighted matrix. These built environment variables mainly refer to station attributes and accessibility, cycling infrastructure, public transport facilities, and land use characteristics. Generally, we found that both trip demand and the ratio of demand to supply at bike stations were positively influenced by population density, length of bike lanes and branch roads, and diverse land-use types near the station, and were negatively influenced by the distance to city center and the number of other nearby stations. However, public transport facilities do not show a significant impact on both demand and D/S at stations, which might be attributed to local modal split. We also found that the PBS usage at stations is positively associated with usage at nearby stations. Model results also suggest that adding a new station (with empty capacity) within a 300 m catchment of a station to share the capacity of the bike station can improve the demand-supply ratio at the station. Referring to both trip demand models and D/S models, regression fits were quite strong with larger R² for weekdays than for weekends and holidays, and for morning and evening peak hours than for off-peak hours. These quantitative analyses and findings can be beneficial to urban planners and operators to improve the demand and turnover of public bikes at bike stations, and to expand or build public bike systems in the future.     

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations.     

Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen,China

Trip purpose is closely related to travel patterns and plays an important role in urban planning and transportation management. Recently, there has been a growing interest in investigating the spatio-temporal patterns of dockless shared-bike usage and its influencing mechanisms. Few, however, have focused on revealing the travel patterns by inferring the purpose of dockless shared-bike trips at the individual level. We present a framework for inferring the purpose of dockless shared-bike users, based on gravity model and Bayesian rules, and conduct it in Shenzhen, China. We consider the comprehensive factors including distance, time, environment, activity type proportion, and service capacity of points of interest (POIs), the last two factors of which were usually neglected in previous transport studies. Especially, we integrated areas of interest (AOIs) and Tencent User density (TUD) social media data characterize the service capacity of POIs, which reflect the area and scale differences of different POI categories. Through the comparison between two improved models and the basic model, it is demonstrated that the introduction of activity type proportion and service capacity of POIs can improve the effectiveness of model for inferring the purposes of dockless shared-bike trips. Based on the obtained trip purposes, we further explore the spatio-temporal patterns of different activities and gain some insights into bike travel demand, which can inform scientific decisions for bicycle infrastructure planning and dockless shared- bike management.     

Perceptually important points of mobility patterns to characterise bike sharing systems: The Dublin case

Since the first Bike Sharing System (BSS) was introduced in Amsterdam (1965), studies about BSSs have constantly increased. BSSs studies are typically focused on user's socio-economic characteristics, bike sharing patterns and purpose of use in the city. This paper increases the knowledge of bike station classification due to users' mobility patterns based on data mining tools. For this purpose stations will be identified by a code based on joining three ratios: the load factor or number of available bikes ratio, the cumulative trips ratio, and the turnover station ratio. The latter is the new ratio proposed in this paper, which measures the effectiveness degree of each station. The higher the rate, the more effective the station is. Data mining tools to work with these three ratios are used in the proposed algorithm. Specifically, the perceptually important points (PIP) process to represent and index each time series of each station, and a rule set to classify the stations, are used. The results could support planning and operations decisions for re-design and management of BSSs in relation to the spatial implications of the stations and the users' mobility patterns, due to the classification reveals imbalances in the distribution of bikes and lead to a better understanding of the system structure. The proposed method is applied to the Dublin Bikes Scheme with good performance results.     

A vehicle for valorising the labour power of commuting: The politics of mobility fixing in Shanghai's Dockless Public Bike Sharing Sector

This paper seeks to contribute to ongoing debates around the politics of hybrid mobilities, smart cities, surveillance capitalism and mobility fixing. Based on a set of qualitative interviews with key stakeholders and secondary sources, the paper uses the emergence of dockless public bike sharing in Shanghai between 2016 and 2018 as its case study. In order to explain the emergence of dockless PBSS and illustrate our contention that this system seeks to create surplus value from the work of mobility (in this case cycle commuting), we examine the processes of valorisation that have occurred in order to both position PBSS as a worthy activity (and therefore remunerate participants affectively) and produce use values for the data generated by users. In doing so we also highlight the ways in which these processes of valorisation seek to ensure that the mobility work of users is not aligned with the products of that labour in order to avoid calls for this mobility work to be remunerated as wage labour (which would reduce any surplus value extraction). Ultimately we argue that these processes of valorisation are not only invasive, but exclusionary in that they prioritise select aspects of social practice that address matters of (governmental) concern, rather than a more rounded interpretation. We argue that there is an urgent need to recognise mobility practices beyond registers that simply prepare the ground for their marketisation.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

Car access and long-term poverty exposure: Evidence from the Moving to Opportunity (MTO) experiment

The U.S. Housing and Urban Development Department designed the landmark Moving to Opportunity (MTO) Experiment for Fair Housing Voucher Program to help low-income households move out of and stay out of concentrated poverty. The program was designed based on the assumption that households benefit from living in higher-opportunity neighborhoods. Early evaluations of the MTO program, however, showed minimal gains for participant families. One explanation for these findings is the short length of time which MTO families typically spent in lower-poverty neighborhoods; the positive effects of longer-term exposure to low-poverty neighborhoods appear more promising.In this study we examine the role of automobiles in increasing the length of time that MTO households remain in lower-poverty neighborhoods over a much longer period than previous studies, 1994–2010. A growing body of research suggests that automobiles provide households with better access to opportunities than other modes of travel, particularly in neighborhoods with lower poverty rates in more dispersed urban environments. We find that automobile access reduces households' neighborhood poverty exposure by between 3% and 4%, and automobile access trails only successful lease-up among policy-relevant factors in reducing exposure. Therefore, to maximize the effects of housing mobility programs, we suggest that voucher receipt ought to be paired with policies to increase participants' access to automobiles.     

Identifying cycling-inducing neighborhoods: A latent class approach

Understanding how spatial attributes of cities and neighborhoods induce cycling is relevant for urban planning and policy making. In this work, ordered logit and latent class models are specified and estimated to analyze how the built environment affects bicycle-commuting frequency. Data come from a survey to 1,487 people in the city of Santiago, Chile, including sociodemographic information, travel behavior patterns and place of residence and work. Using geographic information systems tools, the built environment was characterized with variables calculated for a 500-m-radius buffer around the residential and work locations of each individual. Two models are estimated, first an ordered logit model confirms that built environment variables effect on cycling is similar to what has been reported in the literature, with some new findings such as an increase in cycling when public transport accessibility is low and the role of built environment attributes at the destination. Second, a latent class ordered logit is used to identify two classes of neighborhood in term of their cycling patterns, as a function of their density, presence of cycling infrastructure and distance to the main activity center of the city. This result allows to map the class membership probabilities, potentially helping to identify neighborhoods that encourage cycling and providing relevant information for policy making and infrastructure decisions.     

Helmet non-use by users of bikeshare programs,electric bicycles,racing bicycles,and personal bicycles: An observational study in Taipei,Taiwan

The bikeshare program in Taipei City and New Taipei City, called U-bike, was launched in August 2012 and has more than 7500 bicycles operating out of 769 stations. Research has suggested that bicycle helmet use is a means of reducing morbidity and mortality among bike users. Helmets, however, are not available for rent when a U-bike is rented. The current research conducted an observational study to examine the prevalence of helmet non-use by users of the bikeshare program, electric bicycles, racing bicycles, and personal bicycles in Taipei City and New Taipei City. Trained observers using compact video cameras collected helmet non-use data during various times of the day and on different days of the week. Observers collected data on cyclist attributes, bicycle types, and helmet use at several selected locations within Taipei City and New Taipei City. U-bike users were found to be the least likely to wear helmets. Other noteworthy findings include that violations such as phone use, red-light violations, and travelling at ≥25 km/h were associated with riding without a helmet. Male users of racing bikes tended not to wear helmets, while female users of other bicycle types were less likely to use a helmet. Carrying passengers by users of electric bikes and personal bikes was a determinant of helmet non-use. This paper concludes with a discussion and recommendations for future research.     

Long-term impact of network access to bike facilities and public transit stations on housing sales prices in Portland,Oregon

Planners and economists generally accept that housing market values increase with proximity to transportation facilities through the provision of improved access to activity locations. While the market benefits of rail station access are well-documented, inconsistent and insufficient methods have led to limited agreement on the true value associated with this locational amenity. Far fewer hedonic price studies have assessed the influence of bike facility access on housing sales prices, and those that have generally analyze cross-sectional data. In this study, we estimated a spatial hedonic model using a bootstrapped pseudo panel to determine the joint impact of network proximity to bike lanes and off-street multi-use paths, as well as light rail and streetcar stations, on housing sales in Portland, Oregon, from 2002 to 2013. Our findings revealed housing sales prices increased as network distance to the nearest light rail transit and streetcar station decreased. Likewise, owner-occupied single-family and multifamily housing sales rose in conjunction with reduced street network access to regional multi-use bike paths; however, improved proximity to on-street bike lanes negatively affected housing values. In sum, we believe these findings may help to inform non-automotive transportation infrastructure financing mechanisms that rely on rising property values.     

Rail-transit-induced gentrification and the affordability paradox of TOD

Numerous studies have shown that rail transit has a positive effect on raising property values and tax revenues. Such an effect is widely viewed as an economic benefit for property owners and is key to justifying the high cost of building rail transit infrastructure. In recent years, however, concerns have been raised about rail transit acting as a gentrification trigger and causing the affordability paradox. In this study, I evaluate whether rail transit in suburban Portland caused neighborhood gentrification and reduced home affordability through a longitudinal quasi-experimental design. I use the propensity score matching method to identify control neighborhoods for rail-transit-served neighborhoods. I then make pretest-posttest comparisons between rail-transit-served neighborhoods and their control neighborhoods at multiple observation points. In general, I did not find consistent evidence for rail-transit-induced gentrification in suburban Portland. I did not find evidence that rail transit reduced home affordability for tenants and home owners in rail transit-served neighborhoods either. I observed more changes in the neighborhoods served by the Eastside line (the oldest rail transit line in Portland) than their control neighborhoods in the past three decades: socially, they attracted older and less-educated population; physically, they experienced densification and faster increases of the share of rental units in their housing stock. Rail transit was more likely to be installed along low-income neighborhoods in suburban Portland, confirming the necessity of constructing appropriate control neighborhoods while evaluating the neighborhood and social effects of rail transit.     

Who were these bike lanes built for? Social-spatial inequities in Vancouver's bikeways, 2001–2016

Over the past 15 years, Vancouver, British Columbia, has made substantial investments to their bikeway network, adding over 150 km of protected bike lanes, painted bike lanes, and local street bikeways. This investment in bicycling infrastructure corresponded with increases in city-wide commuting to work by bicycle (from 4.1% in 2001 to 6.1% in 2016). However, there has not been an examination as to who has benefited from the expansion of Vancouver's bikeway network. This study aimed to examine whether increases in bikeway access corresponded with increases in bicycle commuting, whether there are socio-demographic inequities in bikeway access, and if these inequities changed over a fifteen-year period from 2001 to 2016. Using census data and municipal open datasets, we considered access to bikeways overall, and also to specific types of bikeways (protected bike lanes, painted bike lanes, local street bikeways) which confer different comfort and safety benefits. We fit a series of non-spatial and spatial Poisson models using integrated nested Laplace approximation, with random effects for census tract. We found disparities in access did exist and that inequities in access to bikeways have not changed over time. Areas with more children have less access to protected bike lanes (RR: 0.69, 95% CI: 0.55–0.87) and areas where more Chinese people live have less access to protected bike lanes (RR: 0.75, 95% CI: 0.59–0.96). Areas with more university-educated adults had more infrastructure—particularly local street bikeways (RR: 1.11, 95% CI: 1.02–1.21). Indeed, areas with bike commuting had more local street bikeways (RR: 1.15, 95% CI: 1.03–1.27). Our analysis sheds light on certain inequities in the distribution of bikeways in Vancouver which have persisted over time, and can be used to inform policy actions to promote mobility across all neighbourhoods.     

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.