Citi Bike Research Articles

Research on the Influence of Wheelsets on the Visual Imagery of City Bicycles

City bicycles have become popular worldwide because they play an essential role in releasing urban traffic congestion and are environmentally friendly. Since wheelsets are the most prominent shape element in a bicycle, this research explores the influence of various wheel types on the visual image of a city bicycle. First, 10 wheelsets with a high market share in China were selected as the research samples. Meanwhile, vocabularies suitable for describing wheelsets were chosen from many vocabularies. Then, all vocabularies were summarized into six adjectives using factor analysis, and consumers were invited to evaluate all samples using the grouped adjectives. Finally, triangular fuzzy numbers were used to obtain the evaluation scores of 10 wheelsets on six adjectives, and the 10 wheelsets were divided into four groups based on the evaluation scores. The results show that the visual imagery evaluation of the 10 wheelsets had a significant difference in the “trimmed and stable”, “superior and presentable”, and “holistic and balanced” groups, but a slight difference in “novel and individualized”, “rhythmic and metrical”, and “mechanical and ordered.” The research results could serve as a reference for city bicycle operators and help improve product development efficiency, thereby promoting the sustainable development of city bicycles.

Forecasting public bicycle rental demand using an optimized eXtreme Gradient Boosting model

The rational distribution of public bicycle rental fleets is crucial for improving the efficiency of public bicycle programs. The accurate prediction of the demand for public bicycles is critical to improve bicycle utilization. To overcome the shortcomings of traditional algorithms such as low prediction accuracy and poor stability, using the 2011–2012 hourly bicycle rental data provided by the Washington City Bicycle Rental System, this study aims to develop an optimized and innovative public bicycle demand forecasting model based on grid search and eXtreme Gradient Boosting (XGBoost) algorithm. First, the feature ranking method based on machine learning models is used to analyze feature importance on the original data. In addition, a public bicycle demand forecast model is established based on important factors affecting bicycle utilization. Finally, to predict bicycle demand accurately, this study optimizes the model parameters through a grid search (GS) algorithm and builds a new prediction model based on the optimal parameters. The results show that the optimized XGBoost model based on the grid search algorithm can predict the bicycle demand more accurately than other models. The optimized model has an R-Squared of 0.947, and a root mean squared logarithmic error of 0.495. The results can be used for the effective management and reasonable dispatch of public bicycles.

Impact of COVID-19 travel-restriction policies on road traffic accident patterns with emphasis on cyclists: A case study of New York City

Since the COVID-19 outbreak, travel-restriction policies widely adopted by cities across the world played a profound role in reshaping urban travel patterns. At the same time, there has been an increase in both cycling trips and traffic accidents involving cyclists. This paper aims to provide new insights and policy guidance regarding the effect of COVID-19 related travel-restriction policies on the road traffic accident patterns, with an emphasis on cyclists’ safety. Specifically, by analysing the accidents data in the New York City and estimating three fixed effects logit models on the occurrence of different types of accidents in a given zip code area and time interval, we derived the following findings. First, while the overall number of road traffic accidents plummeted in the NYC after the stay-at-home policy was implemented, the average severity increased. The average number of cyclists killed or injured per accidents more than tripled relative to levels in similar times in previous years. Second, the declaration of the New York State stay-at-home order was significantly associated with a higher risk of accidents resulting in casualties. The number of Citi Bike trips in the area at the time overwhelmingly predicted severe risk for cyclists. Last, we applied the models to detect hot zones for cyclists’ severe accidents. We found that these hot zones tend to be spatially and temporally concentrated, making it possible to devise targeted safety measures. This paper contributes to the understanding of the impact of COVID-19 travel-restriction policies on accidents involving cyclists, reveals higher risks for cyclists as an unintended consequence of travel-restriction policies, and provides an analytical tool for road safety impact evaluation should future travel restrictions be considered.

Investigating city bike rental usage and wet-bulb globe temperature.

Extreme heat exacerbates human illness and constrains the intensity and/or duration of outdoor activities. Temperature is an incomplete metric of outdoor heat exposures. By contrast, Wet Bulb Globe Temperature (WBGT) is a heat exposure metric that considers air temperature, moisture, solar radiation, and wind speed. In the USA, bicycle activity increased by 60% from 2000 to 2010. Several studies examined weather conditions and bicycling activities. However, few studies examined how the public should conduct their daily physical activities per weather conditions.This study investigated the relationship between WBGT and shared city bicycle activity in New York City(NYC) and San Francisco (SF), USA. Generalized Additive Models examined nonlinear relationships between WBGT and bicycle activity while controlling for rider demographics and temporal trends. Next, bootstrapping estimated the "peak point", when the relationship between the bike rentals and WBGT notably changed.The analysis also examined whether the heat warning messages affected cycling activities. We found that the number of rented bikes declined at different peak points in each city. The peak point was in NYC at 34.3°C (95% CI 33°C-35°C) and 10.8°C (95% CI 10-12°C) in SF. Somewhat paradoxically, bike rentals increased when heat warnings were issued in both cities.

The design sourcing choice and technological performance in the upscale and downscale markets of an architectural innovation

AbstractIn this study, we investigate the performance impacts of design sourcing choices in addressing an architectural innovation from its originating market to subsequent markets. We maintain that the effects of design internalization and externalization on the technological performance of firms' products may vary across the originating and subsequent markets with different requisite technological demands due to the dynamics of knowledge spillovers and knowledge exchange hazards in those markets. We hypothesize that design internalization is likely to outperform design externalization when facing an architectural innovation in a subsequent upscale market with a higher technological performance requisite than in the originating market. The case is then reverse in a subsequent downscale market with a lower technological performance requisite. We test our hypotheses in the empirical contexts of the US bicycle markets in which the index gear‐shifting technology (i.e., an architectural innovation) originated in the road bicycle market in 1985 and subsequently traversed to the mountain bicycle market (i.e., an upscale market) in 1987 and the city bicycle market (i.e., a downscale market) in 1988. The results are largely in line with our hypotheses. The contributions of our study to the current literature as well as its managerial implications are also discussed.

The Urban Transport Strategy on the Example of the City Bike System in the City of Lublin in Relation to the Covid-19 Pandemic

Abstract The city bike system is now an increasingly popular form of communication. In practice, it has become a complement to the public transport system in large cities, and above all in the most crowded city centers. Importantly, the solution, which is a city bike, can contribute to the reduction of urban congestion, air pollution and to the reduction of noise levels. The paper discusses the issue of city bike system functioning in terms of meeting the transport needs in the area of the city of Lublin. The aim of the work is to describe the city bike system and to indicate the conditions for choosing this means of transport among the inhabitants of the Lublin region. A survey conducted among respondents in 2020 was used. The paper presents own research results and expectations of potential users of this system in the context of displacements, as well as the impact of the Covid-19 pandemic on the system functioning in 2020, and the strategy for operating this industry.

A Consumption Value Evaluation of Rental Preference and Environmental Consciousness on Long-Term Rental City Bikes

A Consumption Value Evaluation of Rental Preference and Environmental Consciousness on Long-Term Rental City Bikes

A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems

City bikes and bike-sharing systems (BSSs) are one solution to the last mile problem. BSSs guarantee equity by presenting affordable alternative transportation means for low-income households. These systems feature a multitude of bike stations scattered around a city. Numerous stations mean users can borrow a bike from one location and return it there or to a different location. However, this may create an unbalanced system, where some stations have excess bikes and others have limited bikes. In this paper, we propose a solution to balance BSS stations to satisfy the expected demand. Moreover, this paper represents a direct extension of the deferred acceptance algorithm-based heuristic previously proposed by the authors. We develop an algorithm that provides a delivery truck with a near-optimal route (i.e., finding the shortest Hamiltonian cycle) as an NP-hard problem. Results provide good solution quality and computational time performance, making the algorithm a viable candidate for real-time use by BSS operators. Our suggested approach is best suited for low-Q problems. Moreover, the mean running times for the largest instance are 143.6, 130.32, and 51.85 s for Q = 30, 20, and 10, respectively, which makes the proposed algorithm a real-time rebalancing algorithm.

Sustainability and the Contribution of Electric Scooter Sharing Business Models to Urban Mobility

There has been a rapid uptake of electric scooter (e-scooter) sharing services in cities in recent years. However, the academic literature is still scarce on how e-scooter operators contribute to sustainable urban mobility through their business models. In this qualitative case study, we analyse the sustainability of these rapidly evolving sharing services through a frame of sustainable business model archetypes, and study value transfer, its obstacles, and its controversies within the Finnish urban transport system. While e-scooter services mainly target delivering functionality over ownership, according to the operators, combinations of approaches are applied that contribute positively to sustainability. These include, for example, increasing the lifecycle of scooters, recycling of scooters and their parts, using renewable energy, commi ing to climate compensation, and promoting road safety and the responsible use of scooters. The findings indicate that in Finland, e-scooter services are seen to have potential in serving the first and last miles of public transport journeys. Due to a lack of binding regulation, co- and self-regulation is emphasized and is supported by active information and data sharing with public authorities. While sustainability is embedded in many ways into the business models of e-scooter operators, there are still potential sources of unsustainability aff ecting the urban transport system. For example, current business models favour convenience over physical activity, which can reduce the amount of active travel and use of city bikes.

If We Build It, Who Will Come? Comparing Sociodemographic Characteristics of Bike Share Subscribers, Cyclists, and Residents of New York City

Bike share programs are becoming increasingly popular across U.S. cities. However, their impact on persistent disparities in cycling by gender, race, and socioeconomic status remains understudied. We examined whether subscribers of Citi Bike, New York City’s (NYC) largest bike share program, reflect the sociodemographic profile of NYC cyclists. Using NYC Community Health Survey data, we described adult NYC residents of neighborhoods with ≥1 Citi Bike stations who rode a bicycle at least once a month. Citi Bike members were also described using first-time subscriber survey data. We compared the sociodemographic characteristics of these groups via a z-score with pooled variance. Approximately 2.2 million residents lived in 15 NYC neighborhoods with ≥1 Citi Bike station, and 449,000 (20.5%) reported cycling at least once a month in the past 12 months. Among first-time Citi Bike subscribers, 23,223 (11.5%) completed the survey. Compared with NYC cyclists, Citi Bike subscribers were more likely to be women, aged 24 to 45, White, college graduates, and from a household with an income >400% than the poverty level. Compared with the general population, cyclists were more likely to be White, male, and from a household with an income >400% than the poverty level. Race/ethnicity and socioeconomic status (not gender) disparities were larger among Citi Bike subscribers than NYC cyclists. With the emergence of cycling as an alternative transportation during the COVID-19 pandemic and the extension of bike share programs, this highlights the need for ongoing, systematic monitoring of bike share user socioeconomic characteristics to evaluate equitable use and access.

The languishing bike: depleting capacities of cycling-bodies

ABSTRACT This article focuses on what may be depleting people’s capacity to ride a bike in cities. ‘Capacity to cycle’ is here understood not as a behavioural trait or in terms of infrastructure provision, but, taking our lead from Deleuze and Guattari, as a desiring cycling-machine. We argue this offers a way to envisage depleting, or enhancing, capacities to cycle as a working socio-material arrangement that make, remake, and unmake cycling-bodies and spaces. The article draws upon mixed-qualitative research conducted with 18 adults who own bikes in 2019, but no longer ride in the City of Sydney, Australia. Three vignettes draw attention to the opposing forces that support and dissolve cycling bodily capacities over the course of repeated, routine city bike journeys. We illustrate those depleting bodily capacities to cycle for transport involve sensations of the self, pedalling through space. It follows that a shift towards cycling mobilities demands related incorporation into planning of the transformative impacts of the near-at-hand and social norms.

A simulation framework for optimizing bike rebalancing and maintenance in large-scale bike-sharing systems

Bike-sharing systems (BSSs) have rapidly attracted worldwide interest for their success in improving quality-of-life in metropolitan areas. One kind of BSS requires docking stations to avoid misplacements of bikes. Such a BSS is faced with challenges caused by imbalanced demands across stations and frequent failures of bikes and docks. To achieve a high level of customer satisfaction, timely bike rebalancing and system maintenance must be performed. In this work, a simulation framework is proposed for evaluating different rebalancing and maintenance strategies. The framework can be integrated with any multi-vehicle static or dynamic rebalancing optimization model. An optimization model solved by an enhanced k-means clustering method (EKM) and an Ant Colony Optimization (ACO) algorithm is provided as an example for demonstrating such integration. A case study based on the configuration and historical data of Citi Bike in New York City is conducted for simulation model validation and for illustrating the managerial impacts of different rebalancing and maintenance strategies on the investment, operation, and service levels of such a large-scale BSS. The application of the proposed simulation framework is not limited to BSSs but also can be extended to other types of shared transport systems with non-floating stations where rebalancing and maintenance optimization are critical for efficient and healthy operation.

Cluster Analysis of Stations Based on Weight SimRank in Sharing Bicycle

With the increasing popularity of shared bikes, the indiscriminate parking of bicycles in cities has increasingly become a difficulty in urban management. The tidal phenomenon of large numbers of urban residents during rush hour is the root cause of the indiscriminate parking of bicycles in many subway stations and commercial areas. Optimizing the scheduling strategy of shared bikes is one of the effective solutions to solve the problem of random parking and reduce the scheduling cost. The cycle is a short - distance vehicle, and its circulation law is in line with the characteristics of the small world of urban traffic. That is, most of the bikes flow within the small world region, while only a small part of the bikes flow between the small world regions. With the massive accumulation of bike-sharing borrow and return data, the method of clustering the borrow and return stations and dividing regions according to the clustering results has attracted the attention of industry experts and researchers. it is effectively to apply in intelligent scheduling related industries. Although there have been some studies on the station clustering in the current literature, because these studies are basically based on the fixed features of the site (site location, pile number, etc.), the results cannot find an effective small world region of bicycles. In order to find out the effective small world region of bicycles, we introduced the idea of SimRank (that is, the similarity of a station is due to the similarity of its bicycle source station and destination station), and assigned weights to association relationships (the number of times of borrowing and returning) to define the similarity algorithm w-SimRank of stations. Then, the station clustering was done in line with skyline thinking. Finally, in order to verify the effectiveness of the algorithm, we implemented the station clustering based on SimRank algorithm, and compared the clustering effect with the W-SimRank algorithm proposed in this paper to verify the effectiveness of the W-SimRank algorithm, and analyzed the influence of the key parameters of the algorithm on the algorithm. And then

A robust analysis of the impacts of the stay-at-home policy on taxi and Citi Bike usage: A case study of Manhattan

On March 22, 2020, the State of New York issued a “stay-at-home” policy, wherein all non-essential businesses were on pause until June 8, 2020. The bike-sharing system (BSS) and yellow taxi system (YTS) in Manhattan were substantially affected. This sudden drop in demand can impact not only short and long-term mobility but also the sustainability of transport network. Given that few empirical studies are focusing on the impacts of the “stay-at-home” policy on the BSS and YTS, this further substantiates the importance of analyzing how the policy affects the overall transportation system in New York City (NYC). This paper aims to fill this gap by quantifying the impacts of the “stay-at-home” policy on the two aforementioned transportation systems. Specifically, the following three research gaps are summarized in this study: I) The hidden biases in current “stay-at-home” policy estimation methods were not properly addressed; II) The policy impacts on BSS and YTS during different periods of the effective day were unclear; III) The sensitivity of uncontrolled confounders in long-term policy impact estimations was poorly discussed. We addressed these important research gaps by introducing robust statistical approaches like regression discontinuity design (RDD) and propensity score matching (PSM) methods, which can overcome methodological challenges such as counterfactual restoration, spatiotemporal heterogeneities, and unmeasured confounders. The BSS and YTS were studied at the aggregated neighborhood levels. Results demonstrate that the impacts on BSS have higher variations than YTS usage. The monthly average treatment effects on the treated (ATT) for BSS ranged from −72% to −28% respectively in March and June, while YTS ranged from −96% to −94%. Evidence suggests that demand for BSS surged on weekends in May and June. Understanding the impact of this short-term yet significant policy change on travel behavior will help optimize supply and demand management strategies, thereby improving the long-term sustainability should similar situations arise in the future.

Characteristics of the social network of bike share members in New York City

BackgroundBike share has been hypothesized to be a phenomenon of social contagion. The current study describes the features of the social network of annual subscribers of the Citi Bike share system in NYC and the characteristics of those who ever took a trip with another member. MethodsAssociations comparing members who rode with others (social) to those who rode alone (non-social) on selected study variables were examined. Social network analysis was performed to examine characteristics of the network of Citi Bike members. A social trip was determined based on start and end trip time and station locations. ResultsThere were 241,340 active Citi Bike members with at least 365 days of membership between May 13, 2013 and December 31, 2018. Those who had taken at least one social trip (n = 139,302, 57.7%) were more likely to be older; have higher household income; have ridden a bike in NYC 12 months before membership; not own a car; self-report as physically active; self-report good health; live in a ZCTA with a Citi Bike station; and list primary reasons for membership as a faster way to get around town or as friends and family joining. Those who never took a social trip were more likely to have requested safety tips from Citi Bike; and to report their primary reason for membership as an alternative to public transportation, personal health and fitness, or support for the program. The social network of Citi Bike members was composed of 139,302 members with 164,735 unique connections to other members. The average number of connections per social member was 2.4 and the maximum was 66. The network was not very dense or well connected with 19,809 subnetworks. ConclusionSocial members differed from non-social members in several ways. Understanding the characteristics of key network members could play a part in promoting and encouraging bike share usage. In particular, organized social trips could increase bike share usage among women.

Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction

Motivated by the Bike Angels Program in New York's Citi Bike and Boston's Blue Bikes, we study the use of (registered) volunteers to re-position empty bikes for riders in a bike sharing system. We propose a method that can be used to deploy the volunteers in the system, based on the real time distribution of the bikes in different stations. To account for (random) route demand in the network, we solve a related transshipment network design model and construct a sparse structure to restrict the re-balancing activities of the volunteers (concentrating re-balancing activities on essential routes). We also develop a comprehensive simulation model using a threshold-based policy to deploy the volunteers in real time, to test the effect of choice restriction on volunteers (suitably deployed) to re-position bikes. We use the Hubway system in Boston (with 60 stations) to demonstrate that using a sparse structure to concentrate the re-balancing activities of the volunteers, instead of allowing all admissible flows in the system (as in current practice), can reduce the number of re-balancing moves by a huge amount, losing only a small proportion of demand satisfied.

Coupled Layer-wise Graph Convolution for Transportation Demand Prediction

Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands. However, in most of the existing research, the graph convolution was implemented on a heuristically generated adjacency matrix, which could neither reflect the real spatial relationships of stations accurately, nor capture the multi-level spatial dependence of demands adaptively. To cope with the above problems, this paper provides a novel graph convolutional network for transportation demand prediction. Firstly, a novel graph convolution architecture is proposed, which has different adjacency matrices in different layers and all the adjacency matrices are self-learned during the training process. Secondly, a layer-wise coupling mechanism is provided, which associates the upper-level adjacency matrix with the lower-level one. It also reduces the scale of parameters in our model. Lastly, a unitary network is constructed to give the final prediction result by integrating the hidden spatial states with gated recurrent unit, which could capture the multi-level spatial dependence and temporal dynamics simultaneously. Experiments have been conducted on two real-world datasets, NYC Citi Bike and NYC Taxi, and the results demonstrate the superiority of our model over the state-of-the-art ones.

Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips

Millions of individuals rely on urban transportation every day to travel inside cities. However, it is not clear how route parameters (e.g., traffic conditions, waiting times) influence users when selecting a particular route option for their trips. These parameters play an important role in route recommendation systems, and most of the currently available applications omit them. This work introduces a new hybrid-multimodal routing algorithm that evaluates different routes that combine different transportation modes. Hybrid-multimodal routes are route options that might consist of more than one transportation mode. The motivation to use different transportation modes is to avoid unpleasant trip segments (e.g., traffic jams, long walks) by switching to another mode. We show that the possibility of planning a trip with different transportation modes can lead to improvement of cost, duration, and quality of experience urban trips. We outline the main research contributions of this work, as (i) an user experience model that considers time, price, active transportation (i.e., non-motorized transport) acceptability, and traffic conditions to evaluate the hybrid routes; and, (ii) a flow clustering technique to identify relevant mobility flows in low-sampled datasets for reducing the data volume and allow the execution of the analytical evaluation. (i) uses a Discrete Choice Analyses framework to model different variables and estimate a value for user experience in the trip. (ii) is a methodology to aggregate mobility flows by using Spatio-temporal Clustering and identify the most relevant of these flows using Curvature Analysis. We evaluate the proposed hybrid-multimodal routing algorithm with data from the Green and Yellow Taxis of New York, Citi Bike NYC data, and other publicly available datasets; and, different APIs, such as Uber and Google Directions. The results reveal that selecting hybrid routes can benefit passengers by saving time or reducing costs, and sometimes both, when compared to routes using a single transportation mode.

BICYCLE POLICY IN MEXICO CITY: Urban Experiments and Differentiated Citizenship

AbstractThis article examines the effect of the development of bicycle policy in Mexico City on existing landscapes of urban citizenship. In Mexico City, NGOs, activists and progressive city officials used Ecobici, the city's bike share system, to promote the bicycle as an innovative, sustainable and socially inclusive mode of transportation in central areas of the city. At the same time, bicycle infrastructure has not yet reached peripheral, low‐income and underfunded districts, where residents rely on bicycles for transportation. I draw on the Cities and Low Carbon Transitions framework to examine bicycle policy as a project aimed at transforming Mexico City's socio‐technical systems for transportation, and I draw on notions of urban citizenship to examine how green urban infrastructures materialize and transform regimes of inequality. The article shows how the bicycle experiment was limited by a consensus that maintained existing power inequalities, favored expert‐driven design targeting middle‐class users, and opted for rapid project implementation over long‐term institutional re‐design and capacity‐building. These dynamics excluded citizens from the planning of bicycle policy and the urban poor from enjoying the benefits of the investments.

Bikeshare and subway ridership changes during the COVID-19 pandemic in New York City.

The COVID-19 pandemic has been unprecedented in its scale and speed, impacting the entire world, and having an impact on metropolitan transportation systems. New York City (NYC) was especially hard hit in March and April 2020. A mandatory stay-at-home order was instituted, with all but essential businesses ordered closed. In this paper we examine the impact on the Citi Bike system and the NYC subway. Usage patterns during the lockdown are compared to corresponding days in 2019. Controlling for weather patterns we examine the effect of the lockdown and subsequent reopening of economic activity up through the end of September 2020. The results show that both subway ridership and bikeshare usage plummeted initially; bikeshare usage has nearly returned to normal while subway ridership remains substantially below pre-COVID levels. Implications for policy suggest that the bikeshare system provides resilience to the overall transportation system during disasters when public transit is considered dangerous or is disrupted.