Quantifying the Bicycle Share Gender Gap

BackgroundPublic bicycle share programs in many cities are used by a small segment of the population. To better understand the market for public bicycle share, this study examined the socio-demographic and transportation characteristics of current, potential, and unlikely users of a public bicycle share program and identified specific motivators and deterrents to public bicycle share use.MethodsWe used cross-sectional data from a 2017 Vancouver public bicycle share (Mobi by Shaw Go) member survey (n = 1272) and a 2017 population-based survey of Vancouver residents (n = 792). We categorized non-users from the population survey as either potential or unlikely users based on their stated interest in using public bicycle share within the next year. We used descriptive statistics to compare the demographic and transportation characteristics of current users to non-users, and multiple logistic regression to compare the profiles of potential and unlikely users.ResultsPublic bicycle share users in Vancouver tended to be male, employed, and have higher educations and incomes as compared to non-users, and were more likely to use active modes of transportation. The vast majority of non-users (74%) thought the public bicycle share program was a good idea for Vancouver. Of the non-users, 23% were identified as potential users. Potential users tended to be younger, have lower incomes, and were more likely to use public transit for their main mode of transportation, as compared to current and unlikely users. The most common motivators among potential users related to health benefits, not owning a bicycle, and stations near their home or destination. The deterrents among unlikely users were a preference for riding their own bicycle, perceived inconvenience compared to other modes, bad weather, and traffic. Cost was a deterrent to one-fifth of unlikely users, notable given they tended to have lower incomes than current users.ConclusionFindings can help inform targeted marketing and outreach to increase public bicycle share uptake in the population.

In recent decades, special attention has been given to the adverse effects of traffic congestion. Bike-sharing systems, as a part of the broader category of shared transportation systems, are seen as viable solutions to these problems. Even if the quality of service in bike-sharing service systems were permanently improved, there would still be some issues that needed new and more efficient solutions. One of these refers to the rebalancing operations that follow the bike depletion phenomenon that affects most stations during shorter or longer time periods. Current work develops a two-step method to perform effective rebalancing operations in bike-sharing. The core elements of the method are a fuzzy logic-controlled genetic algorithm for bike station prioritization and an inference mechanism aiming to do the assignment between the stations and trucks. The solution was tested on traffic data collected from the Citi Bike New York bike-sharing system. The proposed method shows overall superior performance compared to other algorithms that are specific to capacitated vehicle routing problems: standard genetic algorithm, ant colony optimization, Tabu search algorithm, and improved performance compared to Harris Hawks optimization for some scenarios. Since the algorithm is independent of past traffic measurements, it applies to any other potential bike-sharing system.

Gender gap generators for bike share ridership: Evidence from Citi Bike system in 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.

Public bicycle share users are predominantly Caucasian, employed, and have higher incomes and education levels, as compared to the general population. This has prompted bicycle share operators and researchers to increasingly consider equity in bicycle share program access and uptake. The location of bicycle share docking stations has been cited as a major barrier to uptake among lower socioeconomic groups. This study aimed to assess spatial access to bicycle share programs in Canadian cities by comparing the socioeconomic characteristics of dissemination areas inside and outside the bicycle share service areas. We obtained locations of bicycle share stations for the five existing programs in Canada: Vancouver, Hamilton, Toronto, Ottawa-Gatineau, and Montréal. We used the material component of the Pampalon Deprivation Index (2011) as a measure of socioeconomic status for each dissemination area, calculating city-specific quintiles. We compared the distribution of deprivation for dissemination areas inside the bicycle share service area, compared with outside the service area. We found that advantaged areas have better access to bicycle share infrastructure in Vancouver, Toronto, Ottawa-Gatineau, and Montréal, and conversely, that disadvantaged areas have better access in Hamilton. This analysis indicates that in most cities, substantial effort is needed to expand service areas to disadvantaged areas in order to increase spatial access for lower socioeconomic populations.

By understanding gender disparities in bike share usage in New York City, programs can be better tailored to increase cycling and bike share usage among female members. Data from bike trips and a Citi Bike enrollment survey for the period 2013–2018 were used for this analysis. Associations comparing female to male members on selected study variables were examined through the estimation of odds ratios (OR) and 95% confidence intervals (CI) using bivariate logistic regression models and Wilcoxon signed-rank tests for continuous non-normal data. Spatial autocorrelation of bike share station pick ups and drop offs by percentage of female bike share members was detected using local Moran’s I. This study included 226,237 Citi Bike members; of these, over one-third (38.1%) self-identified as female. The optional enrollment survey was completed by 33,945 members; of these, 37.9% self-identified as female. Compared to male gender, female gender was associated with younger age, higher levels of education completed, being a student and not employed, and lower household income, as well as social and health reasons for membership rather than utilitarian reasons. Overall, female members took fewer bike share trips (median: 46.0 per year vs. 78.5 for males). There was spatial correlation between station usage and gender, with female members more likely than male members to use Citi Bikes in less dense neighborhoods. The results from this study highlight the gender disparity in bike share membership and usage in NYC and provide insight into how this gap could be reduced.

The "Citi Bike" bike share program in New York City is the largest bike share program in the USA. We ask whether expanding this program to lower-income communities is cost-effective means of encouraging exercise and reducing pollution in New York City. We built a stochastic Markov model to evaluate the cost-effectiveness of the Citi Bike expansion program, an effort to extend bike share to areas with higher costs and risks over a 10-year time horizon. We used one-way sensitivity analyses and Monte Carlo simulation to test the model uncertainty. The incremental cost-effectiveness ratio of the Citi Bike expansion program relative to the current program (status quo) was $7869/quality-adjusted life year gained. The Citi Bike expansion program in New York City offers good value relative to most health interventions.

From non-cyclists to frequent cyclists: Factors associated with frequent bike share use in New York City

BackgroundDespite rapid expansion of public bicycle share programs (PBSP), there are limited evaluations of the population-level impacts of these programs on cycling, leaving uncertainty as to whether these programs lead to net health gains at a population level or attract those that already cycle and are sufficiently physically active. Our objective was to determine whether the implementation of PBSPs increased population-level cycling in cities across the US and Canada.MethodsWe conducted repeat cross-sectional surveys with 23,901 residents in cities with newly implemented PBSPs (Chicago, New York), existing PBSPs (Boston, Montreal, Toronto) and no PBSPs (Detroit, Philadelphia, Vancouver) at three time points (Fall 2012, 2013, 2014). We used a triple difference in differences analysis to assess whether there were increases in cycling over time amongst those living in closer proximity (< 500 m) to bicycle share docking stations in cities with newly implemented and existing PBSPs, relative to those in cities with no PBSPs.ResultsLiving in closer proximity to bicycle share predicted increases in cycling over time for those living in cities with newly implemented PBSPs at 2-year follow-up. No change was seen over time for those living in closer proximity to bicycle share in cities with existing PBSPs relative to those in cities with no PBSP.ConclusionThese findings indicate that PBSPs are associated with increases in population-level cycling for those who live near to a docking station in the second year of program implementation.

Measuring the vulnerability of bike-sharing system

Evaluation of the impact of a public bicycle share program on population bicycling in Vancouver, BC.

Abstract. The development of the sharing economy has provided an important realization path for urban’s green and healthy development, and has also accelerated the speed of urban development. With the constant capital pouring into the public transport field, dock-less shared bicycle is a relatively new form of transport in urban areas, and it provides a bikesharing service to fulfil urban short trips. Dock-less shared bicycle, with a characteristic of riding and stopping anywhere, has successfully solved the last mile travel problem. Recently, studies focus on the on the temporal spatial characteristics of public bicycle based on public bicycle operation data. However, there are few studies on the identification of riding patterns based on the characteristics of temporal and spatial behavior of residents. In addition, researches have been conducted on public bicycles administered by the government, and the dock-less shared bicycle have different characteristics from public bicycles in terms of scale of use and mode of use. This paper aims to analyze the temporal and spatial characteristics of residents using shared bicycles, and attempts to explore the characteristics of the riding modes of the dock-less shared bicycles.Mobike sharing bicycle dataset of Beijing city were obtained for the research and this dataset contains a wealth of attributes with cover of 396600 shared bicycle users and 485500 riding records from May 10 to May 25 in 2017. Additionally, 19 types of POI (Point of Interest) data were also obtained through the API of Baidu Maps. To examine the patterns of shared bicycle trips, these POI data are categorized into five types including residential, commercial, institution, recreation and transport. Spatiotemporal analysis method, correlation analysis methods and kernel density methods were used to analyse the temporal and spatial characteristics of shared bicycle trips, revealing the time curve and spatial hotspot distribution area of shared bikes. Furthermore, a new matrix of riding pattern based on POI was proposed to identify the riding patterns during massive sharing bicycle dataset.This paper aims to explore the riding behaviour of shared bicycles, and the research results are as follows:(1) Temporal characteristics of riding behaviourThe use of the Mobike bicycles is significantly different on weekdays and weekends (Figure1). Figure 2 clearly shows a morning peak (7–9 h) and evening peak (17–19 h), corresponding with typical commute time. At noon, some users' dining activities triggered a certain close-distance riding behavior, which formed a noon peak. Different from the riding characteristics of the working days, there are many recreational and leisure riding behaviors on the weekends. The distribution of riding time is more balanced, and there is no obvious morning and evening peak phenomenon.(2) Spatial characteristics of riding behavior The spatial distribution of riding behaviour varies with different roads (Figure 2) and people prefer to choose trunk roads for cycling trips. Spatial hotpot detecting method based on the kernel density is applied to identify the active degree of bike sharing trip during a whole weekday (Figure 3). The red colour represents a high active degree and the green and blue colour means the low degree. Note that almost no riding occurred in the early hours of the morning and late at night. The characteristics of three riding peaks are obvious in the figure. A large number of travels occurred in Second Ring to Fourth Ring Road, and some travel activities were concentrated near traffic sites.(3) Patterns of riding behavior Different riding patterns happens in different space and change over the time at two scales of day and hour. During morning peak and evening peak on weekdays, more than 60 percent of riding trips are corresponding with typical commuting activities. The observed commuting pattern of morning peak (Figure 4(a) and (b)) implies that the majority of shared bicycle trips might relate to home, transports, commercial area and some institution. For example, students choose shared bicycles to do some school activities, people prefer to use shared bicycles as a connection tool to bus station and metro stops and people handle daily affairs in some government agencies. However, a large part of the shared bicycle trips on weekends shows the characteristics of non-commuting riding pattern, which means more leisure activities take place at weekends (Figure 4(c) and (d)). Non-commuting pattern of riding behavior mainly occurs among residential areas, metro stops, bus stations and recreational facilities, such as parks, playgrounds, etc.

Bike share’s impact on car use: Evidence from the United States, Great Britain, and Australia

Globally, traffic jams have become a growing cost for many cities. With rapid urbanization in China, traffic congestion is a normal part of daily life. China's goal is to reduce carbon dioxide emissions by nearly 50 in a fifteen-year period. By end of 2011, there were 225 million motor vehicles across country. After buses and rail transportation, developing public bicycle systems (PBSs) became last hope for reducing pressure on transportation and energy consumption. In May 2008, Hangzhou Municipal Government began building a PBS in two phases, with phase one including well-known scenic West Lake area, and phase two including southern and eastern parts of city. On September 4, 2008, Ruan Chengfa, proposed at a meeting held by municipal government that free bicycles be provided in all districts, in order to address issue of the last kilometer of mobility. Keywords: China; Hangzhou Municipal Government; public bicycle systems (PBSs); Ruan Chengfa

The emergence of shared bicycle systems, such as Citi Bike, has greatly changed the pattern of urban mobility in New York City. It has not only provided a new choice for people to travel, but also greatly changed the way people live. In this paper we discuss the impact of Citi Bike on economics, society, environment and other relevant aspects. On the basis of considering people's favorable choices of travel mode, we choose car, walk, subway, bus, bike and For-Hire-Vehicle, totally four modes, to study. We select several representative indicators to evaluate the travel modes, such as the average number of users within one year, the speed, the operating cost and the impact on the environment. Then the data is standardized by the method of Deviation Standardization and we draw the data table using Excel.Then we choose the Entropy Weight Method to calculate the weight of different modes using Matlab, calculate the respective score of travel modes, and finally we get the Citywide Mobility Mode to quantitatively assess the impact of shared bikes on economics, environment and society. We reach a conclusion that without a shared bike system, the way people travel will mostly be replaced by driving. It will lead to more fuel consumption and extraneous expenses, which will increase the burden on consumers and bring pollution to the environment. During our process of modelling, the biggest challenge is to build a reasonable model to simulate the situation without a shared bicycle system, but the assumption itself is unreasonable, so this paper establishes a unified framework for all kinds of travel modes. When the shared bicycle system is not included, the proportion of it is reduced to zero, so the change of the model is observed to evaluate the impact of the shared bicycle system on the city. We hope this research helps to support the construction of transportation systems in New York and other cities around the world.