Bike Sharing Research Articles

Who owns bikes and e-bikes? Insights from a cycling survey in Australia

IntroductionCycling provides numerous mental and physical health benefits, and owning a bike is a prerequisite for engaging in cycling activities in most cases. MethodsUsing data from a 2022 cycling survey (n = 2065), this paper examines bike ownership in Sydney, Australia, including conventional and e-bikes. ResultsWe find being male, younger age, higher education and income levels, and pos-itive attitude contribute to bike ownership, while paying rent, living in apartments and having foreign origin reduce the likelihood of owning a bike. Young adults aged 25–34 have lower conventional bike ownership than adjacent age groups, but reported highest e-bike ownership, and access to shared bikes among all age groups. E-bike ownership is higher in more urbanized parts of the city. ConclusionsFindings from this paper suggest the need for interventions through fi-nancial incentives, and improving the availability of bike storage facilities to increase bike ownership. Affordable and accessible bikeshare schemes address several barriers to bike ownership identified in this paper, and is suggested as a viable alternative to private ownership.

Causes of transportation inequality: The case of bike sharing in the U.S.

The bike-sharing system, as an innovation to solve the first-and-last-mile transportation problem, tends to be disproportionally used much less by lower income users. In this paper, we focus on testing two causes of this inequality: availability of stations and affordability, using a difference-in-difference framework with various treatments. We discovered that stations are more readily available in higher-income areas. However, increasing station availability will not reduce the usage inequality. Instead, we find that equity programs that increase the affordability of low-income users can reduce the usage gap between high and low-income groups. The major findings of this paper suggest that to create a more inclusive and equal urban transportation ecosystem, urban planners, policymakers, and bike-sharing operators need to allocate more resources to support affordability.

Natural barriers facing female cyclists and how to overcome them: A cross national examination of bikesharing schemes

Worldwide, the gender gap in urban cycling is considerable, with most cyclists being young to middle-aged men. In the current study, we first capture the suite of cycling barriers facing women before empirically investigating whether and how much three natural barriers (inclement weather, hilliness, and darkness) impact female users of bikesharing systems. For the analysis, we spatially integrate gender for more than 200 million bikesharing trips with fine-grained weather, gradient, and sunset/sunrise data. Computing a suite of the generalized additive models for ten cities worldwide covering a period of 14 years, we find that wind and precipitation disincentivise cycling, and more so for women than for men. Similarly, steeper gradients are a significant barrier for female bikeshare users for many cities. In every city, women make fewer trips in the dark (i.e., before sunrise and after sunset) compared to men. In higher-cycling cities, regardless of natural barriers, cycling declines less with age for women compared to other cities. To overcome the barriers presented by inclement weather, hilliness, and darkness we recommend (a) partial electrification of bikesharing fleets, (b) reduced exposure along bicycle paths (through manufactured shelters or tree canopies), and (c) adequate nighttime lighting along cycling paths. In the spirit of open science, all data and code on which this paper is based have been provided on Mendeley:https://data.mendeley.com/datasets/vmy42hywwx/1.

Powering bikeshare in New York City: does the usage of e-bikes differ from regular bikes?

ABSTRACT In this study, we investigate the difference between shared electric bicycles (e-bikes) and conventional shared bikes operated by Citi Bike in New York City. We examine differences in usage by examining summary statistics and we develop conditional autoregressive models to examine differences in factors associated with trip generation. These factors include bike infrastructure, subway proximity, area demographics, land use, and elevation. We also control for border effects by identifying peripheral stations. The percentage of e-bike trips fluctuates across hours of the day, with a peak around 3 PM but diminishes during peak commuting hours. We find mostly no significant differences in factors associated with the generation of trips between the two modes. Exceptions include a larger effect on e-bike trip generation when the daytime worker population is larger; other differences are due to the racial composition around the station area, some land use categories, and elevation differentials.

Interpretable Bike-Sharing Activity Prediction with a Temporal Fusion Transformer to Unveil Influential Factors: A Case Study in Hamburg, Germany

Bike-sharing systems (BSS) have emerged as an increasingly important form of transportation in smart cities, playing a pivotal role in the evolving landscape of urban mobility. As cities worldwide strive to promote sustainable and efficient transportation options, BSS offer a flexible, eco-friendly alternative that complements traditional public transport systems. These systems, however, are complex and influenced by a myriad of endogenous and exogenous factors. This complexity poses challenges in predicting BSS activity and optimizing its usage and effectiveness. This study delves into the dynamics of the BSS in Hamburg, Germany, focusing on system stability and activity prediction. We propose an interpretable attention-based Temporal Fusion Transformer (TFT) model and compare its performance with the state-of-the-art Long Short-Term Memory (LSTM) model. The proposed TFT model outperforms the LSTM model with a 36.8% improvement in RMSE and overcomes current black-box models via interpretability. Via detailed analysis, key factors influencing bike-sharing activity, especially in terms of temporal and spatial contexts, are identified, examined, and evaluated. Based on the results, we propose interventions and a deployed TFT model that can improve the effectiveness of BSS. This research contributes to the evolving field of sustainable urban mobility via data analysis for data-informed decision-making.

Research on the Supply of Shared Bicycles in Subway Hub Stations-Taking the Subway Station of Zhengzhou People 's Hospital as an Example

In recent years, with the rapid development of shared bicycles, it has played a great role in alleviating urban traffic pressure and facilitating residents to go out to a certain extent. However, while solving the problem of the last kilometer of travel, the uneven distribution of shared bicycles in subway hub stations is becoming more and more serious, which has a certain impact on the quality of residents ' travel. This paper takes the supply of shared bicycles in urban subway hub stations as the research object, and observes and summarizes the characteristics of slow traffic flow near the entrances and exits of subway hub stations in the morning and evening peak hours. On the premise of ensuring the right of way for pedestrians, the supply and demand of shared bicycles in the subway hub station and the configuration of shared bicycles in the surrounding space of the subway hub station are comprehensively considered, and the supply of shared bicycles is predicted by the estimation model and the field data of the subway hub station are investigated, taking the subway station of Zhengzhou People 's Hospital as an example to carry out field research and predict the actual demand and supply of shared bicycles in the subway hub. Finally, the problems existing in the research on the supply of shared bicycles in metro hub stations and possible future research directions are summarized.

Modeling of the daily dynamics in bike rental system using weather and calendar conditions: A semi-parametric approach

This study proposes a more robust methodological approach to modeling the effect of weather and calendar variables on the number of bike rentals. We employ penalized splines quasi-Poisson regression (a semi-parametric model), which involves some form of regularization, like those used in lasso, ridge, and other types of parametric regularization models. We demonstrate that this modeling approach reveals hidden relationships that a pure parametric model fails to identify.The findings show that visibility, windspeed, season, working day, and year all significantly impact bike rentals. Increased rentals are associated with increased visibility and lower wind speed. Rentals are negatively affected by the spring and winter seasons, while working days and the year show positive trends except in a few cases. The analysis of rentals by registered and casual users reveals similar patterns, though the magnitudes of the effects differ. These findings highlight the importance of considering weather and calendar variables when managing and promoting bike-sharing services. The study has implications for bike-sharing system operators and policymakers, suggesting strategies such as improving visibility and wind protection, seasonally tailoring promotional campaigns, targeting non-working days for casual users, and adapting to changing user demands. The study adds to our understanding of the factors that influence bike rentals and provides suggestions for improving the utilization and accessibility of bike-sharing systems.

A Spatiotemporal Comparative Analysis of Docked and Dockless Shared Micromobility Services

Sustainable urban mobility is an imperative concern in contemporary cities, and shared micromobility systems, such as docked bike-sharing, dockless bike-sharing, and dockless e-scooter-sharing, are recognized as essential contributors to sustainable behaviors in cities, both complementing and enhancing public transport options. Most of the literature on this subject predominantly focuses on individual assessments of these systems, overlooking the comparative analysis necessary for a comprehensive understanding. This study aims to bridge this gap by conducting a spatiotemporal analysis of two different shared micromobility modes of transportation, docked bike-sharing systems and dockless e-scooter-sharing systems operating in the municipality of Lisbon. The analysis is further segmented into arrivals and departures on weekdays and weekends. Additionally, this study explores the impact of sociodemographic factors, the population’s commuting modes, and points of interest (POIs) on the demand for both docked bike-sharing and dockless e-scooter-sharing. Multiscale Geographically Weighted Regression (MGWR) models are employed to estimate the influence of these factors on system usage in different parishes in Lisbon. Comparative analysis reveals that the temporal distribution of trips is similar for both docked bike-sharing and dockless e-scooter-sharing systems on weekdays and weekends. However, differences in spatial distribution between the two systems were observed. The MGWR results indicate that the number of individuals commuting by bike in each parish has a positive effect on docked bike-sharing, while it exerts a negative influence on dockless e-scooter-sharing. Also, the number of commercial points of interest (POIs) for weekday arrivals positively affects the usage of both systems. This study contributes to a deeper understanding of shared micromobility patterns in urban environments and can aid cities in developing effective strategies that not only promote and increase the utilization of these shared micromobility systems but also contribute to sustainable urban mobility.

Comparing e-bike and conventional bicycle use patterns in a public bike share system: A case study of Richmond, VA

Bicycle-sharing systems have emerged as a viable transportation alternative in numerous urban areas, owing to their multifaceted benefits. These benefits include reduced transportation expenses, health improvements, and decreased emission levels. While extensive research has been conducted on travel behaviors in shared bicycle systems, there is currently a lack of research on travel behaviors changes with the introduction of e-bikes. This study presents a comprehensive analysis of the similarities and differences between e-bike (pedelec) and conventional bicycle use in a bike share system in Richmond City, Virginia.The results show that pedelecs are generally associated with longer trip distances, shorter trip times, higher speeds, and lower rates of uphill elevation change. The origin–destination analysis considering the business, mixed use, residential, and other uses shows extremely similar trends, with a large number of trips staying within either business or residential locations or mixed use. The roadway use analysis shows that pedelecs are used farther outside of the city than bikes.

Do Risk-Taking Cyclists Have Different Sociodemographic Characteristics? An Observational Study at Intersections in a French City

The increase in cycling accidents can hinder the increased use of this transportation mode. To identify the sociodemographic factors explaining risky cyclists’ behaviors is therefore important. The relationships between some sociodemographic variables (gender, age, parenthood, use of a shared versus a personal bike) and some risky behaviors (helmet use, red-light running, crossing an intersection with a very short time before the next passing vehicle) remain inconsistent or under-investigated in the literature. These relationships were therefore investigated in a French population. Cyclists ( N = 2,788) were observed at two traffic signal intersections in the city center of Lille, France. Two cameras per site were used to score each cyclist’s variables (with a minimum of intercoder reliability = 80%). Men (versus women) and young (versus older) cyclists are less likely to wear a helmet and more likely to run red lights. Cyclists with (versus without) a child seat were more likely to wear a helmet, suggesting that parenthood influences risk perception. Shared (versus personal) bike users were found to be significantly less likely to wear a helmet and more likely to run red lights. This highlights the importance of further investigating whether shared bike users are more likely to take different types of risk on the road. Various factors (psychosocial, enforcement, road design) are discussed to explain these findings and prevent risks. The importance of adapting road safety interventions to the sociodemographic characteristics of cyclists is also discussed.

Closing the Cycling Gap: Examining Equity Implications of Montreal’s Bikesharing Network Growth

The worldwide growth of bike-sharing systems demonstrates their mass appeal and success. Montreal’s bike-sharing system, BIXI, opened in 2009, is now one of the largest in the world, and has drastically expanded since being taken over by the City of Montreal in 2014. The system now has over 750 stations up from 459 in 2014. This paper analyzes BIXI’s expansion through the lens of equity, revealing how factors including race and income can explain its growth. First, we mapped high-need equity areas that BIXI could have plausibly expanded to, and those that BIXI actually expanded to. We then created a logistic regression model with dissemination areas that gained new access to BIXI as the dependent variable, and income, race, the number of nearby trips, the population count, and the number of bus stops as independent explanatory variables, and controlled for distance from existing stations. Our regression findings demonstrated that race and income were statistically significant in explaining BIXI’s expansion, with a general trend that BIXI was expanding into lower-income neighborhoods and neighborhoods with a greater proportion of visible minorities, as defined by Statistics Canada. Finally, we compared the same explanatory variables in areas that had gained access to BIXI stations, and those in areas that had not but plausibly could have to show the socioeconomic differences in areas gaining new access to service.

Understanding bike-sharing usage patterns of members and casual users: A case study in New York City

Shared bicycle travel has become an important travel mode for urban residents, and bike-sharing platforms are also booming in major cities worldwide. The bike-sharing platform provides users with systematic services: members refer to annual bike-sharing service subscribers and casual users refer to holders of a day pass or single ride ticket. Even though casual users account for a large share of ridership and revenue at bike-share systems in New York City (NYC), very little is known about the characteristics and preferences of casual users and how they compare to members. Based on the open-docked bike-sharing dataset from Citi Bike, we analyze the bike usage patterns of members and casual users in NYC, and how these patterns change in the face of the COVID-19 pandemic on a typical day level. We find that the COVID-19 pandemic has negatively influenced members' bike trip counts on weekdays; bike travel time increases for members during the pandemic and decreases for casual users after the pandemic. To make a profound study concerning spatial heterogeneities, we employ Gaussian Mixture Model (GMM) to cluster the spatiotemporal changes of the station-level bike usage and obtain four clusters for each user type. Combined with the Points of Interest (POI) information, we find that member-related cluster with commuting POIs is significantly affected by the pandemic, while leisure trips are the most severely affected for casual users. Compared with the central area, peripheral clusters with residential and religious POIs are less affected by the pandemic. According to our findings, new operational strategies such as flexible subscriptions can be developed to attract more users, maintain their stickiness, and improve the bike-sharing level of services.

The heterogeneous effects of dockless bike-sharing usage intensity on house prices near subway stations

Dockless bike-sharing (DBS) systems have emerged as a popular mode of transportation in urban areas. While existing literature has explored the potential effects of DBS on urban systems, there is limited research on its impact on housing markets. This study addresses this gap by investigating the heterogeneous effects of DBS usage intensity on house prices at various distances from subway stations in Shanghai. Utilizing Mobike trip data and a dataset of 50,837 second-hand houses sold between May 2016 and December 2018, the analysis reveals that DBS usage intensity positively impacts house prices in areas outside 800 m and within 3000 m from subway stations, resulting in a 1.4 % increase in house prices for every 1,000 DBS rides within a 500 m radius. The study also finds that the marginal effect of DBS usage intensity on house prices is contingent on the distance from subway stations. For distances shorter than 2.33 km, the marginal effect rises with increasing distance. Conversely, for distances exceeding 2.33 km, the marginal effect declines and turns insignificant beyond 3 km. These findings imply that the positive influence of DBS on house prices is more pronounced in areas that are neither too close nor too far from subway stations, where people are more likely to use DBS to connect to subway networks. The findings of this study contribute to a better understanding of the complex relationship between DBS and real estate markets.

The travel pattern difference in dockless micro-mobility: Shared e-bikes versus shared bikes

To facilitate the tailoring of dockless bike-sharing and electric bike (e-bike) sharing services and assist in formulating effective regulations, this study aims to unravel the spatio-temporal travel patterns specific to e-bike-sharing and bike-sharing systems, utilising interpretable machine learning methods and a large-scale trip-level dataset in Kunming, China. The results show that shared bikes and e-bikes exhibit overall similarities and subtle differences in many aspects, such as trip attributes and spatial distribution. Additionally, both shared bikes and shared e-bikes have three basic temporal patterns for commuting and recreational purposes. Regarding the differences, e-bike sharing networks are more dispersed and bigger, and bike sharing tends to form densely connected clusters of flow, exhibiting a local concentration of activity. Besides, the commuting activities within e-bike sharing systems exhibit two patterns: direct travel to the destination and integration with public transit. In contrast, shared bikes predominantly rely on public transit transfers for commuting purposes.

Bikeshare–Metrorail Integration in Washington, D.C.: What are the Characteristics of Neighborhoods that Encourage Capital Bikeshare Trips to and from the Metrorail?

Bikeshare has gained popularity worldwide and can serve as a first mile/last mile feeder for public transport. In many bikeshare systems, docking stations near a metrorail station are popular trip origins and destinations and bikeshare can help increase the ridership of the metrorail system as well. The goal of this study is to identify the determinants of bikeshare trips between neighborhood stations and bikeshare docking stations near the metrorail in Washington, D.C. For this study, we divided the bikeshare stations in Washington, D.C., into two categories: docking stations near a metrorail station (defined as bikeshare docking stations within a quarter mile radius of metrorail stations) and neighborhood stations (bikeshare docking stations outside the quarter mile radius of metrorail stations). We used two stepwise linear regression models of bikeshare trips between neighborhoods and metrorail stations stratifying the analysis by the determinants of neighborhood docking stations into four categories: socio-demographic conditions, transportation related, land use, and points of interests. In the backward elimination model, expanding the bike trail supply by an additional 100 m was associated with an increase in 70 annual bikeshare trips between a neighborhood station and a docking station near the metrorail. The forward selection model showed that a 1% increase in the poverty rate of the neighborhood stations was associated with 52 fewer annual such bikeshare trips. This study also found that adding bikeshare docking stations near national parks and neighborhoods with higher population density can promote bikeshare that starts/ends near metrorail stations.

A feature correlation reinforce clustering and evolutionary algorithm for the green bike-sharing reposition problem

This paper tackles the green bike-sharing reposition problem (GBSRP) considering the reposition quality and carbon emission during the reposition simultaneously. With the expanding scale of today’s bike-sharing system (BSS), the environmental pollution during the reposition becomes obvious. GBSRP aims to satisfy the inventory request of every bike-sharing station through an identified route set while minimizing the routing fuel consumption and relevant carbon emission. This paper provides an effective feature correlation mechanism for both the clustering strategy and algorithm design, which aims at solving large-scale reposition. The original problem is clustered through the feature correlation reinforce clustering (FCRC) strategy. Then, a multi-objective evolutionary algorithm based on decomposition combined with feature correlation mechanism (FC-MOEA/D) is designed considering both the carbon emission and reposition quality. Finally, the carbon emissions of each module of the Comprehensive Modal Emissions Modeling under different reposition qualities are analyzed. The Pareto front of each instance is displayed and compared based on different clustering strategies and algorithms. The results demonstrate the competitiveness of the FCRC strategy and FC-MOEA/D algorithm. We also further discuss the relevant strategies for different reposition quality preferences and how they minimize the carbon emission during the reposition for BSS managers reference.

Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain

The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development.

Exploring the modal integration of bike-sharing and urban rail transit: Multi-Criteria Evaluation in GIS for the city of Salvador (Bahia – Brazil)

Bike-sharing systems (BSS) have become increasingly popular in developing countries. However, few papers have investigated their use in these socioeconomic contexts, mainly as first/last mile connections to urban rail transit. Thus, this paper investigates the potential of implementing and operating BSS in urban rail transit from a developing country to propose strategies and policies for sustainable mobility. The methodology is based on Multi-Criteria Evaluation (MCE) in Geographic Information System (GIS) to identify the suitability of urban rail stations for integration with BSS. Seven key factors were considered: sex, age, travel purposes, public security, land use, terrain slope, and qualification of the road network for cycling. It already delineates the potential users upon adopting the BSS integrated with urban rail transit. Additionally, each urban rail station is individually analyzed, considering the influence of predominant demand and environmental factors in its surroundings.

Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach

Bike-sharing systems are important components of urban transportation systems that facilitate short-distance travel. In recent years, free-floating bike-sharing systems have become popular. However, a major problem in such systems is the increasing number of idle bikes due to their spatial dispersion, which leads to a loss of demand and difficulty in bike rebalancing. To overcome these drawbacks, operators reserve parking areas, and laborers are incorporated into the bike rebalancing operation. We propose a novel dynamic bike rebalancing strategy based on trucks and laborers for free-floating bike-sharing systems. This strategy combines bike collection by laborers and bike transportation by trucks for rebalancing operations. In addition, we consider broken bike detection by laborers and user dynamics in two extension models. Because of the demand uncertainty in bike-sharing systems, we employ an enhanced distributionally robust optimization (EDRO) approach to protect against uncertainty. A target-level constraint is added to the general distributionally robust optimization (DRO) model to further improve the out-of-sample performance. An adaptive robust strategy is also employed to avoid overly conservative solutions. Furthermore, a linear decision rule is adopted to reformulate the EDRO model into a mixed-integer programming model, which guarantees computational tractability. The results of numerical experiments show that the EDRO model outperforms other benchmark models on a series of measures. Moreover, we verify the value of laborers in dynamic rebalancing operations and explore the effect of different bike supply levels and several other parameters on the value of laborers. We also test how the target-level parameter affects the model performance.

SUITABILITY ANALYSIS FOR URBAN BICYCLE TOURISM OF ILOILO CITY USING GIS

Abstract. Urban bicycle tourism refers to the recreational and enjoyable use of bicycles in navigating urban cities like Iloilo City. The city has already established an 11-kilometer cycling infrastructure within its boundaries. It is imperative to maximize its use to combat the negative impacts of rapid urbanization and promote safe and sustainable transportation. This paper shows the use of Geographic Information Systems (GIS) to assess the bike lanes network's suitability and accessibility for building a bike-sharing system that also caters to drop-off and ride-on points at each station. Deletion of duplicates and cleaning of road network data and tourist attraction point data was performed prior to any analysis. Service areas were generated to determine the number of tourist attractions catered by each potential bike share station – defined by main road intersections. Point distance analysis was performed to reduce the number of road intersections to just thirty-nine (39). Bicycle route maps and itineraries were produced to visualize the optimal routes tourists can use for exploring the city based on preferred sites category. From here, Route Analysis was conducted to create four (4) itineraries based on tourism categories: Top 10 Most Rated Tourist Attractions, Food and Dining, Museums and Churches, and Shopping and Leisure. The output maps and itineraries can be presented to the City Tourism Office of Iloilo City as a decision support tool for its programs and policies regarding bike lanes.