Bike Sharing Research Articles

Replacing Car Trips with a Cargo Bike Sharing Service: What Features Do Users Value Most?

While cargo bikes are becoming an increasingly popular alternative to larger, more polluting vehicles in both the logistics and private mobility sectors, there has been comparatively little research on their use for private mobility. The potential of shared cargo bikes to replace car trips has been examined in some studies, but no previous research has investigated the critical factors that make it a valued alternative. By studying users’ willingness to pay, this paper examines the perceived value of a free cargo bike sharing service for users. The research is based on a survey of 321 users of the Fietje cargo bike sharing service in Bremen, conducted in 2022. In this sample, 38 to 55% of shared cargo bikes trips would otherwise have been performed by car. The paper identifies the transport of objects and children as critical features that provide value to users and create the potential to replace car trips. The results also draw attention to the fact that a cargo bike sharing service is likely to be a more effective tool for reducing car use if it is free. Introducing a fee would increase car trips by 14 to 18% of the total trips enabled by the service.

EXPRESS: Quality, Necessity, or Accessibility? Impact of Dockless Bike Sharing on Ineffective Use of Medical Resources

In recent years, patients have increasing autonomy in medical decision-making. With great autonomy in selecting medical institutions, patients with low-risk diseases (LRD) may opt for treatment in prestigious hospitals, which limits the access of patients with high-risk diseases (HRD) to these hospitals. Moreover, due to limited medical knowledge and a fear of uncertainty regarding diseases, patients with greater medical autonomy may request various unnecessary medical tests to alleviate their health-related anxiety. This study takes a novel patients’ perspective to investigate how dockless bike sharing, which addresses the “last-mile problem” and improves geographical accessibility to medical institutions, affects two types of ineffective use of medical resources: disease-institution mismatch and overuse of medical tests. By introducing the concept of quality-accessibility trade-off in patients’ decision-making processes, we shed light on the psychological mechanism underlying the effect of dockless bike sharing on patients’ medical institution choices. We also introduce a new concept, perceived necessity of medical tests, to explain how dockless bike sharing reduces unnecessary medical tests. In Study 1, we designed a quasi-experiment and applied a difference-in-differences model based on real-world electronic health record data to show that dockless bike sharing increased the likelihood of patients choosing hospitals over clinics and decreased the number of unnecessary medical tests. In Studies 2a, 2b, 3a, and 3b, four randomized controlled experiments were employed to replicate the findings and reveal the underlying mechanisms. In sum, we found that dockless bike sharing acted as a “double-edged sword” in terms of its impact on exacerbating disease-institution mismatch and alleviating the overuse of medical tests. This study makes significant theoretical contributions to the Operations Management (OM), sharing economy, and medical resource management literature. It also offers important managerial insights for governments, shared mobility platforms, and healthcare providers.

Potential of Warehouse Sharing and Electric Bicycles Deliveries in the Montreal Region

Potential of Warehouse Sharing and Electric Bicycles Deliveries in the Montreal Region

Examining bike share usage trends in Metropolitan cities through App based data. A case study of Chennai, India

Examining bike share usage trends in Metropolitan cities through App based data. A case study of Chennai, India

Predictive Smart Mobility Systems for Sustainable and Efficient Urban Transportation

Abstract: The rapid urbanization and growth of cities have placed increasing pressure on transportation systems, leading to traffic congestion, environmental degradation, and inefficiencies in mobility. The Smart Mobility Prediction System (SMPS) is designed to address these challenges by leveraging advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and big data analytics to optimize transportation networks. This system gathers realtime data from sensors, traffic cameras, GPS devices, and social media feeds to predict traffic patterns, identify congestion hotspots, and suggest alternative routes to users. By utilizing predictive analytics, the SMPS can forecast traffic conditions, demand for public transportation, and the availability of shared mobility options such as ride-hailing and bike-sharing services. Furthermore, the system can support urban planners and policymakers by providing insights into the performance of transportation systems, enabling them to make informed decisions regarding infrastructure development and traffic management. The implementation of the Smart Mobility Prediction System aims to enhance the efficiency, sustainability, and safety of urban transportation networks, contributing to improved quality of life for city dwellers while reducing environmental impacts.

Challenges and Countermeasures of Entrepreneurial Risk Management in Shared Bicycle Enterprises

With the rise of the sharing economy, shared bicycles, as a convenient and environmentally friendly way of travel, have been rapidly promoted and applied around the world. However, bike-sharing enterprises face many risks in the process of entrepreneurship, including market risk, technical risk, legal risk and operational risk. This paper aims to discuss the challenges of bike-sharing enterprises in entrepreneurial risk management, and put forward corresponding countermeasures, in order to provide reference for the sustainable development of bike-sharing enterprises.

Modeling and Analyzing the Spatiotemporal Travel Patterns of Bike Sharing: A Case Study of Citi Bike in New York

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning.

Optimizing Bike Sharing Demand Prediction: A Comparative Study of Multiple Linear Regression and LSTM Models based on Time and Weather Factors

Optimizing Bike Sharing Demand Prediction: A Comparative Study of Multiple Linear Regression and LSTM Models based on Time and Weather Factors

Forecasting Public Bicycle Utilization in New York City Utilizing the CitiBike Dataset

The integration of public bicycles into urban transportation systems has gained popularity due to their potential to reduce traffic congestion and air pollution, while also addressing the “last kilometer” problem. This study examines the usage patterns and predictive analysis of public bicycles in New York City using the CitiBike dataset from 2013 to 2015. We employ the Spring algorithm for networkx visualization and analyze the loan and return data to understand spatial-temporal usage dynamics. Additionally, clustering techniques, including K-means and DBSCAN, are applied to the dataset to uncover usage trends and patterns. The findings provide valuable insights for the planning and optimization of public bicycle systems, such as CitiBike, and contribute to the promotion of sustainable and environmentally friendly urban transportation.

Forecasting Public Bicycle Utilization in New York City Utilizing the CitiBike Dataset

The integration of public bicycles into urban transportation systems has gained popularity due to their potential to reduce traffic congestion and air pollution, while also addressing the “last kilometer” problem. This study examines the usage patterns and predictive analysis of public bicycles in New York City using the CitiBike dataset from 2013 to 2015. We employ the Spring algorithm for networkx visualization and analyze the loan and return data to understand spatial-temporal usage dynamics. Additionally, clustering techniques, including K-means and DBSCAN, are applied to the dataset to uncover usage trends and patterns. The findings provide valuable insights for the planning and optimization of public bicycle systems, such as CitiBike, and contribute to the promotion of sustainable and environmentally friendly urban transportation.

Improving Bike Share Programs on College Campuses in Southeastern US: A Case Study

Improving Bike Share Programs on College Campuses in Southeastern US: A Case Study

Determinants of Dockless Bicycle-Sharing Adoption and Usage Intensity for Commuting and Errands: Evidence from Disadvantaged Neighborhoods

The emergence of dockless bicycle sharing has transformed urban transportation, particularly in China, by offering a flexible and accessible travel option. However, understanding the factors driving its adoption and usage in disadvantaged neighborhoods is crucial, as these areas often face unique mobility challenges. This study explores these determinants, providing a more comprehensive analysis than prior research by focusing specifically on disadvantaged communities. Using survey data from four such neighborhoods in Xi’an, China, we apply Structural Equation Modeling to investigate the factors influencing individuals’ decisions to adopt and intensively use dockless bicycle sharing for commuting and errands. The results reveal key determinants, including psychological factors, demographic characteristics, and spatial and social contexts, and their interaction mechanisms. Attitudes are found to have a substantial impact on bicycle-sharing behavior for both commuting and errands, while social norms and perceived behavioral control (PBC) mainly influence usage for errands. Interestingly, PBC affects adoption but not usage frequency. The findings also highlight that proximity to schools, subways, and neighborhood aesthetics positively correlate with bicycle-sharing adoption for errands, whereas bicycling infrastructure significantly influences usage intensity. However, none of the neighborhood environment factors were found to significantly affect adoption for commuting purposes. These insights are especially valuable for developing targeted strategies to promote bicycle sharing as a sustainable transportation solution in disadvantaged neighborhoods, where improved access can significantly enhance mobility and quality of life.

From shared to residential mobility services? Carsharing and bike-sharing development under the influence of flexible parking requirements

As a way of decreasing car ownership and use, one of the significant causes of road passenger transport-related emissions, a growing number of municipalities in Sweden and globally promote car- and bike-sharing by implementing Flexible Parking Requirements. Evaluations of how these requirements affect mobility patterns among residents are emerging. Still, research on the implications for the carsharing and bike-sharing business models and overall service development is lacking. This paper aims to address this by investigating how the development of car- and bike-sharing services is affected by Flexible Parking Requirements. We study four cases based on mobility service providers and specific properties in Sweden through the lens of business model literature and actor-network theory. The results uncover specific narratives, assemblages and challenges related to each case. The carsharing services highlighted the difference in success between open-access and exclusive residential mobility services. Bike-sharing services were characterized by diverse weaknesses and threats that must be addressed for functioning services. The results indicate Flexible Parking Requirements being an important actant of change in defining the services offerings and shaping assemblages. A plethora of actants is identified in the assemblage around car- and bike-sharing services. The paper helps to identify the missing pieces in aligning all of them to have persistence and lasting services. In the concluding discussion, the new roles of the apartment blocks and residential mobility services are discussed within the transport system.

Student Bicycle Sharing System in Zagreb – Studocikl

Student Bicycle Sharing System in Zagreb – Studocikl

Optimizing urban bike-sharing systems: a stochastic mathematical model for infrastructure planning

AbstractThis paper addresses the optimization of resource allocation and infrastructure planning in bike-sharing systems, particularly inspired by dynamic demand patterns as observed during the COVID-19 pandemic. We introduce a stochastic mathematical model that considers varying demand scenarios to enhance system performance and resource utilization. The research objectives are to fulfill the total travel demand across scenarios and compute the network's capacity to satisfy demand, thereby enhancing the system's efficiency and meeting users' diverse travel needs. The main contributions of this paper include presenting a stochastic mathematical model for bike-sharing station allocation and path network design, which optimizes resource allocation and infrastructure planning. Through a case study on the Vienna bike-sharing system, the model demonstrates practical applicability and effectiveness, offering insights for improving efficiency and service quality. The sensitivity analysis reveals that as costs for bicycle docks and station building increase, fulfilled demand decreases, emphasizing the crucial role of cost management in meeting demand efficiently.

Promoting sustainable usage behavior in the sharing economy business model: A study based on bike-sharing

Exiting research has noted the significance of sharing economy business model, while insufficient attention has been paid to understand the user's sustainable usage behavior in it. To narrow the research gap and enrich literature, based on the bike-sharing context, this study explores user's sustainable usage behavior in the bike-sharing economy business model. On the basis of Push-Pull-Mooring (PPM) model and the survey data collected from 1029 shared bike users in China, we find that user's sustainable intention of using shared bike can be influenced by push, pull and mooring factors. Specifically, push (e.g., social norm and government and enterprise regulation) and pull (e.g., moral norm and perceived behavioral control) factors significantly promote sustainable usage intention, while mooring factor (e.g., inertia) significantly inhibit it. Meanwhile, mooring factor also weakens the positive influence of push and pull factor on sustainable usage intention. Furthermore, this research further reveals that there is a disparity between sustainable intention and sustainable behavior of using shared bike and uncovers that the “intention-behavior” discrepancy can be bridged by providing punitive measures. Additionally, this research uncovers that user's gender, age and monthly income do not significantly promote sustainable usage intention and behavior, user's educational level significantly promotes sustainable usage intention while not for sustainable usage behavior, and the frequency of using shared bike significantly promotes both sustainable usage intention and behavior. This research provides recommendations for government and enterprise to promote sustainable usage behavior in the bike-sharing context.

The role of intermodality and environmental consciousness in the preferences for MaaS bundles: A hybrid choice modeling approach

The interest in Mobility as a Service (MaaS) continues to increase among researchers, where the main driver of successful implementation is the travelers’ adoption of the service, which can be achieved by providing suitable mobility solution in the form of MaaS packages. This paper examines preferences for MaaS packages where a stated choice experiment is conducted among 519 individuals via an online survey in Hungary, and a hybrid choice modeling is estimated allowing the understanding the impact of latent attitudinal variables on preferences. The results show that almost half of the participants would purchase MaaS bundles, but the potential adopters vary according to socio-demographic and travel characteristics. Participants value the inclusion of PT, bike-sharing services, and online shopping voucher discounts in the bundle, while car-sharing and e-scooter-sharing are not so popular. Additionally, transfer between modes and environmental consciousness influence the bundle uptake positively. The findings suggest that future MaaS operators should carefully design mobility bundles to alter travel behavior and obtain a commercially feasible uptake level. The potential negative impacts of MaaS remains a challenge for policymakers and a topic for future studies.

Origin-Destination Spatial-Temporal Patterns of Dockless Shared Bikes Based on Shopping Activities and Its Application in Urban Planning: The Case of Nanjing

The utilization of dockless shared bikes for shopping purposes has become increasingly prevalent. This research seeks to optimize the configuration of facilities and transportation policies for shared bike travel by analyzing the spatiotemporal patterns of shopping trips from the perspectives of destination (D), origin (O), and O-D correlation in Nanjing’s main city area. As the second-largest commercial center in East China, Nanjing offers a significant context for this research. First, we introduce the “cycling intensity” indicator to analyze the patterns of shared bicycle trips with shopping facilities as destinations at both the subdistrict and road section scales. Second, we utilize spatial autocorrelation analysis and k-means clustering to explore the outflow patterns of shared bicycle trips originating from shopping facilities. Finally, we employ grey correlation analysis to investigate the dynamic flow correlations of shared bicycle O-D trips around various grades of shopping facilities at both subdistrict and road section levels. Concurrently, we endeavored to delineate the practical transformation and application of the research findings. Our results indicate the following: (1) There is a high concentration of cycling intensity around shopping facilities on east–west and north–south roads, with community shopping facilities primarily associated with north–south roads. (2) The outflow of shared bikes from shopping areas can be categorized into four distinct modes. (3) The inflow and outflow of shopping trips exhibit significant synchronicity, particularly on the branch routes. These findings can provide valuable insights for zoning planning, construction of bicycle infrastructure, and formulation of sustainable urban transportation policies.

Towards a System Dynamics Framework for Human–Machine Learning Decisions: A Case Study of New York Citi Bike

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

A Matheuristic Approach Based on Variable Neighborhood Search for the Static Repositioning Problem in Station-Based Bike-Sharing Systems

In this paper, we study a novel static bike-sharing repositioning problem. There is a set of stations spread over a given area, each containing a number of operative bikes, damaged bikes, and free slots. The customers may pick up an operative bike from a station, use it, and return it to another station. Each station should have a target number of operative bikes to make it likely to meet customer demands. Furthermore, the damaged bikes should be removed from the stations. Given a fleet of available vehicles, the repositioning problem consists of designing the vehicles’ routes and calculating the number of operative (usable) and damaged (unusable) bikes that will be moved (loading instructions/loading policy) between stations and/or the depot. The objective is to minimize the weighted sum of the deviation from the target number of bikes for each station, the number of damaged bikes not removed, and the total time used by vehicles. To solve this problem, we propose a matheuristic based on a variable neighborhood search combined with several improving algorithms, including an integer linear programming model to optimize loading instructions. The algorithm was tested in instances based on real-world data and could find good solutions in reasonable computing times.