Bike Availability Research Articles

Cycling into the workshop: e-bike and m-bike mobility patterns for predictive maintenance in Barcelona’s bike-sharing system

Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona’s bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system’s predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

Joint optimization of the inventory routing problem considering the recycling of broken bikes in the bike-sharing system

Bike-sharing system has become an indispensable element of sustainable urban transportation, effectively resolving the "last mile" transportation challenge for city dwellers. A major daily operational task in these systems is planning a fleet to rebalance the bikes over time, ensuring the optimal availability of bikes and docks to users. Recycling is also a daily work with the an increase in the number of broken bikes. However, rebalancing or recycling operation is always regarded as an independent tasks. They are separately studied in existing papers. Thus, this paper develops an operational strategy for recycling broken bikes during the rebalancing process, and studies the combination of the station inventory and vehicle routing problems. First, an inventory routing model is constructed with the aim of minimizing the total costs including procurement, expected user loss, inventory and transportation costs. Then, a two-stage iterative algorithm is developed with both exact and heuristic algorithms. We use real-world data from Capital Bikeshare to test our proposed model and approach, which shows the two-stage iterative algorithm is efficient and outperforms existing solutions in reducing total costs. Finally, the sensitivity analysis is performed on key parameters such as the vehicle’s capacity, unit penalty costs for customer dissatisfaction events, unit inventory holding costs and the observation period of rebalancing. It shows that enterprises can reduce the total cost by altering vehicle’s capacity, reducing the unit inventory holding costs or changing the observation period of rebalancing.

A Study on the Prediction of Bike Availability Using Machine Learning

A Study on the Prediction of Bike Availability Using Machine Learning

Smart Mobility Solutions through Bike Sharing System Design: A Case Study of Jerash Archaeological Site

Intelligent transport systems, or ITS, are gaining significance in transportation planning due to its capacity to enhance traffic flow, reduce environmental impact. This paper offers a case study of the Jerash Archaeological Site in Jordan’s to planning and implementation of an ITS-enabled smart bike-sharing system. This system intends to assist tourists with a sustainable and environmentally friendly mode of transportation to the destination while also minimizing traffic congestion and air pollution. Smart bikes, wireless connectivity, and cloud-based data processing are combined in this technology to offer real-time information on bike availability, usage, and maintenance requirements. The article focuses into detail about the system’s framework, which covers both software and hardware components, as well as the execution and user experience. User feedback, performance, and environmental impact were used to evaluate the system in future. The resultsindicate that the ITS-enabled smart bike-sharing system has the potential to provide a sustainable and efficient transportation solution for visitors to the Jerash Archaeological Site while also reducing traffic congestion and air pollution. The paper discusses the potential for scaling the system to other tourist destinations in Jordan and beyond.

RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems

Bike-sharing systems have been deployed globally. One of the key issues for high-quality bike-sharing systems is to rebalance city-wide stations to maintain bike availability. Traditional strategies usually operate in fixed paths and limited capacities, lacking the flexibility to cope with the highly dynamic and context dependent riding demands, and usually suffer from high costs and long delays. In this work, we propose RedPacketBike, an incentive-driven, crowd-based station rebalancing framework to effectively recruit participants from hybrid fleets based on the accurate forecast of bike demand leveraging deep learning techniques. First, we propose a spatiotemporal clustering method to extract bike demand hotspots from fluctuating bike usage data. Then, we build a context-aware deep neural network named BikeNet to forecast the trends of bike demand hotspots, simultaneously modeling the spatial correlations. Finally, we propose a reinforcement-learning-based method to find optimal station rebalancing schemes by generating station rebalancing tasks with an integer linear programming (ILP) algorithm and allocating tasks to participants from hybrid fleets with dynamic incentive designs and reward expectations. Experiments using real-world bike-sharing system data validate the performance of our framework, achieving a demand forecast error below 4.171 measured in MAE, and a 17.2% improvement of station availability, outperforming the state-of-the-art baselines.

Integrated design of a bus-bike system considering realistic route options and bike availability

This paper examines the design of a transit system that integrates a fixed-route bus service and a bike-sharing service. Bike availability – the average probability that a traveller can find a bike at a dock – is modelled as an analytical function of bike utilization rate, which depends on travel demand, bike usage and bike fleet size. The proposed system also recognizes the greater flexibility provided by biking. Specifically, a traveller can choose between the closest bus stop and a more distant stop for access, egress or both. Whether the closest stop is a better choice depends on the relative position of the traveller’s origin and destination, as well as system design parameters. This interdependence complicates the estimation of average system cost, which is conditional on route choice. Using a stylized analysis approach, we construct the optimal design problem as a mixed integer programme with a small number of decision variables. Results from our numerical experiments show the integrated bus-bike system promises a modest but consistent improvement over several benchmark systems, especially in poorer cities with lower demand density. We find a large share of travellers, more than 20% in nearly all cases tested, opt not to use the nearest bus stop in an optimally designed system. The system also tends to maintain a high level of bike availability: the probability of finding a bike rarely drops below 90% except in very poor cities.

Safety Awareness of Cyclists in the Province of Laguna, Philippines

This study aims to document the cyclists’ safety awareness in the province of Laguna, Philippines in terms of cycling safety gears, bike maintenance, cyclist hand signals, and road signage. The descriptive research design was utilized using a self-made survey questionnaire distributed conveniently to 126 cyclist-respondents within the province. The data was interpreted and analyzed using basic, item analysis, and Pearson correlation. The survey found that cyclists are most aware of the majority of the indicators, but are more aware of some of the indicators used for cyclist safety gear, bike maintenance, and road signage. Likewise, it becomes easy for the cyclists to ask the cyclists hand signals, except the two items. It was observed that the civil status, medical health condition, cycling per hour, bike availability, cycling objectives and cycling related accidents of the cyclist – respondents have significant relationship to the cycling safety awareness.

Supply and Demand Analysis of a Free Floating Bike Sharing System

This article presents an analysis of the supply and demand of a FFBSS recently implemented in the city of Bologna, Italy. The main aspects treated in this paper are: analysis of bike availability; temporal analysis of FFBSS demand; calibration and validation of a novel model that predicts the number of daily trips per available bike. This model is based on a linear combination of several day attributes, including meteorological and day-type attributes. Moreover, an origin to destination analysis is generated showing the spatial distribution of FFBSS trips. The methods are applied to a scenario with almost a million GPS traces recorded between July and October 2018 by the FFBSS in Bologna. Findings could support FFBSS companies to better understand the fluctuation of both the transport demand and supply of this relatively recent transport mode, as to make more efficient decisions when distributing or relocating bicycles.

Dynamic demand-driven bike station clustering

As an eco-friendly transportation option, bike-sharing systems have become increasingly popular because of their low costs and contributions to reducing traffic congestion and emissions generated by vehicles. Due to the availability of bikes and the geographically varied bike flows, shared-bike operators have to reposition bikes throughout the day in a large and dynamic shared-bike network. Most of the existing studies cluster bike stations by their geographical locations to form smaller sub-networks for more efficient optimization of bike-repositioning operations. This study develops a new methodological framework with a demand-driven approach to clustering bike stations in bike-sharing systems. Our approach captures spatiotemporal patterns of user demands and can enhance the efficiency of bike-repositioning operations. A directed graph is constructed to represent the bike-sharing system, whose vertices are bike stations and arcs represent bike flows, weighted by the number of trips between the bike stations. A novel demand-driven algorithm based on community detection is developed to solve the clustering problem. Numerical experiments are conducted with the data captured from the world’s largest bike-sharing system, consisting of nearly 3000 stations. The results show that, with CPLEX solutions as the benchmark, the proposed methodology provides high-quality solutions with shorter computing times. The clusters identified by our methodology are effective for bike repositioning, demonstrated by the balance of bike flows among clusters and geographic proximity of bike stations in each cluster The comparison between clusters found in different hours indicates that bike sharing is a short-distance transportation mode. One of the key conclusions from the computational study is that clustering bike stations by bike flow in the network not only enhances the efficiency of bike-repositioning operations but also preserves the geographic characteristics of clusters.

A crowdsourced dynamic repositioning strategy for public bike sharing systems

<p style='text-indent:20px;'>Public bike sharing systems have become the most popular shared economy application in transportation. The convenience of this system depends on the availability of bikes and empty racks. One of the major challenges in operating a bike sharing system is the repositioning of bikes between rental sites to maintain sufficient bike inventory in each station at all times. Most systems hire trucks to conduct dynamic repositioning of bikes among rental sites. We have analyzed a commonly used repositioning scheme and have demonstrated its ineffectiveness. To realize a higher quality of service, we proposed a crowdsourced dynamic repositioning strategy: first, we analyzed the historical rental data via the random forest algorithm and identified important factors for demand forecasting. Second, considering 30-minute periods, we calculated the optimal bike inventory via integer programming for each rental site in each time period with a sufficient crowd for repositioning bikes. Then, we proposed a minimum cost network flow model in a time-space network for calculating the optimal voluntary rider flows for each period based on the current bike inventory, which is adjusted according to the forecasted demands. The results of computational experiments on real-world data demonstrate that our crowdsourced repositioning strategy may reduce unmet rental demands by more than 30% during rush hours compared to conventional trucks.</p>

Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: A case study of four London Tube strikes

Understanding the interactions between different travel modes is crucial for improving urban transport resilience, especially during times of disruption and transit failure. As a flexible and sustainable travel mode, bikeshare schemes are able to solve “first/last” mile problems in urban transit as well as provide an alternative to motorised traffic. This paper uses OD (origin and destination) trip data from the London Cycle Hire Scheme and temporal docking station bike availability data to explore the impact of four separate London Underground (Tube) strikes on bikeshare usage and behaviours. The results suggest that bikeshare usage generally rises in response to Tube disruptions, but the extent and nature of this rise in use varies according to the type of disruption. A novel measure of station pressure suggests that the scheme very quickly reaches saturated capacity and is unusable in certain parts of London during disruptions. A graph-based analysis reveals several changes in OD flow structures. This implies a modal shift from Tube to bikeshare and a change of route behaviours among bikeshare users. Weekday Tube strikes bring new behaviours and new OD pairs to the bike flow structures, whilst for weekend strikes existing patterns are consolidated. The corollary is that more heterogenous OD trip patterns are introduced by higher volumes of commuting trips and intense competition of cycles/docks. Cyclists are forced into using alternative (second or third preference) docking stations with new behaviours, and possibly users, as journeys that would otherwise be made via the Tube are made via bikeshare. This work comprehensively presents and compares the impacts of Tube strikes under varied circumstances and offers a detailed understanding of the changed cycling behaviours that could be used in transport planning and management.

The role of objective and perceived built environments in affecting dockless bike-sharing as a feeder mode choice of metro commuting

• Built environment effects on bike-sharing as a feeder mode of metro is examined. • Low agreement exists between objective and perceived measures of built environment. • Perceived built environment directly affects the usage of dockless bike-sharing. • Perception mediates the effects of metro accessibility, bus and bike availability. • Effects of built environment are moderated by gender, age, income, and location. The rapid growth of dockless bike-sharing (DBS) systems has attracted increased academic attention in the solutions to first- and last-mile problems. However, only a few studies have examined how the synergy between DBS and metro transit is affected by objective and perceived measures of built environment collectively. This study intends to fill this research gap by focusing on the effects of objective and perceived measures of built environment on DBS–metro integrated use for commuting trips. Results reveal that low agreement between the two measures of built environment and that the perceived measure is more likely to be directly associated with DBS–metro integration than the objective measure. Different built environment attributes may affect DBS–metro integration by unique paths. Moreover, individual characteristics (i.e., gender, age, and income) and location factor moderate the association between the built environment and DBS–metro integration. Particularly, built environment attributes related to transportation service are easier to be moderated than land use and cycling condition attributes. We conclude that the understanding of and interventions for the built environment as objectively measured are necessary but not sufficient for DBS–metro integration. Promoting the perception of the built environment among different population groups is also important for interventions.

Network and station-level bike-sharing system prediction: a San Francisco bay area case study

The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System (BSS) applying machine learning at two levels: network and station. Investigating BSSs at the station-level is the full problem that would provide policymakers, planners, and operators with the needed level of details to make important choices and conclusions. We used Random Forest and Least-Squares Boosting as univariate regression algorithms to model the number of available bikes at the station-level. For the multivariate regression, we applied Partial Least-Squares Regression (PLSR) to reduce the needed prediction models and reproduce the spatiotemporal interactions in different stations in the system at the network-level. Although prediction errors were slightly lower in the case of univariate models, we found that the multivariate model results were promising for the network-level prediction, especially in systems where there are a relatively large number of stations that are spatially correlated. Moreover, results of the station-level analysis suggested that demographic information and other environmental variables were significant factors to model bikes in BSSs. We also demonstrated that the available bikes modeled at the station-level at time had a notable influence on the bike count models. Station neighbors and prediction horizon times were found to be significant predictors, with 15 minutes being the most effective prediction horizon time.

Successfully Initiating a Bike Share Program in Smaller Communities: The College or University as a Focal Point

Successful bike share systems are more common in larger than smaller cities. People use bikes to run errands, shop, go out to eat, or tour the city. But what about smaller communities that make up so much of the United States? For the sake of access to transportation and the economic boost to smaller municipalities, it is important to implement a bike share program that provides its citizens with options for public transportation. Incorporating a bike share system should be considered in smaller cities or municipalities that contain colleges or universities, given the large available student population that would be interested in using bikes for their own transportation needs. The approach is to first build the foundation for bike share success among college students while methodologically planning to increase bike availability to sites with probable high participation usage, including locations centrally located to city commerce in the community. The purpose of this article is to present the foundation to implement a successful bike share program for those smaller cities and communities with a college or university. The benefits of a bike share partnership with a college campus, overall physical activity (PA) and health benefits, potential and current demographic groups to use the bike share scheme, economic benefits, environmental factors, dock versus dockless bike share programs, and the finances and logistics of a bike share scheme will be examined. Yearlong strategies and important considerations for a successful bike share program will be explored.

Unveiling Women’s Needs and Expectations as Users of Bike Sharing Services: The H2020 DIAMOND Project

Within the objectives of the H2020 DIAMOND project, the paper investigates women’s needs and expectations as users of the bike-sharing service managed by Syndicat Mixte Autolib et Velib Métropole in the territory of Paris Region-Petite Couronne (France). The paper presents a thematic literature review focused on gender inclusion in bike-sharing schemes. The proposed methodological approach is based on (i) Geographic Information Systems for the analysis of geolocated open datasets related to land, sociodemographic and mobility characteristics of the areas surrounding each docking stations. This was aimed at identifying a short list of suitable bike-sharing docking stations, which were further characterized through: (ii) structured proprietary data focused on travel demand; (iii) onsite observations focused on universal design indicators; (iv) survey questionnaires focused on women’s concerns, needs and expectations; and (v) social media data from Twitter focused on the opinion of the end-users. Results showed that women use the VELIB’s bike-sharing service much less than men (about 30% of the total number of users), since they are more concerned about the following issues: accessibility (e.g., availability of bikes at the docking stations, distance to the nearest station, type and quality of the cycle paths); safety and security (e.g., perception of danger and insecurity while cycling and using the current bicycle infrastructures); social constraints (e.g., perceptions and cultural stigmatization associated with cycling and bike-sharing); weather and topography (e.g., impact of weather and the urban terrain on cycling and bike-sharing). The final aim of the H2020 DIAMOND project is to support the definition of guidelines and policies for the inclusion of women’s needs in the design of future bike-sharing services.

Barriers and facilitators to bikeshare programs: A qualitative study in an urban environment

IntroductionBikeshare programs have emerged across the US to promote bicycling as an active mode of transportation that could potentially improve health and quality of life. However, bikeshare usage is low in some settings. The purpose of this qualitative study is to explore barriers and facilitators of bikeshare use and to identify potential strategies to increase participation in urban environments. MethodsFocus groups were conducted with urban bikeshare users in Birmingham, Alabama. Thematic analysis was guided by the PRECEDE model, which identifies predisposing (intrapersonal), reinforcing (interpersonal), and enabling (structural) factors related to a health program. ResultsIn the four focus groups, the most prominent barriers to utilization identified were age, disability, stigma, and lack of awareness of programs (intrapersonal), having small children (interpersonal), lack of safety and bicycling infrastructure, bikeshare characteristics such as location, time constraints, cost, ease of use, and availability of bikes (structural). The most prominent facilitators included enjoyment (intrapersonal), peer support (interpersonal), and convenience, location, cost, and availability of electric bikes (structural). Recommendations to increase usage were primarily structural, such as infrastructure improvement, incentive programs, awareness and safety campaigns, expansion to neighborhoods and trails, increasing time users can ride before docking, and providing more electric bikes. ConclusionTo increase bikeshare use in urban settings, use-restricting policies must be addressed.

How fair is the allocation of bike-sharing infrastructure? Framework for a qualitative and quantitative spatial fairness assessment

Abstract How fair is the allocation of the infrastructure of a bike-sharing system (BSS)? Design guidelines for BSSs focus on optimizing the demand but not on who is served who is not. Areas where mainly young Caucasians, highly educated people live, and that have high access to community resources, presented greater access to BSS. Based on the concept of spatial fairness and its subjectivity, we developed a framework for a qualitative and quantitative assessment to help decision-makers and the general public evaluate the allocation of BSS infrastructure. First, from the general concept of justice, we developed our definition of spatial fairness assessment based on the rules of spatial equity, equality, and efficiency. Then, we developed a qualitative and quantitative spatial fairness assessment of BSS. The qualitative assessment aims to understand how underprivileged people perceive the spatial fairness of BSSs taking as case study non-motorized households in Strasbourg feeling socially excluded. The quantitative assessment helps to numerically determine which distribution rule (equity, equality, efficiency) the infrastructure of a BSS follows. This assessment was applied in residential blocks inside the service area of the hybrid BSS in Munich, Germany. We developed a concept of availability as an accessibility indicator. As social indicators, we considered social milieus, access to other opportunities (e.g. health, education), and developed a deprivation index that is a combination of those two. As a result of the qualitative assessment, non-motorized individuals who felt socially excluded were less likely to talk about BSS at all. Furthermore, bicycles’ availability in the bike-sharing system in Munich matched the efficiency and equity rule, although lower availability of bikes correlates to residential blocks where traditional-oriented social groups live. Policy makers, stakeholders, urban and transport planners, and the general public have now available 1) the perception of a group of the underprivileged population about BSS, and 2) a quantitative methodology to identify which distribution rule(s) the infrastructure of a BSS follows and which social groups are spatially advantaged or disadvantaged by it. Further research may be oriented to apply the approach in the same city or applying them to more case studies.

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

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

The Impact of Smartphone Apps on Bike Sharing Systems

Bike-sharing systems are exploding in cities around the world as more people are adopting sustainable transportation solutions for their everyday commutes. However, as more people use the system, riders often encounter that bikes or docks might not be available when they arrive to a station. As a result, many systems like CitiBike and Divvy provide riders with information about the network via smartphone apps so that riders can find stations with available bikes. However, not all customers have adopted the use of these smartphone apps for their station selection. By combining customer choice modeling and finite capacity queueing models, we explore the impact of the smartphone app technology to increase throughput and reduce blocking in bike sharing systems. To this end, we prove a mean-field limit and a central limit theorem for an empirical process of the number of stations with $k$ bikes. We also prove limit theorems for a new process called the ratio process, which characterizes the proportion of stations whose bike availability ratio lies within a particular partition of the interval [0,1]. For the mean field limit, we prove that the equilibrium exists, is unique, and that the stationary distribution of the empirical measure converges to a Dirac mass at the same equilibrium, thus showing an interchange of limits result ($\lim_{t\rightarrow \infty}\lim_{N\rightarrow \infty}=\lim_{N\rightarrow \infty}\lim_{t\rightarrow \infty}$). Our limit theorems provide insight on the mean, variance, and sample path dynamics of large scale bike-sharing systems. Our results illustrate that if we increase the proportion of customers that use smartphone app information, the entropy of the bike sharing network is reduced, and riders experience less blocking in the network.

Introduction: 2018 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research

Competition for the 2018 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research provided the six finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes quality and originality of mathematical models and clarity of written and oral exposition. If you have ever rented a bike from Motivate, the largest provider of bike-sharing systems in the United States, you have benefited from the analytical work done by the winning team from Cornell University, which improves the allocation of docks to stations and created the Bike Angels program, an incentive scheme to crowdsource rebalancing, thus improving bike availability in San Francisco and New York City. The remaining papers describe work on developing a first-of-a-kind market design for the reallocation of fishing shares in South Wales, using primal-dual algorithms for omni-channel retail fulfillment, improving the seat-allocation process for admissions to engineering colleges in India, using structured and unstructured data in predictive modeling for repairing malfunctioning appliances, and rescuing missing tourists in nature reserves in China.