Public Bike-sharing Systems Research Articles

Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing.

Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city’s mobility.

Deciphering the relationship between bikesharing and public transit: Modal substitution, integration, and complementation

Public bikesharing systems (PBSs) have witnessed rapid growth in the last decade. Bikesharing has been found to have a mixed relationship with public transit, but such a relationship has not been well quantified. This research uses real bikesharing trip data in four cities that host the largest bikesharing fleets in the U.S. to investigate when, where, and to what extent bikesharing trips substitute, integrate with, and complement public transit, and what factors contribute to the modal integration between bikesharing and public transit. The results show that it is not where the bikesharing trip takes place that predominantly determines its relationship with public transit but rather when the trip happens (weekday or weekend and what time period during the day) and who takes the trip (a subscriber or customer). Regression models show that modal integration is associated with shorter trip duration, higher public transit service frequency, and higher share of subscribers on weekdays. The relatively high share of bikesharing trips that integrate with public transit helps to improve the experience of riding public transit and in the long term, enhances urban mobility and reduces automobile dependency with multiple beneficial societal and environmental effects.

Balancing public bicycle sharing system by defining response rates for destinations

Public Bicycle Sharing System (PBSS) is used as a way to reduce traffic and pollution in cities. Its performance is related to availability of bicycles for picking up and free docks to return them. Existence of different demand types leads to the emergence of imbalanced stations. Here, we try to balance inventory of stations via defining maximal response rates for each type of rental request. If the maximal response rate for a destination is lower than 100 percent, a part of the proposed destination requests is rejected in the hope of balancing the inventory. The goal is to minimize the mean extra inventory and the mean rejected requests by providing proper amounts of the maximal response rates. An approximation method named as Mean Value Analysis (MVA) is used to develop a genetic algorithm for solving the problem. Different examples are worked through to examine the applicability of the proposed method. The results show that the proposed policy leads to a significant improvement and reduces the users’ dissatisfaction.

Optimal Relocation Strategy for Public Bike System with Selective Pick-Up and Delivery

Public bike-sharing systems in many countries provide convenience as users can rent or return a bike freely at any station, but this may cause a demand–supply imbalance of the bike inventory for certain stations. To solve this issue, this research develops a bike-relocation strategy including both demand prediction and relocating route optimization. First, the bike demand is estimated by a least-square boosting algorithm, and numbers of relocating bikes are decided comparing bike inventories at each station. Second, based on predicted demand, the number of transporting vehicles and relocating routes are optimized by genetic algorithm. The strategy aims to minimize service vehicle numbers and relocating time with selective pick-up and delivery. The proposed strategy is evaluated by applying it to a real-world public bike system in Gangnam-district in Seoul, South Korea, and the results show the system can be improved significantly. Specifically, the bike demand satisfaction ratio increases from 0.87 to 1.00 in the morning peak hour, which shows that the proposed strategy better satisfies the bike demand. The uniformity of spare inventory is also improved, as a coefficient of variation decreases from 0.73 to 0.56. The reasonableness index, which reflects a sufficient number of bike stands, indicates 87% and 92% stations have a proper number of stands at morning peak hour and 24 h, respectively, with respect to predicted demand. The results show that the bike system with the proposed strategy has more reliability with stable inventory, and the operating cost could decrease with fewer relocating vehicles and optimized vehicle routes.

Investigating the effects of a shared bike for tourism use on the tourist experience and its consequences

ABSTRACT As public bike-sharing systems have been widely set up in cities around the world, a shared bike for tourism use can enhance tourists’ experience at destinations and lead to tourists’ post-visit evaluations. Thus, this research explores the attributional effects of the value of using shared bikes (namely, instrumental value and affective value) on tourists’ emotional experience (namely, hedonics and perceived uniqueness) as well as on satisfaction and destination loyalty. We obtain a sample of 302 tourists using shared bikes as transport modes during their visits to examine the proposed relationship model. Results show that both instrumental and affective values of shared bike use positively relate to hedonics and perceived uniqueness experiences. While both hedonics and perceived uniqueness have positive effects on satisfaction, only perceived uniqueness shows a positive effect on destination loyalty. The findings of multi-group analyses indicate that the direct effects of the tourism experience on destination loyalty are significantly positive for the low-motivation group but not for the high-motivation group. Empirical implications and recommendations for future research are also discussed herein.

Real-time prediction of public bike sharing system demand using generalized extreme value count model

Abstract Public Bike Sharing Systems (BSSs) are becoming increasingly popular in recent times. Both the BSS operators and the customers can benefit from the large digital data portals that continuously record the state of the BSS. In this context, the current study developed generalized extreme value (GEV) count models that can predict hourly bike arrivals and departures at each station while accounting for time-of-day, weather, built environment, infrastructure, temporal, and spatial dependency factors. The proposed models were used to analyze the demand patterns in the Capital Bikeshare system and were found to predict the demand at both aggregate and disaggregate levels with reasonable accuracy. Specifically, the total demand in the entire system was predicted within 5% margin of error whereas 75% of the station-level arrival and departure predictions in the next one hour were within a margin of one from the observed counts. The proposed modeling system is useful (a) to BSS customers to better plan their travel based on expected bike and dock availability at the origin and destination ends of their BSS trips, and (b) to BSS operators to anticipate the future demand and optimize their rebalancing plans.

Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips

A sustainable and robust stream of revenues is an essential element of the economic sustainability of bikeshare systems. To this effect, there is an unrelenting need to quantify and understand the impacts of pricing policies and operational considerations on a bikeshare system’s revenue and ridership. A notable gap exists in literature on studies related to the impact of changes in pricing policy on ridership and revenue. The primary objective of this research was to assess the impact of the introduction of a $2 per trip single-trip fare (STF) product for casual users by Capital Bikeshare (CaBi), the bikeshare system in the Washington DC metro area, on its ridership and revenue. Two-year ridership and revenue transaction datasets of CaBi were used in the analysis. A substantial data curation effort was undertaken to fuse elements between the two large transactional datasets. The effort not only facilitated the impact assessment but also enhanced the value of ridership dataset by identifying trips made by casual users by the type of fare product they purchased. The casual bikeshare user revenues were traced to individual bikeshare stations where trips originated, which allowed the comparison between revenues and ridership ‘before’ and ‘after’ the launch of STF at the station level. Over 22 million records on individual bikeshare trips and revenue transactions for 3 years and 330 bikeshare stations were analyzed. The results showed a statistically significant increase in casual user ridership after the introduction of STF. There was a statistically significant decrease in revenue per ride. Statistical tests indicated that these changes might be attributable to the introduction of STF. The methods used in this study are transferable. They can be used for curating ridership data and studying the impacts of bikeshare pricing policy changes on system usage and revenues at various public bikesharing systems with similar characteristics as Capital Bikeshare.

서울시 공공자전거 공유시스템(PBSS)의 시공간적 이용 패턴 분석 - 서울시 여의도동을 중심으로 -

Various policies and studies regarding use of PBSS (Public Bike Sharing System) and Programs (PBSP) have been conducted worldwide as the number systems or programs has increased. Although various phenomena and demands have been generated by the use of PBSS in everyday life, the majority of research and the policies in South Korea have been implemented focused on commuting life. The purpose of this study aimed to understand various PBSS demands using PBSS usage data in 2018 in the Yeouido districts through classifying usage patterns and analyzing features. The rental stations were classified into three types based on weekday/weekend usage rates. The usage of Yeouido’s PBSS accounted for 4.3% of the total usage in Seoul Metropolitan City, while the number of PBSS rental stations accounted for 2% of all rental stations in the Seoul urban areas. Rental stations with a higher weekday utilization rates showed high utilization rates in all four seasons and were mainly distributed in work and residential areas. Other stations showed a concentrated usage pattern in spring (April-May) and autumn (September-October) seasons, and their locations were close to the entrance of nearby parks. Besides, renting and returning were often concentrated at certain rental stations for high weekend utilization as compared to the pattern of high weekday usage. Therefore, PBSS management and programs should be operated to reflect various usage demands rather than uniform PBSS operations. The result of this study is meaningful to provide basic data for effective PBSS operation by monitoring the demand for PBSS usage in spatio-temporal terms.

Balancing public bicycle sharing system using inventory critical levels in queuing network

Public Bicycle Sharing System has recently been developed and installed in many cities as a workable and popular transportation system. There are still some noticeable challenges associated with the operation of the system, like responding to all renting requests and all demands of vacant docks for returning bikes. Balancing the inventory of stations is necessary to minimize the rejected demands of bikes and the empty lockers. Here, critical levels are defined to control requests of different routes in which a demand of a specified destination is accepted if the inventory of the original station is higher than the route’s critical level. The capacity of stations and the fleet size are determined in addition to the different critical levels considering a constraint for the fleet size of the system. After developing the model using the Jackson network, a genetic algorithm is developed to obtain the proper amounts of variables for balancing the inventory of the system as much as possible. Finally, different examples are worked through to evaluate the applicability of the proposed method.

Planning of Public Bicycle (Bike) Sharing System (PBSS): A Case Study of Surat City

In recent years cycling has seen a new beginning, triggered by the thrust towards greater environmental consciousness. In cities where cycling shares are low, efforts are being made to promote cycling as a viable mode of transport. As an emerging economy, Surat is now facing urban challenges that are more complex than Delhi and Mumbai especially in the central zone, CBD area of Surat. Due to congested streets and other limitations, it is not feasible for the provision of other transportation modes in the center area of it and for this reason city planners are planning for providing Public Bicycle Sharing System (PBSS) in Center zone. To understand factors starring to bike sharing adoption and hindrance to adoption, the IUTI-SVNIT Team conducted an intercept survey in the Central zone between January to March 2017. The HH survey reveals that about 75% two-wheeler users and 78% car users are willing to use PBSS if implemented. The results from analysis give an idea about if PBS system is launch then daily commuters, Pedestrian etc get benefits & willing to use this types of NMT facility in their daily trips. The study identify total 81 potential docking station with 1920 fleet size using maximum coverage area method and minimum impedance method in three phase in GIS environment. Author expect that practitioner and researchers will find study useful to overcome the various issues such as short-term parking problem, reducing vehicular congestion, increase livability etc. of dense urban areas.

Identification of Enablers and Barriers for Public Bike Share System Adoption using Social Media and Statistical Models

Public bike share (PBS) systems are meant to be a sustainable urban mobility solution in areas where different travel options and the practice of active transport modes can diminish the need on the vehicle and decrease greenhouse gas emission. Although PBS systems have been included in transportation plans in the last decades experiencing an important development and growth, it is crucial to know the main enablers and barriers that PBS systems are facing to reach their goals. In this paper, first, sentiment analysis techniques are applied to user generated content (UGC) in social media comments (Facebook, Twitter and TripAdvisor) to identify these enablers and barriers. This analysis provides a set of explanatory variables that are combined with data from official statistics and the PBS observatory in Spain. As a result, a statistical model that assesses the connection between PBS use and certain characteristics of the PBS systems, utilizing sociodemographic, climate, and positive and negative opinion data extracted from social media is developed. The outcomes of the research work show that the identification of the main enablers and barriers of PBS systems can be effectively achieved following the research method and tools presented in the paper. The findings of the research can contribute to transportation planners to uncover the main factors related to the adoption and use of PBS systems, by taking advantage of publicly available data sources.

The death and rebirth of bikesharing in Seattle: Implications for policy and system design

What factors determine the ridership of micro-mobility systems such as bikesharing or scooter sharing? This paper presents a case study on bikesharing in Seattle, USA, which has the distinction of being both one of the few cities in the world where a modern public bikesharing system (named Pronto in Seattle) has been shut down, and the first US city to permit dockless bikesharing operations. These dockless services produced more bikesharing trips in four months than Pronto did in its entire two-and-a-half-year run, and in their first year 11 times more rides than Pronto had in its first year. The contrast in performance between these two systems provides a unique opportunity to test alternative theories for why Pronto’s ridership struggled where others have succeeded, offering more general insights into how system designers and regulators can avoid repeating the pitfalls of Seattle’s original bikesharing launch. This case study triangulates qualitatively between popular press reports, interviews with key stakeholders, an original survey of bikesharing users, and ridership data from multiple bikesharing systems to evaluate the contributions of eleven factors to Pronto’s low ridership. It concludes that the most important reasons for Pronto’s struggles were inadequate system scale, station density, geographic coverage area, ease of use, and pricing structure. Critically, these factors all represent explicit choices made by system designers and policymakers, rather than local market or environmental factors beyond their control. The higher ridership of dockless bikesharing in Seattle appears primarily due to differences in these factors, which are not necessarily exclusive to dockless services. The paper closes with a discussion of how policymakers can avoid condemning emerging micro-mobility services to Pronto’s fate of low ridership.

A continuous approximation model for the optimal design of public bike-sharing systems

During the last decade, public bike-sharing systems have gained momentum and popularity. Many cities worldwide have put their trust in bike-sharing to promote bicycle use and move towards more sustainable mobility. This paper presents a parsimonious model from which to derive the optimal strategical design variables for bike-sharing systems (i.e. the number of bicycles, the number of stations and the required intensity of rebalancing operations). This requires an integrated view of the system, allowing the optimization of the trade-off between the costs incurred by the operating agency and the level of service offered to users. The approach is based on the modelling technique of continuous approximations, which requires strong simplifications but allows obtaining very clear trade-offs and insights. The model has been validated using data from Bicing in Barcelona, and the results prove, for example, the existence of economies of scale in bike-sharing systems. Also, station-based and free-floating system configurations are compared, showing that free-floating systems achieve a better average level of service for the same agency costs. In spite of this, the performance of free-floating systems will tend to deteriorate in the absence of a strong regulation. Furthermore, if electrical bikes are used, results show that battery recharging will not imply an active restriction in station-based configurations. In conclusion, the proposed modeling approach represents a tool for strategic design in the planning phase and provides a better understanding of bike-sharing systems.

Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects.

Solving the supply–demand imbalance is the most crucial issue for stable implementation of a public bike-sharing system. This gap can be reduced by increasing the accuracy of demand prediction by considering spatial and temporal properties of bike demand. However, only a few attempts have been made to account for both features simultaneously. Therefore, we propose a prediction framework based on graph convolutional networks. Our framework reflects not only spatial dependencies among stations, but also various temporal patterns over different periods. Additionally, we consider the influence of global variables, such as weather and weekday/weekend to reflect non-station-level changes. We compare our framework to other baseline models using the data from Seoul’s bike-sharing system. Results show that our approach has better performance than existing prediction models.

Algoritma Simulated Annealing untuk Optimasi Rute Kendaraan dan Pemindahan Lokasi Sepeda pada Sistem Public Bike Sharing

The public bike-sharing system has a problem where the number of bicycles at the docking station needs to be balanced to ensure system user satisfaction. The usual solution is to distribute bicycles so that system users can still park for locations that are usually full of bicycles or pick up bicycles at locations that normally lack bicycles. The purpose of solving this problem is to get a vehicle route with the total operating costs of the vehicle. The full vehicle operating costs are associated with the full time taken by the vehicle to distribute the bicycle. Besides, there are also penalty fees related to the lack of bikes or parking slots at the time of operation of the public bike-sharing facility. In this study, two variations of the simulated annealing (SA) algorithm were developed to solve the SBRP problem called SA_BF and SA_CF. The data used comes from a Velib bike-sharing system case study in Paris, France. The results of the experiment show that both the SA_BF and SA_CF algorithms succeeded in solving SBRP. This algorithm has an average difference of 2.21% and 0.36% of the Arc-Indexed algorithm (AI) from previous studies in the first dataset. As for the second dataset, Tabu Search algorithm, SA_BF and SA_CF obtained an average difference of 0.65%, 1.08% and 0.38% of the optimal results.

Who are the 'super-users' of public bike share? An analysis of public bike share members in Vancouver, BC.

Public bike share programs have been critiqued for serving those who already bicycle, or more well-off individuals who already have a multitude of transportation options. While substantial research focuses on characteristics of public bike share members, it often overlooks their intensity of use which may relate more directly to transport and health gains. In this study we link system data with member survey data to characterize “super-users” of Vancouver's public bike share system. We used system data from September 1, 2016–August 31, 2017 to calculate member-specific trip rates (trips/month). We linked system data to demographic and travel data for members who completed an online survey in 2017 (1232 members who had made 89,945 trips). We defined super-users as those who made 20 or more trips/month. We used a logistic regression to model demographic and travel characteristics associated with super-users as compared to regular users. Of the 1232 members, 204 were super-users. Super-users made 47% of the trips and had a median trip rate of 29.3 trips/month. In adjusted models, super-users were more likely to be young, male, have household incomes below $75,000, and live and work near bike share docking stations. Super-users had fewer transportation options than regular users, with lower odds of having a personal bike or car share membership. Amongst members, we found a distinct demographic profile for super-users relative to regular users, suggesting that usage is an important consideration when quantifying transport and health gains, and the resulting equity implications of public bike share programs.

Enhanced fuzzy‐logic‐based power‐assisted control with user‐adaptive systems for human‐electric bikes

With the increasing attention to sustainability, several studies have attempted to develop human-electric bikes which provide the power to assist the human rider from an electric motor when the rider is pedalling. For such systems, the design of the power-assisted controller plays a critical role to ensure the comfort and safety of the rider. In practical situations, a bike may be utilised by multiple riders such as the users of bike-sharing systems. However, the influence of different human riders is seldom discussed in the power-assisted controller design. In this study, an enhanced fuzzy-logic-based control approach with a user-adaptive mechanism has been presented to online adjust the fuzzy parameters for power-assisted regulation so as to ensure the riding comfort and safety. Simulation results display that the proposed user-adaptive approach improves the comfortability and safety for the riding in urban areas. It is believed that the techniques presented in this study could further be adopted in public bike-sharing systems for short-range city commuters.

Spatial-temporal characteristics of green travel behavior based on vector perspective

In this paper, the spatial-temporal characteristics of green travel behavior in public bike-sharing system are studied. From the vector perspective, a coarse graining method of green travel direction is proposed, the methods and steps of analyzing the spatial characteristics and temporal window of green travel behavior are established, which opens up new horizons in the study of green travel behavior, develop the existing coarse granulation methods and temporal network theory, and provides a new way of route optimization and high-frequency task window recognition for the dynamic rebalancing scheme. At the same time, we conduct an empirical analysis by the travel data of the public bike-sharing system in Nanjing from March 20, 2017 to March 26, 2017, and verify the dynamic spatial characteristics and time-varying of green travel behavior based on vector perspective.

Assessing service and price sensitivities, and pivot elasticities of public bikeshare system users through monadic design and ordered logit regression

Pricing is one of the major factors that affects ridership and revenue of the bikeshare systems. This paper examines bikeshare users' sensitivity to changes in price and preferences on service by conducting an intercept survey at Capital Bikeshare (CaBi) stations. Monadic price testing approach was used to design survey questions and elicit responses on sensitivity of users to price changes in fare products. Ordered logit regression results indicated that higher household income groups and ‘White’ users are less sensitive to price compared to other income groups and other races/ethnicities, respectively. An illustrative application of the demand curves is presented, which showed that low-income groups are more sensitive to price than the middle and high-income groups. White users were found to be approximately 20% less price sensitive than other races for both casual users purchasing single-trips and annual members. The price elasticities revealed that females are about 30% and 10% more price sensitive than males for single-trip fare (STF) and annual membership, respectively. Also, sightseeing trips are 30% less price sensitive than work trips for STF purchasers. An illustrative application of income-based elasticities indicated that reducing the STF to $1.50 from the current $2.00 per trip and annual membership to $73.00 (from $85 per year) were found to improve both ridership and revenue. It is expected that the contributions from this study would provide insights and guidance on evaluating future pricing policy changes at various bikeshare systems.

Prioritising barriers for successful implementation of public bicycle-sharing system

Among Asian countries, India has a vast potential to adopt a public bicycle-sharing system (PBSS) as a sustainable mode of transport after China. However, various barriers pose as hindrances in the implementation of PBSS in Indian urban areas. The primary objective of this paper is to identify and prioritise PBSS barriers, which inhibit the implementation of PBSS. For this study, a total of 31 barriers out of 41 reported barriers worldwide are shortlisted and ranked using the fuzzy analytic hierarchy process. A group of experts assisted in ranking the barriers by using a pair-wise comparison survey. The main contribution of this paper is to identify critical barriers and to rank them according to the Indian scenario. These findings can help practitioners, municipal engineers, urban planners and decision makers to formulate PBSS planning policy, which will overcome the PBSS implementation issues.