Public Bike-sharing Systems Research Articles

Will they ride them back? User behavior on public bikesharing schemes: Case study of Hangzhou, China

In order to study user behavior to support the management of Station-based Bikesharing Systems (SBS), Free-floating Bikesharing Systems (FBS), and the whole public bikesharing system, this study scrutinized factors that influence bikeshare-user patterns of one-way trips or round trips and examined the use frequency characteristics including both systems. Based on the data collected by questionnaires, descriptive analysis shows that the user profile of education background, private vehicle ownership, riding days during a week, departure time of day, trip type, mode transfer and service satisfaction for both systems is similar. To detect the factors associated with cycling for a round-trip versus one-way, this study uses step-wise logistic regression analysis to compare the difference between the targeted users. Putting samples of the two systems together and applying an ordinal logistic regression model, the outcomes show that as the increase of use frequency, trips are more likely to be one-way trips, generated by SBS users. They are likely to work at state-owned enterprises, having travel purposes more likely related to family or personal affairs rather than work affairs and owning cars. Finally, recommendations are given for operators of both schemes as well as government organizations responsible for supervising these schemes.

A Public Value-Based, Multilevel Evaluation Framework to Examine Public Bike-Sharing Systems. Implications for Cities’ Sustainable Transport Policies

Abstract This article proposes a multilevel bike-sharing assessment framework based on the concept of public value. This approach makes it possible to combine customer satisfaction with the transport service system with determinants of demand for bicycle services in the form of value. The framework aims to evaluate the parameters of public bike systems (PBS) that determine user value, and that co-create user value, system value, and social and ecological value, to identify the characteristics of the bicycle that need improvement in order to meet users’ needs and optimize quality. The framework uses empirical verification through satisfaction surveys of PBS users in Lodz, Poland. The results of the study were subjected to factor analysis, which revealed four groups of factors that satisfy public bike users: (1) impact on the health, environment, mobility and traffic in the city, (2) reliability, and comfort, (3) intramodality, (4) price and technical availability.

Air Pollution and Public Bike-Sharing System Ridership in the Context of Sustainable Development Goals

A bicycle-sharing system (BSS) has been implemented in Seoul, South Korea to promote green transportation policy as a Sustainable Development Goal (SDG) to mitigate climate change, reduce traffic jams, and promote physical activity. However, the concentration of air pollutants in Seoul often exceeds the standards of the World Health Organization, thereby creating a conflict with SDG 3 (Health). Therefore, it is important to recognize the trade-offs between actions targeted at SDGs as they might offset each other. In this context, a primary concern is investigating how the behavior of BSS users regarding outdoor air pollution appears. This study explores the relationship between ambient air pollution and the behavior of BSS riders in Seoul. We conducted a time-series analysis of associations between particulate air pollution and participation in the BSS. We used generalized additive models, adjusted for mean temperature, humidity, rainfall, day of the week, long-term trends, and seasonality to construct an exposure–response relationship. We observed a nonlinear relationship between increasing air pollution and bicycle ridership. This study method can be used as a basis for similar analyses to investigate BSS policies in other cities.

External Environmental Analysis for Sustainable Bike-Sharing System Development

The paper introduces a discussion regarding the development of a public bike-sharing system, considering random factors, based on selected external environmental analysis methods. The global energy crisis is forcing scientists to continuously improve energy-efficient sustainable methods and scientific solutions. It is particularly important in transportation since transport activities and the constant increase in the number of vehicles have a large share in global energy consumption. The following study investigates the social, technological, economic, environmental, and political aspects of bike-sharing systems in cities. The research purpose of the article is to select the most important macro-environmental factors and their mutual interaction influencing the sustainable development of bike-sharing systems based on the Polish cities case study. The evaluation was carried out through expert methods with STEEP environmental analysis, evaluation of factors with the weighted score, and structural analysis method with MICMAC computer application. The classification of key factors influencing the development of a bike-sharing system has divided them into five groups. It can support public transport service providers and organizers. This can optimize the planning process with decision-making based on future environmental trends.

Bike-sharing systems: Effects on physical activityin a Spanish municipality

Introduction: Urban mobility (such as walking or biking) and public Bike-Sharing Systems (BSS) have begun to emerge through programs and services that drive change from the strong culture of dependence on motor vehicles in most developed countries. At the international level, the 2030 Agenda of the United Nations (UN) opted to establish objectives for sustainable development, addressing the need to establish changes in urban mobility. The World Health Organization (WHO) has concluded that physical inactivity and being overweight / obesity are the fourth and fifth risk factors for non-communicable diseases in the world, respectively. The objective of this study is to know if a BSS has been able to serve as a means of promoting Physical Activity (PA) in the urban environment. Material and Methods: A quantitative and longitudinal study has been designed with data collection and analysis from July 2009 to January 2012. The sample consisted of a total of 3,268 users and a total of 59998 observations were recorded (Men: n = 42,411; Women: n = 17,587). SPSS 21.0 and ANOVA were used for statistical analysis. Results: Users between the ages of 20 and 44 have spent an average of 30.7 minutes, between 45 and 64 years an average of 36 minutes and between 65 and 79 years an average of 51.1 minutes. Conclusions: The data suggest that the WHO (2010) PA recommendations may be being met. BSS can favor the practice of PA in the urban environment, and thus, be able to promote less polluted and traffic-congested environments. Men from 65 to 79 years old have had the most average minutes of use. The routes that took the most minutes were those that go between the same stations and those that pass between stations on the urban outskirts.

Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.

The impact of gasoline shortages on public bike-sharing systems

PurposeThis paper aims to analyze the effect of a recent disruption of Mexico's gasoline supply chain on the usage of public bike-sharing systems in Mexico City and Guadalajara.Design/methodology/approachThe authors use a detailed data set to understand the usage patterns of Ecobici and Mibici. The authors assess both systems with a differences-in-differences econometric model using the least popular stations as a control group.FindingsThe authors find that the number of rides increased significantly shortly after the event because less popular stations became more utilized.Social implicationsThe authors show that when the effects of gasoline shortages were noticeable, usage rates increased in Guadalajara and Mexico City, but the rise primarily came from the users selecting more bikes from the less popular stations. Therefore, the authors show that citizens in both cities regarded bike-sharing as an adequate means of transportation, maximizing system usage during a disruptive time. This finding suggests that cities should invest in improving public bike-sharing systems to reduce carbon emissions and increase their population's well-being.Originality/valueThe authors use a publicly available data set to understand how citizens answered to a major disruption. Furthermore, this is one of the first papers that align supply chain risk management with sustainable transportation and analyzes its effects on citizen behavior in a Latin American setting.

Bi-objective optimization for customers’ satisfaction improvement in a Public Bicycle Sharing System

In recent years, the popularity of the Public Bicycle Sharing System (PBSS) for urban transportation is increasing. The fleet size of the system and the capacity of its stations are some key factors in establishing a successful sharing system. These factors affect the number of rejected demands and the lack of free docks for returning bicycles because of demand variation at stations. This paper presents a bi-objective optimization solution for PBSS in which the first objective function minimizes the mean number of rejected requests for renting bikes and free docks for returning bikes per each satisfied demand. In other words, this function tries to minimize the mean number of dissatisfied users per each replied demand for renting. The second objective function minimizes the total number of bicycles and docks of the system. The goal is to minimize users’ dissatisfaction with the least possible fleet size considering capacity constraints. The model is discussed under Jackson Network and the Mean Value Analysis (MVA). Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to solve and examine the efficiency of the proposed model via different numerical examples.

Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

Research on flourishing public bike-sharing systems has been widely discussed in recent years. In these studies, many existing works focus on accurately predicting individual stations in a short time. This work, therefore, aims to predict long-term bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas. To address the problem, we propose LDA (Long-Term Demand Advisor), a framework to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for original and newly established stations, we propose several feature extraction methods and an algorithm to model the correlations between urban dynamics and long-term demands. Our work is the first to address the long-term demand of new stations, providing the government with a tool to pre-evaluate the bike flow of new stations before deployment; this can avoid wasting resources such as personnel expense or budget. We evaluate real-world data from New York City’s bike-sharing system, and show that our LDA framework outperforms baseline approaches.

Efficiency Comparison of Public Bike-Sharing Repositioning Strategies Based on Predicted Demand Patterns

Public bike-sharing systems are used worldwide, and the imbalance between supply and demand for bicycles and operational inefficiency is becoming increasingly severe. For a system to operate efficiently, it is necessary to relocate bicycles among rental stations to minimize a lack of bikes at the station causing unmet demand. Recent studies have presented various repositioning strategies for bike-sharing systems and compared their efficiency. However, little consideration has been paid to the strategy of the spatial and temporal patterns of bike-sharing demand and the inventory level. This study aims to analyze the spatiotemporal patterns of the forecasted demand for the bike-sharing system and to compare the efficiency of different repositioning strategies to choose the most efficient one. We use three repositioning strategies with different additional constraints related to unbalanced stations and present computational results with real data in Seoul. Two indices represent the temporal variation of predicted inventory at each station and the coefficients of the spatial variation for hourly unmet demand. Linear classifiers are derived by linear discriminant analysis to classify the efficiency of each strategy according to developed indices. The study reveals that adding constraints of imbalanced stations to the strategy according to the spatiotemporal characteristics of forecasted inventory can help to reduce unmet demand. The result of this study enables proactive decision-making using proposed indices in operating bike-sharing systems and contributes to improving the efficiency and reliability of systems.

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.

Using multivariate adaptive regression splining (MARS) to identify factors affecting the performance of dock-based bikesharing: The case of Chicago’s Divvy system

This study explores factors contributing to the uneven success of past expansions of dock-based public bikesharing systems, in which middle- and - higher-income neighborhoods have tended to benefit considerably more than poorer neighborhoods. After a review of the differing performance of the three phases of expansion by Chicago's Divvy bikeshare system, this study uses multivariate adaptive regression splining (MARS) to select among more than 100 community- and station-level factors to explain variations in Divvy system usage at the station level. MARS demonstrates that neighborhood racial and ethnic diversity, proportion of condominium units, and job accessibility to public transit are strongly and positively correlated with total annual station trips, whereas percentage unemployed, average distance to Divvy stations, and percentage of residential foreclosures are negatively correlated. Model results are compared with those of earlier studies to foster insights into ways to more accurately predict the use of bikesharing systems across urban neighborhoods.

Prioritizing Facilitators for Successful Implementation of PBSS in Indian Urban Areas Using BWM Method

The demand for mobility is increasing day by day along with the rise in population. In the absence of clear guidelines and policy regarding the use of shared transport or Mass transportation, the use of personalized vehicles has grown rapidly. The personal vehicle owned individually, in which one cannot use others even belonging to the same family this leads to more traffic on the road. This leads to more consumption of fossil fuel and responsible for climate change. The Public Bicycle Sharing System (PBSS) is the concept, based on shared mobility in which users enable to use it for short-distance trips and drop it at any location through check-in/check-out operations without any accountability and likewise anyone can again use it with the same procedure. These multiple uses of the system conserve natural resources and increase the national economy by reducing the consumption of fossil fuel for mobility. The PBSS in urban areas, normally used for short-haul trips. However, by integrating it into public transport, the coverage of PT may also be increased, which attracts more users. In India implementation of this system is introduced in mid-2017 at Bhopal and within the short span, nearly 14 cities adopted PBSS in their urban areas. However, the majority of the system presently facing ridership issues due to the absence of bicycle-friendly infrastructure. Hence, it is essential to overcome such barriers using an appropriate facilitator. The BWM approach used in this study identifies the top facilitators based on the expert opinion survey to implement PBSS successfully. The result of this study shows that city infrastructure facilitators, technological facilitators, as well as a dedicated bicycle lane, government policy are the most effective facilitators which can improve the ridership and promote a circular economy through shared mobility. This study will help the decision-makers in developing the facilitator and enhance the ridership of PBSS.

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification.

Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that of BiciMAD’s use fall in the category of transport, which demonstrates our first statement.

Predicting demand for a bike-sharing system with station activity based on random forest

Public bike-sharing (PBS) systems have expanded to major cities around the world in efforts to mitigate air pollution, traffic congestion and traffic accidents. Users can pickup and drop-off bicycles at any station, and thus inventory imbalances can occur. To improve system efficiency, system operators should establish appropriate repositioning strategies based on accurate predictions of demand for bicycles. This study aims to predict station-level demand for pickup and drop-off of bicycles using station activity information. In addition to time and weather information, the number of pickups and drop-offs at a station 1–3 h before the prediction was used as a predictor. A random forest machine learning technique is adopted for the demand prediction. The PBS database in Seoul, South Korea was used for the case study. To compare prediction accuracy by station usage patterns, the stations are classified into four clusters. The analysis results show that prediction accuracy including lag information provides mprovements of up to 20%, and the forecast for drop-off is more accurate than the forecast for pickup. This study practically contributes to increasing operational efficiency and reducing operating costs by improving demand predictability in a PBS system.

Social Distancing and Outdoor Physical Activity During the COVID-19 Outbreak in South Korea: Implications for Physical Distancing Strategies.

The novel coronavirus disease (COVID-19) outbreak has put the entire world in a pandemic situation. In response, strict screening, quarantine protocols, and contact tracing have been conducted in South Korea. The purpose of this study was to examine effects of social distancing on the Public Bicycle Sharing System (PBSS) during the COVID-19 outbreak. We used the PBSS public dataset of Seoul, South Korea. Difference-in-differences (DID) analysis was used. In the DID approach, the 2 groups are distinguished based on designated year. Cases of PBSS use were observed in 2 time periods: pre- and post-strict social distancing in Seoul, Korea. Average PBSS usage per day doubled during 2019-2020 (30 697 vs 77 996, P < .001). Commuters and weekend users increased during the social distancing period in 2020 compared with the same period in 2019. DID analysis showed statistically significant positive effects of high levels of social distancing on PBSS usage, commuters, weekend users, and new subscribers. In conclusion, social distancing during the COVID-19 outbreak increased outdoor physical activity. Meaningful outdoor physical activity during the COVID-19 pandemic can be safe from infection and psychologically stabilized as long as keeping meticulous physical distancing, such as hand hygiene, wearing facial masks, and surface cleaning of public resources.

Cluster Analysis of Public Bike Sharing Systems for Categorization

The world population will reach 9.8 billion by 2050, with increased urbanization. Cycling is one of the fastest developing sustainable transport solutions. With the spread of public bike sharing (PBS) systems, it is very important to understand the differences between systems. This article focuses on the clustering of different bike sharing systems around the world. The lack of a comprehensive database about PBS systems in the world does not allow comparing or evaluating them. Therefore, the first step was to gather data about existing systems. The existing systems could be categorized by grouping criterions, and then typical models can be defined. Our assumption was that 90% of the systems could be classified into four clusters. We used clustering techniques and statistical analysis to create these clusters. However, our estimation proved to be too optimistic, therefore, we only used four distinct clusters (public, private, mixed, other) and the results were acceptable. The analysis of the different clusters and the identification of their common features is the next step of this line of research; however, some general characteristics of the proposed clusters are described. The result is a general method that could identify the type of a PBS system.

A Public Bicycle Sharing System Considering Renting and Middle Stations

Recently, public bicycle sharing system (PBSS) has become one of the most favorite urban transportation systems that can help governments to decrease environmental problems such as pollution and traffic. This paper studies a sharing system that includes two types of stations. The first category contains stations that users can rent or return back bicycles and each bicycle can be rented by any new user who arrives to the stations. The second group is the stations which are near shopping centers, historical and other places that users and tourists can stop and visit them. These stations are used only for parking the rented bicycles for a period of time and after that, the users must ride their bicycles and turn them back to their destination stations. After discussing the network of the model under the closed Jackson network, the Mean Value Analysis (MVA) method will be used to calculate the mean queue of each station and analyzing the proposed model.

Text mining for the evaluation of public services: the case of a public bike-sharing system

This study conducted text mining analysis of the review text data (13,615 accounts) posted on SNS by users of the public bike-sharing service in South Korea. A total of 11,954 reviews were processed with SKT KoBERT and classified them either positive or negative. Subsequently, various text mining techniques were used to determine the factors affecting the users’ polarity. The study results revealed that the identification of the positive and negative factors affecting service quality through an analysis of reviews by text mining contributes to the improvement of the public bike-sharing system.

A stakeholders perspective on improving barriers in implementation of public bicycle sharing system (PBSS)

Abstract Public bicycle sharing system (PBSS) has been adopted around the globe to reduce traffic congestion, CO2 emissions and for positive health effects. The first system was initially started in the year 1965 at Amsterdam and now it has been expanded almost in all the continents of the globe. The adoption of this concept was initially seen in developed countries. Now in the past decade various developing countries also have implemented the PBSS in their urban areas. However, the developing countries are facing many implementations and less ridership issues due to various barriers. This paper identifies the barriers which are hinders in the way of implementation of PBSS. An identified 31 barrier was categorized under six groups for ranking. The Fuzzy Analytical Hierarchical Process (FAHP) is applied to examine the relevance and prioritize them with the help of different stakeholders. Total 31 stakeholders were selected as experts from different working backgrounds for this study. The experts were grouped into six namely decision makers, academicians, consultants, scientists and researchers, operators, and users. The perspective of each group is also examined and checked using Kendall' coefficient. The majority of experts opine that City infrastructure barrier and travel characteristics barriers are most influential barriers for successful implementation of PBSS while technological and geographical barriers are least influential barriers. The overall results and opinions by different group of experts will help the decision makers to formulate effective policy to tackle all the barriers prior to implementation of PBSS in Indian urban areas. With the help of this framework, time and financial resources of the government can be utilized in an optimum way and the savings can be utilized for improving necessary city infrastructure.