Public Bicycle Research Articles

Resource-constrained task assignment for event-driven sensing using public bicycles in smart cities

Many cities have provided public bicycle services to reduce traffic congestion and air pollution. Mobility of public bicycles makes them very suitable for event-driven sensing for smart cities, i.e., collecting data relevant to special events such as car accidents or street parades. The problem is how to assign a set of bicycles to best fulfil the sensing mission, considering the constrained storage, battery energy, and communication capability of bicycles. The problem is referred to as the resource-constrained task assignment for event-driven sensing (ReConTAES). The goal is to minimise the number of bicycles used, while balancing the energy consumption of the selected bicycles. We first formulate the problem as a mixed integer programming and then propose a set of greedy heuristics to solve the problem. We evaluate the proposed algorithms by using real trajectories to show their feasibility.

Recognizing Social Function of Urban Regions by Using Data of Public Bicycle Systems

Obtaining the classification of urban functions is an integral part of urban planning. Currently, public bicycle systems are booming in these years. It conveys human mobility and activity information, which can be closely related to the social function of an urban region. This paper discusses the potential use of public bicycle systems for recognizing the social function of urban regions by using one year's rent/return data of public bicycles. We found that rent/return dynamics, extracted from public bicycle systems, exhibited clear patterns corresponding to the urban function classes of these regions. With seven features designed to characterize the rent/return pattern, our method based on Smooth support vector machine (SSVM) is proposed to recognize social function classes of urban regions. We evaluate our method based on the large-scale real-world dataset collected from the public bicycle system of Hangzhou. The results show that our method can efficiently recognize different types of urban function areas. Classification results using the proposed SSVM achieved the best classification accuracy of 96.15%.

The study of Coopetition between Public Bus and Bike Sharing based on Environmental Protection

Recently, the traditional travel modes and public transportation system in urban areas have undergone some subtle changes under the impact of shared bikes, especially for public bus, which is the most impacted by the shared bikes. Therefore, this paper mainly aims at studying the impact of shared bicycles on the public bus from both competitive and cooperative perspectives. More specifically, this study will explore the coopetition relationship between these two modes from travellers’ behaviour perspective. An SP survey has been conducted on traveller in Beijing and a Multinomial Logit model was adopted to quantitatively analyse the effect of various factors on travellers’ modal choice between bus and shared bikes. In this study, the estimation results show that the travel distance, road infrastructure, and travelers’ psychological factors all suggest a competition between bus and bike sharing. On the contrary, distance between bus transfer stations will also lead to modal cooperation. Finally, some policy implications have been proposed to better facilitate public transports and promote the sustainable development of whole urban transport system.

The implementation results of safe bicycle transport in Taipei City

Background: In 2013, there were 7,016 bicycle accidents, resulting in 62 deaths and 10,162 injuries in Taiwan. Since then, there have been more than 7,000 accidents every year, and the number of accidents in 2017 has reached a peak of 8,106 records for the past five years. Among them, the number of electric bicycle accidents increased the most, with 1,183 compared to 2013 and the number of persons injured also increased by 1,310 whereas the number of bicycle accident from 6,262 to 6,169. Methods: Taipei City creating a friendly, safe and secure urban environment to ride: Bicycle Road Network Deployment: bicycle-exclusive lanes, walkways, bicycle and pedestrian shared path and so on in order to meet a variety of traffic needs. Safe and comprehensive road environment: Set up bike stands, road surfacing, street trees, signs, (pavement) markings, dedicated signs, intersection rotation and other traffic amenities to create a safe and comfortable riding environment. To promote cycling to the station to transfer MRTs is tantamount to encouraging people to take more public transport. Planning a city bicycle rental system: To provide a public rental system to expand the convenience, visibility and the community of users launched a new type of public transport - public bicycle rental system (YouBike). The Youbike started demonstration operation, initially setting up 11 station points and 500 bicycles in the peripheral regions of Xinyi District on March 11th, 2009, and now there are 400 station points in all districts. Bicycle Safe Riding Education: Most people refuse to wear helmet protection, and do not install safety devices such as reflectors and warning lights. Causes easy collision at night. The top three reasons for bicycle accidents are not giving way according to regulations, inappropriately making U-turns (turn direction) and Breaching traffic signs and sign controls. Furthermore, wrong-way driving and Careless overstepping are also the main causes of accidents. Smart transportation Setting: To build a safe environment from the appropriate space. Its city area cycle path from 41 kilometers in 2006 (16.4 for dedicated lanes and 24.6 for bicycle and pedestrian shared path) to an extended 391 kilometers in 2018 (86.7 kilometers for dedicated lanes and 304.3 for bicycle and pedestrian shared path). The number of bike stands in Taipei urban area has increased from 5,202 in 2006 to 42,121 in 2018(809%). Appropriately adjust the lane allocation ratio through management means and let the left-turning bicycle turn in a two-stage left turn. This way allows the bicycle to start earlier than the steam locomotive when the green light starts to light up. Results: The implementation results of safe bicycle transport in Taipei City effectively reduced the death and rate caused by head injuries in bicycle accidents: the number of casualties (6,262 dropped to 6,169, down 1.49%), the number of deaths (59 to 30, a drop of 49.15%) or the number of injured persons (9,099 to 8,711), all presented a declining trend.  Conclusion: Changing car-oriented road planning, complete bicycle infrastructure, and respecting the driving awareness of bicycle users is the best protection for the public. All policies should have comprehensive safety policies to ensure personal safety. Just like separate two types of transport, the overall design of the exclusive separation bicycle road, the policy of forcing all ages riding locomotives to wear helmets, etc. In the future, we will continue to plan for self-defense special road layout, intersection ramp configuration, traffic engineering and parking and transfer facilities, so that all roads in the city are safe and friendly, and we will become the first place for bicycle safety riding. Successful experience is extended to the whole country.

A Big Data Analysis of PM2.5 and PM10 from Low Cost Air Quality Sensors near Traffic Areas

ABSTRACT Particulate matter (PM) pollution (including PM2.5 and PM10), which is reportedly caused primarily by industrial and vehicular emissions, has become a major global health concern. In this study, we aimed to reveal spatiotemporal characteristics and diurnal patterns of PM2.5 and PM10 data obtained from 50 air quality sensors situated in public bike sites in Kaohsiung City on June and November 2018 using principal component analysis (PCA). Results showed that PM concentrations in the study were above the standard World Health Organization criteria and were found to be associated, although complicated, with relative humidity. Specifically, the relationship between PM concentrations and relative humidity suggest a clear association at lower PM concentrations. Temporal analysis revealed that PM2.5 and PM10 occurred at higher concentrations in winter than in summer, which could be explained by the long-range transport of pollutants brought about by the northeast monsoon during the winter season. Both PM fractions displayed similar spatial distribution, wherein PM2.5 and PM10 were found to be concentrated in the heavily industrialized areas of the city, such as near petrochemical factories in Nanzih and Zuoying districts in north Kaohsiung and near the shipbuilding and steel manufacturing factories in Xiaogang district in south Kaohsiung. A pronounced diurnal variation was found for PM2.5, which generally displayed higher peaks during the daytime than in the nighttime. Peaks generally occurred at 7:00–9:00 a.m., noontime, and 5:00–7:00 p.m., while minima generally appeared at nighttime. The diurnal pattern of PM was greatly influenced by a greater number of industrial and human transportation activities during the day than at night. Overall, a number of factors such as relative humidity and type of season, transboundary pollution from neighboring countries, and human activities, such as industrial operations and vehicle use, affects the PM quality in Kaohsiung City, Taiwan.

Associations of built environments with spatiotemporal patterns of public bicycle use

This study examines the association of built environment attributes with spatiotemporal patterns of public bicycle use. The study observations are based on the rental records of YouBike, which is a public bicycle system (PBS) in Taipei Metropolitan Area, Taiwan, from July to December 2015. A hierarchical clustering method is applied to identify the spatiotemporal patterns of YouBike use, and multinomial logit regression is used to clarify the associations. Empirical results indicate that the spatiotemporal patterns of PBS utilization differ in weekdays and holidays and are associated with locations, land use and facilities in a city. The empirical evidence fills the knowledge gap on the factors associated with the spatiotemporal patterns of PBS use and provides system operators with a valuable basis for the integrated planning and management of PBSs and built environments.

IBike: Intelligent public bicycle services assisted by data analytics

Bicycle sharing systems are becoming increasingly prevalent in urban environments. These systems provide an environmentally friendly transportation alternative in cities. The management of these systems faces many optimization problems. The most important of these problems are the individual maintenance of bicycles, rebalancing and shared facilities, and the use of systems by creating requirements in asymmetrical patterns. A series of data mining tasks based on real data sets is performed to solve the problem of unbalanced bicycle use.By analyzing the characteristics of each station, the stations are modeled from the perspective of individuals and clusters by means of different models. The evaluation indicators used to address the accuracy of the results provide an effective method for shared bicycle predictions.

Dynamic bicycle scheduling problem based on short-term demand prediction

As a low-cost environmentally-friendly travel mode, public bicycles have been widely applied in many large cities and have greatly facilitated people’s daily lives. However, it is hard to find bicycles to rent or places to return at some stations in peak hours due to the unbalanced distribution of public bicycles. And the traditional scheduling methods have hysteresis, in general, the demands might have changed when the dispatch vehicle arrives the station. To better solve such problems, we propose a dynamic scheduling (DBS) model based on short-term demand prediction. In this paper, we first adopt K-means to cluster the stations and adopt random forest (RF) to predict the check-out number of bikes in each clustering. In addition, the multi-similarity inference model is applied to calculate the check-out probability of each station for check-out prediction, and a probabilistic model is proposed for check-in prediction in the cluster. Based on the prediction results, an enhanced genetic algorithm (E-GA) is applied to optimize the bicycle scheduling route. Finally, we evaluated the performance of the models through a one-year dataset from Chicago’s public bike-sharing system (BSS) with more than 500 stations and over 3.8 million travel records. Compared with other prediction methods and scheduling approaches, the proposed approach has better performance.

Organizational and infrastructural solutions in creating competitive advantage of sustainable urban transport – Gdańsk case study

Abstract The paper presents conclusions of the survey conducted in Gdańsk in October 2017. The study aimed to compare travel times between the largest residential districts in Gdańsk and the area of the business-academic center. The research was aimed at showing time losses on the commuting trips and their organizational and infrastructural causes, which lead to the extension of the actual travel time. Based on the identified time losses and their causes, recommendations regarding further organizational and investment work were presented to improve the competitiveness of public urban transport and bicycles in relation to the cars.

Prediction model of demand for public bicycle rental based on land use

Land use is a primary factor affecting the demand for public bicycle rentals. Demand for public bicycle rentals during different periods of time were predicted using the following procedures. First, walking distances from the rental stations where riders returned the public bicycles to the final destinations were obtained by field investigation, and the 85th percentile statistical values were used as the scopes of influence of those stations. Then, a relationship model among the rental demands for public bicycles and the features of land use inside the influence scope of the rental station was established based on a linear regression model. Finally, considering the public bicycle system in the old urban region of Zhenhai in Ningbo city, the newly established prediction model for rental demand was tested. Results show that the model can predict the daily rental demand, rental demand during the morning peak, returns during the morning peak, rental demands during the evening peak, and returns during the evening peak. The demand prediction model can provide a significant theoretical basis for preparing the layout stations, operation and management strategies, and vehicle scheduling in the public bicycle system.

Public Bicycle Operating System Based on Space-Time Security and the Internet of Things

The bad behaviors of some users and the drawbacks of public bicycles have hindered the promotion of public bicycles. The current problems include low utilization rate, uneven distribution, high loss rate and insecure lock. However, there is few feasible research in this new field. To address these issues of public bicycles, we propose a public bicycle operating system(PBOS). PBOS involves three key technologies: 1) To acquire a dynamic password and realize bicycle self-rescue, we devise an intelligent lock that utilizes the Internet of Things (IoT) to establish Bluetooth connection with user’s mobile phone. 2) To avoid bicycle loss and improve the security of data transmission, we design a space-time security protocol to work between bicycle’s intelligent lock, mobile app, and server. 3) To increase the average utilization rate and distribution, we present a cooperative game model for bicycle scheduling. Finally, we evaluate the performance and validate the theoretical properties of PBOS through extensive simulations.

서울시 공공자전거 이용에 영향을 미치는 물리적 환경 요인 분석 : 대여소별 거리에 따른 요인의 영향력 차이를 중심으로

This study examines the relationship between the usage of public bike and physical environment factors around the public bike stations using the public bike rental history data from 2016 to 2017 in Seoul, Korea. Focusing on the different influences of determinant factors by distance to public bike station, this study identifies influential factors that affect the usage of public bike. The results of the analysis are as follows. First, both the land use and physical environmental variables of bike station areas show strong associations with the usage of public bike. Second, the usage of public bike is also associated with neighborhood living facilities, business facilities, land use mix, the distance to subway station, public facilities and universities. This finding indicates that public bike has played a role as a transportation mode for the short-distance travel and commuting purposes in everyday life. Third, this study shows that the usage of public bike is strongly associated with the average slope, traffic volume around public bike stations, distance to streams or rivers, and the types of bike lane. This finding also indicates that surrounding environmental factors play an important role in the usage of public bike. Finally, this study identifies the different influences of determinant factors on the usage of public bike by distance to public bike station. This study suggests policy implications for the potential locations of public bike stations in the future.

Who is in the near market for bicycle sharing? Identifying current, potential, and unlikely users of a public bicycle share program in Vancouver, Canada

BackgroundPublic bicycle share programs in many cities are used by a small segment of the population. To better understand the market for public bicycle share, this study examined the socio-demographic and transportation characteristics of current, potential, and unlikely users of a public bicycle share program and identified specific motivators and deterrents to public bicycle share use.MethodsWe used cross-sectional data from a 2017 Vancouver public bicycle share (Mobi by Shaw Go) member survey (n = 1272) and a 2017 population-based survey of Vancouver residents (n = 792). We categorized non-users from the population survey as either potential or unlikely users based on their stated interest in using public bicycle share within the next year. We used descriptive statistics to compare the demographic and transportation characteristics of current users to non-users, and multiple logistic regression to compare the profiles of potential and unlikely users.ResultsPublic bicycle share users in Vancouver tended to be male, employed, and have higher educations and incomes as compared to non-users, and were more likely to use active modes of transportation. The vast majority of non-users (74%) thought the public bicycle share program was a good idea for Vancouver. Of the non-users, 23% were identified as potential users. Potential users tended to be younger, have lower incomes, and were more likely to use public transit for their main mode of transportation, as compared to current and unlikely users. The most common motivators among potential users related to health benefits, not owning a bicycle, and stations near their home or destination. The deterrents among unlikely users were a preference for riding their own bicycle, perceived inconvenience compared to other modes, bad weather, and traffic. Cost was a deterrent to one-fifth of unlikely users, notable given they tended to have lower incomes than current users.ConclusionFindings can help inform targeted marketing and outreach to increase public bicycle share uptake in the population.

Shared Bicycle Scheduling Model Based on Price Incentive Mechanism

In order to realize the rational delivery and effective dispatch of urban public self-vehicles, according to the randomness and time-varying of public bicycle demand, a demand forecast based on random forest and spatiotemporal clustering is proposed, and based on this, the user-based site rebalancing price incentive mechanism is implemented. Combining the demand of public bicycles with time factors, meteorological factors, associated sites, and other variables, using logarithmic optimization to reduce the impact of outliers, establish a random forest regression model. Secondly, based on this, a dynamic price incentive model is constructed to realize the rebalancing of user-based rental vehicles. The validity and feasibility of the dynamic price incentive model are verified by taking the historical data of public bicycle operation in the Bay Area as an example.

Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data

Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures.

Research on the station location problem for public bicycle systems under dynamic demand

Abstract This paper proposes a station location problem for public bicycle systems under a zoning scheme with consideration for multi-period demand. The objective of the problem is to minimize the fixed construction costs and variable operational costs of the public bicycle stations. A mixed integer programming model is formulated for the problem. Owing to the computational intractability of the problem under study, a genetic algorithm and a variable neighborhood search algorithm are developed to solve the problem. Comprehensive computational experiments are conducted to illustrate the effectiveness and efficiency of the proposed approach.

Biking to School: The Role of Bicycle-Sharing Programs in Adolescents.

The purpose of this study was to: (1) describe modes of transport to school, with a specific focus on the use of public bicycle share programs (PBSP); and (2) assess sociodemographic, psychosocial, and environmental correlates of bike and PBSP use to go to school. A group of 465 adolescents from the International Physical Activity and the Environment Network (IPEN) Adolescent Study (Valencia, Spain) participated in the research. Mixed regression analyses were conducted on the data obtained. Not having bicycle access, a PBSP card, or a public bike lane close to the school is negatively associated with bicycle use. PBSP is positively associated with physical self-efficacy and bicycle availability and negatively associated with the number of vehicles per drivers at home and not having PBSP stations close to the school. The strongest predictive factor, as regards cycling as an active mode of transport to school among adolescents is having a PBSP card. Schools have a crucial role in promoting bicycle use through collaboration when designing public policies that consider access to bike lane networks and PBSP stations and therefore should encourage local government implementation policies and/or increase PBSP stations within 250 m of the school.

Exploring the importance of detailed environment variables in neighborhood commute mode share models

This paper analyzes the relationship between detailed neighborhood environment variables and commute mode share using a dataset drawn from across the United States and includes model validation results. Representing one of the first studies of its kind, we use United States journey-to-work data to explore the following questions: 1) Which detailed environment variables have significant associations with the proportion of people in a neighborhood who take public transit, walk, or bicycle to work? 2) Does adding detailed environment variables to existing, nationally available neighborhood variables improve the predictive accuracy of work commute mode share models? We use a set of 120 randomly selected census tracts to estimate fractional multinomial logit models that predict walk, bicycle, transit, and automobile commute mode shares. The Base Model includes a set of nine significant, nationally available variables identified from a previous analysis of 5,000 tracts. We test 18 additional detailed neighborhood environment variables and identify five variables that have significant associations with commute mode share: sidewalk coverage (positive association with transit and walk), proximity to a rail station (positive association with transit), bicycle facility density (positive association with bicycle), freeway presence (negative association with walk), and mixed land use (positive association with transit, walk, and bicycle). While these detailed environment variables add clarity to our understanding of factors that influence travel behavior, our validation analysis using 50 separate census tracts does not provide conclusive evidence that these variables improve model accuracy. Further studies with larger sample sizes are needed to determine the optimal set of variables to include to predict automobile, transit, pedestrian, and bicycle commute mode shares.

Do metro interruptions increase the demand for public rental bicycles? Evidence from Paris

Urban policy makers are increasingly interested in cycling as part of a sustainable and reliable transport network. One of the ways through which cities promote cycling is by providing a public bicycle system (PBS) with electronic docking stations. This paper studies demand for public rental bicycles following local and temporary metro interruptions in Paris. I construct a unique data set by linking metro interruptions announced in Twitter communication by the Parisian metro operator to usage data on the Vélib’ PBS. I find that, as a direct consequence of a metro interruption, the consumption of bicycles within 100 m of metro stations increases by 0.72 bicycles per hour per docking station on average, and with 1.54 bicycles per hour per docking station during the first 20 min; an increase of approximately 11% and 22% respectively. Due to their effects on demand, metro interruptions increase the probability of empty stocks at docking stations with 15%. The findings highlight that cycling is a local net substitute for metro service, and that public rental bicycles can alleviate time losses stemming from interruptions in public transport.

Hybridizing bike-sharing systems: the way to improve mobility in smart cities

New generation of bike-sharing systems introduce a wide range of smart solutions. Dockless bicycles equipped with GPS and accessible by smartphone applications as well as electric bikes are considered to be solutions to many socioeconomic and environmental urban problems. However, older generation of bike-sharing systems equipped with dockings stations have some advantages over free floating public bicycles. The aim of this paper is to examine if hybridization of both systems may become an opportunity to improve bike-sharing services. The paper presents characteristics of both types of bike-sharing schemes and describes the examples of hybrid models to show benefits of this solution. Chosen methodology is the case study of selected European bike-sharing systems which combine features of both schemes – the dockless and the station-based.