Bike-sharing Stations Research Articles

Determining the optimal locations for bike-sharing stations: methodological approach and application in the city of Thessaloniki, Greece

Bike-sharing systems are an important part of many cities’ transportation systems and they are constantly being introduced in more and more cities worldwide. Thus, the strategic decisions for these systems are essential both for their successful operation and the efficient operation of cities’ transportation systems. The present paper aims to develop a methodological approach for determining the optimal locations for installing bike-sharing stations, taking into account the operators’ perspective. Through the developed methodological approach, it is sought to select locations which maximize the demand and the area (built environment) coverage and at the same time minimize the needs for bike redistribution within the day. Thus, the optimal selection of locations for bike-sharing stations is being set as a multi-objective optimization problem. The proposed methodological approach is being applied in the city of Thessaloniki, Greece, where a dock-based and a dockless bike-sharing system operate. The results indicate that the selected stations slightly vary based on the assigned weights in each of the three objectives; higher weight in the demand coverage objective results in more selected stations close to the city’s waterfront where bicycling demand is higher, while higher weight in the area coverage results in more selected stations in the inner city.

Station Importance Evaluation in Dynamic Bike-Sharing Rebalancing Optimization Using an Entropy-Based TOPSIS Approach

As an eco-friendly travel mode, bike-sharing has prevailed around the world. However, the systems are imbalanced due to the asymmetric spatial and temporal distribution of user demand. Station prioritization strategies are needed to rebalance more shared bikes for more important stations. This paper proposes an evaluation method of station importance in dynamic bike-sharing rebalancing. Firstly, a short-term demand prediction model is applied to capture the temporal and spatial characteristics of bike-sharing trip data and predict bike-sharing demand at the station level. Based on the prediction results, the method of determining rebalancing quantity is proposed with consideration of bike-sharing usage throughout the rebalancing period. Then, three criteria are employed to evaluate the importance of bike-sharing stations, including rebalancing quantity, closeness to inventory threshold, and distance from the key station. An entropy-based Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) approach is proposed to weigh different criteria and evaluate station importance. Furthermore, the experiments on bike-sharing data from Nanjing City demonstrate the effectiveness of the proposed methods. This research is helpful for operators and managers to dynamically rebalance shared bikes with high efficiency and improve the service quality of bike-sharing systems.

Gender disparities in New York City bike share usage

By understanding gender disparities in bike share usage in New York City, programs can be better tailored to increase cycling and bike share usage among female members. Data from bike trips and a Citi Bike enrollment survey for the period 2013–2018 were used for this analysis. Associations comparing female to male members on selected study variables were examined through the estimation of odds ratios (OR) and 95% confidence intervals (CI) using bivariate logistic regression models and Wilcoxon signed-rank tests for continuous non-normal data. Spatial autocorrelation of bike share station pick ups and drop offs by percentage of female bike share members was detected using local Moran’s I. This study included 226,237 Citi Bike members; of these, over one-third (38.1%) self-identified as female. The optional enrollment survey was completed by 33,945 members; of these, 37.9% self-identified as female. Compared to male gender, female gender was associated with younger age, higher levels of education completed, being a student and not employed, and lower household income, as well as social and health reasons for membership rather than utilitarian reasons. Overall, female members took fewer bike share trips (median: 46.0 per year vs. 78.5 for males). There was spatial correlation between station usage and gender, with female members more likely than male members to use Citi Bikes in less dense neighborhoods. The results from this study highlight the gender disparity in bike share membership and usage in NYC and provide insight into how this gap could be reduced.

Dynamic bike sharing traffic prediction using spatiotemporal pattern detection

This study proposes a two-step pattern detection methodology for dynamic bike share station traffic prediction using historic traffic and spatiotemporal characteristics. The model is developed on the 15-minute aggregated Washington, D.C. Capital Bikeshare data to predict bike share station traffic for both short- and long-term horizons ranging from 15 min to 4 h. The results show the prediction accuracy equals 100% for 15-minute, 1-hour, and 2-hour horizons and slightly more than 95% for 3-hour and 4-hour horizons at the system level. Not surprisingly, the prediction accuracy drops at the station level. For 15-minute and 1-hour horizons, the prediction accuracy equals 77% and 82%, and it ranges from 24% to 31% for 2-hour, 3-hour, and 4-hour horizons. The results also show that temporal characteristics contribute more than spatial characteristics in the short-time horizons, but the contribution is flipped for long-time horizons. The proposed models have the capacity to estimate bike share traffic for both short- and long-time horizons in less than 20 s of runtime, which illustrates the practicality of the models in dynamic bike sharing traffic prediction, and the potential of the proposed model to be updated in real-time and incorporate the most recent observations into predictions.

Impact of Weather, Activities, and Service Disruptions on Transportation Demand

This paper aims to estimate short-term transportation demand fluctuations because of events such as meteorological events, major activities, and subway service disruptions. Four different modes are analyzed and compared, being bikesharing, taxi, subway, and bus. Case study includes 3 years of transactional data on working days collected in Montreal, Canada. Generalized additive models (GAM) are developed for every mode. The dependent variable is the hourly number of trip departures from one subway station neighborhood. Independent variables are data from various events. Different models are calibrated for every subway station neighborhood to better understand spatial differences. Also, performance of GAM and autoregressive integrated moving average models are compared for prediction on different horizons. Results suggest that presence of rain decreases bikesharing, subway, and bus demand, while increasing taxi demand. In fact, after four consecutive hours of rain, bikesharing demand decreases by 28.0%, subway and bus demand decreases by 4.6%, while taxi increases by 13.9%. Wind is only found significant for bikesharing. Temperature is found significant for all four modes but has a larger effect on bikesharing and taxi. Moreover, demand increases significantly during subway service disruptions for the three alternative modes studied, especially for taxi, suggesting an increase in demand of 182% during disruptions of 1 h. Furthermore, activities influence demand for all four modes, but subway seems to be the most affected one. This method allows for a better understanding of travel behaviors and makes it possible to consider a more dynamic adaptation of the transportation service supply to match travel demand based on various events. This could lead to better co-planning of events and transportation service, for example by temporarily increasing subway frequency or changing the position of some bikesharing stations.

Attractiveness of Bike-Sharing Stations from a Multi-Modal Perspective: The Role of Objective and Subjective Features

When analysing the performance of bike-sharing scheme (BSS) stations, it is common to find stations that are located in specific points that capture the interest of users, whereas nearby stations are clearly underused. This uneven behaviour is not totally understood. This paper discusses the potential factors influencing station attractiveness, supported by the related literature on cyclists’ and pedestrians’ preferences and the characteristics of the stations themselves. The existing literature addresses these topics independently, while this work unites them by proposing a non data-extensive methodology that allows the attractiveness of BSS stations to be assessed. Attractiveness in this context is understood as the set of physical, environmental and service-related features of a bike station that make it more appealing for BSS users than nearby stations. Special attention is paid to differentiating objective features, based on facts, from subjective features, those influenced by personal perceptions. This classification becomes important in this context because subjective aspects can change from one geographical location to another, making the findings related to these aspects difficult to apply to other regions. Moreover, the assessment of the stations’ levels of safety and security is included. Thus, the proposed measure of attractiveness of BSS stations provides a balanced overview of several features. The consideration of station attractiveness when designing BSS layouts will help to refine the design of new layouts and will assist in conducting an appropriate diagnostic evaluation of the existing ones. This tool will allow urban and transportation planners to reduce re-balancing costs and to maximise user satisfaction at a low cost, which have a direct impact on improving the urban sustainability. The proposed method is applied to the Dublin bike sharing scheme, Dublinbikes, with good performance results.

Hierarchical Prediction Based on Network-Representation-Learning-Enhanced Clustering for Bike-Sharing System in Smart City

The sharing bike system is emerging as a new type of transportation with the advance of the smart city in recent years. More and more people will choose to ride a sharing bicycle for short-distance travel. While sharing bikes provide convenient services to customers, there are also many unfavorable factors in the sharing bike system, which have a certain impact on the riding experience of customers. One of the obvious disadvantages is the imbalance of usage because of the abnormal distribution of sharing bike stations in different areas. Despite some prediction work have been done, most of them merely consider the geographical factors between station whereas the user preferences and global network information are not fully considered. In this work, we propose a hierarchical model for sharing bike prediction, which can predict the number of rents/returns of each sharing bike station in the future to achieve resource redistribution. The proposed model is composed of two steps, including the sharing bike station clustering via network representation learning by considering the migration trends and geographic location information of bike-sharing between station. In the hierarchical prediction step, the total number of all bike-sharing station is predicted with an inference model based on multiple similarities. Finally, the number of rents/returns at each station can be derived. Our method is evaluated based on two publicly available sharing data sets by comparison with several baseline methods. Extensive experimental results on two open sharing bike data sets demonstrate that our proposed hierarchical can achieve the best prediction results. Our proposed method has a 17.34% improvement in root-mean-squared logarithmic error and a 10.4% improvement in return prediction.

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.

An equality-based model for bike-sharing stations location in bicycle-public transport multimodal mobility

Abstract Bike-sharing systems can be implemented to complete the coverage of public transport networks which could be insufficient to serve an entire urban area. Some methodologies that maximise coverage or accessibility are suggested in the literature. In this paper, we propose a bike-sharing stations location model that includes not only these issues but also equality aspects. The model aims to minimise inequalities in bicycle-public transport mobility among observed population groups trying to maintain specified levels of accessibility and coverage at the same time. We evaluated the performance of the model on a test network and carried out a sensitivity analysis according to the available budget. The results showed that maximising accessibility or coverage alone, without considering equality, may lead to an unequal distribution of accessibility among the population, producing discrimination between different groups. The outcomes of the application revealed the significance of the model in evaluating equality in the network design phase for achieving not only a satisfactory bike-sharing system and public transport multimodal accessibility of each zone but also a high equality measure among the considered population groups. Budget availability also played an essential role since a minimum budget value is needed to achieve higher levels of equality. The proposed approach could serve transport and public authorities as a decision support system in planning future investment as well as promoting multimodal mobility because it links bike-sharing stations with stops/stations of the public transport lines networks.

Bikesharing, equity, and disadvantaged communities: A case study in Chicago

Abstract Bikeshare systems are rapidly becoming more prevalent in metro and micropolitan areas around the world. However, low-income populations, people of color, and transit-dependent households are not highly representative of the bikeshare user’s profile. Some bikeshare programs in the United States, such as Divvy in Chicago, try to address this equity problem by promoting their systems among low-income communities. Currently, there is limited research estimating bikeshare ridership in these communities and quantitatively analyzing the impacts of financial barriers in disadvantaged areas at the station level. This research fills this gap by analyzing the current utilization of bikeshare systems among disadvantaged populations. The study develops a Negative Binomial regression model to estimate bikeshare ridership using data from Chicago’s bikeshare system. The results show that bikeshare stations in disadvantaged communities generate around two-thirds of the average annual trips across all stations. The employment rate plays an important role in increasing bikeshare ridership, especially for disadvantaged areas. Additionally, the research found that the proportion of trips by annual members is significantly lower in disadvantaged communities than in other areas. However, interestingly, residents in disadvantaged communities tend to make longer bikeshare trips once they are annual members. Their dependence on bikeshare systems may result from accessibility improvement (e.g., work commute by bikeshare). Based on all the findings, we discuss planning implications for more socially inclusive and equitable bikeshare systems.

A hybrid algorithm for the static bike-sharing re-positioning problem based on an effective clustering strategy

This paper studies the bike-sharing re-positioning problem (BSRP) frequently encountered in modern bike-sharing systems that are widely deployed around the world. To cope with customer demand fluctuations and improve service level, BSRP aims to identify the optimal vehicle routes to visit bike-sharing stations in order to balance their inventories, picking up excess bikes from surplus stations and adding needed bikes to insufficient stations, with the objective of minimizing total traveling cost and inventory cost. The mathematical model of the studied problem is first given, detailing the considerations of multiple depots available for re-positioning vehicles and the extra objective of inventory cost minimization. An effective clustering strategy is then proposed to put bike-sharing stations into self-sufficient groups, which is shown to be able to greatly decompose the problem complexity for large-scale instances. A destroy-and-repair algorithm is developed to improve the clusters, and an adaptive variable neighborhood search algorithm is designed to conduct intra-cluster and inter-cluster vehicle routing optimization. Performance of the hybrid algorithm is validated on three sets of benchmark instances, and compared with CPLEX as well as state-of-the-art algorithms from the literature, which demonstrates that the proposed algorithm is highly competitive in solving BSRPs.

The Bike-Sharing System as an Element of Enhancing Sustainable Mobility—A Case Study based on a City in Poland

The bike-sharing system allows urban residents to rent a bike at one of the rental stations located in the city, use them for their journey, and return them to any other or the same station. In recent years, in many cities around the world, such systems were established to encourage their residents to use bikes as an element of enhancing sustainable mobility and as a good complement to travel made using other modes of transport. The main purpose of this article is to present the results of an analysis of the functioning of the bike-sharing system in Warsaw (Poland). Moreover, the article presents an analysis of the accessibility to individual stations. An important aspect is that the bike-sharing system has been popular among users and that more people use it. Therefore, the city should be provided with a dense network of conveniently located bike-sharing stations. Also, the quality of the bike-sharing system should be an adjustment to the user’s expectations. In connection with the above, the article also presents the results of the analysis of factors affecting bike-sharing system usage as well as the level of satisfaction connected with bike-sharing system usage. The results of the analysis showed that there is a strong positive correlation between these variables. The obtained results can be helpful for carrying out activities whose purpose is to increase the bike-sharing system usage as well as to increase the level of satisfaction connected with bike-sharing system usage.

The suitability level of bike-sharing station in Yogyakarta using SMCA technique

Yogyakarta is experiencing the increasing number of population (1,13%) from 2014 to 2017 that influences the rising number of motorized vehicles. Besides, it is also led by the easiness to access vehicles ownership in Yogyakarta. In 2017, Yogyakarta ranked fourth as the most congested city in Indonesia that indicates the importance of changing the transportation mode from private vehicles to public transportation. Through the mission of Yogyakarta to strengthen urban planning and environmental sustainability, it is necessary to implement green transportation along with transport demand management to reduce the disadvantages of road congestion in the city. Hence, bike-sharing system can be an option that provides new alternative mobility. However, the important thing to manage bike-sharing systems is the optimal location of bike-sharing station. In Yogyakarta, there is already a bike-sharing systems but only in 12 stations. This research aims to identify the best location for siting bike-sharing station based on spatial analysis using Analytical Hierarchy Process (AHP) and Spatial Multi-Criteria Analysis (SMCA). According to the analysis, the urban core of Yogyakarta and its surrounding are the most suitable location for bike-sharing station.

Optimal locations for bikeshare stations: A new GIS based spatial approach

This study identifies three new bike station locations for Baltimore City's bikeshare program using a location allocation spatial analysis tool. Over 1.6 million Global Positioning System (GPS) coordinates of bikeshare trips over a period of four months were used to identify the routes most frequently used by bikers. It was hypothesized that potential new bike station locations have a relationship with proximity to transit, attractions and restaurants/pubs. A negative correlation was found between route usage intensity and proximity to attractions and restaurants/pubs, which means that the closer the proximity to attractions or restaurants/pubs, the higher the road usage intensity is. A new methodology was developed by modifying Huff's gravity model, replacing facility location size with a suitability score. The bike station suitability score is calculated with regard to proximity to transit, attractions, restaurants/pubs and existing bike stations. A location allocation model is developed to maximize market share, i.e., the potential bike station locations will be accessible to maximum population while also being within a 400-m (0.25-mile) radius of attractions, restaurants/pubs and at least 300 m from existing bike stations. The location of the proposed bike stations through location allocation is within a block of the planned bike stations by the City of Baltimore, which corroborates the findings of this study. This methodology will be useful to identify new facility locations in any metropolitan city where maximum exposure is a necessity.

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.

Maximum Closeness Centrality k-Clubs: A Study of Dock-Less Bike Sharing

In this work, we investigate a new paradigm for dock-less bike sharing. Recently, it has become essential to accommodate connected and free-floating bicycles in modern bike-sharing operations. This change comes with an increase in the coordination cost, as bicycles are no longer checked in and out from bike-sharing stations that are fully equipped to handle the volume of requests; instead, bicycles can be checked in and out from virtually anywhere. In this paper, we propose a new framework for combining traditional bike stations with locations that can serve as free-floating bike-sharing stations. The framework we propose here focuses on identifying highly centralized k-clubs (i.e., connected subgraphs of restricted diameter). The restricted diameter reduces coordination costs as dock-less bicycles can only be found in specific locations. In addition, we use closeness centrality as this metric allows for quick access to dock-less bike sharing while, at the same time, optimizing the reach of service to bikers/customers. For the proposed problem, we first derive its computational complexity and show that it is NP-hard (by reduction from the 3-SATISFIABILITY problem), and then provide an integer programming formulation. Due to its computational complexity, the problem cannot be solved exactly in a large-scale setting, as is such of an urban area. Hence, we provide a greedy heuristic approach that is shown to run in reasonable computational time. We also provide the presentation and analysis of a case study in two cities of the state of North Dakota: Casselton and Fargo. Our work concludes with the cost-benefit analysis of both models (docked vs. dockless) to suggest the potential advantages of the proposed model.

Joint analysis of the impacts of built environment on bikeshare station capacity and trip attractions

Shared micromobility is proliferating throughout the world. Many researchers have extensively studied the links among factors representing the built and natural environment and bikeshare demand. One common feature of the existing demand models is that they view bikeshare infrastructure as a group of exogenous variables along with other influential factors. Indeed, this assumption is seldom true in planning practices. Bikeshare system operators usually allocate resources in dense urban areas based upon the environmental correlates. This study contributes to the literature by jointly exploring the determinants of bikeshare station capacity (i.e., the number of docking points) and trip arrivals at the station-level. The research dataset is constructed from the Citi Bike system in New York City in September 2016. The analytical results reveal that the effects of built environment characteristics on bikeshare usage could be carefully considered during the system installation process. We find existing bicycle facilities do not significantly influence the supply of docking points at the station-level. However, they exert direct and positive effects on hourly trip arrivals. The findings improve our understanding of the bikeshare system installation process.

Robust bike-sharing stations allocation and path network design: a two-stage stochastic programming model

ABSTRACT Nowadays, the significance of developing sustainable transport systems is being reconsidered due to the impact produced by an extensive use of private cars. In this scenario, bicycles are becoming more popular. To encourage cycling, bike-sharing systems have been developed. However, as safety is people’s main concern of cycling, bike-sharing systems should be part of an integral project that includes not only docking stations but a dedicated cycle path network connecting them. A two-stage stochastic programming model that maximizes the travel demand covered by the system is developed. Various demand scenarios in terms of different time periods of a day, travel intensities and levels of commuting demand are considered in the model, in order to obtain robust solutions. A real-world case study based on the city of Montevideo in Uruguay is conducted. The obtained solution shows that the model tends to develop bicycle lane network with good connectivity between transit stations and normal stations, and the obtained network possesses good robustness.

Quantifying greenhouse gas emissions reduction from bike share systems: a model considering real-world trips and transportation mode choice patterns

The emerging bike share systems provide convenient mobility to short-distance travelers for both leisure and commuting purposes. Many cities are rolling out bike share programs. However, few studies have evaluated how bike share systems (BSS) are used to quantify their sustainability impacts. This study proposes a Bike Share Emission Reduction Estimation Model (BS-EREM) to quantify the environmental benefits from bike share trips and compare the greenhouse gas (GHG) emission reductions from BSS in eight cities in the United States, including New York, Chicago, Boston, Philadelphia, Washington D.C., Los Angeles, San Francisco, and Seattle. The BS-EREM model stochastically estimates the transportation modes substituted by bike share trips, considering factors such as trip distance, trip purpose, trip start time, the accessibility of public transits, and historical distributions of transportation mode choices. Based on average life cycle emission factors of different transportation modes, our analysis reveals that the annual GHG emission reductions contributed by the eight BSSs in year 2016 range from 41 tons of CO2-eq (Seattle) to 5417 tons of CO2-eq (New York City), while the emission reductions per trip range from 283 to 581 g CO2-eq. The total annual emission reduction is linearly correlated to the number of trips, bikes, and docks. The bike share stations located in the center of a city contributed to more total GHG emission reductions due to the high trip volumes, while the stations that are further away have higher emission reductions on a per trip basis due to longer trips and higher car substitution rate.

Leveraging Integrated Bike-Sharing with Existing Bus Rapid Transit (BRT) to Reduce Motor Vehicle in Central Jakarta Municipal

Jakarta is one of the most congested cities in the world due to a plethora of motor vehicles used in the city. One of the government actions to address the issue is by implementing Bus Rapid Transit (BRT) as an alternative public transportation mode. However, according to the convenience walking distance standard, the BRT coverage the inhabitant to choose motor vehicle rather than walking. This paper purposes Bike-Sharing as the smart transportation mode to overcome such issue and predict the three potential places to establish sharing-bike stations according to the convenience walking distance standard. In this paper the walking distance is classified into 100 mater range (300 meter, 400 meter and 500 meter) projected using the euclidean distance principle. As the result for 300 meter standard, there are 809 potential bike-sharing stations consist of 164 main stations and 645 feeder stations, while the 400 meter standard needs 541 potential stations with 140 stations serve the BRT station directly and 401 stations as the feeder. Furthemore, with 500 meter standard, 359 stations consist of 131 main stations and 228 feeder stations is needed.