Free-floating Bike-sharing System Research Articles

Recovering Real Demand for Free-Floating Bike-Sharing System Considering Demand Truncation, Migration, and Spatial Correlation

Recovering Real Demand for Free-Floating Bike-Sharing System Considering Demand Truncation, Migration, and Spatial Correlation

Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach

Bike-sharing systems are important components of urban transportation systems that facilitate short-distance travel. In recent years, free-floating bike-sharing systems have become popular. However, a major problem in such systems is the increasing number of idle bikes due to their spatial dispersion, which leads to a loss of demand and difficulty in bike rebalancing. To overcome these drawbacks, operators reserve parking areas, and laborers are incorporated into the bike rebalancing operation. We propose a novel dynamic bike rebalancing strategy based on trucks and laborers for free-floating bike-sharing systems. This strategy combines bike collection by laborers and bike transportation by trucks for rebalancing operations. In addition, we consider broken bike detection by laborers and user dynamics in two extension models. Because of the demand uncertainty in bike-sharing systems, we employ an enhanced distributionally robust optimization (EDRO) approach to protect against uncertainty. A target-level constraint is added to the general distributionally robust optimization (DRO) model to further improve the out-of-sample performance. An adaptive robust strategy is also employed to avoid overly conservative solutions. Furthermore, a linear decision rule is adopted to reformulate the EDRO model into a mixed-integer programming model, which guarantees computational tractability. The results of numerical experiments show that the EDRO model outperforms other benchmark models on a series of measures. Moreover, we verify the value of laborers in dynamic rebalancing operations and explore the effect of different bike supply levels and several other parameters on the value of laborers. We also test how the target-level parameter affects the model performance.

A Cluster-Then-Route Framework for Bike Rebalancing in Free-Floating Bike-Sharing Systems

Bike-sharing systems suffer from the problem of imbalances in bicycle inventory between areas. In this paper, we investigate the rebalancing problem as it applies to free-floating bike-sharing systems in which the bicycles can be rented and returned almost anywhere. To solve the rebalancing problem efficiently, we propose a framework that includes (1) rebalancing nodes at which requirements for the redistribution (pickup or delivery) of bicycles are determined, (2) “self-balanced” clusters of rebalancing nodes, and (3) bicycle redistribution by service vehicles within each cluster. We propose a multi-period synchronous rebalancing method in which a rebalancing period is divided into several sub-periods. Based on the anticipated redistribution demand at each node in each sub-period, the service vehicle relocates bicycles between nodes. This method improves the efficiency of the system and minimizes rebalancing costs over the entire rebalancing period, rather than for a single sub-period. The proposed framework is tested based on data from the Mobike (Meituan) free-floating bike-sharing system. The test results demonstrate the effectiveness of the proposed methodologies and show that multi-period synchronous rebalancing is superior to single-period rebalancing.

A Rolling-Horizon Strategy for Dynamic Rebalancing of Free-Floating Bike-Sharing Systems

A Rolling-Horizon Strategy for Dynamic Rebalancing of Free-Floating Bike-Sharing Systems

A Demand Truncation and Migration Poisson Model for Real Demand Inference in Free-Floating Bike-Sharing System

The free-floating bike-sharing system enables users to pick up bikes from everywhere. However, the spatial-temporal imbalance of bikes becomes severer in such systems. Consequently, bikes are sometimes not available at desired location and time, and the pick-up demand of users is truncated. On demand truncation, the bike user would either give up using the bike or migrate to the nearby to pick up the bike. Thus, the demand of bikes observed from smart locks does not equate with the real demand. However, capturing the real demand is crucial for system planning and operation. Therefore, a statistical model, namely the demand truncation and migration Poisson (DTMP) model, is proposed to analyze the real demand. It is able to jointly model the real demand of FFBS and the migration behavior of users. In particular, we analyzed the demand truncation and migration processes to establish the relationship between the expected value of observed demand and that of real demand. Then, the real demand of using bikes was assumed to be related to the influential factors and the demand function was formulated. Subsequently, the likelihood for each observation was established. The performance of the proposed model was evaluated through field-testing data. The results revealed that the proposed DTMP model is superior to the baseline Poisson model from both the fitness and accuracy perspectives. Finally, the spatial-temporal distribution of the real demand and unmet demand of bikes in typical hours are presented. The results can guide the efficient management of the bike-sharing system.

A location-routing model for free-floating shared bike collection considering manual gathering and truck transportation

With the booming of free-floating bike-sharing systems globally, collecting target bikes effectively becomes increasingly more important in practice. Although many existing studies deal with the transportation of shared bikes with trucks, the process of manual gathering scattered target bikes along truck transportation is seldom explicitly addressed. This study tackles an integrated free-floating shared bike collection problem, which includes manually moving the target bikes to selected gathering sites and transporting the gathered bikes back to the depot with trucks. We propose a location-routing model to optimize the gathering site locations, the bike gathering assignments, and the truck delivering routes integrally under the goal of minimizing a total operation cost, and we develop a heuristic approach to solve the large-size instance of our model. A case study, which is based on the real data of Mobike company in Beijing, China, is conducted to verify the effectiveness of our planning model and solution approach and to generate managerial insights and suggestions for better practices of free-floating shared bike collection.

Exploring Influential Factors of Free-Floating Bike-Sharing Usage Frequency before and after COVID-19

In order to better understand the impact of COVID-19 on the free-floating bike-sharing (FFBS) system and the potential role of FFBS played in the pandemic period, this study explores the impact mechanism of travel frequency of FFBS users before and after the pandemic. Using the online questionnaire collected in Nanjing, China, we first analyze the changes of travel frequency, travel distance, and travel duration in these two periods. Then, two ordered logit models are applied to explore the contributing factors of the weekly trip frequency of FFBS users before and after COVID-19. The results show that: (1) While the overall travel duration and travel distance of FFBS users decreased after the pandemic, the trip frequency of FFBS users increased as the travel duration increased. (2) Since COVID-19, attitude perception variables of the comfort level and the low travel price have had significantly positive impacts on the weekly trip frequency of FFBS users. (3) Respondents who use FFBS as a substitution for public transport are more likely to travel frequently in a week after the outbreak of COVID-19. (4) The travel time in off-peak hours of working days, weekends, and holidays has a significantly positive correlation with the trip frequency of FFBS users. Finally, several relevant policy recommendations and management strategies are proposed for the operation and development of FFBS during the similar disruptive public health crisis.

Reposition optimization in the free-floating bike-sharing system considering transferring travels from urban rail transit

The popular free-floating bike-sharing (FFBS) system serves as a first- and last-mile solution to connect urban rail transit (URT). Recently, a number of studies have investigated how to enhance the integration between FFBS and URT. Nevertheless, how to reposition the FFBS system to promote URT ridership is still a challenge. This study proposes a bike-sharing repositioning framework to balance the demand and supply of FFBS and promote URT ridership. First, a repositioning quantity determination method based on the geographically weighted regression model is applied to determine the number of bikes to be delivered to or collected from each transfer area. Second, we establish an optimization model to minimize the total repositioning cost considering carbon price. Both the repositioning of normal bikes and recycling of broken bikes are included in this model. Subsequently, a hybrid Cuckoo Search algorithm is designed to solve the problem. The extended 2-opt, random removal and insertion algorithms are introduced to generate new solutions, which enhance the search ability. Finally, several numerical studies are conducted using data collected in Shenzhen, China, to verify the effectiveness of the proposed methods. The results demonstrate that the methodology proposed in this study is reasonable and effective. The findings are expected to guide traffic authorities to get environmental and highly utilized scheduling schemes for the integration between FFBS and URT systems.

Using Geopandas for locating virtual stations in a free-floating bike sharing system

Free-floating bike-sharing systems can have a positive influence on the mobility of urban centers and developing efficient localization strategies is crucial to avoid crowding at peak times and increase service availability. Our study aims to efficiently resolve the location of virtual bike stations in a Latin American city through a geospatial data wrangling methodology that allows us to respond opportunely to the potential demand forecasted for the city. This approach is implemented in Python, and it uses the Geopandas and LocalSolver libraries to determine locations for the virtual bike stations that maximize the system coverage. The decision-making process is supported by a binary integer mathematical programming model, and the instances are built from intercity travel surveys that provide realistic data based on travel demand. The developed decision support system prototype provides a recommendation about where virtual bike stations should be located during peak hours and improve general availability by more than 37%. Moreover, when the system's users participate in the relocation of bicycles, the model can eliminate up to 80% of the CO2 emissions and nearly 50% of the operational costs associated with the relocation process.

A worker‐and‐system trade‐off model for rebalancing free‐float bike sharing systems: A mixed rebalancing strategy

AbstractBecause of imbalanced spatial‐temporal user demands, situations with no available bikes or docks often occur. In order to better response to user demands, many researchers are devoted to rebalancing bike sharing systems (BSS). Existing researches focus primarily on rebalancing BSS by trucks or by workers individually, resulting in two issues: (a) the high cost and massive emissions associated with trucks‐based rebalancing; and (b) the low efficiency associated with workers‐based rebalancing. This paper combines two rebalancing methods together and proposes a mixed rebalancing strategy to tackle the aforementioned issues. This strategy rebalances free‐floating BSS (FBSS) by trucks on the eve of peak hours and by workers during peak hours, in which the worker‐based rebalancing plan is given by a worker‐and‐system trade‐off model which simultaneously considers the impact of workers' costs and real‐time inventory of stations. A case study is conducted on two scenarios of the Beijing Mobike dataset: the Beigongdaximen subway station and the Jinyijiayuan residential area. The results show that our mixed rebalancing strategy can effectively rebalance FBSS by improving the following four indices: rebalancing cost, usage rate of shared bikes and docks, survival time of stations, and demand satisfaction.

Unbalanced usage of free-floating bike sharing connecting with metro stations

Free-floating bike-sharing systems allow users to unlock nearby bikes and leave the bikes at any authorized parking area, acting as a time-saving approach for solving the first/last mile problem by connecting trip origins/destinations to metro stations (i.e., bike-and-ride). Inspired by a recent study uncovering the scaling-free phenomenon on the bike-sharing mobility network, this paper addresses the following question: does the power-law distribution also appear in bike-and-ride activities? How does the unbalanced usage pattern of bike-and-ride vary by time and space? To answer the questions, we examine one-week trip orders of free-floating bike sharing in Beijing. We find that the unbalance activities of bike-and-ride show a similar segmented scaling-free feature displaying a first sub-linear regime followed by another super-linear regime on weekdays and weekends. The unbalanced features on weekdays are more substantial than those on weekends and uncovered by the time-varying unbalance in different regimes. Moreover, the unbalanced state corresponding to each metro station obeying non-stationary and dramatically changing peak hours on weekdays suggests that commuting between the suburban and urban areas may be the primary source of the unbalance usage of bike-and-ride in Beijing. Our findings can improve the efficient redistribution of bike-sharing supply and promote the coupling efficiency among transportation modes.

Dynamic Rebalancing of the Free-Floating Bike-Sharing System

In the paper, we propose a novel method to analyze the rebalancing of the free-floating bike-sharing system. First, we construct a visualization method to analyze the rebalancing of the system. Then, for the first time, we set up a coarse-grained way to study dynamics rebalancing during rush hours. Finally, we complete the empirical analysis with the real-time cycling data of the Nanjing Mobike Sharing-bike Company. The results show that: static rebalancing is weak, and dynamic rebalancing during rush hours is serious. Therefore, increasing the number of shared bikes in parking spots can ease the rebalancing. At the same time, we find that commuting to and from work is not the main factor that constitutes the rebalancing of the free-floating bike-sharing system, though the rebalancing is proportional to travel frequency.

Operation Characteristics of a Free-Floating Bike Sharing System as a Feeder Mode to Rail Transit Based on GPS Data

The jobs-housing imbalance and long commuting distances for residents in many megacities in China are promoting the increase in mode share with rail transit. The emergence of free-floating bike sharing (FFBS) provides an attractive and cost-effective multi-modal solution to the first/last mile problem. This study identifies the mobility patterns of free-floating bikes as a feeder mode to 277 rail transit stations in Beijing using detailed GPS data, and the relationships between these patterns, culture and spatial layout of the city are examined. The results show that the distribution of free-floating bikes, as a feeder mode to rail transit, exhibits an aggregating feature in the spatial-temporal pattern on weekdays. According to the results of the Clusters method and ANOVA analysis, the operation characteristics of free-floating bikes are related to the location of the transit station and the job-to-housing ratio around that area, and imbalanced usage of shared bikes across the city may result from the extreme values of job-to-housing ratios. Based on the fitted distance decay curve, accessing distance is greatly influenced by urban morphology and location. Based on these findings, recommendations for planning, management, and rebalancing of the FFBS system as a feeder mode to rail transit are proposed to promote the integration of FFBS and the rail transit system.

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.

Supply and Demand Analysis of a Free Floating Bike Sharing System

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

Free-floating bike-sharing systems: New repositioning rules, optimization models and solution algorithms

In this work different repositioning rules are investigated and compared in the context of free-floating bike-sharing systems. A static complete reposition setting is adopted, i.e., the system is operated (re-balanced) when the number of users riding bicycles can be neglected as it happens in many cities, for instance during the night. The popular ‘healthy or broken’ repositioning rule is revisited and examined along with two newly proposed rules, i.e., ‘pickup or delivery’ and ‘pickup and delivery’ rules. A discussion is also provided in terms of measuring the degree of an unbalancing in such a system. A mathematical model is proposed for each repositioning rule. Depending on the rule one will be facing a multi-trip vehicle routing or a multi-trip pickup and delivery vehicle routing problem, which is a problem not much investigated in the literature. An approximate algorithm is also devised for the problem, which is adapted to the multi-trip vehicle routing, and also suitable for multi-trip pickup and delivery vehicle routing problem with some corresponding adjustment. Computational tests are reported on to assess the methodological contributions of this work. These tests consider both randomly generated instances and two instances using real data. The results shown that ‘pickup and delivery’ rule is better than others when distribution degree of repositioning scenario is less than 0.5, while if the distribution degree exceeds 0.5, the ‘pickup or delivery’ rule is the most cost-effective one.

Dynamic Intra-Cell Repositioning in Free-Floating Bike-Sharing Systems Using Approximate Dynamic Programming

In bike-sharing systems, the spatiotemporal imbalance of bike flows leads to shortages of bikes at some locations and overages at some others, depending on the time of the day, resulting in user dissatisfaction. Repositioning needs to be performed timely to deal with the spatiotemporal imbalance and to meet user demand in time. In this paper, we study the dynamic intra-cell repositioning of bikes by a single mover in free-floating bike-sharing systems. Considering that users can drop off bikes almost anywhere in free-floating systems, we study the simultaneous reposition of bikes among gathering points and collection of bikes scattered along the paths between gathering points under stochastic demands at both the gathering points and along the paths. We formulate the problem as a Markov decision process (MDP), design a policy function approximation (PFA) algorithm, and apply the optimal computing budget allocation method (OCBA) to search for the optimal policy parameters. We perform a comprehensive numerical study using test instances constructed based on the real data set of a major free-floating bike-sharing company in China, which demonstrates the outperformance of the proposed PFA policy against the benchmark policies and the practical implications on the value of repositioning and the impact of bike scatteredness.

Reposition optimization in free-floating bike-sharing system: A case study in Shenzhen City

In recent years, the free-floating bike-sharing system has attracted a large number of travelers for its convenience and non-pollution in the urban public transit. Although many researchers have been carried out, there still exist challenges in repositioning optimization to improve the operating efficiency in the bike-sharing system. This study introduces an improved repositioning model for the free-floating bike-sharing system and designs Non-dominated Sorting Genetic Algorithm II (NSGA-II) to conduct optimization. Firstly, the Gaussian mixture model (GMM) is adopted for clustering to obtain the stations in the free-floating bike-sharing system. Then, a multi-objective optimization model is established considering minimizing the total cost of the repositioning operation and maximizing the satisfaction degree of demands of the delivery stations. To solve the model, an improved NSGA-II is proposed, in which different ways of crossover and mutation operators are applied to maintain the diversity of the population. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to determine the best compromise solution. Finally, using trajectory data from the free-floating bike-sharing system in Shenzhen City, we further compare the results with traditional Genetic Algorithm (GA). The comparison demonstrates that the proposed method is reasonable and effective in the reposition optimization for free-floating bike-sharing system.

Reposition Optimization in the Free-Floating Bike-Sharing System Considering Transferring Travels from Urban Rail Transit

Reposition Optimization in the Free-Floating Bike-Sharing System Considering Transferring Travels from Urban Rail Transit

Mobility pattern recognition based prediction for the subway station related bike-sharing trips

The free-floating bike-sharing (BS) system plays an important role in connection with the public transit system. However, few studies have addressed the impacts of the subway network on the BS system and integrated the features quantitatively into the BS trip prediction framework. Based on the observation of the close relationship between the BS and the urban rail transit, our study focuses on the trip forecasting of the BSs around the subway stations. Firstly, the subway station related sites are investigated based on the BS dataset in Beijing, China. Secondly, multiple categories of features are extracted, including the subway station related site categories by clustering, the BS site mobility patterns by tensor decomposition, as well as other features (e.g., temporal, POI, meteorological, and air quality information). Finally, a three-layer ensemble learning model based method (i.e., the SAP-SF method) under the stacking strategy is proposed with integrations of multiple features and the several selected machine learning algorithms. It is applied to the simultaneous prediction of the hourly return numbers for a large-scale BS system involving a total of 280 sites in Beijing. The output performance is also examined by comparing the results with those obtained from the benchmark models. It is indicated that the features of subway station related site categories and site mobility patterns jointly contribute to the improvement of BS trip prediction. The accuracy can be increased layer by layer and is superior to the single machine learning algorithm. The research finding can provide useful information for system administrators to perform service level checks and to rebalance BSs around subway stations.