Redistribution Of Bicycles Research Articles

Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain

The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development.

A novel simulation based approach for user-based redistribution in bike-sharing system

The increased demand for a safe, reliable, cost-effective, clean, and social distancing-friendly mode of commuting in the post-COVID world has motivated the transportation research community to closely analyse and optimise the operations of the existing Bike-Sharing Systems (BSS). One of the key elements in BSS operation is the redistribution of bicycles to cope with the spatiotemporal asymmetry in the demands across the docking stations. Operators usually employ trucks to do the rebalancing at regular intervals. Alternatively, strategies encouraging user-based rebalancing that allow the continuous redistribution of bicycles can be implemented. Dynamic user-based rebalancing strategies are often considered a feasible approach to improve the reliance and performance of the BSS and have been gaining prominence. This paper presents a simulation-driven dynamic user-based redistribution framework using the novel Distance-Willingness-Reward (DWR) matrix method. The framework shows the capability to generate dynamic incentives for the users, motivating them to participate in the rebalancing process, leading to the continuous redistribution of bicycles at each station. The Distance-Willingness-Reward (DWR) matrix method generates the required dynamic incentives for each user, and the bike requirements at each docking station are evaluated using an improved suggestion measure, the Station Impact Index (SII). Simulation experiments are designed and run for 30 days of operation considering 30 stations. Results show that the proposed approach gives better performance compared to the existing operator-based rebalancing and no rebalancing approaches.

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.

Identification and Spatiotemporal Analysis of Bikesharing-Metro Integration Cycling

An essential function of dockless bikesharing (DBs) is to serve as a feeder mode to the metro. Optimizing the integration between DBs and the metro is of great significance for improving metro travel efficiency. However, the research on DBs–Metro Integration Cycling (DBsMIC) faces challenges such as insufficient methods for identification and low identification accuracy. In this study, we improve the enhanced two-step floating catchment area and incorporate Bayes’ rule to propose a method to identify DBsMIC by considering the parameters of time, distance, environmental competition ratio, and POI service power index. Furthermore, an empirical study is conducted in Shenzhen to verify the higher accuracy of the proposed method. Their spatiotemporal behavior pattern is also explored with the help of the kernel density estimation method. The research results will help managers improve the effective redistribution of bicycles, promote the coupling efficiency between transportation modes, and achieve sustainable development of urban transportation.

Static bike repositioning problem with heterogeneous distribution characteristics in bike sharing systems

Bike sharing is one of the recent trends in urban areas as a solution for the mobility problem, where it is seen as a significant component of local transportation due to the need to ensure last-mile transportation. Bike sharing systems enable bicycles to be rented at a station for a ride and be dropped off at another station which provides flexibility to users. However, incorporating a bike sharing system into the transportation network of a city involves its own challenges including balancing the supply and demand within stations, and scheduling bikes’ repositioning.The bike repositioning scheduling problem arises due to the variable demand for various types of bicycles that can be rented from and left to any station. In order to ensure sustainability in bike sharing operations, bikes should be redistributed to the stations considering the demand, during for example a day or night. In this context, with the ultimate motivation of proposing a bike sharing system to meet within-day demand dynamics in a metropolitan city, we address the Static Bike sharing Rebalancing Problem (SBRP) with multiple capacitated vehicles for redistribution of bicycles. On purpose, we formulate a mixed-integer linear program in which the objective is minimizing the total cost subject to a set of constraints including truck capacity, demand satisfaction, inventory balance, and flow preservation. We have obtained the results from solutions to the SBRP through a real case study discussing the tendency on bike sharing systems before and during Covid-19 pandemic, in which total distance traveled according to the repositioning schemes for different months in a year, repositioning, and the truck type requirements for different days in a week, and the unsatisfied demand concerning the actual demands are analyzed. Computational experiments have shown that the distance traveled by trucks varies for the days of the week in different months of the year depending on the change in demand.

Vehicle scheduling approach and its practice to optimise public bicycle redistribution in Hangzhou

Public bicycle sharing programmes (PBSPs) have become increasingly popular across many urban areas in China. Hangzhou PBSP is the world's largest and forms this case study. The management of this large inventory of bicycles is a particularly challenging issue with the goal to ensure the demand for bicycles is met at all times across the network. To this end, an efficient scheduling approach is needed with the capacity to guide the redistribution of bicycles across the self-service stations. Drawing on 7 years of disaggregate trip data, this study first captures the usage dynamics across both space and time to extract the candidate stations and redistribution periods for vehicle scheduling. A region partition method with K-means clustering is proposed to satisfy the real-time requirement of large-scale PBSPs' redistribution. Moreover, drawing on the variations in demand a back-propagation neural network short-term prediction model is computed to inform the necessary prospective redistribution of bicycles to ensure demand is always met. Finally, a vehicle scheduling model employing a rolling horizon scheduling algorithm is established and implemented in a GIS-based prototype system. The prototype is evaluated through its effectiveness and found benefit for following 18 months of practical operation in Hangzhou PBSP.

Bidirectional Incentive Model for Bicycle Redistribution of a Bicycle Sharing System during Rush Hour

Redistribution is an important part of operational activities in a bicycle sharing system (BSS). This paper proposes that there are two types of users in a BSS: leisure travelers and commuters. The operators and the government are adopting the bidirectional incentive model (BIM) to improve their service level of redistribution. That is, the BIM stimulates leisure travelers to actively respond to bicycle resetting needs of the system; on the other hand, it guides commuters by encouraging them to avoid travelling in peak periods. This is beneficial to achieve the goals of reducing the scheduling pressure on bicycles during rush hour, and even to realize the self-resetting of the BSS. In this paper, we explore three scenarios for implementing BIM through cooperation between the operator and the government. By exploiting Stackelberg games in all models, we illustrate the quantity of users in three different travel behaviors, and surplus value of these users respectively. We also consider the trend of the profit of the operator and the government when some changes of parameters are made. The numerical analysis and case discussion find that the strategy of the operator implementing BIM with a subsidy is the best method for developed regions. In a developing region, the strategy of implementing the BIM with a direct government subsidy to users is the best choice in a small or tourist city. In these cities, the proportion of leisure travelers is always larger than 50%, resulting in a significant incentive effect. The strategy of the operator implementing BIM without a subsidy is the best choice for the large and medium-sized city.

Operational Redistribution Model for a Large-Scale Bicycle-Sharing System

In recent years, bicycle-sharing systems (BSSs) have been getting more and more popular in many cities all over the world, particularly in developing countries. However, a significant operating problem was the imbalance that occurred in the distribution of bicycles, especially in large-scale BSSs during peak hours. This problem could significantly reduce the level of service and number of potential users. To improve the level of service of BSSs, the necessity of redistribution was analyzed, and an operational redistribution model (ORM) that could deal with large-scale redistribution was developed. The objective of the ORM was to minimize the generalized operation costs of BSSs, which were the penalty cost of unserved user demand and the cost of redistribution of bicycles. The overall system performance was analyzed under several scenarios. The results demonstrated that an ORM could effectively improve the level of service of a BSS and could provide a detailed work plan for each redistribution truck to implement. For redistribution in a large-scale BSS, the partition of subzones was important to achieve a high level of service with relatively low generalized costs. In addition, an optimal number of subzones could be found through the scenario-based optimization process.

Determining the Optimal Layout Design for Public Bicycle System within the Attractive Scope of a Metro Station

Public bicycle acts as a seamless feeder mode in combination with the citywide public transit, as well as a competitor for the inner-city short trips. The primary objective of this study is to address the layout planning of public bicycle system within the attracted scope of a metro station. Based on the land use function, population, and bicycle mode share, bicycle rental stations are divided into three types, namely, the metro station, district station, and resident station, and later the quantity of bicycle facilities in each rental station is estimated. Then, the service stations are selected from these bicycle rental stations to provide the service of periodical bicycle redistribution. An improved immune algorithm is proposed to determine the number and locations of service stations and the optimal route options for the implement of redistributing strategy. Finally, a case study of Nanjing Tianyin Road metro station is conducted to illustrate the proposed model and clarify some of its implementation details.

The bike sharing rebalancing problem: Mathematical formulations and benchmark instances

Bike sharing systems offer a mobility service whereby public bicycles, located at different stations across an urban area, are available for shared use. These systems contribute towards obtaining a more sustainable mobility and decreasing traffic and pollution caused by car transportation. Since the first bike sharing system was installed in Amsterdam in 1965, the number of such applications has increased remarkably so that hundreds of systems are now operating all over the world.In a bike sharing system, users can take a bicycle from a station, use it to perform a journey and then leave it at a station, not necessarily the same one of departure. This behavior typically leads to a situation in which some stations become full and others are empty. Hence, a balanced system requires the redistribution of bicycles among stations.In this paper, we address the Bike sharing Rebalancing Problem (BRP), in which a fleet of capacitated vehicles is employed in order to re-distribute the bikes with the objective of minimizing total cost. This can be viewed as a special one-commodity pickup-and-delivery capacitated vehicle routing problem. We present four mixed integer linear programming formulations of this problem. It is worth noting that the proposed formulations include an exponential number of constraints, hence, tailor-made branch-and-cut algorithms are developed in order to solve them.The mathematical formulations of the BRP were first computationally tested using data obtained for the city of Reggio Emilia, Italy. Our computational study was then extended to include bike sharing systems from other parts of the world. The information derived from the study was used to build a set of benchmark instances for the BRP which we made publicly available on the web. Extensive experimentation of the branch-and-cut algorithms presented in this paper was carried out and an interesting computational comparison of the proposed mathematical formulations is reported. Finally, several insights on the computational difficulty of the problem are highlighted.

Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems

We develop practical operations research models to support decision making in the design and management of public bicycle-sharing systems. We develop a network flow model with proportionality constraints to estimate the flow of bicycles within the network and the number of trips supported, given an initial allocation of bicycles at each station. We also examine the effectiveness of periodic redistribution of bicycles in the network to support greater flow, and the impact on the number of docks needed. We conduct our numerical analysis using transit data from train operators in Singapore. Given that a substantial proportion of passengers in the train system commute a short distance—more than 16% of passengers alight within two stops from the origin—this forms a latent segment of demand for a bicycle-sharing program. We argue that for a bicycle-sharing system to be most effective for this customer segment, the system must deploy the right number of bicycles at the right places, because this affects the utilization rate of the bicycles and how bicycles circulate within the system. We also identify the appropriate operational environments in which periodic redistribution of bicycles will be most effective for improving system performance.

Robust Multi-period Fleet Allocation Models for Bike-Sharing Systems

This paper presents mathematical programming models that generate optimal daily allocation of bicycles to the stations of a bike-sharing system. First, a time-space network is constructed to describe time-dependent bike flows in the system. Next, a bike fleet allocation model that considers average historical demand and fixed fleet size is established based on the time-space network. In addition to fleet allocation in multiple periods, this model generates least cost empty bicycle redistribution plans to meet demand in subsequent time periods. The model aims to correct demand asymmetry in bike-sharing systems, where flow from one station to another is seldom equal to the flow in the opposing direction. An extension of the model that relaxes the fleet size constraint to determine optimal fleet size in supporting planning stage decisions is also presented in the paper. Moreover, we describe uncertain bike demands using some prescribed uncertainty sets and develop robust bike fleet allocation models that minimize total system cost in the worst-case or maximum demand scenarios derived from the uncertainty sets. Numerical experiments were conducted based on the New Taipei City’s public bike system to demonstrate the applicability and performance of the proposed models. In addition, this research considers two performance measures, robust price and hedge value, in order to investigate the tradeoff between robustness and optimality, as well as the benefit of applying robust solutions relative to nominal optimal solutions in uncertain demand situations.

A Geo-Aware and VRP-Based Public Bicycle Redistribution System

Public Bicycle System (PBS) has been developed for short-distance transportation as a part of the mass transportation system. The supply and demand of bikes in PBS is usually unbalanced at different stations and needs to be continuously and widely monitored and redistributed. The bicycle redistribution is a part of the vehicle routing problem (VRP). We can apply solutions to the VRP to redistribute bicycle efficiently. However, most solutions to the VRP use the Euclidean distance as the condition factor, which does not take road conditions, traffic regulations, and geographical factors into account, resulting in unnecessary waste of delivery time and human resources. In this work, we propose an actual path distance optimization method for the VRP to adapt the several additional constraints of road problems. We also implement a system that integrates real-time station information, Web GIS, the urban road network, and heuristics algorithms for PBS. The system includes a simulator inside that can assist PBS managers to do the route planning efficiently and find the best scheduling strategy to achieve hotspot analysis and the adjustment of station deployment strategies to reduce PBS operation cost.

GPS-CDMA 모뎀 일체 단말기 및 LBS 기반 자전거 관제 시스템 개발

Most of the developed countries and Korea have continued to invest much money in developing low-carbon vehicles such as electric car, methanol car and hydrogen car to replace the conventional fossil fuel vehicles. Government and local governments of each country, however, grope to construct roads for bicycle and public bicycle rental systems because bicycle is the only ultimate and feasible non-pollution transportation. Most of the current bicycle monitoring systems have achieved automation of rental process in bicycle stations, but they can not monitor bicycles in use. This paper develops GPS-CDMA modem combined terminals and implements a LBS-based bicycle monitoring system using them for public bicycle rental system. The monitoring system collects location information from GPS-CDMA modem combined terminals attached on bicycles and presents the moving tracks of bicycles on a GIS map for easy return and redistribution of bicycles. Also, the system helps to prevent from theft and vandalism of bicycles and to recommend the nearest bicycle station.

Bikesharing in Europe, the Americas, and Asia

Growing concerns about global motorization and climate change have led to increasing interest in sustainable transportation alternatives such as bikesharing (the shared use of a bicycle fleet). Since 1965, bikesharing has grown across the globe on five continents: Europe, North America, South America, Asia, and Australia. Today, approximately 100 bikesharing programs are operating in an estimated 125 cities, with more than 139,300 bicycles. Bikesharing's evolution is categorized into three generations: (a) white bikes (or free bike systems), (b) coin-deposit systems, and (c) information technology-based systems. In this paper, a fourth generation is proposed: demand-responsive, multimodal systems. A range of existing bikesharing business models (e.g., advertising) and lessons learned are discussed, including (a) bicycle theft and vandalism, (b) bicycle redistribution, (c) information systems (e.g., real-time information), (d) insurance and liability concerns, and (e) prelaunch considerations. Although limited in number, several studies have documented bikesharing's social and environmental benefits, which include reduced auto use, increased bicycle use, and a growing awareness of bikesharing as a daily mobility option. Despite bike-sharing's ongoing growth, obstacles and uncertainty remain: these include future demand, safety, sustainability of business models, limited cycling infrastructure, challenges to integrate with public transportation systems, technology costs, and user convenience (e.g., limited height adjustment on bicycles, lack of cargo space, and exposure to weather). More research is needed for a better understanding of bikesharing's effects, operations, and business models in light of its reported growth and benefits.