Query: bike sharing rebalancing user

title: A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
abstract: Bike sharing provides an environment friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing operators to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP) which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide and conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24 timeslot look ahead optimization, and outperforms state of the art methods in both service level and bike distribution. It also transfers well when applied to unseen areas.

title: Understanding intention and behavior toward sustainable usage of bike sharing by extending the theory of planned behavior
abstract: Bike sharing, especially dockless bike sharing (DBS) plays a key role in enhancing sustainable mobility in Chinese cities. Unsustainable usage modes and user behavior are increasingly serious problems confronting DBS in China that may substantially threaten its profits and supply. While existing studies have mainly focused on how to promote DBS, the issue of the sustainable usage of shared bikes has been largely overlooked. This paper examined the determinants affecting users' sustainable usage intention and behavior by incorporating awareness of consequences and moral obligation into the theory of planned behavior. Based on partial least squares structural equation modeling, the questionnaire data of 705 users (67.92% of all respondents) who had used DBS in sustainable ways were empirically analyzed. The results revealed that perceived behavioral control and moral obligation are the most important factors driving users' sustainable usage intention and behavior and that behavioral attitude and subjective norms also present significant positive effects on sustainable usage intention. In contrast to the hypothesized relationship, the awareness of consequences does not play a significant role in sustainable usage intention. Moreover, the empirical results ascertained that the extended model has stronger interpretation ability than the original model in predicting DBS users' sustainable usage behavior. Eventually, some targeted managerial implications are proposed. This study enhances the current understanding of the sustainable usage behavior of DBS users and provides timely insights for policymakers and operators promoting sustainable bike sharing practices in China and other countries operating DBS programs.

title: Comparative life cycle assessment of station based and dock less bike sharing systems
abstract: Bike sharing system (BSS) is growing worldwide. Although bike sharing is viewed as a sustainable transportation mode, it still has environmental footprints from its operation (e.g. bike rebalancing using automobiles) and upstream impacts (e.g. bike manufacturing) Thus, evaluating the environmental impacts of BSS from the life cycle perspective is vital to inform decision making for the system design and operation. In this study, we conducted a comparative life cycle assessment (LCA) of station based and dock less BSS in the U.S. The results show that dock less BSS has a greenhouse gas (GHG) emissions factor of 118 g CO2 eq/bike km in the base scenario, which is 82% higher than the station based system. Bike rebalancing is the main source of GHG emissions, accounting for 36% and 73% of the station based and dock less systems, respectively. However, station based BSS has 54% higher total normalized environmental impacts (TNEI) compared to dock less BSS. The dock manufacturing dominants the TNEI (61% of station based BSS and the bike manufacturing contributes 52% of TNEI in dock less BSS. BSS can also bring environmental benefits through substituting different transportation modes. Car trip replacement rate is the most important factor. The results suggest four key approaches to improve BSS environmental performance: 1) optimizing the bike distribution and rebalancing route or repositioning bikes using more sustainable approaches, 2) incentivizing more private car users to switch to using BSSs, 3) prolonging lifespans of docking infrastructure to significantly reduce the TNEI of station based systems, and 4) increasing the bike utilization efficiency to improve the environmental performance of dock less systems.

title: Setting Inventory Levels in a Bike Sharing Network
abstract: Bike sharing systems (BSSs) allow customers to rent bicycles at automatic rental stations distributed throughout a city, use them for a short period of time, and return them to any station. One of the major issues that BSS operators must address is nonhomogeneous asymmetric demand processes. These demand processes create an inherent imbalance, thus leading to shortages either of bicycles when users are attempting to rent them and of vacant lockers when users are attempting to return them. The predominant approach taken by operators to cope with this difficulty is to reposition bicycles to rebalance the inventory levels at the different stations. Most repositioning studies assume that a target inventory level or range of inventory levels is known for each station. In this paper, we focus on determining the correct target level for repositioning according to a well defined objective. This is a challenging task because of the nature of the user behavior that creates the interactions among the inventory level.

title: User satisfaction based model for resource allocation in bike sharing systems
abstract: Over the past decade, the number of ongoing bike sharing programs has remarkably risen. In this framework, operators need appropriate methodologies to support them in optimizing the allocation of their resources to globally enhance the bike sharing program, even without massive and costly interventions on the existing configuration of the system. In this paper, we propose an optimization model able to determine how to employ a given budget to enhancing a bike sharing system, maximizing the global user satisfaction. During the day, each bicycle station has a certain number of bikes that fluctuates according to the travel demand; it happens, however, that for certain time slots, the station is full or empty. Then, we propose to consider as key performance indicators the zero vehicle time and the full port time, that reflected respectively the duration of vehicle shortage and parking stall unavailability in the stations. Both these indicators, together with the lost users of the system, need to be kept to a minimum if the final aim is maximizing the customer satisfaction, i.e. not forcing the user to use other stations or turn/shift to other travel modes. We have analyzed the historical usage patterns of the bike sharing stations, smoothing their trends (by wavelets) and operated a preliminary spatio temporal clustering. Our model verifies the necessity of adding or removing racks to each station, setting at the same time the optimal number of bikes to allocate in them, and decide the eventual realization of further stations. Then, an application, both on a small test and a real size network, is presented, together with a sensitivity analysis.

title: Comparison of usage regularity and its determinants between docked and dockless bike sharing systems: A case study in Nanjing, China
abstract: Bike sharing systems have rapidly expanded around the world. Previous studies found that docked and dockless bike sharing systems are different in terms of user demand and travel characteristics. However, their usage regularity and its determinants have not been fully understood. This research aims to fill this gap by exploring smart card data of a docked bike sharing scheme and GPS trajectory data of a dockless bike sharing scheme in Nanjing, China, over the same period. Both docked and dockless bike sharing users can be classified into regular users and occasional users according to their usage frequency. Two systems are cross compared regarding their travel characteristics. Then, binary logistic models are applied to reveal the impacts of travel characteristics and built environment factors on the regularity of bike sharing usage. Results show that for both bike sharing systems, regular users and occasional users share similar riding time and distance, while significant differences in the spatio temporal distribution between docked and dockless bike sharing systems are observed. The regression model results show that the "Trips during morning and afternoon peak hours" are positively associated with the regularity of both docked and dockless bike sharing usage. However, the "Riding distance" variable is negatively associated with the usage regularity of both systems. Built environment factors including working point of interest (POI) residential POI, and transit POI promote the usage regularity of both bike sharing systems. Finally, policy implications are proposed, such as increasing the density of docking stations in suburban areas and developing high quality parking area for dockless bike sharing around public transport stations. This study can help operators or governments to launch or improve the service of bike sharing systems.

title: Optimizing Rebalance Scheme for Dock Less Bike Sharing Systems with Adaptive User Incentive
abstract: Recently, the development of Bike Sharing Systems (BSSs) brings environmental and economic benefits to the public. However, BSSs frequently suffer from the imbalanced bike distribution, including dock less BSSs. The underflow or overflow of bikes in a region may lead to a lower service level to BSSs or congestion to the city. In the paper, we consider rebalancing the dock less BSS by providing users with monetary incentives. The long term objective is to maximize the number of satisfied users who successfully complete their rides over a period of time. The operator of the dock less BSS can not only encourage a user to rent bikes at the neighborhood of its source with a source incentive, but also incentivize them to return bikes at the neighborhood of its destination with a destination incentive. To learn the differentiated incentive price for rebalancing bikes across time and space, we extend a novel deep reinforcement learning framework for user incentive. The source and destination incentives are integrated in an adaptive way by adjusting the detour level at the source and/or destination by avoiding bike underflow and overflow. In the experiment, we evaluate our approach in comparison with two existing pricing schemes. The locations of sources and destinations are abstracted from a selected dataset from Mobike. The experiment results show that our adapted learning algorithm outperforms the original one that only considers source incentive as well as another state of the art approach in maximizing the long term number of satisfied users.

title: The Static Bike Sharing Rebalancing Problem with Forbidden Temporary Operations
abstract: This paper introduces and solves the static bike rebalancing problem with forbidden temporary operations. In this problem, one aims at finding a minimum cost route in which a vehicle performs a series of pickup and delivery operations while satisfying demand and capacity constraints. In addition, a vehicle can visit stations multiple times but cannot use them to temporarily store or provide bikes. Apart from bike rebalancing, the problem also models courier service transportation and repositioning of inventory between retail stores, where temporary operations are frequently disliked because they require additional manual work and service time. We present some theoretical results concerning problem complexity and worst case analysis, and then propose three exact algorithms based on different mathematical formulations. Extensive computational results on instances involving up to 80 stations show that an exact algorithm based on a minimal extended network produces the best average results.

title: Considering user behavior in free floating bike sharing system design: A data informed spatial agent based model
abstract: Although bike sharing has been recognized as an active and sustainable transportation mode, the dramatic expansion of free floating bike sharing (FFBS) services generates problems such as illegal parking and low utilization. An effective FFBS system needs to be highly regulated. This study combines Big Data and spatial agent based modeling to understand the interactions between stakeholders to assist the bike sharing system design. The key design decisions considered are the locations and capacities of bicycle parking lots in the system, as well as the connected bike lanes between parking lots. The model has been applied to the case of Hong Kong for demonstration. The results show that the parking lots with higher capacities are mostly close to the metro stations, and the cycleways are disconnected even for those that have high cycling occupancy. The results indicate that for most target people to be willing to change the parking location, the minimum fare discount rate for doing so should be set to 30% The average trip time can be reduced by 3.8% and per user cost can be reduced by 2.4% with an expected investment of 0.12 million USD to build new cycle tracks and connect existing cycleways.

title: Monitoring the first dockless bike sharing system in Greece: Understanding user perceptions, usage patterns and adoption barriers
abstract: The city of Rethymno in Crete is the first Greek city to host a Dockless Bike Sharing System (DBSS) This is a scheme that means to promote sustainable mobility and help mitigating high levels of car traffic induced particularly during summer by a consistently increasing number of tourists. The present paper examines the efficiency of this DBSS aiming at: a) analysing the current usage patterns, b) identifying and discussing the various reasons that encourage or deter its use and c) capturing a few key user perceptions reflecting and affecting scheme acceptance and usage. The study adopts a mix method approach consisting of secondary data analysis, field observations and a quantitative survey, completed by 534 DBSS users. The findings illustrate that DBSS is used primarily for short distance trips (up to 1,5 km) and very short rentals (up to 15 min) Traffic safety concerns and limitations in the existing cycling infrastructure are two of the factors adversely affecting the scheme's usage, since many questioned whether the scheme was an effective investment under the present situation with the given constrains of Rethymno's transport system. The majority of both frequent and occasional users thought that the scheme is affordable, easy to use and suitable for both tourist and local populations. These findings constitute the first ever footprint of DBSS usage in Greece and can be used as an input for delivering appropriate policy interventions in future urban transportation strategies looking to promote and reinforce bike sharing usage and increase cycling uptake. The paper also offers valuable guidance to mobility providers about how bike sharing businesses can prosper long term in an environment where shared mobility schemes constitute novel socio technical interventions.