Problems and Countermeasures of Bike-Sharing System in China Based on the theory of Public Goods

Under the new normal of China’s economic development,bike-sharing,a new product of the“Internet+”era,opens the door to a novel lifestyle for the society,and is in the stage of rapid development.Nevertheless,with the explosive growth of the number of bicycles,some new problems gradually come out,such as disorderly parking,serious damage and waste of resources,which in turn restrict its further development.Based on the theory of public goods,this article studies the quasi-public goods attributes and problems of bike-sharing,as well as provides feasible measures for its long-term development,so as to realize the co-governance and sharing among enterprises,consumers and government.

Review of bike-sharing system studies using bibliometrics method

Compared to motorized modes,bike-sharing systems(BSSs)are generally recognized as an environmentally friendly mode of transport and mobility.Due to the advantages and benefits of BSSs,they have spread globally in the past decade.The mapping knowledge domain(MKD)technique is an important tool for bibliometric analysis that can directly reflect the development status and trends in a research field and has been extensively applied in natural science,medical science,engineering and technology,humanities and the social sciences.In this paper,we conduct a systematic analysis of the development trend in bike-sharing studies taken from Web of Science(WoS)Core Collection articles published between 2010 and 2020 using the MKD software tool VOSviewer and Cite Space.The results show that the topics of the cited documents can be divided into three categories:(a)development,operation mode and lessons learned;(b)BSS static rebalancing problem;(c)spatiotemporal characteristics and demand prediction.Next,we conduct document co-citation analysis and keyword co-occurrence analysis to visually explore the research trends in bike-sharing studies.Our results also find that,(a)bicycle rebalancing problem,(b)travel behavioral movements and barrier,(c)impact factors and characteristics of the internal usage demand,(d)innovation and sustainability for BSSs in the future,and(e)built environment and land-use,are the five major research areas and interests for bike-sharing studies.Finally,examining the trends in BSSs studies by identifying keyword bursts allows an on-demand characteristics analysis based on multisource data fusion technology.This study expands the application field of the MKD analysis method and promotes the development of BSSs against a background of new technology innovation.

Reducing CO_(2)emissions from the rebalancing operation of the bike-sharing system in Beijing

With the development of the bike-sharing system(BSS)and the introduction of green and low carbon development,the environmental impacts of BSS had received increasing attention in recent years.However,the emissions from the rebalancing of BSS,where fossil-fueled vehicles are commonly used,are usually neglected,which goes against the idea of green travel in a sharing economy.Previous studies on the bike-sharing rebalancing problem(BRP),which is considered NP-hard,have mainly focused on algorithm innovation instead of improving the solution model,thereby hindering the application of many existing models in large-scale BRP.This study then proposes a method for optimizing the CO_(2)emissions from BRP and takes the BSS of Beijing as a demonstration.We initially analyze the spatial and temporal characteristics of BSS,especially the flow between districts,and find that each district can be independently rebalanced.Afterward,we develop a rebalancing optimization model based on a partitioning strategy to avoid deciding the number of bikes being loaded or unloaded at each parking node.We then employ the tabu search algorithm to solve the model.Results show that(i)due to over launch and lack of planning in rebalancing,the BSS in Beijing shows great potential for optimization,such as by reducing the number of vehicle routes,CO_(2)emissions,and unmet demands;(ii)the CO_(2)emissions of BSS in Beijing can be reduced by 57.5%by forming balanced parking nodes at the end of the day and decreasing the repetition of vehicle routes and the loads of vehicles;and(iii)the launch amounts of bikes in specific districts,such as Shijingshan and Mentougou,should be increased.

Estimating model for urban carrying capacity on bike-sharing

As the demand for bike-sharing has been increasing,the oversupply problem of bike-sharing has occurred,which leads to the waste of resources and disturbance of the urban environment.In order to regulate the supply volume of bike-sharing reasonably,an estimating model was proposed to quantify the urban carrying capacity(UCC)for bike-sharing through the demand data.In this way,the maximum supply volume of bike-sharing that a city can accommodate can be obtained.The UCC on bike-sharing is reflected in the road network carrying capacity(RNCC)and parking facilities’carrying capacity(PFCC).The space-time consumption method and density-based spatial clustering of application with noise(DBSCAN)algorithm were used to explore the RNCC and PFCC for bike-sharing.Combined with the users’demand,the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users’demand,so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly.The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing,China.Compared with the field survey data,the effect of the proposed algorithm was verified.

Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment

Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)environments.A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems.In the real world,the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage.In addition to pure manual rebalancing,in several works,attempts were made to predict the demand for bikes.In this paper,we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion.We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering.We carefully analyzed and discovered the group of features that impact the demand of bikes,from historical bike-sharing records and 5G IoT environment data.We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand.We performed sufficient experiments on two real-world datasets.The results confirm that compared to some existing methods,our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.

Analysis and O-D Demand Estimation of a Public Bike-Sharing Program in Las Vegas

Bike-share systems are an effective way of mitigating congestion on the road. In addition, bike-share systems have been built in universities to serve for trips to work/commuting as well as the trips on campus. In Las Vegas, a bike-share system was proposed at the University of Nevada, Las Vegas. This study analyzed factors that influence the usage of bike-share program and estimated the origin-destination demand. To achieve these objectives, first, a literature review was conducted on university bike-sharing systems in the U.S. and abroad. Then, a survey with a questionnaire was distributed to UNLV to obtain the users’ preferences to the locations of the proposed bike-share stations and their likelihood and frequency to use the bike-share program. In total, 241 faculty, staff, and students responded to the survey. About 50% of those participating in the survey expressed willingness to use the bike-share system for commuting and 60% said they are willing to use bike share for on-campus travel. Commuting and on-campus travel are two different types of travel, and the factors to determine whether an individual would use the bike-share system are quite different for each. It was estimated that there would be 3450 members for a bike-share program at UNLV, each making bicycle trips with varying frequencies, producing 1966 trips per day.

Determination of the Size of a Proposed Bike-Sharing Program in Las Vegas, Nevada

Bike-share systems have been installed in cities worldwide as a way to attract travelers to use transit rather than the automobile. This has been proved to be an effective way of mitigating congestion on the road. The objective of this study is to develop a method to determine the size of the bike-share program in terms of the number of bicycles, the number and location of the stations, the number of docks at each station. To achieve the objectives of this study, a literature review was conducted on university bike-sharing systems in the U.S. and abroad. Various cases of bike-share programs were analyzed, in which each case consisted of a different number and location of bike-share stations. The demand corresponding to these stations was used as the input to a simulation model developed in this study to determine the number of docks in stations and bicycles in the system on and around campus at UNLV. These sizing parameters of the bike-share system then were used in a cost and benefit analysis to determine which cases could achieve maximum benefit, given a limitation of the initial costs. It was found that provision of one peripheral station and three internal stations at strategic locations provide relatively higher benefit cost ratio at lower initial cost.

Free-floating bike-sharing green relocation problem considering greenhouse gas emissions

This paper introduces the problem of green bike relocation considering greenhouse gas emissions in free-floating bike-sharing systems(FFBSSs)and establishes a mathematical model of the problem.This model minimizes the total imbalance degree of bikes in the FFBSS and the greenhouse gas emissions generated by relocation in the FFBSS.Before the relocation phase,the FFBSS is divided into multiple relocation areas using a two-layer clustering method to reduce the scale of the relocation problem.In the relocation phase,the relocation route problem is converted into a pickup and delivery vehicle-routing problem.Then,an adaptive variable neighbourhood tabu search algorithm with a three-dimensional tabu list is proposed,which can simultaneously solve the relocation problem and the routing problem.A computational study based on the actual FFBSS used in Shanghai shows that this method can effectively solve the green relocation problem of FFBSSs.

Optimization of bike-sharing parking spot layout based on usage requirements

In this paper,two problems that exist in parking spot layout of bike-sharing which need to be solved are addressed.One is the size of parking spot,while another is the location of parking spot.First,the generation and attraction model of traffic travel is used to predict the usage requirements of bike-sharing,to acquire the parking requirements of bike-sharing.Then,we establish the best station spacing model to calculate the optimal quantity of parking spots.Moreover,the method of shortest distance clustering is applied to cluster the parking demand spots.Finally,the location optimization of bike-sharing parking spot is carried out by using gravity location model.In summary,the layout of bike-sharing parking spot is finished based on the abovementioned four steps.

How Should Cities Improve Dockless Bike-Sharing Services?

Recently, transport authorities in Guangzhou in south China's Guangdong Province released in formation that it had conducted talks with bike-sharing companies with business in the city, demanding that they suspend the deployment of new bicycles until they have rectified and improved their current practices.

Self-Organized Bike Redistribution in Urban City

Bicycle is an affordable and environmental friendly alternative to private cars and public transportation. Recently, some big cities in China established the bike-sharing system (BSS) through which people can rent bikes offered by government or commercial companies. However, due to limited parking space, it is often difficult for bikers to park their bicycles in bike stations. This paper envisions approaching this problem by using a self-organized bike redistribution strategy: as time passes by, bike society will form an equilibrium state of bike redistribution.

Detection approach for unusable shared bikes enabled by reinforcement learning and PageRank algorithm

Existing research models can neither indicate the availability of shared bikes nor detect unusable ones owing to a lack of information on bike maintenance and failure.To improve awareness regarding the availability of shared bikes,we propose an innovative approach for detecting unusable shared bikes based on reinforcement learning and the PageRank algorithm.The proposed method identifies unusable shared bikes depending on the local travel data and provides a ranking of the shared bikes according to their availability levels.Given a sliding time window,the value function for the reinforcement learning model was determined by considering the cumulative number of unavailable shared bikes,the proportion of rental cancelations at the same stations,and the mean time between the cancelations.Reinforcement learning was then used to identify shared bikes with the worst availability.An availability ranking for the shared bikes below the reward threshold was performed using the PageRank algorithm.The proposed detection approach was applied to a trip dataset of a real-world bike-sharing system to illustrate the modeling process and its effectiveness.The detection results of unusable shared bikes in the absence of failure and feedback data can provide essential information to support the maintenance management decisions regarding shared bikes.