Research on the Traveling Characteristics and Comparison of Bike Sharing in College Campus-A Case Study in Hangzhou

Bike sharing emerging from college campus in China's Mainland has become a major part in the daily traveling of Chinese urban residents.It changes the traveling behavior of urban residents,and simultaneously,raises higher requirements on urban transportation facility construction and management.However,the return of bike sharing to college campus causes more troubles to schools.The fundamental cause is the closed peculiarity of campus traveling comparing with city traveling,and also the discrepancy between college campuses of different types.This paper investigates the traveling characteristics of bike sharing in college campus in three different locations in Hangzhou City,Zhejiang Province of China in the questionnaire,and compares the discrepancy with urban bike sharing traveling characteristics and the discrepancy in bike sharing use between college campuses of different types.From the perspective of parking,maintenance and operation,and hardware design,the paper eventually raises suggestions to optimize independent college campus bike sharing service facility and management consistent with urban system.The research may also offer beneficial reference to the release of bike sharing facilities consistent with urban system in all sorts of independent parks,especially college campus.

Analysis of Financial Accounting Problems Based on Sharing Economy——Taking Bike Sharing as an Example

Bike sharing is a new type of"Internet+"model.At present,China has not yet introduced relevant Internet operating systems and accounting standards.There is still a big problem in the financial accounting and processing of the bike sharing platform.This paper firstly summarizes the development status of bike sharing,then analyzes the related financial accounting problems of bike sharing with the example of Mobike,and points out the main problems faced by the financial accounting of bike sharing short-time rental model,and finally proposes countermeasures for the financial accounting of bike sharing short-time rental model.

Understanding bike trip patterns leveraging bike sharing system open data

Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and sta- tion management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips （i.e., origin-destination station pairs） is much larger than the number of stations, we define the trip infer- ence as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data from Washington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.

Challenges and Opportunities in Algorithmic Solutions for Re-Balancing in Bike Sharing Systems

In recent years,the booming of the Bike Sharing System(BSS)has played an important role in offering a convenient means of public transport.The BSS is also viewed as a solution to the first/last mile connection issue in urban cities.The BSS can be classified into dock and dock-less.However,due to imbalance in bike usage over spatial and temporal domains,stations in the BSS may exhibit overflow(full stations)or underflow(empty stations).In this paper,we will take a holistic view of the BSS design by examining the following four components:system design,system prediction,system balancing,and trip advisor.We will focus on system balancing,addressing the issue of overflow/underflow.We will look at two main methods of bike re-balancing:with trucks and with workers.Discussion on the other three components that are related to system balancing will also be given.Specifically,we will study various algorithmic solutions with the availability of data in spacial and temporal domains.Finally,we will discuss several key challenges and opportunities of the BSS design and applications as well as the future of dock and dock-less BSS in a bigger setting of the transportation system.

The New Bike Sharing A Concept for Urban Commuters

Shanghai saw the launch last April of bike-sharing startup MoBike,with its fleet of station-free bicycles distinguished by their app-activated rear-wheel locks.The company’s aim was to provide urban dwellers with affordable,convenient,short-distance transportation.The bikes were an immediate success.A few months later they

Inspiration of “Internet + Shared bike” for Healthy Exercise of University Students

As the economy and technology keep growing,the mode of shared bikes gains popularity under these circumstances.At the current period,university students become fond of using shared bikes,which changes body health and life style of university students.When this mode combines the characteristics and functions of shared bikes,it has a great impact on the awareness and motivation of work-out of university students.Supported by Internet technology,shared bikes meet the need of people that they can use them at any time,which is new and innovative to the university students.This article provides university students with shared bike service in campus by analyzing the influence of“Internet+shared bike”on health and sports of university students.It will promote the effective application of shared bikes.

Understanding the demand predictability of bike share systems:A station-level analysis

Predicting demand for bike share systems(BSSs)is critical for both the management of an existing BSS and the planning for a new BSS.While researchers have mainly focused on improving prediction accuracy and analysing demand-influencing factors,there are few studies examining the inherent randomness of stations'observed demands and to what degree the demands at individual stations are predictable.Using Divvy bike-share one-year data from Chicago,USA,we measured demand entropy and quantified the station-level predictability.Additionally,to verify that these predictability measures could represent the performance of prediction models,we implemented two commonly used demand prediction models to compare the empirical prediction accuracy with the calculated entropy and predictability.Furthermore,we explored how city-and system-specific temporallyconstant features would impact entropy and predictability to inform estimating these measures when historical demand data are unavailable.Our results show that entropy and predictability of demands across stations are polarized as some stations exhibit high uncertainty(a low predictability of 0.65)and others have almost no check-out demand uncertainty(a high predictability of around 1.0).We also validated that the entropy and predictability are a priori model-free indicators for prediction error,given a sequence of bike usage demands.Lastly,we identified that key factors contributing to station-level entropy and predictability include per capita income,spatial eccentricity,and the number of parking lots near the station.Findings from this study provide more fundamental understanding of BSS demand prediction,which can help decision makers and system operators anticipate diverse stationlevel prediction errors from their prediction models both for existing stations and for new ones.

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.