Query: riding experience bike-sharing

title: From value co creation to value co destruction? The case of dockless bike sharing in China
abstract: Sharing economy platforms have gained momentum in urban areas worldwide by offering the potential for efficient resource utilisation and novel value creation. A recent addition to the mobility sector of the sharing economy is the dockless bike sharing system (DBSS) which emerged in late 2015 with the aim of complementing urban mobility and contributing to urban sustainability. However, recently, debate and controversy have emerged about the potential negative consequences of DBSS. Building on the value creation literature, and drawing on practice theory and the resource integration perspective, we investigate how users participate in value co creation and co destruction activities related to DBSS. Through a thematic analysis of 8813 social media (i.e. Sina Weibo) tweets from April 2016 to December 2017, we find that riding experience is the most important practice in the core using process for both value co creation and co destruction, and that post riding practices can result in significant value co destruction. In the value formation process of DBSS, the critical firm resources are product service resources and relational resources, and the critical customer resources are emotional resources, relational resources and energy resources. We also argue the enabling role of peripheral practices in the transition between value co creation and co destruction. We contribute to the literature by proposing a value co creation and co destruction framework for DBSS derived from key social practices and resources.

title: Hierarchical Prediction Based on Two Level Affinity Propagation Clustering for Bike Sharing System
abstract: Bike sharing system is a new transportation that has emerged in recent years. More and more people will choose to ride bicycle sharing at home and abroad. While we use shared bicycles conveniently, there are also unfavorable factors that affect the customer's riding experience in the bicycle sharing system. Due to the rents or returns of bikes at different stations in different periods are imbalanced, the bikes in the system need to be rebalanced frequently. Therefore, there is an urgent need to predict and reallocate the bikes in advance. In this paper, we propose a hierarchical forecasting model that predicts the number of rents or returns to each station cluster in a future period to achieve redistribution. First, we propose a two level affinity propagation clustering algorithm to divide bike stations into groups where migration trends of bikes among stations as well as geographical locations information are considered. Based on the two level hierarchy of stations, the total rents of bikes are predicted. Then, we use a multi similarity based inference model to forecast the migration proportion of inter cluster and across cluster, based on which the rents or returns of bikes at each station can be deduced. In order to verify the effectiveness of our two level hierarchical prediction model, we validate it on the bike sharing system of New York City and compare the results with those of other popular methods obtained. Experimental results demonstrate the superiority over other methods.

title: Research on the Psychological Model of Free floating Bike Sharing Using Behavior: A Case Study of Beijing
abstract: As a clean, sustainable transport tool, bicycles have significant advantages in short distance travel. Despite many efforts assumed in Beijing to improve the cycling environment, the popularity of cycling remains relatively low. However, the advent of the free floating bike sharing (FFBS) system has engendered an unexpected cycling enthusiasm in Beijing. Therefore, it is of great importance to delve into why travelers prefer FFBS as a transportation form from a psychological perspective. In this paper, 352 valid questionnaires were collected from an online survey, and an extended theory of planned behavior (TPB) was adopted to examine the psychological determinants of intention and actual behavior to use FFBS. The results showed that men and car owners prefer vehicles and show a lower willingness to use FFBS. In contrast, residents under the age of 60, residents with FFBS riding experience, and residents skilled in cycling are inclined to use FFBS; the economic convenience of FFBS is the most important attractant for FFBS, while bad weather is the biggest hindrance factor for residents to use FFBS; however, imperfection in infrastructure has no significant impact on reducing residents' willingness to use FFBS. These results have important implications for planners to better understand the FFBS use behavior, and several suggestions are proposed to support the policymaking about FFBS.

title: Sharing and Riding: How the Dockless Bike Sharing Scheme in China Shapes the City
abstract: Over the last three years, the dockless bike sharing scheme has become prevalent in the context of the boom in the sharing economy, the wide use of mobile online payment, the increasing environmental awareness and the inherent market demand. This research takes Beijing as a case study, investigates the users' characteristics, their behaviour change, and perceptions of dockless bike sharing scheme by the quantitative survey, and then analyzes the reasons behind it and how it has changed the residents' life in Beijing. This new kind of dockless shared bikes, with great advantages of accessibility, flexibility, efficiency and affordability, helps to solve the 'last mile' problem, reduce the travel time, and seems to be very environmentally friendly and sustainable. However, with the help of interview and document analysis, this research finds that the shared bikes are not the effective alternative for the frequent car users. Nevertheless, it also has numerous negative consequences such as 'zombie' bikes blocking the sidewalks and vandalism to the bikes. The public is also worried about their quality and safety, especially the issues of 'right of way' How to coordinate and solve these problems is not only related to the future direction of the dockless bike sharing scheme but also to the vital interests of the general public. Therefore, it is important to emphasize that governments, enterprises, and the public participate in multi party cooperation and build synergic governance networks to carry forward the advantages and avoid the negative effects of the new bike sharing system.

title: Riding a Bike Not Owned by Me in Bad Air: Big Data Analysis on Bike Sharing
abstract: The sharing economy has significantly changed the way of living for years. The emergence and expansion of sharing economy empowered by the mobile information technologies and intellectual algorithms reconfigure how people use transportation means. In this paper, the bike sharing phenomenon is highlighted. Combining a big data set provided by the Seoul government about user logs and air quality data set, the empirical findings reveal that temperature change is tightly associated bike sharing activities. Also, the concentration of particulate matter is weakly related to bike sharing, but the trend should be carefully examined. By considering external environmental factors to bike sharing businesses, this work is differentiated. To further understand empirical data, data mining methods and econometric approaches were adopted.

title: Study on the Cycling Attraction Area of Metro Station Based on Sharing Bike Riding Data: An Empirical Study in Beijing

title: Bike sharing: Is Singapore riding the wrong way? Experts weigh in on which model suits the Republic's needs best

title: "Just Like Riding a Bike" A Model Matching Approach to Predicting the Enjoyment of a Cycling Exergame Experience
abstract: Innovations in technology have led to the introduction of exercise devices that feature interactive virtual worlds, often referred to as exergames. Using the theory of model matching, this study seeks to determine if controller naturalness and perceived realism are predictors of immersion in an exercise based virtual environment and, in turn, if perceived immersion is a predictor of enjoyment of this exercise experience. Participants rode a 3 mile virtual course on an Expresso HD upright bicycle and then reported their attitudes toward the experience. Findings suggest that extending the theory of mental models from traditional video game play to the study of certain exergame based technologies is an appropriate application of the theory. Additionally, given the influence of immersion on the overall enjoyment of the fitness experience identified in this study, these findings also have implications for the development and use of exergames that are immersive, realistic, and utilize naturalistic controllers to promote commitment to physical activity.

title: Bike sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning
abstract: In many big cities, the bike sharing system (BSS) and taxi play critical roles in transportation services. They both offer on demand transportation options and allow flexible riding scheduling and routing. Previous literature has compared BSS and taxi to other transport modes, such as public transit and private automobile, but little is known about the spatiotemporal factors that influence travel choices between these two alternatives. Understanding travel patterns of BSS and taxi is critical in traffic demand analysis and sustainable transportation planning. Also, an in depth examination of the patterns of travel behaviors, especially when one would choose BSS over a taxi, will provide valuable insights on human mobility and active living research. In this study, we investigated the spatiotemporal patterns of BSS and taxi trips in Chicago from 2014 to 2016. To model travel choices between BSS and taxi, we applied machine learning techniques to simulate the means of transport based on environmental and temporal factors. Results show seasonal trip variations of the BSS and a declining trend of taxi trips. BSS speed is relatively stable while taxi speed varies primarily because of time and locations. Based on the random forest model, which has demonstrated the best fit with high processing speed, travel distance and the number of parks and recreational facilities seem to be critical spatial predicting factors of the travel choice. Given any time and location, the model can recommend the travel choices between BSS and taxis for users. This study shows the significance of machine learning techniques in urban mobility research. Results of the study may potentially support people's transportation decision making and facilitate sustainable transportation planning.

title: User habits analysis in a bike sharing system
abstract: The concept of mobility is changing. Nowadays there are different systems to help people move in a sustainable way. The concept of sharing vehicles like cars or bicycles is more and more spreading. The bike sharing is growing up in the last years in many cities in different parts of the world. People prefer bikes because they are a healthy and a fast way to move especially across small distances. Moreover, people enjoy riding a bike. There are two types of bike sharing: the station based bike sharing and the free floating bike sharing. The first one is usually public, the user has to do the registration on the web site and then the service can be used. But it is based on station and this means that the user must take and park the bike in a station that must be found through the mobile app. The free floating bike sharing is a relatively new system. The most important feature is that docking stations are not needed and the cost of installing and maintaining the racks are avoided. For this reason, the cost of the installation for the companies is less than the station based system. Thanks to this new system searching a station to park the bike is not necessary, hence it is easier for the user to leave the bike. In the last few years not a lot of studies were made on the bike sharing system especially for what concern the free floating bike sharing. The aim of this master thesis is to analyse the data coming from a free floating bike sharing system to know the habits of the customers. To this purpose two types of data research are made: Real time data: to access this type of data some APIs are tested in order to have in real time the data of users' movements in a certain city; Historical data: the website of Deutsche Bahn makes available a dataset of a bike sharing system owned by them named "Call A Bike" That dataset contains the historical data of the users in many German cities. For the first approach the Ofo system in the city of Milan considered. Data are retrieved from a server thanks to a POST request and at the same time a crawler is built up in order to process the data. But in this case some issues with the collection of data occur such as the coverage of the city, in fact the requests give back all the bikes present in a certain radius of a given point, hence the division of the city is not trivial and the token used for the request has a deadline, so a lack of data occurs. For the second approach a dataset of the Call A Bike system is found from the Deutsche Bahn website. From these data four main cities are selected (Munchen, Stuttgart, Frankfurt and Hamburg) and the most important features are analysed in order to understand how the usage of the system changes in the different years.