Social media and travel behaviour

Social media has emerged as a trend that greatly influences transportation and travel behaviour. This influence is identified both on the way that travellers make decisions concerning transportation related matters and the fact that social media allow tracing back the way that these decisions were made and allow for the collection of data, related to understanding these decisions. This chapter introduces these interdependences between Social Media (SM) and travel behaviour by presenting a collection of use cases and methods. Firstly, an introduction to the existing dominant SM is presented, including current availability of data and a discussion of helpful frameworks that could facilitate transportation related research on the subject. The pertinent literature on the User Generated Content (UGC) generation process and users’ personality characteristics is reviewed, in order to gain understanding on the characteristics of the users, who generate the content. Secondly, the relation of SM to travel behaviour is established by investigating the impact of UGC to the transportation system and vice versa. The capabilities of SM to allow for behavioural interventions is discussed towards the direction of inducing more socially responsible behaviour. Finally, related case studies are presented

POI prediction based on user selection influences

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Ciências da Computação.Tendo em vista a dificuldade de escolha gerada pela grande diversidade de estabelecimentos encontrados nas cidades atualmente e a crescente aderência da população a redes sociais baseadas em localização, realiza-se uma pesquisa aplicada exploratória quali-quantitativa com enfoque indutivo sobre os frameworks existentes de recomendação e de predição de pontos de interesse. Foram identificados 5 fatores que influenciam usuários de redes sociais baseadas em localização a visitarem novos estabelecimentos: geografia, tempo, amizade, personalidade e escolhas feitas por usuários similares. Baseado nesses frameworks e nas influências identificadas, desenvolve-se um modelo unificado que será capaz de prever que estabelecimentos cada usuário visitará com base em parte de sua trajetória individual. Para tanto, divide-se a trajetória de cada usuário em duas partes, a primeira para análise e a segunda para validação, de modo que, ao aplicar o modelo desenvolvido à primeira parte da trajetória, idealmente chega-se na segunda. O modelo é aplicado à base de dados da rede social Gowalla. Diante disso, verifica-se que dentre os 5 fatores identificados, a escolha de usuários similares e a amizade obtiveram a melhor acurácia, e a união dos 5 fatores apresentou resultados melhores que cada fator individualmente, porém, diferentemente dos outros frameworks estudados, a melhora não foi significativa, o que impõe a constatação de que o problema precisa ser estudado mais a fundo.Taking into account the difficulty of choice generated by the great diversity of points of interest currently found in cities and the population increasing adherence to location-based social networks, a qualitative and quantitative exploratory applied research with inductive reasoning is performed on existing points of interest prediction and recommendation. 5 factors that influence users from location-based social networks to visit new points of interest were identified: geography, time, friendship, personal taste and choices made from similar users. Based on the identified influences and the frameworks, an unified model that is capable of predict what points of interest each user will visit based on their individual trajectory is developed. To validate the model, each user trajectory is divided in two parts, the first for analysis and the second for validation, so that by applying the developed model to the former, ideally the latter is achieved. Finally, the model is applied to a Gowalla social network dataset. Facing the results, it is stated that among the five influential factors identified (geography, time, friendship, personal taste and similarity with other users), the similarity with other users and the friendship achieved the best prediction accuracy, and the unified model presented better results than each factor individually, however, unlike the other studied frameworks, the improvement was negligible, which imposes that the problem needs further research

POI prediction based on user selection influences

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Ciências da Computação.Tendo em vista a dificuldade de escolha gerada pela grande diversidade de estabelecimentos encontrados nas cidades atualmente e a crescente aderência da população a redes sociais baseadas em localização, realiza-se uma pesquisa aplicada exploratória quali-quantitativa com enfoque indutivo sobre os frameworks existentes de recomendação e de predição de pontos de interesse. Foram identificados 5 fatores que influenciam usuários de redes sociais baseadas em localização a visitarem novos estabelecimentos: geografia, tempo, amizade, personalidade e escolhas feitas por usuários similares. Baseado nesses frameworks e nas influências identificadas, desenvolve-se um modelo unificado que será capaz de prever que estabelecimentos cada usuário visitará com base em parte de sua trajetória individual. Para tanto, divide-se a trajetória de cada usuário em duas partes, a primeira para análise e a segunda para validação, de modo que, ao aplicar o modelo desenvolvido à primeira parte da trajetória, idealmente chega-se na segunda. O modelo é aplicado à base de dados da rede social Gowalla. Diante disso, verifica-se que dentre os 5 fatores identificados, a escolha de usuários similares e a amizade obtiveram a melhor acurácia, e a união dos 5 fatores apresentou resultados melhores que cada fator individualmente, porém, diferentemente dos outros frameworks estudados, a melhora não foi significativa, o que impõe a constatação de que o problema precisa ser estudado mais a fundo.Taking into account the difficulty of choice generated by the great diversity of points of interest currently found in cities and the population increasing adherence to location-based social networks, a qualitative and quantitative exploratory applied research with inductive reasoning is performed on existing points of interest prediction and recommendation. 5 factors that influence users from location-based social networks to visit new points of interest were identified: geography, time, friendship, personal taste and choices made from similar users. Based on the identified influences and the frameworks, an unified model that is capable of predict what points of interest each user will visit based on their individual trajectory is developed. To validate the model, each user trajectory is divided in two parts, the first for analysis and the second for validation, so that by applying the developed model to the former, ideally the latter is achieved. Finally, the model is applied to a Gowalla social network dataset. Facing the results, it is stated that among the five influential factors identified (geography, time, friendship, personal taste and similarity with other users), the similarity with other users and the friendship achieved the best prediction accuracy, and the unified model presented better results than each factor individually, however, unlike the other studied frameworks, the improvement was negligible, which imposes that the problem needs further research

Consumer Location Based Service Perceptions and Response: a focus on Location Based Services and Emerging Mobile Lifestyles

Location Based Services (LBS) and electronically mediated lifestyles (e-lifestyles) represent emergent new areas with approaches (e.g. apps and e-activities) billed to change customer experiences and responses. Marketers are confronted with a challenge of understanding how consumers engage with mobile services and how to design appropriate strategies towards that (Donovan, 2013). A review of extant literature has indicated that the implementation of marketing strategies based on LBS is still in its infancy, and yet to gain widespread acceptance by consumers. The role of individual differences in consumer response to LBS is not reported in any substantive way in the literature- yet we know that e-lifestyles are now shaping different consumer responses to LBS. This PhD addresses this important area, with a focus on the role of e-lifestyles in consumer response to location-based services. The study relied on a sequential multimethod qualitative method of enquiry. Initially, in the first phase of data collection, relevant LBS websites were observed over a three-month period to explore consumer familiarity, attitudes and experience, offering some rich insights into consumer LBS awareness. In phase two of the research, specialist interviews (thirty-eight in total) were used in conjunction with cartoon tests as an effective way to establish the role of e-lifestyles, situational decision making as well as capturing actual (typical) consumer response in LBS encounters. In phase three, three focus groups were conducted with different user groups (young students, young professionals and older established working participants with families) to examine the role of individual factors in consumer LBS response. Findings in the study point to good experience with LBS with some selective engagement depending on user group profile, which broke down into ‘Involved’, ‘Observer’ or ‘Transaction’ orientations. Phase two (innovative cartoon tests) led to findings that mapped actual consumer response pathways in simulated encounters- four response pathways unique to this study emerged (immediate, delayed/future response, socially-mediated response and indifference). Findings also point towards influential individual factors such as variation on the basis of life stage, distinct patterns of proactivity and reactivity to LBS messages and the importance of situational factors on the nature of LBS response. This study contributes to the body of knowledge on LBS and e-lifestyles theory by providing deeper insights on actual consumer response process in typical LBS encounters (e.g. the UK context). It adds fresh insights into typical response processes by using specialist scenarios reflective of typical LBS encounters to map key response pathways, capturing ‘live’ customer experiences of different forms of LBS and interrogating the rationale behind individual responses using LBS scenarios. Findings also offer a clearer classification of customer response types (e.g. proactive and self-referencing LBS; reactive and cross-referencing LBS). By combining situational context, e-lifestyle and individual attributes influencing individual response to LBS in a single study, this research takes forward the argument of Weiss (2013) on the need for more in-depth examination of consumer response to LBS and takes further previous LBS adoption studies (Zhou, 2012)

Information Outlook, December 2010

Volume 14, Issue 8https://scholarworks.sjsu.edu/sla_io_2010/1007/thumbnail.jp

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Social, Casual and Mobile Games

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Social, casual and mobile games, played on devices such as smartphones, tablets, or PCs and accessed through online social networks, have become extremely popular, and are changing the ways in which games are designed, understood, and played. These games have sparked a revolution as more people from a broader demographic than ever play games, shifting the stereotype of gaming away from that of hardcore, dedicated play to that of activities that fit into everyday life. Social, Casual and Mobile Games explores the rapidly changing gaming landscape and discusses the ludic, methodological, theoretical, economic, social and cultural challenges that these changes invoke. With chapters discussing locative games, the new freemium economic model, and gamer demographics, as well as close studies of specific games (including Candy Crush Saga, Angry Birds, and Ingress), this collection offers an insight into the changing nature of games and the impact that mobile media is having upon individuals and societies around the world

Towards a Protocol for the Collection of VGI Vector Data

A protocol for the collection of vector data in Volunteered Geographic Information (VGI) projects is proposed. VGI is a source of crowdsourced geographic data and information which is comparable, and in some cases better, than equivalent data from National Mapping Agencies (NMAs) and Commercial Surveying Companies (CSC). However, there are many differences in how NMAs and CSC collect, analyse, manage and distribute geographic information to that of VGI projects. NMAs and CSC make use of robust and standardised data collection protocols whilst VGI projects often provide guidelines rather than rigorous data collection specifications. The proposed protocol addresses formalising the collection and creation of vector data in VGI projects in three principal ways: by manual vectorisation; field survey; and reuse of existing data sources. This protocol is intended to be generic rather than being linked to any specific VGI project. We believe that this is the first protocol for VGI vector data collection that has been formally described in the literature. Consequently, this paper shall serve as a starting point for on-going development and refinement of the protocol

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)