Mobility Trends and their effect on Traffic Safety During the Covid-19 Pandemic: Case Study Republic of Croatia

Safety analysis focuses on how traffic safety can change while mobility analysis is used to determine how people change travel behavior. The integration of mobility, safety and behavioral data related to COVID-19 can provide valuable insights to decision makers. Wide availability of mobile sensors has given us the opportunity to be able to assess changes in the performance and mobility of transport systems in, almost real time. The researchers also measured the impact of COVID-19 on human mobility using public mobile location data available from many companies such as Google and Apple, which is very useful for changing human mobility. The platforms produce aggregated metrics of daily mobility, including the purpose of travel, the mode of travel, and imputations of social demographics. Based on a comprehensive data set of people who participated in the collected accident data and mobile device data, we record the impact of COVID-19 on traffic safety. The paper systematically and statistically approaches the assessment of road safety in Croatia during the COVID-19 pandemic.

Mobility Trends and their effect on Traffic Safety During the Covid-19 Pandemic: Case Study Republic of Croatia

Safety analysis focuses on how traffic safety can change while mobility analysis is used to determine how people change travel behavior. The integration of mobility, safety and behavioral data related to COVID-19 can provide valuable insights to decision makers. Wide availability of mobile sensors has given us the opportunity to be able to assess changes in the performance and mobility of transport systems in, almost real time. The researchers also measured the impact of COVID-19 on human mobility using public mobile location data available from many companies such as Google and Apple, which is very useful for changing human mobility. The platforms produce aggregated metrics of daily mobility, including the purpose of travel, the mode of travel, and imputations of social demographics. Based on a comprehensive data set of people who participated in the collected accident data and mobile device data, we record the impact of COVID-19 on traffic safety. The paper systematically and statistically approaches the assessment of road safety in Croatia during the COVID-19 pandemic.

Intelligent Resource Allocation Method for Wireless Communication Networks Based on Deep Learning Techniques

In this paper, a deep learning approach is used to conduct an in-depth study and analysis of intelligent resource allocation in wireless communication networks. Firstly, the concepts related to CSCN architecture are discussed and the throughput of small base stations (SBS) in CSCN architecture is analyzed; then, the long short-term memory network (LSTM) model is used to predict the mobile location of users, and the transmission conditions of users are scored based on two conditions, namely, the mobile location of users and whether the small base stations to which users are connected have their desired cache states, and the small base stations select the transmission. The small base station selects several users with optimal transmission conditions based on the scores; then, the concept of game theory is introduced to model the problem of maximizing network throughput as a multi-intelligent noncooperative game problem; finally, a deep augmented learning-based wireless resource allocation algorithm is proposed to enable the small base station to learn autonomously and select channel resources based on the network environment to maximize the network throughput. Simulation results show that the algorithm proposed in this paper leads to a significant improvement in network throughput compared to the traditional random-access algorithm and the algorithm proposed in the literature. In this paper, we apply it to the fine-grained resource control problem of user traffic allocation and find that the resource control technique based on the AC framework can obtain a performance very close to the local optimal solution of a matching-based proportional fair user dual connection algorithm with polynomial-level computational complexity. The resource allocation and task unloading decision policy optimization is implemented, and at the end of the training process, each intelligent body independently performs resource allocation and task unloading according to the current system state and policy. Finally, the simulation results show that the algorithm can effectively improve the quality of user experience and reduce latency and energy consumption.

Visual Emotion-Aware Cloud Localization User Experience Framework Based on Mobile Location Services

<p>Recently, the study of emotional recognition models has increased in the human-computer interaction field. With high recognition accuracy of emotions’ data, we could get immediate feedback from mobile users, get a better perception of human behavior while interacting with mobile apps, and thus make the user experience design more adaptable and intelligent. The harnessing of emotional recognition in mobile apps can dramatically enhance users’ experience. Therefore, in this paper, we propose a visual emotion-aware cloud localization user experience framework based on mobile location services. An important feature of our proposed framework is to provide a personalized mobile app based on the user’s visual emotional changes. The framework captures the emotion-aware data, process them in the cloud server, and analyze them for an immediate localization process. The first stage in the framework builds a correlation between the application’s default language and the user’s visual emotional feedback. In the second stage, the localization model loads the appropriate application’s resources and adjusts the screen features based on the real-time user’s emotion obtained in the first stage and according to the location data that the app collected from the mobile device. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high-quality application experience in terms of a user’s emotional levels and deliver an excellent level of usability that was before not possible.</p>

Literature Review of Location-Based Mobile Games in Education: Challenges, Impacts and Opportunities

With the universal use of mobile computing devices, there has been a notable increase in the number of mobile applications developed for educational purposes. Gamification strategies offer a new set of tools to educators and, combined with the location services provided by those devices, allow the creation of innovative location-based mobile learning experiences. In this literature review, we conduct an analysis of educational mobile location-based games. The review includes articles published from January of 2010 to October of 2020, and from 127 records screened, 26 articles were analysed in full-text form. This analysis allowed us to answer the following six predefined research questions: Who are the target audiences for location-based games? In which subjects are location-based games most used? Which strategies are implemented with mobile devices to improve the student’s learning process? What are the main impacts of location-based games on students’ learning? What are the main challenges to the development of location-based games for education? What future tendencies and research opportunities can be identified from the analysis of the current state of the art?

Japan’s voluntary lockdown: further evidence based on age-specific mobile location data

Changes in people’s behavior during the COVID-19 pandemic can be regarded as the result of two types of effects: the “intervention effect” (changes resulting from government orders for people to change their behavior) and the “information effect” (voluntary changes in people’s behavior based on information about the pandemic). Using age-specific mobile location data, we examine how the intervention and information effects differ across age groups. Our main findings are as follows. First, the age profile of the intervention effect shows that the degree to which people refrained from going out was smaller for older age groups, who are at a higher risk of serious illness and death, than for younger age groups. Second, the age profile of the information effect shows that the degree to which people stayed at home tended to increase with age for weekends and holidays. Thus, while Acemoglu et al. (2020) proposed targeted lockdowns requiring stricter lockdown policies for the oldest group in order to protect those at a high risk of serious illness and death, our findings suggest that Japan’s government intervention had a very different effect in that it primarily reduced outings by the young, and what led to the quarantining of older groups at higher risk instead was people’s voluntary response to information about the pandemic. Third, the information effect has been on a downward trend since the summer of 2020. It is relatively more pronounced among the young, so that the age profile of the information effect remains upward sloping.

In the right place at the right time: a review of mobile location-based marketing and a research agenda

Objetivo: Com base na presença cada vez mais difundida do celular no contexto do varejo, o objetivo da presente pesquisa é examinar os efeitos potenciais do conteúdo da mensagem móvel e dos dados de geolocalização como motivadores de visitas às lojas, conectando os esforços online ao comportamento offline. Método: Este artigo fornece uma revisão da literatura sobre o que se sabe a respeito dos temas mobile marketing, comunicação baseada em localização e o efeito de notificações push nas atitudes e comportamento dos clientes.Resultados: Sintetizamos argumentos para notificações push baseadas em localização relacionadas a visitas ao site offline e oferta de cupons, conteúdo personalizado e de alto envolvimento. Com base em várias descobertas de marketing, identificamos um conjunto de proposições.Contribuições teóricas: Esta revisão pretende contribuir para as teorias de mobile marketing, comportamento do cliente omnicanal e também para a compreensão das promoções baseadas em geolocalização. Ao identificar estratégias que os profissionais de marketing podem empregar para promoções mais eficazes, a presente revisão fornece uma estrutura abrangente para sintetizar as descobertas atuais no marketing baseado em geolocalização e identifica lacunas em nosso conhecimento atual, a fim de estimular o desenvolvimento de pesquisas neste campo.Implicações gerenciais: A principal suposição que apoia o artigo é a de que o conteúdo e o momento da mensagem (considerando a geolocalização do cliente) aumentam as visitas à loja física. O contexto e a conveniência são apresentados como os principais impulsionadores do efeito (visitas ao ponto de venda físico geradas por notificações móveis), considerando que o contexto e a conveniência são representados pela geolocalização e pelo conteúdo da mensagem. Isso fornece a base para um modelo de estratégias de mobile marketing para que empresas atraiam clientes para locais físicos. A contribuição desta revisão está em iluminar o caminho da ativação mobile na estratégia de cross-channel do varejo.

An Application of K-Means Clustering on Tourist Activity in Maui Using Mobile Location Data

Mobile location data are a major form of Big Data that hold many possibilities for study and insight into human behaviour. This research used mobile location data to investigate the differences in the activity patterns of tourists in Maui, Hawai’i. Mobile data used in this study were app-based location data collected as a stream of mobile phone locations with a timestamp. Tourists were clustered using K-Means based on time spent at attraction types. Different travel experiences were analyzed based on traveler’s accommodation choices, the average distance travelled from accommodation to attraction, and vacation length, which all varied significantly between the tourist clusters. This work provided a new use for K-means clustering with mobile location data to provide insightful information to marketing professionals and tourism management bodies.

An Application of K-Means Clustering on Tourist Activity in Maui Using Mobile Location Data

Mobile location data are a major form of Big Data that hold many possibilities for study and insight into human behaviour. This research used mobile location data to investigate the differences in the activity patterns of tourists in Maui, Hawai’i. Mobile data used in this study were app-based location data collected as a stream of mobile phone locations with a timestamp. Tourists were clustered using K-Means based on time spent at attraction types. Different travel experiences were analyzed based on traveler’s accommodation choices, the average distance travelled from accommodation to attraction, and vacation length, which all varied significantly between the tourist clusters. This work provided a new use for K-means clustering with mobile location data to provide insightful information to marketing professionals and tourism management bodies.