Mobility Behavior Research Articles

Свойства трафика видеоконференций 5G и его прогнозирование методами искусственного интеллекта

The objectives and prospects of short- and long-term forecasting of data traffic in 5G networks are outlined. An overview of current traffic forecasting methods is presented. A large number of works indicate a variety of ap-proaches to the analysis and forecasting of mobile traffic. These approaches include both traditional statistical methods and deep learning methods, which is an important factor for the development of network technologies and optimization of radio access networks. Forecasting and management plays an important role both at the level of the entire 5G network and at the level of its individual components. With the increase in the volume of big data, traffic forecasting becomes a difficult task due to mobility and different user behavior. In connection with this circumstance, using the example of mobile traffic data from Google Meet, MS Teams and Zoom video conferencing systems, the features of this type of traffic are analyzed and it is concluded that the distribution of time intervals between packet arrivals corresponds to a stable distribution. Based on the results of studying some machine learning methods designed to predict time series, a comparative analysis of the effectiveness of these methods for short-term forecasting of traffic intensity was performed. The following models were used for analysis and prediction: naive seasonal, exponential smoothing, linear regression, ARIMA, Theta and NBEATS. It was found that linear regression provides the best quality indicators for Google Meet traffic, the exponential smoothing method shows the best quality indicators for MS Teams, and the Theta method has the best indicators for Zoom.

Short-Term Mobility and Risky Sexual Behaviors Among Men Who Have Sex with Men in Guangdong, China.

This study investigated regional differences in short-term mobility patterns among men who have sex with men (MSM) in Guangdong Province, and explored the associations with risky sexual behaviors. An online survey was conducted from June to August 2023, collecting socio-demographic information, intra-provincial mobility details and sexual behaviors. Among 1,808 participants, 60% reported moving within the province over the past six months, primarily for tourism, business/work, or visiting family and friends. Participants were categorized into Pearl River Delta (PRD) and Non-Pearl River Delta (Non-PRD) regions. Significant differences in education level and income were observed between mobile and non-mobile MSM in both areas (p < 0.05). Logistic regression revealed that mobile MSM, relative to non-mobile MSM, were more likely to engage in risky sexual behaviors, with adjusted odds ratios (aORs) for multiple partners, commercial sex, and sexualized drug use ranging from 1.44 to 2.96 in PRD and 2.67 to 4.44 in Non-PRD. Additionally, associations between mobility and behaviors such as having casual partners, group sex, and inconsistent condom use with casual partners were exclusively identified in Non-PRD. These findings indicate that mobility among MSM in Guangdong Province is substantial and follows similar patterns in both regions. Mobile MSM exhibit higher rates of risky sexual behaviors, with notable regional disparities. Addressing the impact of short-term mobility on risky sexual behaviors among MSM is crucial, considering regional variations.

A paradigm shift for evaluating natural attenuation of radioactive iodine in soils and sediments: Species-specific mechanisms and pathways.

The primary approach to assessing monitored natural attenuation (MNA) is currently based on a conceptual model utilizing the total contaminant concentrations, assuming a single aqueous species. However, many contaminants, such as metals and radionuclide - including iodine, can exist in multiple species that behave chemically differently in the environment and can exist simultaneously. For example, radioiodine often occurs concurrently as three major aqueous species: iodide (I-), iodate (IO3-), and organo-I, which undergo distinct attenuation pathways and exhibit markedly different mobility and geochemical behavior. Here, current literature is reviewed with the objective to: 1) demonstrate differences in iodine species' geochemical behavior and natural attenuation mechanisms; 2) show that a species-specific (or multi-species) approach provides greater details on contaminant migration and attenuation; and (3) discuss the logistics of a species-specific approach to developing conceptual models for assessing overall contaminant mobility. The species-specific approach results in a more accurate assessment of mass flux and maximum groundwater concentrations; and, therefore, a more defensible risk evaluation to support short- or long-term remediation and/or natural attenuation strategies. Although iodine is the focus of this paper, this methodology could be applied to other risk-driving contaminants such as mercury and uranium, which have even more complex aqueous speciation than iodine, or technetium and chromium, which have complex solid phase speciation and natural attenuation reaction networks. Accounting for species-specific geochemical behavior, while implementing MNA strategies can greatly reduce uncertainty, and, therefore, remedial costs required to ultimately achieve remediation regulatory objectives.

Modeling and Evaluating the Impact of Mobile Usage on Pedestrian Behavior at Signalized Intersections: A Machine Learning Perspective

Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and safety at signalized urban intersections. Data were collected from 11 signalized intersections in New Delhi, India, using video recordings. Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). The outputs of the models focus on predicting mobile usage behavior and its association with compliance behaviors such as crosswalk and signal adherence. The results show that 6.9% of the pedestrians used mobile phones while crossing the road. Advanced machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN), have been applied to analyze and predict MU behavior. Key findings reveal that younger pedestrians and females are more likely to exhibit distracted behavior, with pedestrians crossing alone being the most prone to mobile usage. MU was significantly associated with increased levels of crosswalk violation. Among the machine learning models, the CNN demonstrated the highest prediction accuracy (94.93%). The findings of this study have a practical application in urban planning, traffic management, and policy formulation. Recommendations include infrastructure improvements, public awareness campaigns, and technology-based interventions to mitigate pedestrian distractions and to enhance road safety. These findings contribute to the development of data-driven strategies to improve pedestrian safety in rapidly urbanizing regions.

Behind the software: The impact of Unobtrusiveness, Goal Setting and persuasive features on BMI.

Studies have demonstrated that interventions targeting weight loss and body mass index (BMI) reduction can be successful, although the specific factors that influence their effectiveness are still unclear. Behavior change support systems (BCSS) are an approach that aims to help users in their efforts to modify their behavior. A useful tool for assessing BCSS is the Persuasive Systems Design model (PSD), where different features and postulates can be employed. However, it is unknown whether the grouping of software features and design principles, along with behavioral traits, provide a better combination to achieve effective BMI reduction. This study investigates the impact of PSD features, postulates behind the design, and behavioral traits on BMI reduction after six months of utilizing a mobile health behavior change support system (mHBCSS). We examined a subset of 96 individuals from a randomized controlled trial using a mHBCSS for a period of six months. Data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). We found that 15.3% in the variance of BMI reduction was explained by the ability of setting goals. Furthermore, users who perceive a system as highly persuasive are more likely to establish goals (R2=0.207). Among PSD features, Dialogue Support and Primary Task Support explained 54.9% of the variance in Perceived Persuasiveness. In addition, both Dialogue Support and Credibility Support have a mutual effect on Primary Task Support (R2=0.685). Finally, the system's unobtrusiveness explained 41.1% of the variance in Dialogue Support. PSD framework and behavior change theories provide significant influence on BMI reduction. Setting a clear and organized objective assists individuals in successfully pursuing their intended results. The findings of this study can help developers and health professionals decide which PSD features and postulates to include to make mHBCSS interventions targeting BMI reduction more effective.

Impact of pandemic COVID-19 on the E-banking customer behavior: Case of Indonesia

This study aims to evaluate the factors affecting customers' acceptance and use of the internet and mobile banking during the COVID-19 pandemic. In Indonesia, the COVID-19 epidemic has encouraged residents to utilize other payment methods rather than cash, which easily spreading the virus. Internet and mobile banking are examples of technological innovations which can assist banking customers during the COVID-19 pandemic since it is safer, easier to access, and needs less human interaction. This paper utilizes a Unified Theory of Acceptance and Use of Technology (UTAUT) Model to assess consumers' intentions to use the internet and mobile banking. Previous research discussed the mobile banking adoption during the Covid-19 in various fields. Therefore, it is important to analyze the mobile banking intention and behavior in Indonesia. The data collected from questionnaires are used to evaluate the conceptual model, which shows the relationship between UTAUT constructs, performance expectancy, experience expectancy, and social influence. From 158 questionnaires with diverse demographic backgrounds gathered, we found that effort expectancy, performance expectancy, and social influence have a positive impact on the behavioral intention. Furthermore, it found that behavioral intention and facilitating conditions have influence on the using behavior. These results give a suggestion to both the banking industry and government to improve their mobile banking system to improve financial inclusion.

Navigating mobile device heightened use: an integrated model to assess mobile user engagement

Purpose The widespread use of mobile devices raises concerns about their negative effects on users’ well-being but also presents business opportunities. To harness this phenomenon effectively, understanding various aspects of user engagement is essential. This study aims to develop and empirically evaluate a conceptual framework for examining user engagement in mobile technology. Design/methodology/approach This study explores user engagement with mobile technology. The authors developed a conceptual model and collected data through an online survey, focusing on engagement with mobile apps, services and devices. The authors analyzed the data to identify factors influencing each aspect of the mobile engagement. Findings The findings suggest that engaging mobile apps should be visually appealing, novel, reliable and enjoyable. Mobile services engage users when they are easily accessible and adaptable to diverse needs. These results emphasize the importance of dissecting mobile technology engagement into app, service and device engagement for a deeper understanding of their impacts. Originality/value This study underscores the significance of comprehensively understanding user engagement in mobile technology. By delineating engagement dimensions and their determinants, it offers insights for businesses to optimize strategies in leveraging mobile user behavior. This structured approach enhances theoretical understanding and has practical implications for designing user-centric mobile technology solutions.

Optimizing Learning Path Design in Mobile Learning Platforms for Online Courses

With the rapid advancement of information technology, online education, particularly vocational education, has become a vital avenue for enhancing skills and knowledge. Vocational education necessitates flexible and personalized learning path design to accommodate the diverse needs and behaviors of learners. Mobile learning platforms, as a pivotal form of modern online education, provide learners with the convenience of accessing educational resources anytime and anywhere. However, existing methods for optimizing learning paths exhibit notable limitations, primarily in accurately capturing learners’ dynamic behaviors and in providing personalized and intelligent path design. Therefore, how to optimize learning paths based on learners’ dynamic behavior data has become a research hotspot in academia and educational practice. At present, many studies focus on matching analysis based on students’ static characteristics with course content but overlook learners’ behavioral changes and dynamic needs during the learning process. Traditional recommendation algorithms and rule-based path design methods are difficult to cope with complex learning behaviors and diverse learner needs. This study addresses these limitations by proposing an optimization model for mobile learning path design based on multi-view prediction of dynamic student behaviors. The model extracts mobile interaction features from student groups, constructs a multiple mobile behavior collaborative encoder, and employs multiple task label prediction techniques to achieve personalized and intelligent optimization of learning paths. The results demonstrate that this approach significantly enhances the learning efficiency and experience of learners, offering novel insights and technological support for the path design of mobile learning platforms.

Designing social behaviour change in households: sustainable mobility intervention and follow-up study

Limiting global warming to 1.5 degrees requires consistent action by people to change their lifestyles in order to limit annual household-related carbon emissions to 2.5 tonnes per person by 2030. As the required mobility reduction measures have already been identified, the challenge remains in scaling up the changes into a mainstream practice. Our study explored whether the involvement of close social communities, especially households, in the change process could be effective in achieving the required measures. Through a Climate Puzzle game intervention and a six-month follow-up study with 12 households in Espoo, Finland, we investigated the role of close social communities in implementing the planned sustainable mobility behaviour change. The findings are presented through 12 household narratives. These narratives show that the adoption of new sustainable mobility behaviours is influenced by both (infra)structural and social relationship factors and that close social communities can hinder or facilitate the shift of everyday mobility behaviours towards being more sustainable in diverse ways. The findings suggest that design interventions should target larger social communities rather than just individuals in order to achieve the 2.5 tonne target. The study also indicates that socially tailored interventions and low-carbon solutions should be developed and targeted at city districts to help households create and maintain lifestyle changes.

Analysis of factors influencing mobile learning behaviour of English curriculum students based on UTAUT model

Analysis of factors influencing mobile learning behaviour of English curriculum students based on UTAUT model

Study on Variation in Mobility and Travel Behavior during the Covid-19 Pandemic: A Case Study in Western Province, Sri Lanka

Study on Variation in Mobility and Travel Behavior during the Covid-19 Pandemic: A Case Study in Western Province, Sri Lanka

Avoidance rather than change: What influence does the price shock for mineral oils have on the mobility behaviour of users of motorised private transport in Germany?

Avoidance rather than change: What influence does the price shock for mineral oils have on the mobility behaviour of users of motorised private transport in Germany?

Behavior of a mobile robot with symmetrical structure of mecanium wheels when movement along a curvolinear trajectory

The article presents a comparative analysis of two variants of symmetrical placement of omnidirectional mecanum wheels in a mobile robot. The modeling of the mobile robot behavior with two symmetrical variants of mecanum wheel arrangement along curvilinear trajectories in the MATLAB Simulink environment is considered. Graphs of the robot center trajectories in the X–Y axes are obtained for two variants of symmetrical mecanum wheel configurations when moving along the trajectories of a lemniscate, ellipse, hypotrochoid, and track. Dependences of the robot position errors in the X–Y axes on time are obtained for two variants of wheel topologies when moving along four trajectories. Dependences of the robot body rotation angle deviation on time are analyzed for two variants of wheel topologies when moving along four curvilinear trajectories. The comparative analysis made it possible to give recommendations on movement along the ellipse and lemniscate, which are characterized by smaller position errors and deviations in the rotation angle compared to more complex curves.

Research on audience news and information behavior in the application on mobile phone in Chongqing

In the era of rapid advancements in mobile phone media, media integration has progressed into a stage where audience-centred market integration prevails, rendering the exploration of audience information behaviour[1] a paramount priority for both communication research and media practice. This paper delves into news information behaviour within the mobile media landscape, leveraging T.D. Wilson's information behaviour theory [2] as a starting point. It integrates theories such as Uses and Gratifications, Japanese information behaviour theory, media dependency theory, innovation diffusion theory, and consumer behaviour theory [3] to devise a communication information behaviour model. Through interdisciplinary theoretical linkages, this study aims to contribute to theoretical innovations in exploring the behaviour of communication audiences. Utilizing a robust foundation of literature reviews, the primary research methods employed are quantitative research and surveys. Data analysis is conducted using statistical software to discern the characteristics of audience news information behaviour in the mobile media environment. Furthermore, the study compares and contrasts the information behaviour patterns among typical users across different news audience segments. By synthesizing interview insights, the key factors influencing audience news information behaviour are identified, leading to the formulation of a communication news information behaviour model. Employing structural equation modelling (SEM)[4] techniques and statistical software, this paper delves into the intricate relationships between variables that influence the adoption behaviour of mobile news, ultimately constructing a mobile news adoption behaviour model.

Cognitive risk and behaviour related to the effects of mobile phone use on sleep quality: an analysis of data from Chinese college students

ABSTRACT This study assessed the relationships among cognitive risk, phone use behaviors, and sleep quality. We used a questionnaire, which included the Pittsburgh Sleep Quality Index (PSQI), mobile phone use behaviours, and questionnaires on mobile phone use cognitive risk to gather information from 1204 college students. T-test, chi-square test, and Wilcoxon signed rank test were applied to test differences in measurement data. Generalized linear regression analysis was used to analyze the factors influencing sleep quality. The percentage of people with poor sleep quality significantly increased during COVID-19, with a significant increase in sleep latency and diurnal dysfunction and a significant decrease in sleep persistence, sleep disturbance, and sleep efficiency. The PSQI scores significantly decreased during the COVID-19 pandemic (0.27 and 0.34 for overall and females in the ‘Often take a break’ subgroup), only females in the ‘Often increase the distance between the screen and the eyes’ subgroup had a significant decrease in the PSQI score (0.37). Bedtime use and bedtime light use in females significantly increased during the epidemic. Regression analysis revealed that rest or increased screen distance significantly improved sleep quality compared to rarely rest or rarely increased screen distance and had a more significant effect on sleep quality during the COVID-19 pandemic compared to before. The time spent using a phone with lights off before bedtime was significantly associated with poor sleep quality. Often resting, increasing the distance between the screen and the eyes, reducing phone use time, and reducing the time of lights off when using mobile phones are all protective factors for sleep quality. Females were more often affected by poor sleep quality.

Behaviors and attitudes towards mobile learning during course work and scientific research among dental, medical and non-medical undergraduates: a cross-sectional study

BackgroundMobile learning is a valuable supplement to traditional education methods, but the current research on mobile learning is still insufficient. This study aimed to assess dental undergraduates’ behaviors and attitudes towards mobile learning during course work and scientific research and to compare these findings to those of medical and non-medical students.MethodsA cross-sectional survey was conducted among undergraduates at Sichuan University, China. Participants completed an online questionnaire covering demographic information, mobile learning behaviors in course work and scientific research, and attitudes towards mobile learning.ResultsA total of 849 valid responses were collected, including 290 dental students, 307 medical students, and 252 non-medical students. The average behavior scores for using mobile devices in course work and scientific research were 3.02 and 3.09 (out of 5), respectively. Smartphones were the most frequently used devices for course work across all majors, while laptops predominated for scientific research. Mobile device preferences for various learning activities were similar across all majors, with minor variations. Over 80% of dental students reported using mobile learning for more than one hour daily for course work, and they had the highest proportion of daily mobile learning exceeding 90 min. Overall, students demonstrated positive attitudes towards mobile learning, with average attitude scores of 3.58, 3.66, and 3.47 (out of 5) for dental, medical, and non-medical students, respectively.ConclusionResult show that dental undergraduates used mobile learning extensively and exhibited positive attitudes towards it for both course work and scientific research. This study highlights mobile device preferences and their alignment with various academic activities, which can inform the strategic development of educational technologies in dental curricula.

A Survey to Capture the Mobility Behavior of Residents in the Republic of Cyprus

In today's highly mobile world, people of all age groups constantly move between various locations, such as their homes, grocery stores, schools, and workplaces, reflecting the dynamic nature of modern society. The recent COVID-19 pandemic has underscored the profound impact of human mobility on the rapid spread of the virus within and between urban areas. The current study investigates the daily mobility behaviors of residents of the Republic of Cyprus. Through a comprehensive survey, the research documents travel patterns, commuting habits, and transportation preferences across various age groups and districts of Cyprus. With a total of 787 responses from all districts of Cyprus, data was collected through an online survey from 20/11/2023 to 20/02/2024. Data analysis includes descriptive methods and statistical modeling techniques in order to provide insights into the patterns of human mobility. Key findings include insights into travel distances, frequencies, duration and means of transportation, and timelines of mobility activities during typical workdays, as well as during weekends.

The Impact of Interactive Mobile Learning on Enhancing University Students’ English-Speaking Proficiency

In the context of globalization, proficiency in English-speaking is crucial for the academic and career development of university students. However, traditional English teaching methods often fall short in effectively enhancing students’ speaking proficiency. With the advancement of information technology (IT), interactive mobile learning has gradually emerged as a vital teaching tool, offering new avenues for teaching English speaking. By leveraging the convenience and diversity of mobile devices, interactive mobile learning provides students with a flexible environment to improve their speaking proficiency. The current study predominantly focuses on the integration of technology and education, but there is limited investigation into the specific applications and effectiveness of interactive mobile learning in enhancing Englishspeaking proficiency. Furthermore, existing studies tend to provide fragmented analyses of mobile learning interaction behaviors and their constituent elements, failing to fully reveal the advantages of this approach. This study aims to construct an interactive mobile learning framework tailored to university students’ English-speaking practice, analyze the interaction behaviors and elements within this framework, and explore its impact on improving students’ speaking proficiency. The goal is to enrich the theoretical system of interactive mobile learning and provide practical insights for English-speaking teaching in higher education.

Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios.

In the realm of infectious disease control, accurate modeling of the transmission dynamics is pivotal. As human mobility and commuting patterns are key components of communicable disease spread, we introduce a novel travel time aware metapopulation model. Our model aims to enhance estimations of disease transmission. By providing more reliable assessments on the efficacy of interventions, curtailing personal rights or human mobility behavior through interventions can be minimized. The proposed model is an advancement over traditional compartmental models, integrating explicit transmission on travel and commute, a factor available in agent-based models but often neglected with metapopulation models. Our approach employs a multi-edge graph ODE-based (Graph-ODE) model, which represents the intricate interplay between mobility and disease spread. This granular modeling is particularly important when assessing the dynamics in densely connected urban areas or when heterogeneous structures across entire countries have to be assessed. The given approach can be coupled with any kind of ODE-based model. In addition, we propose a novel multi-layer waning immunity model that integrates waning of different paces for protection against mild and severe courses of the disease. As this is of particular interest for late-phase epidemic or endemic scenarios, we consider the late-phase of SARS-CoV-2 in Germany. The results of this work show that accounting for resolved mobility significantly influences the pattern of outbreaks. The improved model provides a refined tool for predicting outbreak trajectories and evaluating intervention strategies in relation to mobility by allowing us to assess the transmission that result on traveling. The insights derived from this model can serve as a basis for decisions on the implementation or suspension of interventions, such as mandatory masks on public transportation. Eventually, our model contributes to maintaining mobility as a social good while reducing exuberant disease dynamics potentially driven by travel activities.

The Essence of Multimodal Travel and Key Standards for Data Exchange in Public Transport

The concept of multimodal transport has recently attracted much interest. It is user-oriented, i.e. the passenger planning his trip. To properly plan and prepare a mobility offer, it is necessary to know the mobile behaviour of travellers or their changes. Both the travel history and communication preferences of passengers can contribute to the design of a mobility distribution model in which various modes of transport offered by different operators are integrated into one service provided within one digital platform. The study aims to present the concept of multimodal travel and show the added value for the traveller: the synergy of combining ticket purchase and payment channels and a wide range of transport options. The authors highlight two key public transport data exchange standards: Open Journey Planner (OJP) and General Transit Feed Specification (GTFS). Their goal is to improve the quality of public transport services and provide travellers with better access to information. The article was developed as part of the Network Management Planning and Control &amp; Mobility Management in a Multimodal Environment and Digital Enablers project – an acronym of the MOTIONAL project – implemented by the Research and Development Ecosystem created at PKP S.A. The article's content is part of the WP20 work package Development: Integration of Railways with other means of transport. In particular, it refers to Task 20.1, Improving railway integration using B2B intermodal services, which includes Subtask 20.1.1, Providing MaaS platforms for B2B intermodal services.