Mobility Model Research Articles

On the calibration and improvement of human mobility models in intercity transportation system

As one of the research highlights of physics-level modelling, human mobility has generated numerous universal laws over the past decades. However, the majority of research has concentrated on the intracity networks, leaving the intercity mobility systems with insufficient attention despite its increasingly crucial role in the development of urban agglomerations. Related research gaps further extend to the limited understanding of spatiotemporal heterogeneity in intercity human mobility. To bridge these gaps, our study systematically validated and improved the modelling framework of intercity mobility flows utilizing real-world data sources. Specifically, building upon the nationwide Location-based Services (LBS) datasets in China, the applicability of classic human mobility models, including gravity model and intervening opportunities-class models, was extensively explored in the intercity domain by developing fitting models that incorporated multi-class urban attributes. Then, we contributed to proposing improved models that consider the diverse attraction effect of the origin and potential destinations. Moreover, our research scope was expanded to incorporate spatiotemporal heterogeneity through model comparisons among various city sets during both regular period and holiday. The findings suggested that our improved models effectively enhance the modelling accuracy while strengthening the explanatory power. They especially demonstrate a balanced performance even when handling datasets with spatiotemporal heterogeneity. Consequently, this study provides valuable insights into understanding intercity human mobility from the intrinsic mechanism of opportunity attraction. Our models hold practical significance in accurately modelling intercity mobility flows utilizing observable urban attributes and spatial layouts, further providing effective tools for preemptive traffic management.

ICT Use and Entrepreneurial Mobilization of Chinese Grassroots Activists In and Beyond Cyberspace

While prevailing scholarship commonly asserts that information and communication technologies (ICTs) significantly facilitate mass mobilization, a growing body of literature questions the mobilization potential of technologies alone. This literature proposes that ICTs might redirect offline participation by promoting low-risk and low-cost online collective actions, especially in the absence of linkage between offline and online tactics. In the context of these debates, this article explores the nuanced synergies between the online and offline mobilization strategies of individual activists and their underlying motives. It does so through an in-depth ethnographic case study of popular protest mobilization in a Chinese locality. Through an integration of the traditional entrepreneurial mobilization model with the concept of mediatization, this study delves into the intricate mobilization strategies and underlying motives of activists. The findings reveal that through a strategic convergence of offline and online tactics, including extension, amalgamation, and substitution, activists adeptly choreographed and presented protest repertoires, thereby fostering the development of mass mobilization. The research posits that the efficacy of ICTs in mobilization is contingent upon activists’ adeptness in leveraging the norms and practices of ICTs to pursue collective objectives suited to social contexts, thus circumventing the pitfalls of “clicktivism.”

A DPSIR-Driven Agent-Based Model for Residential Choices and Mobility in an Urban Setting

A DPSIR-Driven Agent-Based Model for Residential Choices and Mobility in an Urban Setting

Hybrid optimization-based secured routing in mobile ad-hoc network

Mobile Ad hoc Networks (MANETs) pose significant routing challenges due to their decentralized and dynamic nature. Node mobility results in frequent changes in network topology, leading to unsFIG connectivity and link quality. Traditional RPs designed for static networks are inadequate for MANETs. To deal with these issues, a secure routing approach is proposed using the Red Panda-Lyrebrid Optimization (RePLO) algorithm, which combines the advantages of the Red Panda Optimization (RPO) and Lyrebird Optimization Algorithm (LOA) algorithms. The proposed approach consists of five steps: (i) configuring the system model, (ii) developing the energy model, (iii) creating the mobility model, (iv) selecting cluster heads using the RePLO algorithm, and (v) routing using the RePLO algorithm. The RePLO algorithm optimizes cluster head selection and routing while considering specific constraints such as delay, distance, energy, & security for Cluster Head (CH) selection, and link quality and enhanced trust for routing optimization. The effectiveness of the proposed approach is evaluated using various metrics to demonstrate its efficiency in MANET routing. By integrating multiple optimization techniques and considering critical constraints, the RePLO algorithm offers a systematic and secure solution for MANET routing. The evaluation results confirm the efficacy of the proposed approach in improving network performance, reliability, and security. Overall, the RePLO algorithm presents a promising approach to tackle the routing issues inherent in MANETs, paving the way for more robust and efficient communication in mobile ad hoc networks.

Data-Driven Modeling of Tire-Soil Interaction with POD-Based Model Order Reduction

Abstract A data-driven model capable of predicting time-domain solutions of a high-fidelity tire-soil interaction model is developed to enable quick prediction of mobility capabilities on deformable terrain. The adaptive model order reduction based on the proper orthogonal decomposition (POD), for which the high-dimensional equations are projected onto the reduced subspace, is utilized as the basis for predicting the time-domain tire-soil interaction behavior. The projection-based model order reduction, however, requires many online matrix operations due to the successive updates of the nonlinear functions and Jacobians at every time step, thereby hindering the computational improvement. Therefore, a data-driven approach using a Long Short Term Memory (LSTM) neural network is introduced to predict the reduced order coordinates without the projection and time integration processes for computational speedup. With this model, a hybrid data-driven/physics-based off-road mobility model is proposed, where four separate LSTM-POD data-driven tire-soil interaction models are integrated into the physics-based multibody dynamics (MBD) vehicle model through a force-displacement coupling algorithm. By doing so, the individual data-driven tire-soil interaction model can be constructed efficiently, and the MBD and LSTM models are assembled as a single off-road mobility model and analyzed with existing off-road mobility solvers. The predictive ability and computational benefit of the proposed data-driven tire-soil interaction model with the POD-based model order reduction are examined with several numerical examples.

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

Asynchronous localization of dynamic node through underwater acoustic sensor networks

In most cases, the operation of underwater acoustic sensor networks (UASNs) relies on accurate sensor location information. However, UASNs present more challenges than terrestrial sensor networks, such as the propagation speed variation with depth (i.e., stratification effect), asynchronous clock, node mobility, etc. In this paper, an efficient method of asynchronous localization is proposed by taking into account the stratification effect and node mobility. Firstly, a network is constructed that consists of surface buoys, AUVs, and target sensors. Secondly, an iterative least squares (LS) method is developed to locate AUV by establishing the relationship between propagation delay and location estimation. Thirdly, the passive mobility velocity of AUV is used to develop and solve the mobility model of the target sensors. Then, with the support of AUV, an asynchronous localization method is developed to estimate the target position, which improves the localization accuracy through motion and stratification compensation strategies. Moreover, the proposed method is validated through Cramer Rao lower bound (CRLB) analysis. Finally, simulation is performed to show that the proposed method is more efficient in reducing the estimation errors.

A sidewalk-level urban heat risk assessment framework using pedestrian mobility and urban microclimate modeling

Climate change and the associated increase in heat-related hazards pose a pressing threat to urban residents’ health and well-being. People, when walking in particular, are at risk of experiencing heat stress as they navigate urban environments. This study proposes a novel heat risk assessment framework combining pedestrian mobility modeling with urban microclimate modeling. Using this framework, we assessed pedestrian heat-related exposure and risk in urban areas by integrating the Universal Thermal Climate Index (UTCI) as the hazard and pedestrian trips to critical destinations as exposure. We considered temporal variation, in both hazard and exposure, by examining different time periods during the day—morning peak, midday, and evening peak. In addition to hazard and exposure, we also considered vulnerability by focusing on young children and older adults. We contribute to improving the spatial resolution of heat risk assessment by analyzing the hazard for pedestrian trips between home locations and five critical destinations—bus stops, rail stations, parks, schools, and commercial amenities—at the address-point level and using a pedestrian network comprising sidewalks and crosswalks. Our framework helps identify sidewalks with high heat exposure levels as well as home locations with high cumulative heat risk, accounting for walking trips to critical destinations along feasible routes. We demonstrated the effectiveness of this framework by applying it to a 36-square-kilometer area of central Los Angeles, CA. Our findings offer valuable information to urban planners and policy-makers, supporting evidence-based prioritization of intervention sites, climate adaptation strategies, and policy decisions essential for climate-proof planning. By implementing targeted interventions in areas where heat hazard is expected to affect the most vulnerable pedestrians, planners can create heat-resilient, pedestrian-friendly environments while prioritizing the health and well-being of vulnerable groups. This study contributes to the growing knowledge of robust risk assessment methodologies for climate-proof planning, specifically with regard to addressing outdoor heat-related risks during extreme heat events.

Model of Sustainable Household Mobility in Multi-Modal Transportation Networks

Nowadays, urban and suburban areas face increasing environmental pressures, and encouraging sustainable transportation behaviors at the household level has become crucial. This paper presents a model of a decision support system (DSS) for promoting sustainable household mobility choices in multi-modal transport networks. The system was modeled using an enhanced Petri Net approach, allowing for the dynamic representation of complex transport networks and multi-modal journey options. The model incorporated various sustainability factors. These were combined into a single environmental impact score, which was considered alongside travel time and cost in the route optimization process. Simulation results demonstrated the DSS’s capability to guide users toward more sustainable mobility choices. The model also showed potential as a tool for policymakers to assess the impact of various sustainable transportation initiatives and infrastructure investments. This paper discussed the versatile applications of the system. It also addressed the limitations of Petri Net models in transportation systems and suggested future research directions.

Diabetes Prevention Strategies in Ghana: Opportunities for Community-Based Interventions

Diabetes was emerging as a critical public health challenge in Ghana, driven by rapid urbanization, lifestyle changes, and increasing rates of obesity. This article reviewed the growing prevalence of diabetes in Ghana, particularly type 2 diabetes, and highlighted the urgent need for effective prevention strategies. It also examined the epidemiological trends, identified key risk factors such as obesity, physical inactivity, and unhealthy diets, and discussed the role of genetic predisposition in the rising diabetes rates. The article explored various models of community-based interventions, including the Community Mobilization Model, Ecological Model, Social Marketing Model, Health Belief Model, and Participatory Action Research, as promising approaches to diabetes prevention in Ghana. These models leveraged local knowledge, cultural practices, and community structures to promote healthier lifestyles, increase awareness, and facilitate early detection. The article also identified challenges such as inadequate funding, healthcare infrastructure disparities, and cultural barriers that hinder the implementation of prevention programs. However, it highlighted opportunities for leveraging mobile health technologies, public-private partnerships, and the integration of diabetes prevention with existing health services. The methodology used involved a comprehensive review of relevant literature and case studies from other low- and middle-income countries to identify effective community-based strategies for diabetes prevention. The article concluded with recommendations for scaling up community-based interventions to reduce the incidence of diabetes and improve public health outcomes in Ghana. Keywords: Diabetes Prevention, Community-Based Interventions Ghana, Risk Factors, Health

Geomorphology and sediment mobility on sand banks: A study of Dogfish Bank, Hecate Strait, Northeast Pacific Ocean

AbstractMovement of sediment along shallow continental shelves is a natural process with wide‐ranging environmental and economic implications, making it of high importance to marine spatial planning efforts in the offshore. Development of marine renewable energy, for instance, requires detailed understanding of the morphodynamics of mobile bedforms to select foundation types and ensure safe installation of infrastructure in shallow shelf environments. This study evaluates geomorphology and sediment mobility of Dogfish Bank (< 20 mbsl) in the Hecate Strait offshore British Columbia, Canada, using hydroacoustic and airborne bathymetric data combined with seismic profiles and grain‐size information. These data reveal current‐swept features ranging from sediment‐depleted lag to sediment‐abundant sand ridges and dunes, with sand ribbons and furrows in‐between. Seismic reflection data show up to 15 m of surficial sand concentrated beneath north‐aligned sand ridges that dominate the bathymetry of northwest Hecate Strait. Sand ribbons (typically understood sediment‐limited features in shallow marine environments) are notably maintained over seabed with comparable sand thickness to adjacent dunes (i.e. sediment‐abundant features), suggesting local spatial variability in hydrodynamics and sediment characteristics (principally grain size) influence expression of mobile bedforms. Repeat mapping between 2008 and 2019 shows dunes and ribbons both migrate northwards, with largest seafloor changes along northeast‐facing lee sides of dunes, matching closely with published models of sediment mobility which suggest northward bedform migration is largely driven by storms. Median total migration distance is 164 m (northward) for dunes (time‐averaged rate of 14.9 m/year). Sand ribbons show less migration (median northward distance of 73 m) and migrate in a depth‐dependent manner. Because sand ribbons are typically flow‐parallel features, their lateral migration likely results from varying current directions and flow acceleration over shallower seabed. Sand ribbon migration should therefore a consideration in studies examining seabed change, particularly when they are formed over unconsolidated sediment.

Enhancement of the impacts 2050 model to enable a whole system sustainability assessment of rideshare

Emerging concepts, such as Mobility as a Service (MaaS), could evolve to provide sustainable mobility, especially in densely populated urban areas. However, recent studies highlight the challenge of evaluating how the complex interactions of user demographics, mode choice, vehicle automation, governance, and efficiency will impact the sustainability of future mobility. Given this challenge, this research identifies a whole system (STEEP - social, technical, economic, environmental, and political) framework as essential to assess the overall sustainability of emergent urban mobility systems such as rideshare. The need is a single tool that can rapidly explore the long-range sustainability impact of such alternative future mobility scenarios for a given city region. This paper documents enhancements made to Impacts 2050, a strategic-level model of urban mobility, to address this need, including updates to the statistical travel behavior model and the addition of rideshare including trip occupancy. Results obtained with the enhanced Impacts 2050 showed that, while rideshare use increased significantly for some scenarios, its overall mode share remained limited. In addition, though rideshare enabled users to shed car ownership, the overall percentage increase of “no car ownership” was low. An urban mobility sustainability scorecard based on STEEP and generated by output from the enhanced Impacts 2050 is presented.

Numerical study on the forward and inverse problems of the mobile pump truck frame

Aiming at the requirements of strong mobility and high flexibility of rescue and relief mobile pump trucks, this paper designs a new type of mobile pump truck frame based on existing mobile vehicle frame models. The materials used for the frame are 40Cr and Q235, and the finite element method is utilized to carry out static mechanical analysis and dynamic characteristic analysis. Simultaneously utilizing topology optimization and multi-objective genetic algorithm to optimize the design of the frame structure. The results show that the optimized pump truck frame can meet the strength design requirements of four typical working conditions: full load bending, full load torsion, emergency turning and emergency braking, while avoiding resonance phenomena caused by road surface and diesel engine vibration. Compared with the original frame model, the weight of the optimized frame is reduced by 87.88 kg, with a weight reduction rate of 10.89%, realizing the lightweight design requirements.

Clarifying new urban mobility services based on a threefold business model framework

Market entrants have brought a variety of new urban mobility services over the past years, which are rooted in the sharing economy and have their origin in digitalization. Digital data serve as key resource of a business model and, accordingly, digital technologies are the basis of key activities. Building our analysis on the resource-based view and on the business model debate, we ask: what degrees of digitalization do urban mobility service business models exhibit? We perform a systematic literature review and a qualitative content analysis. As a result, we identify a continuum of highly and lowly digitalized business models. We derive a threefold business model framework, substantiated in conventional mobility, hybrid, and data-driven business models. (1) Conventional mobility business models are dominated by mobility as a key resource, digitalization is low and performed by key partners, (2) hybrid models contain both conventional mobility and data-driven key resources, and (3) data-driven models take digital data as key resources, while conventional mobility is carried out by key partners. As a first main contribution, we conceptualize conventional versus purely data-driven business models along the continuum of data-driven business model components. New urban mobility services are the group of both hybrid and data-driven business models, while conventional urban mobility stands on its own. As a second contribution, we clarify the Mobility-as-a-Service (MaaS) concept by corroborating it as a purely data-driven business model with key partners provisioning mobility.

Mobile Learning and Resource Sharing Mode of College English Translation Based on 5G Mobile Communication Technology

The application of 5G and other mobile communication technologies to education and teaching, and the construction of College English Translation mobile learning and resource sharing model and related models have important scientific value and significance for the effective organization and management of learning resources and the improvement of the speed and efficiency of college english translation resource retrieval. This research mainly combines the characteristics of complementarity and expansion of mobile learning technology for traditional online learning technology. Based on Moodle online teaching platform, this research develops a mobile learning and resource sharing mode for college english translation based on 5G mobile communication technology, designs and implements a mobile learning model based on Moodle platform, and designs the system architecture, functional modules, learning mode and learning process, Finally, the main functional modules of the system model are implemented and tested. To promote mobile learning in the teaching field to play a helping role in a wide range. The experimental results show that the AKAZE algorithm is used to optimize the model, and the overall accuracy rate reaches 74.7%, and the test probability value P (siq)=0.00, which is significantly less than the significant level of 0.05. Therefore, we believe that the application of mobile learning instructional design mode will help students improve their academic performance.

A Deep Learning Framework for Evaluating the Over-the-Air Performance of the Antenna in Mobile Terminals.

This study introduces RTEEMF (Real-Time Evaluation Electromagnetic Field)-PhoneAnts, a novel Deep Learning (DL) framework for the efficient evaluation of mobile phone antenna performance, addressing the time-consuming nature of traditional full-wave numerical simulations. The DL model, built on convolutional neural networks, uses the Near-field Electromagnetic Field (NEMF) distribution of a mobile phone antenna in free space to predict the Effective Isotropic Radiated Power (EIRP), Total Radiated Power (TRP), and Specific Absorption Rate (SAR) across various configurations. By converting antenna features and internal mobile phone components into near-field EMF distributions within a Huygens' box, the model simplifies its input. A dataset of 7000 mobile phone models was used for training and evaluation. The model's accuracy is validated using the Wilcoxon Signed Rank Test (WSR) for SAR and TRP, and the Feature Selection Validation Method (FSV) for EIRP. The proposed model achieves remarkable computational efficiency, approximately 2000-fold faster than full-wave simulations, and demonstrates generalization capabilities for different antenna types, various frequencies, and antenna positions. This makes it a valuable tool for practical research and development (R&D), offering a promising alternative to traditional electromagnetic field simulations. The study is publicly available on GitHub for further development and customization. Engineers can customize the model using their own datasets.

User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks

With the rise of the Internet of Things (IoT), mobile crowdsourcing has become a leading application, leveraging the ubiquitous presence of smartphone users to collect and process data. Spatial crowdsourcing, which assigns tasks based on users’ geographic locations, has proven to be particularly innovative. However, this trend raises significant privacy concerns, particularly regarding the precise geographic data required by these crowdsourcing platforms. Traditional methods, such as dummy locations, spatial cloaking, differential privacy, k-anonymity, and encryption, often fail to mitigate the risks associated with the continuous disclosure of location data. An unauthorized entity could access these data and infer personal information about individuals, such as their home address, workplace, religion, or political affiliations, thus constituting a privacy violation. In this paper, we propose a user mobility model designed to enhance location privacy protection by accurately identifying Points of Interest (POIs) and countering inference attacks. Our main contribution here focuses on user mobility modeling and the introduction of an advanced algorithm for precise POI identification. We evaluate our contributions using GPS data collected from 10 volunteers over a period of 3 months. The results show that our mobility model delivers significant performance and that our POI extraction algorithm outperforms existing approaches.

Should I Stay, or Should I Go Now? Neighborhood, Family, and Youth Predictors of Intracity Mobility.

The effects of residential mobility on youth development outcomes related to violence and victimization have traditionally been understood from an outward mobility perspective. Less attention has been paid to predictors of intracity moves, a more common mobility practice. Research, however, should identify factors associated with intracity mobility before scholars can explore and fully understand the impacts of moving within the city on youth development outcomes such as violence and victimization and whether they are akin to those identified for moves beyond city limits. Using data from the Project on Human Development in Chicago Neighborhoods, we apply a multilevel ecological framework to assess neighborhood, family, and youth factors associated with intracity mobility. We employed hierarchical generalized linear modeling to examine within and between-neighborhood models of intracity mobility among youth and their families who moved within Chicago (N = 426) and those who did not (N = 867). Our investigation highlights as important factors family dynamics instability and aggression and violence at the neighborhood level. Findings show that parental divorce/separation, harsh disciplinary practices, negative school experiences such as racial/ethnic conflict, gangs, or heightened security measures, and exposure to violence predict intracity mobility. Critically, negative school experiences proved to be a robust determinant of moving as cross-level interactions suggest neighborhood crime significantly shapes how these experiences inform intracity mobility. This study emphasizes the importance of examining mobility beyond urban city limits, noting distinct predictors of intracity mobility across systemic levels. The findings fill a gap in our understanding of neighborhood turnover and stability, while also providing a foundation for exploring within and between-neighborhood differences in intracity mobility.

A New Toolbox for Managing Sites Impacted by Light Non-Aqueous Phase Liquids (LNAPLs)

The Concawe LNAPL Toolbox is one of the first completely web-based tools for managing and remediating light non-aqueous phase liquids (LNAPL) impacted sites. An off-line downloadable version is also available for users who do not want to access it via the internet. LNAPLs are typically historic hydrocarbons released into soils and groundwater, such as liquid fuels, crude oil, and condensates. The toolbox was developed by Concawe, the Scientific Division of European Fuel Manufacturers Association, with the support of the engineering and environmental science-consulting agency GSI Environmental. The toolbox, written on the R-Shiny platform, was released in 2022 and includes over 20 different tools, such as, for example, infographics, nomographs, calculators, mobility models, videos, and checklists. These tools are organized into three tiers, with Tier 1 offering basic graphics and essential information, Tier 2 providing simple quantitative models and calculators, and Tier 3 featuring access and explanations of more complex tools. The toolbox is designed to address six key questions that are important for environmental consultants and regulators managing LNAPL sites, including determining the amount of LNAPL, estimating LNAPL migration and persistence, assessing LNAPL risk over time, determining the effectiveness of LNAPL recovery, and estimating Natural Source Zone Depletion (NSZD). The Concawe LNAPL Toolbox is accessible free of charge via a webpage on an internet browser or by downloading the toolbox for use on a personal computer.

Optimization of distribution function and model parameters for molecular communication via diffusion with OtoO approximation

The analysis is generally conducted in stationary receiver and transmitter models in a diffusion environment for the fundamental Molecular communication (MOC) models. However, a mobile MOC model is employed in this study, deviating from the existing literature. This mobile MOC model considers the mobility of all variables in the diffusion environment, including the transmitter, receiver, and molecules. Firstly, a novel MOC model is proposed, departing from the conventional normal distribution for the mobility of variables. Instead, alternative distribution functions such as the Pareto distribution, extreme value distribution, t-distribution, and generalized extreme value distribution are employed. Furthermore, the system's performance is enhanced by optimizing the distribution function and model parameters, such as the diffusion coefficient, using the optimization of optimization (OtoO) approach. In this approach, the Multi-Verse Optimization (MVO) algorithm serves as the primary algorithm, while the Grey Wolf Optimization (GWO) algorithm functions as the auxiliary algorithm. Essentially, the MVO algorithm optimizes the parameters of the MOC model, while simultaneously, the GWO algorithm optimizes the impact of the optimization processes of MVO on the parameters ``p'' and ``N'' as well as the constant parameter of the distribution function. By optimizing both the parameters of the MOC model and the distribution function, the number of received molecules is significantly increased. Therefore, this study not only improves the results of the MOC model structure based on different distribution functions but also optimizes all parameters of the proposed model using the MVO-GWO OtoO approach.