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Although agent-based modelling of crime has made great progress in the last decades, drawing concrete conclusions from the modelling results that can be directly applied to real-world environments has thus far remained challenging, mainly due to the fact that existing studies use scaled-down scenarios (due to computational constraints) and employ relatively simple behavioural models for perpetrators. In order to study different hypotheses of street robbery at the scale of a realistic urban agglomeration, a model has been developed that fully incorporates the mobility behaviour of a civilian population at high spatial (1m) and temporal (1s) resolution, co-simulated alongside a perpetrator agent population that is endowed with the ability to learn through experience how to travel across and roam within the urban landscape, resulting in a stochastic "common-knowledge" behaviour that mimics the intelligence that real perpetrators possess about their own environments. This works extends with pedestrian travel an existing, high-resolution, mobility simulation framework of private vehicle and public transport. The dynamic decision-making and re-routing of perpetrator agents, the environmental variables at link-level, the realistic vision of perpetrators, and the perpetrator movement and travel learning, which is modelled with a state-of-the-art deep reinforcement learning algorithm, are integrated into the extended simulation framework. In order to facilitate modelling at this large scale (that is, millions of agents in a spatial extent of 10’000s of sq. km), the data processing and agent routing is performed on the CPU (Central Processing Unit), while the movement, perception and decision-making of perpetrator agents is performed on GPU (Graphical Processing Unit) hardware. This work is demonstrated on a scenario developed for the City of Cape Town, South Africa. The results show that perpetrator agents effectively optimise for the specified reward signals. Four situational factors that influence the occurrence of street robbery are systematically investigated, that is, the relative target value of different civilians, the availability of potential guardians, the illumination due to sunlight and public lighting installations, and the area-level socio-economic deprivation. The good spatial and temporal comparisons of simulated robberies with geocoded robbery data verify the accuracy of the final hypothesis specification using the extended simulation framework. This hypothesis and framework are then used to assess the effectiveness of public lighting as a tool to reduce street robbery. As an outcome of this work, criminologists, urban planners and policymakers will be able to use this approach to (i) investigate crime hypotheses in different urban environments and contexts, thereby improving our understanding of how different factors contribute to street robbery, and (ii) investigate the effectiveness of different infrastructure intervention strategies in the medium to long term.

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19.This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. The model is validated against the infection dynamics in Berlin (Germany).The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reinfection rate R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%.Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.Author summaryEvidently, there is an interest in models that are able to predict the effect of interventions in the face of pandemic diseases. The so-called compartmental models have difficulties to include effects that stem from spatial, demographic or temporal inhomongeneities. Person-centric models, often using social contact matrices, are difficult and time-consuming to build up. In the present paper, we describe how we built a largely data-driven person-centric infection model within less than a month when COVID-19 took hold in Germany. The model is based on our extensive experience with mobility modelling, and a synthetic data pipeline that starts with mobile phone data, while taking the infection dynamics and the disease progression from the literature. The approach makes the model portable to all places that have similar so-called activity-based models of travel in place, which are many places world-wide, and the number is continuously increasing. The model has been used since its inception to regularly advise the German government on expected consequences of interventions.

The COVID-19 pandemic has highlighted the importance of public health policies and crisis management. The spread of diseases is a complex phenomenon with many time-dependent variables, which hampers an accurate prediction of epidemic evolution. Models of epidemic spread play an important role in guiding in designing public health policies, enabling hypothetical scenarios simulation and rapid analyses of ongoing epidemics.Over the last century disease spread models evolved from deterministic compartmental models into complex metapopulation and agent-based simulations. Today’s solutions consider many factors, not limiting to the disease itself but also simulating socio-demographic structure and population flows. In the era of globalisation, human mobility became the major factor of rapid disease spread. Although current models consider international and regional travels, used mobility models are simplistic. This limits the accuracy and spatio-temporal resolution of these simulations, providing daily cases updates aggregated to large regions.We propose an agent-based mobility model, offering a simulation of hourly temporal resolution depicting mobility with less than a few hundreds of meters spatial precision. Agent-based models allow each simulation agent to assign different characteristics, e.g. susceptibility to infection, mobility behaviour.We integrate our mobility model with disease spread simulation, using an agent’s interaction to detect virus transmission. In every time step of the model, the interaction between the agents, their current state and localisation of interaction are used to determine the probability of infection. Social interactions in the context of the spread of the disease are a fundamental element influencing the temporal and spatial extent of the disease. An essential aspect of our model is the integration of the simulation environment with the points-of-interests (POIs), which represent the destination of the majority of non-home-work related activities. We validate the accuracy of mobility replication and present hypothetical scenarios of disease spread in one of the large European cities, presenting capabilities of our solution.

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. Results show that in Berlin (Germany), behavioral changes of the population mostly happened before the government-initiated so-called contact ban came into effect. Also, the model is used to determine differentiated changes to the reinfection rate for different interventions such as reductions in activity participation, the wearing of masks, or contact tracing followed by quarantine-at-home. One important result is that successful contact tracing reduces the reinfection rate by about 30 to 40%, and that if contact tracing becomes overwhelmed then infection rates immediately jump up accordingly, making rather strong lockdown measures necessary to bring the reinfection rate back to below one.

Artykuł stanowi oryginalną syntezę badań dostępności i mobilności prowadzonych w Instytucie Geografii i Przestrzennego Zagospodarowania Polskiej Akademii Nauk (IGiPZ PAN) w kontekście uwarunkowań przestrzennych rozwoju przemysłu w Polsce, ze szczególnym uwzględnieniem rozkładu przestrzennego ludności, PKB oraz lokalizacji spółek prawa handlowego. Autorzy podjęli się analizy dostęp- ności potencjałowej w transporcie ciężarowym, gdzie na miarę atrakcyjności celu wpływają zarówno liczba ludności, jak i PKB. Do analizy empirycznej wykorzystano autorską aplikację OGAM. W części artykułu poświęconej mobilności wykorzystano rezultaty modelowego rozkładu ruchu na krajowej i wojewódzkiej sieci drogowej oraz oprogramowanie VISUM. Przedstawiono oryginalną metodę, która porównuje wyniki modelu grawitacyjnego z rzeczywistym rozkładem ruchu, wynikającym z pomiarów natężenia ruchu w sieci, w odniesieniu do podróży między spółkami prawa handlowego odbywanych przez przedsiębiorców (biz- nesmenów, przedstawicieli handlowych itd.). Rozkład teoretyczny ruchu pojazdów osobowych porównano z wykorzystaniem analizy kartograficznej i pokazano przeszacowanie lub niedoszacowanie ruchu względem wyników generalnego pomiaru ruchu (GPR) w 2010 roku. W badaniu dostępności i mobilności wykorzystano macierz czasów podróży na poziomie gminnym (2321 jednostek). Prędkości obliczono na podstawie modelu prędkości IGiPZ PAN. Wskazano, że modele potencjału i grawitacji należą do mało popularnych metod analizy uwarunkowań transportowych rozwoju przemysłu w Polsce i dają duże możliwości w zakresie wskazywania potencjalnych lokalizacji dla nowych inwestycji w kontekście transportu towarów i podróży służbowych.

We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of human-mobility on deriving this upper bound on maximum mobility predictability. By using several statistical tests on three real-world mobility datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, contrasting with the initial Markovian assumption. We show that this assumption of exponential decay of information in mobility trajectories, coupled with the inadequate usage of encoding techniques results in entropy inflation, consequently lowering the upper bound on predictability. We highlight that the current upper bound computation methodology based on Fano&rsquo, s inequality tends to overlook the presence of long-range structural correlations inherent to mobility behaviors and we demonstrate its significance using an alternate encoding scheme. We further show the manifestation of not accounting for these dependencies by probing the mutual information decay in mobility trajectories. We expose the systematic bias that culminates into an inaccurate upper bound and further explain as to why the recurrent-neural architectures, designed to handle long-range structural correlations, surpass this upper limit on human mobility predictability.

In the modern world, with the growth of the volume of processed data arrays, the logic of solving problems also becomes more complex. This leads more and more often to the need to use high-performance computational clusters, such as supercomputers. Created multi-agent simulation applications require not only significant resources but often perform time-consuming complex scenarios, which significantly affects the efficiency of the executed process. However, there are various mechanisms for optimizing application execution for different needs. Unfortunately, the specificity of multi-agent simulation does not allow the use of traditional and modern algorithms due to the iteratively variable workload and limitations of a system software installed on the supercomputers. In this paper, we propose a four-level scheme for organizing the symbiotic execution (co-design) of multi-agent applications on supercomputers, as well as an effective two-level algorithm for optimizing the flow of the execution of an urban mobility simulation application. The algorithm is based on evolutionary approach and machine learning techniques.

Accurate arrival-time predictions in public transportation systems can improve the perceived quality-of-service offered and increase usage of these systems. To date, predictions were primarily based on periodic updates of mobility information that however exhibit a tradeoff between deterministic performance and system efficiency. To further improve on this tradeoff, event-triggering is emerging as a promising operations paradigm. This work describes an innovative design of a public transportation tracking system within which arrival-time predictions are made utilizing an event-triggering framework. As a first step towards this direction, behavior models are derived through extensive analysis of real mobility data. Thereafter, an event-triggering algorithm is developed to detect changes in the model in an online fashion. The efficiency and applicability of the proposed data-driven event-triggering paradigm is demonstrated through a real-world transit scenario that compares event triggering updating techniques. 682 686

Use of unmanned aerial vehicles (UAVs) for road traffic surveillance is an exciting idea for improving surveillance quality, as a component of intelligent transportation systems and smart cities. Calibrated mobility models help study and analyse several mobility related issues, for their successful deployment in large urban environments. This paper discusses an energy-aware and scalable mobility model for a team of cooperative UAVs monitoring urban road traffic. It also accompanies an extensible framework for territory distribution, to optimise the number of UAVs required for maximising coverage, without compromising operational performance. Since the problem of territory distribution in general is NP-hard, a genetic algorithm-based strategy, considering edge-disjoint paths, is recommended. Simulation results show that the proposed model considerably improves area coverage in comparison with other mobility models. A published dataset simulating vehicular traffic for a mid-size urban city is used to ascertain scalability to realistic urban territories.

The evolution of Internet of Things and Information and Communication Technology is determined by systems’ ability to efficiently store and process massive amount of data connected with rapid flow of information. The smart devices creating Internet of Things produce massive amount of data, and while smart devices are independent, data analyst needs an effective mechanism to collect all these data, aggregate them and analyse. In this paper author presents Distributed Spatio- Temporal Data Warehouse systems as a platform to collect and analyse Internet of Things data such as smart sensor data and mobility data, Studia Informatica, Vol 38, No 4 (2017)

Delay Tolerant Network (DTN) is an emerging networking technology that is widely used in the environment where end-to-end paths do not exist. DTN follows store-carry-forward mechanism to route data. This mechanism exploits the mobility of nodes and hence the performances of DTN routing and application protocols are highly dependent on the underlying mobility of nodes and its characteristics. Therefore, suitable mobility models are required to be incorporated in the simulation tools to evaluate DTN protocols across many scenarios. In DTN mobility modelling literature, a number of mobility models have been developed based on synthetic theory and real world mobility traces. Furthermore, many researchers have developed specific application oriented mobility models. All these models do not provide accurate evaluation in the all scenarios. Therefore, model selection is an important issue in DTN protocol simulation. In this study, we have summarized various widely used mobility models and made a comparison of their performances. Finally, we have concluded with future research directions in mobility modelling for DTN simulation.

With the advent of smartphones, opportunistic mobile crowdsensing has become an instrumental approach to perceive large-scale urban dynamics. In this context, the present work presents a novel approach based on such a sensing paradigm to automatically identify and monitor the areas of a city comprising most of the human transit. Unlike previous approaches, the system performs such detection in real time at the same time the opportunistic sensing is carried out. Furthermore, a novel multilayered grill partitioning to represent such areas is stated. Finally, the proposal is evaluated by means of a real-world dataset.

Data traces, consisting of logs about the use of mobile and wireless networks, have been used to study the statistics of encounters between mobile nodes, in an attempt to predict the performance of opportunistic networks. Understanding the role and potential of mobile devices as relaying nodes in message dissemination and delivery depends on the knowledge about patterns and number of encounters among nodes. Data traces about the use of WiFi networks are widely available and can be used to extract large datasets of encounters between nodes. However, these logs only capture indirect encounters between nodes, and the resulting encounters datasets might not realistically represent the spatial and temporal behaviour of nodes. This paper addresses the impact of overlapping between the coverage areas of different Access Points of WiFi networks in extracting encounters datasets from the usage logs. Simulation and real-world experimental results show that indirect encounter traces extracted directly from these logs strongly underestimate the opportunities for direct node-to- node message exchange in opportunistic networks.

We report on the experimental results of the MOS field-effect and Hall mobilities measured on etched surface of GaN grown on silicon substrate. The peak MOS Hall mobility was extracted to be 23 cm2/V-s, and the Hall mobility was then modelled using physics-based model. The effects of Columbic scattering, phonon scattering, and surface roughness were evaluated, and the limiting factor of the GaN MOS mobility was identified as Columbic scattering caused by the combination of high interface charges and traps at the GaN MOS interface. The GaN MOS surface roughness extracted from the modelling was comparable to silicon MOS interface. (© 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Wireless networks of moving objects have drawn significant attention recently. These types of networks consist of a number of autonomous or semi-autonomous wireless nodes/objects moving with diverse patterns and speeds while communicating via several radio interfaces simultaneously. To overcome current shortcomings, a number of research challenges have to be addressed in this area, ranging from initial conceptualization and modelling, to protocols and architectures engineering, and development of suitable tools, applications and services, and to the elaboration of realistic use-case scenarios by taking into account corresponding societal and economic aspects. By applying a systematic approach the objective of this book is to assess the state of the art and consolidate the main research results achieved in this area. It was prepared as the Final Publication of the COST Action IC0906 “Wireless Networking for Moving Objects (WiNeMO)”. The book contains 15 chapters and is a show-case of the main outcomes of the action in line with its scientific goals. The book will serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, and research strategists working in this field.

This article uses a real example to demonstrate the importance of mobility modelling (objective criteria) on correctly planned investments in infrastructure. Some radical conclusions are drawn which differ greatly from those suggested by more subjective mental models. Strategic developments on inter-urban networks are evaluated by applying a mobility model including a model for estimating O–D matrices based on traffic counts and a model for the optimal location of traffic counting stations at the same time as a population accessibility model. An important decision making tool is provided for planning investments in road infrastructure by rationalising the high public spending required for this type of work. The models are applied to various possible projects at determined points on a regional road network in the Autonomous Community of Cantabria (Northern Spain) following the proposals and demands of different social and political groups. The models find a 68% reduction in the number of links required to obtain O–D matrices. This provides considerable savings in data collection costs (approximately 50% less) involving future traffic counts on similar and especially regional road networks. First Published Online: 03 Apr 2013

People mobility enormously augmented in the last decades. However, despite the increased possibilities of fast reaching far places, the places that a person commonly visits remain limited in number. The number of visited places of each person is regulated by some laws that are statistically similar among individuals. In our previous work, we firstly argued that a person visit most frequently always few places, and we confirmed that by some initial experiments. Here, in addition to further validating this result, we build a more sophisticate view of the places visited by the people. Namely, on top of our previous work, which identifies the class of Mostly Visited Points of Interest, we define two next classes: the Occasionally and the Exceptionally Visited Points of Interest classes. We argue and validate on real data, that also the occasional places are very limited in number, while the exceptional ones can grow at will, and by the analysis of the classes of visited points we can distinguish the type of users mobility. This paper firstly demonstrates this property in large experimental scenario, and put the basis for new understanding of people places in several areas as localization, social interactions and human mobility modelling.

In this paper, we propose a mobility model for opportunistic networks in a commercial area MMCA. The commercial area is divided into two parts: internal and external areas. We modified the traditional susceptible-infected-removed SIR model for predicting the performance of the message forwarding in MMCA. Using the opportunistic networks environment ONE simulation platform, we validated this modified SIR by comparing the theoretical results and modelled data from ONE. We further analyse the performances of MMCA with the number of nodes, walking speeds, communication ranges and the size of areas.

For the simulative and emulative performance evaluation of tactical indoor communication systems modeling mobility is an important task. Typical assumptions are a uniformly distribution of destinations, shortest paths movement without obstacles, and randomly chosen speeds. These assumptions do not hold for tactical indoor scenarios. In this paper, we introduce a new rule-based indoor mobility model for tactical scenarios. We show that the realistic modeling has a great impact on the evaluation of routing protocols. Furthermore, we demonstrate that even complex movements can be represented by small rulesets.

Cooperative systems for transportation can bring new intelligence for vehicles, roadside systems, network operators and individuals by creating a communications platform allowing vehicles and infrastructure to share information. However, to verify the system performance, better mobility models are required since the research community has been using unrealistic models for very long. We propose to facilitate a discrete mobility modelling environment considering both mirco and macro mobility issues. The simulation results prove that the data transmission protocol, which is highly relevant to node mobility characteristics, is able to produce better results under both vehicle-to-vehicle and vehicle-to-infrastructure scenarios.

The subject of the present study is to find the best way of making a simulation model for the exposure assessment of mobile individuals. For that purpose we consider and apply several very different approaches to model movement patterns in a more or less random way and compare them in terms of the exposure resulting for the individuals. The models combine random movement with agenda-driven movement. We do not aim to involve all details of real conditions into the models, but explain and review the general concepts and provide an inter-comparison of these concepts. Stationary and ergodic behaviour are explained and applied as well as performance criteria for a comparative evaluation of the investigated algorithms. In particular, the present study investigates the exposure to air contaminants of persons moving in heterogeneously polluted urban areas by help of movement simulations. For that purpose we applied four different movement algorithms: Levy-modulated correlated random walk (LMCRW), Potential field controlled walk (PTW), Reference point mobility model (RPM), and RPM with a pre-defined daily agenda of targets (RPMA). We find that none of the studied algorithm results in purely random trajectories. PTW and RPMA prove to be suitable for human mobility modelling, because they provide conditions for very individual-specific trajectories and exposure. Suggesting these models we demonstrate the plausibility of their results for exposure to air-borne benzene and the combined exposure to benzene and nonane. It appears however that inter-individual variation in the individual-specific short-term exposure diminishes with runtime and when long-term exposure is considered.

Since the long-channel behaviour of MOS devices can be retained in spite of a severe reduction of the MOS transistor channel length, there are still many applications for long-channel MOS transistor models. This paper is a review of such models and its aim is to elucidate the influence of two effects: the dependence of the surface potential on the gate voltage in the strong inversion and mobility modulation along the channel due to the effective transverse electric field variation. The paper contains also a brief discussion of quantum-mechanical effects and mobility modelling.

In this paper, we propose a novel concept map for mobility models and use it in a short survey of existing proposals. We then review necessary requirements, and define key components for the generation of mobility models adapted to vehicular ad hoc networks (VANETs). Based on this, we first adapt our concept map to vehicular motion, then present a framework for the generation of vehicular mobility models that include all parameters vehicles experience while moving, and finally propose two derived mobility models at the stage of research.

In wireless personal communication systems, location and call management processes are fundamentally determined by the movement of the user. Modelling the mobility of a user is critical when evaluating current or future personal communication service protocols and management techniques. Synthetic and trace modelling are two methods of modelling the mobility of a single user or a group of users. Synthetic models are complex, computationally heavy, and lack realism. Trace models are based on empirical data and are difficult to modify to reflect future mobility. This paper presents PotFlow, a new synthetic method based on potential fluid dynamics that allows modelled users to have smooth continuous velocity trajectories in a simple, flexible, low computational manner.

Aggregate mobility modelling, studying macroscopic rule of human movement, benefits the performance of mobility management. This paper is primarily focused on establishing an architecture based on field theory, using scalar and vector field to describe traffic and mobility, respectively. On the basis of their temporal-spatial evolution, we try to discover the relationship between traffic field and mobility field. This field architecture fits for mobility management in large temporal-spatial scale, since it not only benefits qualitative analysis of traffic and mobility in the perspective of field, but also provides a theoretical foundation and insight for issues in mobile communication.