We study the mean-field limit of a particle-based system modeling the behavior of many indistinguishable pedestrians as their number increases. The base model is a modified version of Helbing's social force model. In the mean-field limit, the time-dependent density of two-dimensional pedestrians satisfies a four-dimensional integro-differential Fokker-Planck equation. To approximate the solution of the Fokker-Planck equation we use a time-splitting approach and solve the diffusion part using a Crank-Nicholson method. The advection part is solved using a Lax-Wendroff-Leveque method or an upwind Backward Euler method depending on the advection speed. Moreover, we use multilevel Monte Carlo to estimate observables from the particle-based system. We discuss these numerical methods, and present numerical results showing the convergence of observables that were calculated using the particle-based model as the number of pedestrians increases to those calculated using the probability density function satisfying the Fokker-Planck equation.

In this thesis, we develop multiscale models for particle simulations in population dynamics. These models are characterised by prescribing particle motion on two spatial scales: microscopic and macroscopic.At the microscopic level, each particle has its own mass, position and velocity, while at the macroscopic level the particles are interpolated to a continuum quantity whose evolution is governed by a system of transport equations.This way, one can prescribe various types of interactions on a global scale, whilst still maintaining high simulation speed for a large number of particles. In addition, the interplay between particle motion and interaction is well tuned in both regions of low and high densities. We analyse links between models on these two scales and prove that under certain conditions, a system of interacting particles converges to a nonlinear coupled system of transport equations.We use this as a motivation to derive a model defined on both modelling scales and prescribe the intercommunication between them. Simulation takes place in inhomogeneous domains with arbitrary conditions at inflow and outflow boundaries. We realise this by modelling obstacles, sources and sinks.Integrating these aspects into the simulation requires a route planning algorithm for the particles. Several algorithms are considered and evaluated on accuracy, robustness and efficiency. All aspects mentioned above are combined in a novel open source prototyping simulation framework called Mercurial. This computational framework allows the design of geometries and is built for high performance when large numbers of particles are involved. Mercurial supports various types of inhomogeneities and global systems of equations. We apply our framework to simulate scenarios in crowd dynamics.We compare our results with test cases from literature to assess the quality of the simulations. / <p>Master Thesis in Industrial and Applied Mathematics</p>

With the significant improvement in IoT technology and smart devices, data collection and distributed computation have led a foundation for Mobile crowd-sensing (MCS). MCS utilizes the capabilities of embedded sensors in smart devices for gathering data. MCS benefits both data provider (participant/user), and data requester, i.e. data providers via incentives/rewards, data requesters by delivering required data. Apart from the benefits gained through acquiring data, confronting challenges such as participant privacy, data trustworthiness, malicious attacks (from illegitimate users) need to be addressed to build robust and reliable data solicitation. In addition to that, it is necessary to consider user motivation and user preference, comfort during its engagement in crowd-sensing. User preferences/constraints can be due to privacy concerns in terms of location, the sensitivity of data or energy usage and many more. With this in mind, the main contributions of the thesis can be listed as follows. 1) We design user selective trustworthy data acquisition frameworks. We introduce a variety of user selection criteria to form participant communities based on participants reliability and income. To evaluate the trustworthiness of our selective reputation-based data acquisition, we consider malicious users in the environment and calculate the total rewards given to malicious users. Simulations results show that community formation based on the acquired income of participants ended up with a substantial loss to the cloud platform as well as participants. Contrary to that, reputation-based community formation has shown nearly equal platform utility (profit), negligible loss of user utility compared to benchmark Non-selective data acquisition with 7% malicious probability. 2) Moreover, we attempt to enable users to modify (allow/deny access to) their builtin sensor set according to their comfort levels. We formulate three comfort levels high (only allow access to sensors that would not directly reveal personal identity such as accelerometer, light sensor, etc.), moderate (obstruct access to sensitive data, e.g. camera), zero comfort (allow access to all users). We introduce Static modification, where users pre-arrange their sensor set before the start of data collection. Our feasibility study shows that pre-arrangement of the sensor set favours user comfort, user utility at the cost of loss in platform utility and performs better than selective reputation-based recruitment for the considered settings. 3) We apply Adaptive sensor modification on top of pre-arrangement of sensor set through which participants are authorized to re-arrange their sensor availability based on reliability scores. Simulation results show that the Adaptive comfort-aware approach performed better than static in terms of platform utility and achieved comparatively better user comfort with reasonable loss in user utility.

Since the identification, in the UK, of the need for further information on the dynamic loading of grandstands in the early 1990s, a number of research projects have investigated the issues relating to dynamic loading of structures due to groups of people participating in synchronised activities. These studies have, to date, largely focused on producing load models to accurately represent the dynamic crowd load and the human-structure interaction. However, whilst the vibrational response of grandstand structures is becoming better understood, the question arises as to what level of dynamic response is acceptable to the users. Currently there is very little experimental data available regarding human perception of vibrations in such crowd loading situations. As a result those producing design standards and design guides have very little information on which to base serviceability requirements. To address this, tests have been carried out at the University of Bath using a section of grandstand, whose dynamic properties could be varied, with the aim of developing acceptability criteria. Groups of participants were subjected to a range of vibrations induced by selected members of the group jumping in synchrony. Both those seated and jumping during the tests were asked to rate their perception and emotion of each vibration as well as the acceptability of the vibration in a real grandstand situation. These ratings were then used to statistically model perception and emotion to find the key vibration characteristics influencing the human response of both the seated and jumping participants prior to developing acceptability curves for each group. It was found that those seated are more sensitive to vibrations than those jumping to create them. The response of the jumpers is relatively simple and can be fairly accurately modelled using just a single variable namely the square of vertical RMS displacement. The seated response is much more complex but can be relatively accurately represented using logarithm of vertical RMS acceleration. The proposed acceptability criteria and load model generally tie in well with previously published guidelines provided that the serviceability criteria are in the same format as the original research upon which they were based (i.e. peak accelerations).

No description available.

Our study estimates the crowd-out of private health insurance following SCHIP expansions for children. We use panel data from the 2001 panel of the Survey of Income and Program Participation (SIPP). We use multivariate regression models to the crowd-out of private health insurance. This difference-in-differences approach controls for other factors that affect both the control group and treatment group, and measures the extent of crowd-out private coverage in the treatment group relative to the control group. We find that nearly 26 percent of the transitions from private coverage into SCHIP coverage were made by children who would have had private coverage in the absence of the expansions. This paper provides evidence that the SCHIP expansions have overall displacement effect of 52.9 percent for private coverage for those children who had private coverage or were uninsured from the first interview in 2001. This dissertation provides empirical evidence on the impact of SCHIP on single mothers¡¯ working decisions using recent CPS (Current Population Survey) data during 1999-2005. The empirical work requires a measure of the change in eligibility requirements; we compute a measure suggested by Yelowitz (1995). The major findings of this paper are: first, SCHIP expansions are found to have a significant positive impact on hours-worked decision; second, most models yielded results that indicated that SCHIP expansions have a generally insignificant impact on the decision to work.

Thesis (Ph.D, History) -- Queen's University, 2009-07-20 14:36:15.104 / The thesis is an interdisciplinary history of the crowd in late-Victorian London. It examines the crowd using novels, newspapers, and periodicals, Home Office, Metropolitan Police, and Parliamentary records, and the personal papers of politicians and city officials. The thesis focuses on riots, demonstrations, and processions beginning in 1848 through to the Trafalgar Square mle in 1887 as well as the way novelists imagined the crowd at the fin de sicle. In the process, it re-evaluates the urban environment that gave rise to the crowd and it explores the crowds influence on space, geography, and movement. The thesis rethinks crowd activity after mid century as the coming together of crowds and new concerns with modernity. It brings together the Marxist tradition of interpreting the crowd with writing on cultural and intellectual history as well as sociological and geographical theory in order to assess the crowds experience at street level. It aims to expand the traditional crowd model to include the spatial attitudes and practices that shaped the crowds relationship to the city and the citys relationship to the crowd. The thesis shows that the crowd, through its struggle for space, was not only a condition of the city, but one of the compelling features of urban modernity after mid century. The thesis traces the crowd in London in six chapters. An introductory chapter first locates the crowd historiographically. Chapter two focuses on the extent to which Londons improvement project mobilized the crowd. Chapter three describes the crowds battle for private space, after huge swathes of the urban population were dis-housed, and the challenges this posed to spatial ordering. Chapter four examines the battle for public space in the form of the radical political crowds occupation and production of space, between 1848-1868, as well as the states heavy-handed response. Chapter five describes the culmination of earlier issues in Trafalgar Square in 1887. Finally, chapter six explores the way novelists imagined the crowd in late-Victorian slum fiction. / Ph.D

This thesis explores techniques for synthesizing crowds from imagery. Synthetic photorealistic crowds are desirable for cinematic gaming, special effects and architectural visualization. While motion captured-based techniques for the animation and control of crowds have been well-studied in computer graphics, the resulting control rig sequences require a laborious model-based graphics pipeline to render photorealistic videos of crowds. Over the past ten years, data-driven techniques for rendering imagery of complex phenomena have become a popular alternative to model-based graphics. This popularity is due in large part to difficulties in constructing the sufficiently-detailed models that are required to achieve photorealism. A dynamic crowd of humans is an extremely challenging example of such phenomena. Example-based synthesis methods such as video textures are an appealing alternative, but current techniques are unable to handle new challenges posed by crowds. This thesis describes how to synthesize video-based crowds by explicitly segmenting pedestrians from input videos of natural crowds and optimally placing them into an output video while satisfying environmental constraints imposed by the scene. There are three key challenges. First, the crowd layout of segmented videos must satisfy constraints imposed by environmental and crowd obstacles. This thesis addresses four types of environmental constraints: (a) ground planes in the scene which are valid for crowd traversal, such as sidewalks, (b) spatial regions of these planes where crowds may enter and exit the scene, (c) static obstacles, such as mailboxes and walls of a building, and (d) dynamic obstacles such as individuals and groups of individuals. Second, pedestrians and groups of pedestrians should be segmented from the input video with no artifacts and minimal interaction time. This is challenging in real world scenes due to significant appearance changes while traveling through the scene. Third, segmented pedestrian videos may not have enough frames or the right shape to compose a path from an artist-defined entrance to exit. Plausible temporal transitions between segmented pedestrians are therefore needed but they are difficult to identify and synthesize due to complex self occlusions. We present a novel algorithm for composing video billboards, represented by crowd tubes, to form a crowd while avoiding collisions between static and dynamic obstacles. Crowd tubes are represented in the scene using a temporal sequence of circles planted in the calibrated ground plane. The approach consists of representing crowd tube samples and constraint violations with a conflict graph. The maximal independent set yields a dense crowd composition. We present a prototype system for the capture, analysis, synthesis and control of video-based crowds. Several results demonstrate the system's ability to generate videos of crowds which exhibit a variety of natural behaviors.

Crowdfunding is an online fundraising technique, where individuals or groups ask for contributions from a large number of people, i.e. “the crowd”. Crowdfunding first appeared in 2003 and has doubled its financial scope every year since 2011. Yet, very little is known about it, especially from a societal perspective. In this thesis I have attempted to understand how the organizations surrounding crowdfunding have structured themselves into an organizational filed, and also how “the crowd” might have granted this field special characteristics. This since “the crowd” is an element not earlier taken into consideration when studying field emergence. I have conducted a content analysis with data from 170 crowdfunding platforms and 190 media articles. I found that several events coincided in 2009 and opened up for the emergence of the crowdfunding field, and also that the understanding of crowdfunding’s purpose has shifted since 2003, from culture projects to start-ups. Regarding the special characteristic of the field it seems like the online nature enabled a fierce structuration pace, and also that “the crowd” constitutes an entity that cannot be captured by our traditional understanding of fields. The results suggest that when “the crowd” takes on functions in a field, the functions become invisible, and this has implications for future research regarding organizational fields.

The operating environment of cellular networks can be in a constant state of change due to variations and evolutions of technology, subscriber load, and physical infrastructure. One cellular operator, which we interviewed, described two key difficulties. Firstly, they are unable to monitor the performance of their network in a scalable and fine-grained manner. Secondly, they find difficulty in monitoring the service quality experienced by each user equipment (UE). Consequently, they are unable to effectively diagnose performance impairments on a per-UE basis. They currently expend considerable manual efforts to monitor their network through controlled, small-scale drive-testing. If this is not performed satisfactorily, they risk losing subscribers, and also possible penalties from regulators. In this dissertation, we propose Tattle1, a distributed, low-cost participatory sensing framework for the collection and processing of UE measurements. Tattle is designed to solve three problems, namely coverage monitoring (CM), service quality monitoring (QM) and, per-device service quality estimation and classification (QEC). In Tattle, co-located UEs exchange uncertain location information and measurements using local-area broadcasts. This preserves the context of co-location of these measurements. It allows us to develop U-CURE, as well as its delay-adjusted variant, to discard erroneously-localized samples, and reduce localization errors respectively. It allows operators to generate timely, high-resolution and accurate monitoring maps. Operators can then make informed, expedient network management decisions, such as adjusting base-station parameters, to making long-term infrastructure investment. We propose a comprehensive statistical framework that also allows an individual UE to estimate and classify its own network performance. In our approach, each UE monitors its recent measurements, together with those reported by co-located UEs. Then, through our framework, UEs can automatically determine if any observed impairment is endemic amongst other co-located devices. Subscribers that experience isolated impairments can then take limited remedy steps, such as rebooting their devices. We demonstrate Tattle's effectiveness by presenting key results, using up to millions of real-world measurements. These were collected systematically using current generations of commercial-off-the-shelf (COTS) mobile devices. For CM, we show that in urban built-up areas, GPS locations reported by UEs may have significant uncertainties and can sometimes be several kilometers away from their true locations. We describe how U-CURE can take into account reported location uncertainty and the knowledge of measurement co-location to remove erroneously-localized readings. This allows us to retain measurements with very high location accuracy, and in turn derive accurate, fine-grained coverage information. Operators can then react and respond to specific areas with coverage issues in a timely manner. Using our approach, we showcase high-resolution results of actual coverage conditions in selected areas of Singapore. For QM, we show that localization performance in COTS devices may exhibit non-negligible correlation with network round-trip delay. This can result in localization errors of up to 605.32m per 1,000ms of delay. Naïve approaches that blindly accepts measurements with their reported locations will therefore result in grossly mis-localized data points. This affects the fidelity of any geo-spatial monitoring information derived from these data sets. We demonstrate that using the popular localization approach of combining Global-Positioning System together with Network-Assisted Localization, may result in a median root-mean-square (rms) error increase of over 60%. This is in comparison to simply using the Global-Positioning System on its own. We propose a network-delay-adjusted variant of U-CURE, to cooperatively improve the localization performance of COTS devices. We show improvements of up to 70% in terms of median rms location errors, even while subjected to uncertain real-world network delay conditions, with just 3 participating UEs. This allows us to refine the purported locations of delay measurements, and as a result, derive accurate, fine-grained and actionable cellular quality information. Using this approach, we present accurate cellular network delay maps that are of much higher spatial-resolution, as compared to those naively derived using raw data. For QEC, we report on the characteristics of the delay performance of co-located devices subscribed to 2 particular cellular network operators in Singapore. We describe the results of applying our proposed approach to addressing the QEC problem, on real-world measurements of over 443,500 data points. We illustrate examples where “normal” and “abnormal” performances occur in real networks, and report instances where a device can experience complete outage, while none of its neighbors are affected. We give quantitative results on how well our algorithm can detect an “abnormal” time series, with increasing effectiveness as the number of co-located UEs increases. With just 3 UEs, we are able to achieve a median detection accuracy of just under 70%. With 7 UEs, we can achieve a median detection rate of just under 90%. 1 The meaning of Tattle, as a verb, is to gossip idly. By letting devices communicate their observations with one another, we explore the kinds of insights that can elicited based on this peer-to-peer exchange.