GPGPU Computing for Microscopic Simulations of Crowd Dynamics

We compare GPGPU implementations of two popular models of crowd dynamics. Specifically, we consider a continuous social force model, based on differential equations (molecular dynamics) and a discrete social distances model based on non-homogeneous cellular automata. For comparative purposes both models have been implemented in two versions: on the one hand using GPGPU technology, on the other hand using CPU only. We compare some significant characteristics of each model, for example: performance, memory consumption and issues of visualization. We also propose and test some possibilities for tuning the proposed algorithms for efficient GPU computations

Grouping behaviour and decision making in road tunnels evacuation in smoke conditions Experimental approach

We have performed a set of evacuation experiments in a road tunnel. In each experiment pedestrians were gathered in a bus, the bus was stopped in the tunnel, next the tunnel was filled with artificial smoke and pedestrians had to evacuate. We compared evacuation times and behaviours for different levels of visibility, defined by extinction coefficient Cs range

Pedestrian Spatial Self-organization According to its Nearest Neighbor Position

AbstractThe paper describes relation between positions of individuals in a crowd, namely dependence between position of a pedestrian and position of his/her nearest neighbors. Two main characteristics have been analyzed: nth nearest neighbors’ spatial and angular distributions. At first sight, people in human crowd seem to be located randomly, however, our findings indicate that there are clearly visible patterns in analyzed characteristics. We discover symptoms of strong correlations between position of closely located pedestrians. Simple, local movement rules for pedestrian are proposed to explain observed patterns

Algorytmy modelowania inteligentnych zachowań w zagadnieniach dynamiki pieszych z zastosowaniem niehomogenicznych automatów komórkowych rozprawa doktorska /

Tyt. z ekranu tytułowego.Praca doktorska. Akademia Górniczo-Hutnicza im. Stanisława Staszica (Kraków), 2007.Bibliogr.Dostępna także w wersji drukowanej.Tryb dostępu: Internet.Automaty komórkowe w modelowaniu, definicje pojęcia automatu komórkowego, klasyfikacja automatów komórkowych, charakterystyka, systemy wieloagentowe, modelowanie dynamiki pieszych, charakterystyczne zjawiska w ruchu pieszych, rozwiązania architektoniczne w obiektach użyteczności publicznej, przegląd modeli dynamiki pieszych, modele ciągłe względem czasu, dyskretne, proponowana definicja CA, przesłanki do stworzenia nowego modelu CALconst, jego formalizacji, definicja automatu komórkowego ze stałą siatką, rozszerzony model, proponowane modele dynamiki pieszych, model bazowy, klasyczna, nieklasyczna formuła CA w dynamice pieszych, ogólna charakterystyka modelu, formalny zapis modelu bazowego, model SPA, formalny opis, SPA-BNE, zapis, algorytm ruchu z uwzględnieniem blokad, dynamika tłumów, eksperymenty, charakterystyka badań eksperymentalnych, symulacja paniki, eksperyment, analiza zapisu video, ankiet, podsumowanie eksperymentu, symulacja kontrolowanej ewakuacji, analiza ankiet, kontrolowana ewakuacja, sytuacja zwyczajnego opuszczania pomieszczenia, normalne opuszczanie pomieszczenia, badania symulacyjne, implementacja modeli, opis aplikacji, informacje ogólne, opis struktury wewnętrznej aplikacji, graficzny interfejs użytkownika, symulacje komputerowe, informacje wstępne, symulacja sytuacji normalne

Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship

Forecasting electricity demand is of utmost importance for ensuring the stability of the entire energy sector. However, predicting the future electricity demand and its value poses a formidable challenge due to the intricate nature of the processes influenced by renewable energy sources. Within this piece, we have meticulously explored the efficacy of fundamental deep learning models designed for electricity forecasting. Among the deep learning models, we have innovatively crafted recursive neural networks (RNNs) predominantly based on LSTM and combined architectures. The dataset employed was procured from a SolarEdge designer. The dataset encompasses daily records spanning the past year, encompassing an exhaustive collection of parameters extracted from solar farm (based on location in Central Europe (Poland Swietokrzyskie Voivodeship)). The experimental findings unequivocally demonstrated the exceptional superiority of the LSTM models over other counterparts concerning forecasting accuracy. Consequently, we compared multilayer DNN architectures with results provided by the simulator. The measurable results of both DNN models are multi-layer LSTM-only accuracy based on R2—0.885 and EncoderDecoderLSTM R2—0.812

Pedestrian behavior during evacuation from road tunnel in smoke condition-Empirical results.

Five evacuation experiments were performed in a road tunnel in order to test how pedestrians react when exposed to reduced visibility, how the decision making process is carried out, and finally what is the impact of various circumstances like: different level of smokiness, competitive behavior or learning effect on an evacuation process. In four experiments pedestrians were exposed to artificial, non-toxic smoke. During evacuation of a group of people gathered in low and moderate level of smokiness (when Cs 0.7m-1 we have observed decision making by small groups and characteristic double-lines patterns. In four experiments the same group of participants was involved, and a learning effect was observed: increasingly shorter pre-movement time and decreasing time required to leave the main tunnel. We show, that movement speed in smoke is influenced by the evacuees' attitude and familiarity with environment and evacuation procedures and not only by the visibility level

Verification and Validation of Evacuation Models – Methodology Expansion Proposition

AbstractIn November 2013, Technical Note 1822: The Process of Verification and Validation of Building Fire Evacuation Models was released by NIST. The note was intended to open discussion about evacuation modeling, rather than provide definitive guidelines. The aim of our paper is to add creative contribution to V&V topic.We propose adding some qualitative tests and distinguishing a base set of tests for all kinds of models from extended set of tests for specialized models. Moreover, the inclusion of additional tests is suggested, as well as the inclusion of some improvements and extensions to the tests proposed in the note

Bio-Hybrid Hydrogels Incorporated into a System of Salicylic Acid-pH/Thermosensitive Nanocarriers Intended for Cutaneous Wound-Healing Processes

In this paper, the preparation method of bio-hybrid hydrogels incorporated into a system of salicylic acid-pH/thermosensitive nanocarriers to speed up the wound-healing process was developed. This combination creates a dual drug delivery system, which releases the model hydrophobic active substance—salicylic acid—in a gradual and controlled manner for an extended time. Our research team has determined the various properties of bio-hybrid hydrogels based on their physicochemical (swelling degree, and degradation), structural (FT-IR), morphological (SEM), and mechanical (elongation tests) traits. Moreover, empty pH/thermosensitive nanocarriers and their salicylic acid-containing systems were characterized using the following methods: DLS, TG/DTG, and DSC. Additionally, salicylic acid release profiles directly from thermosensitive nanocarriers were compared to the bio-hybrid matrix. These studies were conducted in PBS (pH = 7.4) for 7 days using the USP4 method. To evaluate the antibacterial properties of the obtained materials, the inhibition of growth of Staphylococcus aureus, Escherichia coli, Candida albicans, and Aspergillus niger—as the main microorganisms responsible for human infections—were tested. The obtained results indicated that the pH/thermosensitive nanocarrier–salicylic acid system and bio-hybrid hydrogels are characterized by antibacterial activity against both S. aureus and E. coli