Cellular Automata Model Research Articles

Heterogeneous traffic flow of expressway with Level 2 autonomous vehicles considering moving bottlenecks

With the development of autonomous driving technology, autonomous vehicles (AV) will gradually replace human-driven vehicles (HDV), but it will still be a mixed traffic flow of autonomous vehicles with different levels of automation for a long time. In addition to cars, trucks regarded as moving bottleneck are also an important part of traffic flow of expressway. In this paper, the elimination rule of moving bottleneck is introduced into the traffic flow model, and the improved hybrid traffic flow cellular automata model is proposed. Next, through numerical simulation, the existence and influence of moving bottleneck caused by heavy duty trucks are hypothesized and verified. The simulation results show that with the proportion of trucks increases, the influence range of moving bottleneck expands, resulting in the decrease of capacity and traffic congestion. Then, the impact of Level 2 autonomous vehicles (hereinafter referred to as L2 AVs) on the traffic flow of moving bottlenecks is explored. The simulation results show that with the proportion of L2 AVs increases, traffic capacity increases, and traffic congestion ratio at medium and high density decreases. Under medium density, the average velocity fluctuates greatly, while under high density, the average velocity decreases due to limitation of density. In summary, L2 AVs can alleviate influence of moving bottlenecks, increase traffic capacity, improve traffic congestion, and lift road safety. The cellular automata model of traffic flow including moving bottleneck proposed in this paper is based on the actual road situation, considering the fact that most of autonomous vehicles on the expressway currently are L2 AVs, and moving bottleneck is mainly caused by heavy trucks. This research contributes to an in-depth analysis of L2 AVs on heterogeneous traffic flows under moving bottleneck environment.

A hybrid model of recursive cellular automata, DNA sequences, and chaotic system for image encryption

A hybrid model of recursive cellular automata, DNA sequences, and chaotic system for image encryption

A multiclass logistic regression, Markov chain, and cellular automata integration for modelling urban land use change

Cellular Automata (CA) models employ simple transition rules and neighbourhood interactions to determine cells' evolution. Integrating CA with other algorithms is often essential in order to obtain necessary statistical inferences. The integration of Binary Logistic Regression (BLR) and Markov chain (MC) with CA forms common CA integrations. For convenience, urban land use change is often modelled as a binary process (e,g. ‘undeveloped land’ and ‘developed land’), whereas in the real world urban land use change phenomenon could go beyond two processes (e. g. ‘undeveloped land’, ‘sparsely developed land’, ‘semi-highly developed land’, ‘highly developed land’, ‘very highly developed land’, etc.). The BLR-MC-CA hybrid model yields a binary outcome. In order to extend the BLR-MC-CA hybrid model to multivariate problems, we introduce a novel hybrid model, by simply replacing the BLR with the new Multiclass Logistic Regression (MLR) to obtain the new MLR-MC-CA hybrid model. Satellite remote sensing imageries acquired in 1984, 2000, and 2020 are used as input data. Four scenarios of urban land use change are modelled: ‘nonurban region’, ‘highest urban region’, ‘high urban region’, and ‘medium urban region’. These four scenarios are modelled with twelve urban land use drivers: distance to water body, distance to residential areas, distance to industrial and commercial areas, distance to major roads, distance to railway, distance to Lagos Island, distance to international seaport, distance to University of Lagos, income potential, population potential, distance to international airport, and distance to Lagos State University. Urban transitions 1984–2000 and 2000–2020 are used to simulate the urban states in 2000 and 2020 respectively; as well as to obtain the urban state in 2060. The validation of the experiment is largely based on McNemar's test and Receiver Operating Characteristics (ROC) curves. The results of the experiment indicate significant fit between the reference and the predicted data.

Hantavirus Expansion Trends in Natural Host Populations in Brazil.

Hantaviruses are zoonotic agents responsible for causing Hantavirus Cardiopulmonary Syndrome (HCPS) in the Americas, with Brazil ranking first in number of confirmed HCPS cases in South America. In this study, we simulate the monthly spread of highly lethal hantavirus in natural hosts by conjugating a Kermack-McCormick SIR model with a cellular automata model (CA), therefore simultaneously evaluating both in-cell and between-cell infection dynamics in host populations, using recently compiled data on main host species abundances and confirmed deaths by hantavirus infection. For both host species, our models predict an increase in the area of infection, with 22 municipalities where no cases have been confirmed to date expected to have at least one case in the next decade, and a reduction in infection in 11 municipalities. Our findings support existing research and reveal new areas where hantavirus is likely to spread within recognized epicenters. Highlighting spatial-temporal trends and potential expansion, we emphasize the increased risk due to pervasive habitat fragmentation and agricultural expansion. Consistent prevention efforts and One Health actions are crucial, especially in newly identified high-risk municipalities.

A Harris Hawks optimization-based cellular automata model for urban growth simulation

A Harris Hawks optimization-based cellular automata model for urban growth simulation

Estimating future bathymetric surface of Kainji Reservoir using Markov Chains and Cellular Automata algorithms

The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation.

Diffusion and pattern formation in spatial games.

Diffusion plays an important role in a wide variety of phenomena, from bacterial quorum sensing to the dynamics of traffic flow. While it generally tends to level out gradients and inhomogeneities, diffusion has nonetheless been shown to promote pattern formation in certain classes of systems. Formation of stable structures often serves as a key factor in promoting the emergence and persistence of cooperative behavior in otherwise competitive environments, however, an in-depth analysis on the impact of diffusion on such systems is lacking. We therefore investigate the effects of diffusion on cooperative behavior using a cellular automaton (CA) model of the noisy spatial iterated prisoner's dilemma (IPD), physical extension, and stochasticity being unavoidable characteristics of several natural phenomena. We further derive a mean-field (MF) model that captures the three-species predation dynamics from the CA model and highlight how pattern formation arises in this new model, then characterize how including diffusion by interchange similarly enables the emergence of large scale structures in the CA model as well. We investigate how these emerging patterns favors cooperative behavior for parameter space regions where IPD error rates classically forbid such dynamics. We thus demonstrate how the coupling of diffusion with nonlinear dynamics can, counterintuitively, promote large-scale structure formation and in return establish new grounds for cooperation to take hold in stochastic spatial systems.

Modeling and validation of hydrogen porosity formation in aluminum laser welding

Laser welding is used to weld aluminum alloy 1100 for battery cell manufacturing in the automotive industry. However, these welds can have porosity that can decrease their performance in the battery packs. One type of porosity is gas porosity, commonly understood to be due to hydrogen entrapment in the weld during solidification. Hydrogen is dissolved in the molten aluminum at elevated temperatures during laser heating and can be entrapped in the weld pool upon solidification. When welding anodized aluminum, the amount of hydrogen porosity is increased because the anodized oxide layer is broken up and the broken-up oxide can act as heterogeneous nucleation sites for hydrogen pores. A cellular automaton model has been developed to predict the nucleation and growth of hydrogen porosity during laser welding. This model has been validated with microstructure analysis, LECO® hydrogen analysis, and X-ray micro-computed tomography. This model can be used by welding engineers to reduce porosity and control weld quality during laser welding of aluminum alloys.

Multifactor dynamic coupled model for building fire evacuation: Case study of the Karamay Friendship Palace fire

Occupant evacuation during a building fire is a complex system that includes several highly coupled subsystems, such as building characteristics, occupant characteristics, fire behavior, and evacuation behavior. To study the interaction mechanism between these subsystems to maximize occupant safety during fire evacuation in a building, a multifactor dynamic coupled model based on the Fire Dynamic Simulator and floor field cellular automata model for occupant evacuation in a fire was established. In the model, the dynamic impact of smoke diffusion on occupant movement direction and speed, decrease in occupant health points caused by smoke and trampling, and changes in occupant exit choices in the presence of closed exits were considered in detail. The catastrophic fire accident in the Friendship Palace of Karamay, Xinjiang, China, in 1994 was used as the analysis case, in which the spatial model of the Friendship Palace was reconstructed according to real data and architectural design methods. The safety exits open or closed, radius Rtra, possibility of information transmission Psuc, and wait time twait were discussed to study the influence of these parameters on the evacuation process and results. The study shows that the model could reproduce a fire accident well with Rtra = 4, Psuc = 0.5, and twait = 30. The closed exits increased evacuation time by over 120.3 % and resulted in 439 deaths. These conclusions are important for providing better estimates, guiding architectural designs, and enhancing building safety management.

Forecasting urban expansion of Vadodara City, Gujarat (India): An integrated modeling approach with spatial, economic and demographical parameters

Urbanization is the inevitable phenomenon associated with the development of the region. The cities and towns tend to expand their territory exerting pressure on the environment and resources. Often this growth turns into sprawl, owing to irrational planning policies, coarse data, and generalized approaches irrespective of the underlying characteristics of the region. Thus, the need for urban planners is to understand the impact of past policies and their future course. The spatial urban expansion models like Cellular automata (CA) prove to be effective to trace urban development along with predicting the future. It equips the inclusion of the multiple spatial parameters to understand its degree of influence on regional urbanization. The present study focuses on the prediction of urbanization for 2031 in the Vadodara urban development authority (VUDA) region in Gujarat (India) using the cellular automata model. The study includes multiple variables comprising the social, demography, and land price variation along with the physical and site-situation suitability. A total of 32 years of time series dataset for identification of the land use pattern was generated. The result shows the city of Vadodara predominantly service industries centric growth and having industrial development on the peri-urban stretch will push the city limit to expand and according to the calibrated CA model, around 25 sq. km of the area is going to be transformed into the built-up usage in the VUDA region by the year 2031. The expansion of the built-up cover will mostly transform agricultural land, scrubland, and open land usage. Thus, a balanced horizontal versus vertical growth of the city will be required to have sustainable city development.

Post-mining planning under the prism of land use alternatives: a cellular automata model

Post-mining planning under the prism of land use alternatives: a cellular automata model

Minimal cellular automaton model with heterogeneous cell sizes predicts epithelial colony growth

Regulation of cell proliferation is a crucial aspect of tissue development and homeostasis and plays a major role in morphogenesis, wound healing, and tumor invasion. A phenomenon of such regulation is contact inhibition, which describes the dramatic slowing of proliferation, cell migration and individual cell growth when multiple cells are in contact with each other. While many physiological, molecular and genetic factors are known, the mechanism of contact inhibition is still not fully understood. In particular, the relevance of cellular signaling due to interfacial contact for contact inhibition is still debated. Cellular automata (CA) have been employed in the past as numerically efficient mathematical models to study the dynamics of cell ensembles, but they are not suitable to explore the origins of contact inhibition as such agent-based models assume fixed cell sizes. We develop a minimal, data-driven model to simulate the dynamics of planar cell cultures by extending a probabilistic CA to incorporate size changes of individual cells during growth and cell division. We successfully apply this model to previous in-vitro experiments on contact inhibition in epithelial tissue: After a systematic calibration of the model parameters to measurements of single-cell dynamics, our CA model quantitatively reproduces independent measurements of emergent, culture-wide features, like colony size, cell density and collective cell migration. In particular, the dynamics of the CA model also exhibit the transition from a low-density confluent regime to a stationary postconfluent regime with a rapid decrease in cell size and motion. This implies that the volume exclusion principle, a mechanical constraint which is the only inter-cellular interaction incorporated in the model, paired with a size-dependent proliferation rate is sufficient to generate the observed contact inhibition. We discuss how our approach enables the introduction of effective bio-mechanical interactions in a CA framework for future studies.

Simulating the spread of COVID-19 with cellular automata: A new approach

Between the years 2020 and 2022, the world was hit by the pandemic of COVID-19 giving rise to an extremely grave situation. Many people suffered and died from this disease. Also, the global economy was badly hurt due to the consequences of various intervention strategies (like social distancing, lockdown) which were applied by different countries to control this pandemic. There are multiple speculations that humanity will again face such pandemics in the future. Thus, it is very important to learn and gain knowledge about the spread of such infectious diseases and the various factors which are responsible for it. In this study, we have extended our previous work [S. Chowdhury, S. Roychowdhury and I. Chaudhuri, Eur. Phys. J. Spec. Top. 231, 3619 (2022), doi:10.1140/epjs/s11734-022-00619-1] on the probabilistic cellular automata (CA) model to reproduce the spread of COVID-19 in several countries by modifying its earlier used neighborhood criteria. This modification gives us the liberty to adopt the effect of different restrictions like lockdown and social distancing in our model. We have done some theoretical analysis for initial infection and simulations to gain insights into our model. We have also studied the data from eight countries for COVID-19 in a window of 876 days and compared it with our model. We have developed a proper framework to fit our model on the data for confirmed cases of COVID-19 and have also re-checked the goodness of the fit with the data of the deceased cases for this pandemic. Our model is compared with other well-known CA models and the ODE-based SEIR model. This model fits well with different peaks of COVID-19 data for all the eight countries and can be possibly generalized for a global prediction. Our study shows that the rate of disease spread depends both on infectivity of a disease and social restrictions. Also, it shows an overall decrement in mortality rate with time due to COVID-19 as more and more people get infected as well as vaccinated. Our minimal model with modified neighborhood condition can easily quantify the degree of social restrictions. It is statistically concluded that the overall degree of social restrictions is above the mean when we considered all eight countries. Finally to conclude, this study has given us various important insights about this pandemic which can help in preparing for combating epidemics in future situations.

Potential-based dynamic parking navigation for autonomous vehicles: Near-priority vs. distant-priority

We aim to enhance the parking efficiency for a group of autonomous vehicles in a large parking lot during peak hours. Two parking principles, i.e. the near-priority (NP) and distant-priority (DP) principles, are proposed and quantitatively examined. The NP principle characterizes individual parking behavior, where autonomous vehicles tend to select available parking spaces that are closer in proximity. Conversely, the DP principle is proposed from the perspective of the entire parking system, prioritizing the allocation of more distant parking spaces within a certain range around each entrance. Two time indicators, including the overall parking time and the average parking time, are adopted to evaluate the performance of the two principles. A potential-based cellular automata (CA) model is proposed to formulate the dynamic parking process of vehicles in a two-dimensional space, where vehicle navigation is driven by a so-called potential field. Then, two dynamic navigation algorithms are developed for parking navigation under the NP and DP principles. Furthermore, by conducting a set of comparative simulation experiments, we have obtained some management insights into peak parking management in the era of autonomous driving.

In vitro characterization of Trypanosoma cruzi infection dynamics in skeletal and cardiac myotubes models suggests a potential cell-to-cell transmission in mediating cardiac pathology.

Chagas disease predominantly affects the heart, esophagus, and colon in its chronic phase. However, the precise infection mechanisms of the causal agent Trypanosoma cruzi in these tissue types remain incompletely understood. This study investigated T. cruzi infection dynamics in skeletal (SM) and cardiac myotubes (CM) differentiated from H9c2(2-1) myoblasts (control). SM and CM were generated using 1% fetal bovine serum (FBS) without or with retinoic acid, respectively. Initial invasion efficiencies and numbers of released parasites were equivalent between undifferentiated and differentiated cells (~0.3-0.6%). Concomitantly, parasite motility patterns were similar across cell lines. However, CM demonstrated significantly higher infection kinetics over time, reaching 13.26% infected cells versus 3.12% for SM and 3.70% for myoblasts at later stages. Cellular automata modeling suggested an enhanced role for cell-to-cell transmission in driving the heightened parasitism observed in CM. The increased late-stage susceptibility of CM, potentially mediated by cell-to-cell transfer mechanisms of the parasite, aligns with reported clinical tropism patterns. The myotube infection models provide novel insights into Chagas disease pathogenesis that are not fully attainable through in vivo examination alone. Expanding knowledge in this area could aid therapeutic development for this neglected illness.

HashGAT-VCA: A vector cellular automata model with hash function and graph attention network for urban land-use change simulation

Vector cellular automata (VCA) models, which excel at representing spatiotemporal dynamics of irregularly shaped land parcels, have been widely employed in land use change simulations. However, current research faces the following issues: (1) most VCA models neglect the spatial heterogeneity of driving factors within each land parcel when evaluating the environmental driving effects; (2) when calculating the neighborhood effects, simple statistics of land use types in neighboring parcels are often used, overlooking the influence of driving factors within neighboring parcels; (3) the ability to explore the interactions between land parcels is often limited. To address the aforementioned issues, this study proposes a HashGAT-VCA model for investigating urban land use changes. The model utilizes a Hash function to encode the non-uniform distribution of each driving factor within each irregularly shaped land parcel into a fixed-length vector, and constructs a graph structure between land parcels based on their spatial topological relationships. By employing a Graph Attention Network (GAT), the model explores the mechanisms of environmental driving effects and inter-parcel interactions to calculate the probability of land use change for each parcel. The proposed HashGAT-VCA model was applied to simulate urban land use changes in Shenzhen, China, from 2009 to 2012. Compared to other VCA models, the HashGAT-VCA demonstrated higher simulation accuracy. The results indicated that HashGAT-VCA can effectively capture the impacts of the heterogeneously distributed driving factors and the interactions between land parcels on land use changes. Additionally, this study simulated land use patterns for the years 2025 and 2030 under ecological control strategies, providing decision support for urban planning.

Continuous dynamic recrystallization behaviors in a single-phase deformed Ti-55511 alloy by cellular automata model

In this study, the continuous dynamic recrystallization (CDRX) behaviors of a hot-deformed Ti-55511 alloy are investigated in β single-phase domain. CDRX is found to play a dominant role in the microstructure evolution during thermomechanical forming of the Ti-55511 alloy. In order to quantitatively and visually simulate the hot forming process, a probabilistic cellular automata (CA) method is established. This CA technology integrates the dislocation evolution model, subgrain nucleation rate model, misorientation evolution model, grain boundary (GB) migration model, and topology deformation model. The results of a systematic study of both microstructural evolution and macroscopic mechanical response at various strains, strain rates, and temperatures are presented. The average absolute relative error (AARE), correlation coefficient (R), and root-mean squared error (RMSE) values between the experimental and simulated stresses are 4.63 %, 0.9963, and 3.81 MPa, respectively. The deviation values of subgrain size range from 1.27 % to 17.25 %, when the temperatures varies from 920 to 980 °C. The predicted results well agree with the measured results, demonstrating the transition from a coarse and static recrystallized structure to a refined and continuous dynamic recrystallized structure. A low Zener-Hollomon (Z) parameter can effectively enhance the progress of CDRX. Higher temperatures and lower strain rates result in a larger volume fraction of CDRX. Quantitative analyses confirm that the incubation period for CDRX is significantly longer than that for subgrain formation, indicating a sustained transformation from the low-angle grain boundaries (LAGBs) to the high-angle grain boundaries (HAGBs) through the absorption of dislocations.

A Computational Perspective: Escape Panic Analysis in Confined Pedestrian Environments

An in-depth investigation of the phenomena of escape fear, which has significant repercussions in the actual world, is carried out by means of sophisticated simulations. Specifically, this study makes use of a sophisticated two-dimensional cellular automaton (CA) model in order to decipher the complex dynamics that are characteristic of escape situations. The predicted characteristics, which include arch creation, disruptive interference, and self-organized queuing, are not only theoretical speculations; rather, they are manifestations of events that have been witnessed in our digital reality. Surprisingly, the simulations reveal a scale-free behaviour that had not been expected before, which adds an extra degree of complexity to the escape dynamics. There is a seamless integration of real-world restrictions into the simulations. These constraints include occupancy rates, pedestrian weariness, and the non-rigidity of bodies. In light of the fact that conducting experiments with actual systems presents both ethical and practical obstacles, numerical simulations have emerged as an indispensable instrument for investigating critical sampling rates, exit widths, and the substantial influence that these factors have on escape dynamics. Key Words: Self-organized queuing, Escape panic, Numerical simulations, Exit widths, Exit throughput rate, Pedestrian movement.

Dynamic traffic noise simulation at signal-controlled intersections based on cellular automata model

Predicting and assessing road traffic noise is a crucial task in urban environmental management. To accurately predict traffic noise at signal-controlled intersections, this study introduces a dynamic traffic noise simulation model based on cellular automata. Firstly, a cellular automata traffic flow model for a multi-lane signal-controlled intersection is established. The vehicle noise emission and noise propagation models are incorporated into the dynamic simulation of traffic flow to accurately calculate traffic noise. Subsequently, the simulated data is compared with the measured data. The results demonstrate that the proposed method can effectively predict the statistical distribution of traffic noise at the intersection, with prediction errors of less than 2 dB(A) for Leq, L50, L90, and less than 3 dB(A) for L10. Finally, the proposed method is employed to simulate the operational status and noise emissions of vehicles at intersections under various traffic conditions. Based on the simulation results, the intrinsic connection between traffic operation and noise emission is explored, and the characteristics of traffic noise emission and distribution at intersections are analyzed. The method and its findings are anticipated to serve as a valuable reference for the management of road traffic noise.

Numerical and Theoretical Analysis of a New One-Dimensional Cellular Automaton Model for Bidirectional Flows

Numerical and Theoretical Analysis of a New One-Dimensional Cellular Automaton Model for Bidirectional Flows