Cellular Automata Model Research Articles

Cellular automaton model for the analysis of design and plan of bus station in the mixed traffic environment

With the quick development of Connected Autonomous Vehicles (CAVs), CAVs would gradually become an important part of urban traffic. Hence, the impacts of CAVs on urban traffic should be further explored. This study aims to analyze the mixed traffic comprising CAVs and human-driven vehicles (HDVs) in different urban scenarios in which different bus station types (i.e., roadside and bay bus stations) and capacities are considered. To do this analysis, this study proposes a cellular automaton model which simulates car following behaviours of vehicles using a two-state safe speed model, and designs specific modeling rules for lane-changing behaviours and vehicle behaviours near bus stations with consideration of the differences between CAVs and HDVs. The analysis results indicate that the impacts of various bus station types and capacities on the mixed traffic flow vary with traffic volumes, while increasing the CAV penetration rate can reduce the traffic congestion caused by bus stop-and-go. Furthermore, the study explores the optimal number of bus routes for different types of bus stations in different urban traffic scenarios, and several possible policy implications have been given according to the analysis results.

Extended Floor Field Cellular Automata Model for Pedestrian Emergency Evacuation Considering Panic Emotion

Panic emotion appears frequently in an emergency evacuation, affecting evacuation efficiency and pedestrian safety. Individual diversity in emotion propagation causes pedestrians to behave differently. To better describe the emotion propagation in the movement of pedestrians, we analyze the characteristics of the emotions of different pedestrians in emergencies. Considering pedestrian heterogeneity, we divide pedestrians into three types (conservative, moderate, and radical) according to the strength of the expression of individual emotions. Based on an analysis of the characteristics of the three types of pedestrian, an improved susceptible-infected-recovered (SIR) model is built to describe the pedestrian emotion propagation process. In addition, a conflict avoidance method that considers pedestrian speed is proposed to demonstrate pedestrian motion more realistically. Based on this foundation, a cellular automata (CA) model that integrates emotion propagation and conflict avoidance techniques is employed to replicate the process of pedestrian emergency evacuation. The simulation results suggest that a higher proportion of pedestrians with initial panic emotions and a radical behavior type leads to a prolonged evacuation time. Many pedestrians in emergency evacuations gather at the exit in a short time (approximately 37 time steps), the density then reaches a high-level (approximately 5.5 pedestrians/m2) for a period of time, and the duration increases with the increase in the number of pedestrians. Methods for guiding pedestrians to evacuate in a rational manner are worthy of promotion and research.

Spatially explicit simulation and forecasting of urban growth using weights of evidence based cellular automata model in a millennium city of India

The present study focuses on quantifying and simulating the future urban growth based on the land use/land cover (LULC) data created from the Landsat images of the year 1999, 2011, and 2022. These LULC maps help in analysing the expansion of urban areas over the years and forecast their potential growth in the future. The spatio-temporal processes of urban growth are quantified, and future patterns are simulated and forecasted using Weights of Evidence based Cellular Automata model built in Dinamica EGO (Environment for Geoprocessing Objects) platform. The process of urban growth was manifested through prominent contributing factors of infill expansion namely, distance to built-up areas, distance to main roads, population density, and public services etc. The model's performance was evaluated using Kappa statistics and the percentage of correct prediction (PCP) based two-way comparison method. For this purpose, the simulated map was first compared with the observed information of year 2022 using Kappa indices followed by the PCP value (90.40%) exhibiting high predictive ability of the model. These findings corroborate that the model can forecast the future urban growth scenarios effectively with reasonable accuracy. Based on the outcomes, the forecasting of future urban growth scenarios for years 2033 and 2044 was accomplished. Analysis of the LULC changes displays that urban land use will experience the highest increase. Growth in the study area is predicted to increase by 23.5% and 26.7% in year 2033 and 2044 respectively where new urban settlements can appear. The results demonstrated that an integrated geospatial model provides essential information about the pattern, simulation, and prediction of urban growth associated with various driving variables.

Self-Reproduction and Evolution in Cellular Automata: 25 Years After Evoloops.

The year 2024 marks the 25th anniversary of the publication of evoloops, an evolutionary variant of Chris Langton's self-reproducing loops, which proved constructively that Darwinian evolution of self-reproducing organisms by variation and natural selection is possible within deterministic cellular automata. Over the last few decades, this line of Artificial Life research has since undergone several important developments. Although it experienced a relative dormancy of activity for a while, the recent rise of interest in open-ended evolution and the success of continuous cellular automata models have brought researchers' attention back to how to make spatiotemporal patterns self-reproduce and evolve within spatially distributed computational media. This article provides a review of the relevant literature on this topic over the past 25 years and highlights the major accomplishments made so far, the challenges being faced, and promising future research directions.

Predicting Prayagraj's Urbanization Trajectory using CA-ANN Modelling: Population Pressures and Land Use Dynamics

Land Use/Land Cover (LULC) dynamics provide a crucial role in the monitoring, planning, and management of resources. They also offer valuable information for developing strategies to balance conservation efforts, resolve conflicts between different land uses, and address pressures from growth. The present study focuses on the assessment of LULC dynamics, their forecasting, and their changes for Prayagraj city (including its surroundings) of India. Using long-term spatiotemporal Landsat datasets (1988–2018), we have explored the interlinkages between the change dynamics and human population pressure to explore the impact of agriculture and urbanization on the city landscape. Future growth prediction is carried out by incorporating Cellular Automaton (CA) and Artificial Neural Network (ANN) models. Six exploratory layers (viz., roads, educational institutes, railway transition, slope, river, and restricted area) are used in the learning process to determine LULC change (1997–2008) simulation. The validation of real and predicted LULC is carried out for 2018, where the correctness percentage and kappa value are found to be 90.29% and 0.87, respectively. Then, the ANN- Multilayer perceptron (MLP) and CA model are applied to predict LULC–2028 using the same trained transition probabilities. Results show that Built-land has grown highly by 10.03%, whereas Agriculture land and Forest land have significantly decreased by 13.43% and 3.03%, respectively, from 1988 to 2018. The predicted LULC of 2028 reveals that Built-land will keep growing by 2.83% during 2018–2028 at the cost of Agriculture land and Forest land, especially in northern, south–western and southern region, including city's inner sphere. United Nations' human population projection reveals that the city is expected to reach a population of 1.625 million by 2028. This indicates that tremendous pressure will be placed on land resources, particularly on agricultural, barren, and forested areas. To address this alarming scenario, it is imperative to delineate future development areas, ensuring better urban planning for the environmental sustainability and economic prosperity of Prayagraj city.

Urban growth simulation and scenario projection for the arid regions using heuristic cellular automata

Arid regions tend to form compact urban patterns that have significant implications on urban growth and future urban patterns. Spatial simulation and projection using cellular automata (CA)-based models are important for achieving sustainable urban development in arid regions. In response to this need, we developed a new CA model (GSA-CA) using the gravitational search algorithm (GSA) to capture and project urban growth patterns in arid regions. We calibrated the GSA-CA model for the arid city of Urumqi in Northwest China from 2000 to 2010, and validated the model from 2010 to 2020, and then applied to project urban growth in 2040. The results indicated that the optimal performance of the model was achieved when the fraction of the population was 0.5. GSA-CA achieved an overall accuracy of 98.42% and a figure of merit (FOM) of 43.03% for the year 2010, and an overall accuracy of 98.52% with FOM of 37.64% for 2020. The results of the study help to adjust urban planning and development policies. The developed model has the potential to be employed in simulating urban growth and future scenarios in arid regions globally, including Northwest China and Africa.

A polycrystal plasticity-cellular automaton integrated modeling method for continuous dynamic recrystallization and its application to AA2196 alloy

Continuous dynamic recrystallization usually dominates the microstructural evolution in hot working of aluminum alloys, in which the high-angle grain boundaries of new grains mainly originate from the gradual increase in subgrain misorientation angles. In this work, an integrated computational method is proposed to simulate continuous dynamic recrystallization process of aluminum alloys by coupling three-dimensional cellular automaton and visco-plastic self-consistent models. The stress response, dislocation accumulation and recovery, and evolution of crystal orientations are computed in the context of polycrystal plasticity; the formation and rotation of subgrains, followed by stored energy and curvature-driven boundary migration, are captured and visualized by cellular automaton. The non-octahedral slip mode {110}<110> is additionally introduced to capture the 〈001〉 texture during hot compression. A universal cell topology deformation method is adopted to achieve an effective track of grain morphology evolution during plastic deformation. The proposed simulation framework is validated through simulating the isothermal uniaxial compression process of AA2196 alloy under different temperatures and strain rates. The orientation dependence of CDRX during compression is numerically reproduced by correlating the subgrain formation and rotation process with the activation state of slip systems. The simulated macroscopic flow stress, 3D microstructure and inherent microstructural characteristics such as subgrain size, subgrain boundaries and textures are in good agreement with the experimental results. The proposed method provides an effective and efficient tool for multi-scale simulation of hot forming process of aluminum alloys.

A novel ANN-CA and MCDA integrated framework for predicting urban expansion and its implications on future flood risk, Accra Metropolis

Urban development in African countries significantly impacts environmental sustainability and city resilience, particularly in flood risk management. The Accra Metropolis, in particular, faces increasingly prevalent annual floods that disproportionately affect the urban poor living in informal settlements, resulting in significant loss of life and disruption of livelihoods. This study developed a GIS-based flood risk mapping framework that integrates artificial neural network (ANN) and cellular automata (CA) modelling with multi-criteria decision analysis. This hybrid approach was employed to predict flood scenarios for the Metropolis in 2032 and 2042. The framework incorporated six hydro-geomorphological indicators influencing extreme events: LULC, slope, elevation, drainage density, soil type, and proximity to rivers. The study analysed LULC projections, revealing a trend of substantial urban expansion with an estimated 10.9% increase in built-up areas within the next 20 years. This growth is expected to significantly heighten future flood risk in both the central upstream and downstream portions of the Metropolis, particularly in low-lying informal settlements close to the Odaw River and Korle Lagoon. Model performance was validated by ROC curve analysis, with AUC values of 0.927 and 0.922 for 2032 and 2042, respectively, which resonates with the records of previous flood distribution studies of the area. This study highlights the crucial need for sustainable urban development, improved infrastructure, and proactive flood mitigation measures in Accra to assist urban planners, policymakers, and stakeholders in managing flood risks effectively.

From flood to turbidity current: combined models to simulate continent to ocean sediment transport in the Var system, France

Turbidity currents are a form of gravity-driven flow that occurs in subaqueous environments. These currents contain a low volumetric percentage of particles but are very important in transporting them from coastal to deep-marine environments. In the Var sediment routing system, southeastern France, there is a sedimentary record of turbidity currents formed by submarine landslides and flooding of the Var River. In 2006, there was continuous monitoring of the marine section that recorded four turbidity currents. To explore the connectivity between the terrestrial source and marine basin we linked two reduced complexity models of sediment transport and landscape change. The aim of the paper is to combine two models, one dedicated to the prediction of river water and sediment run-off, and the second dedicated to the transport, erosion, and sedimentation by a turbidity current. The landscape evolution model CAESAR-Lisflood was used to model discharge and sediment yield. The behaviour of turbidity currents was modelled with the cellular automata model CATS. From calibration of the two models against observations of discharge and suspended sediment in the Var River system, we find that in 2006, two rainfall events would have led to hyperpycnal turbidity currents. These two events match well with two of the four-recorded events. Focusing on the largest event we find that the source pulse of sediment from the terrestrial environment is short-lived, less than half a day, but contains a significant quantity of fine particles. The event transferred on the order of 105 m3 of suspended sediment from source to sink. This study demonstrates that hyperpycnal turbidity currents can be generated with concentrations as low as 2-6 kg/m3 at the Var River mouth far below the theoretical 40 kg/m3 threshold, suggesting active convective sedimentation in the surface plume and sea-water dilution at the flooding river mouth. A substantial amount of sediment (35% of the input volume) is directly transferred towards the deepest part of the system during a short-living hyperpycnal turbidity current. However, a considerable (65%) part remains in the surface as a hypopycnal plume feeding the hemipelagic sedimentation.

Urban land use simulation and carbon-related driving factors analysis based on RF-CA in Shanghai, China

As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city’s overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches.

Methodological Insights into Implementing cellular automata models for simulating seagrass dynamics: Responses to global change effects

This study introduces an innovative methodology employing Cellular Automata (CA) models to simulate seagrass dynamics in response to global environmental changes. The primary objective is to outline a procedural framework for constructing and deploying CA models applied to seagrass ecosystems, and potentially to other marine or terrestrial environments. The methodology encompasses various components, including conceptualization, workflow delineation, model parameterization, and execution steps. By utilizing Mediterranean and Zanzibari (East Africa) seagrass ecosystems as case studies, we demonstrate the versatility and applicability of the proposed approach across diverse geographical regions, species composition and model components. Through these case studies, we demonstrated how CA models can effectively capture the dynamics of seagrass communities subjected to climate change, invasive species, and nutrient regimes. Despite its strengths, the proposed CA model has limitations, including parameterization complexity and uncertainties related to species-specific environmental thresholds, growth rates and species interactions, alongside the difficulty of validating our models with real-world scenarios. Addressing these limitations in future studies will enhance the model's accuracy and applicability. This study serves as a foundation for future research in other regions and ecosystems, facilitating a better understanding of the complex interactions driving ecosystem dynamics.•This study introduces a methodology using Cellular Automata (CA) models to simulate seagrass dynamics detailing conceptualization, workflow, parameterization, and execution.•Case studies in Mediterranean and East Africa ecosystems demonstrate the versatility of CA models in capturing the impacts of climate change, invasive species, and nutrient regimes.•Despite strengths, the CA model has limitations and uncertainties like parameterization complexity and model validations suggesting future research to enhance accuracy and applicability.

Identifying vegetation patterns for a qualitative assessment of land degradation using a cellular automata model and satellite imagery.

We aim to identify the spatial distribution of vegetation and its growth dynamics with the purpose of obtaining a qualitative assessment of vegetation characteristics tied to its condition, productivity and health, and to land degradation. To do so, we compare a statistical model of vegetation growth and land surface imagery derived vegetation indices. Specifically, we analyze a stochastic cellular automata model and data obtained from satellite images, namely using the normalized difference vegetation index and the leaf area index. In the experimental data, we look for areas where vegetation is broken into small patches and qualitatively compare it to the percolating, fragmented, and degraded states that appear in the cellular automata model. We model the periodic effect of seasons, finding numerical evidence of a periodic fragmentation and recovery phenomenology if the model parameters are sufficiently close to the model's percolation transition. We qualitatively recognize these effects in real-world vegetation images and consider them a signal of increased environmental stress and vulnerability. Finally, we show an estimation of the environmental stress in land images by considering both the vegetation density and its clusterization.

Assessing the suitability of the SLEUTH cellular automata model for capturing heterogeneous urban growth in developing cities: A case study in Northern Nigeria

Cellular automata (CA) models like SLEUTH (an acronym for slope, land use, excluded area, urban extent, transport-network and hill shade) have predominantly been developed and applied in developed countries. Modeling can serve as a tool to guide policy measures in facing urbanization challenges. However, developing cities have peculiar differences (heterogeneity, poor planning, and low infrastructure) thus the existing modeling approaches may not be able to apprehend heterogeneous urban growth. This research will use selected cities with similar spatial extents as controls but disparate urban extents, and growth indices to analyze the performance of SLEUTH simulations. Presumably, a comparison of the model simulations of the cities would display some significant differences, due to these variations and the scale of observation that has to be used for the model simulations. The results for the successfully calibrated cities (Kano/Funtua couple: 0.48/0.02. Katsina/Kaduna: 0.48/0.83 respectively) showed that in each city couple, the more expansive city with the most compact urban settlement pattern had a higher prediction accuracy, also predicted images of the cities showed underestimation of the urban areas over the years with the exception of Katsina city. The study further showed the model's effectiveness in modeling cities in developing countries, such as Nigeria. It is recommended that the type of urban growth experienced by cities be taken into consideration when implementing SLEUTH. Limitations of the study are centered on the inherent limitations of the model, the possibility of the occurrence of errors in data preparation, the scale and urban settlement type, which play an important role in the success of the calibration. Future research could be focused on adding other relevant inputs to the model and creating a metric that ascertains the best satellite image resolutions for a particular study area's growth coefficient values.

Enhancing ecological connectivity in the Qilian Mountains: Integrating GCA and optimized MST models for ecological corridor construction

Amidst global climate change and increased anthropogenic activities, the Qilian Mountains face critical ecological and environmental challenges, including vegetation degradation and declining carbon stocks. Therefore, effective mitigation strategies are critical to building China’s ecological security and enhancing carbon storage. This study constructed and optimized an ecological corridor in the Qilian Mountains using models, such as minimum spanning tree and carbon stock calculation. It combined geographic meta cellular automata and other methods to model ecological recovery in the Qilian Mountains following corridor construction and investigated the areas of particular concern. The results show that: (1) the distribution of the 118 extracted ecological source sites (2020) exhibited a pattern of “dense in the east and sparse in the west.” Compared with the results of the 2010 extraction, the source site area had shrunk by 0.87 % and number of sites had increased by 45.68 %. This implies that the shrinkage and fragmentation of source sites are still increasing, underscoring the urgent need for ecological corridors. (2) Compared to the pre-optimization period, the network node connectivity of the corridor network formed by the optimized 114 primary corridors and 131 secondary corridors has increased by 110 % and the network connectivity has increased by 112 %, which enhances the connectivity of the corridors. (3) From 2000 to 2020, the carbon stock in the Qilian Mountains decreased and then increased; with the construction of a corridor mountain range, the carbon stock can be further increased by 1.4748 million tons. Additionally, the Geographic Cellular Automata (GCA) model simulated that in 2030, the area of forest land in the Qilian Mountains, area of grassland, and carbon stock will increase by 71.75 km2, 313.87 km2, and 6.663 million tons, respectively. These results confirm that ecological protection and corridor construction promote ecological restoration and enhance carbon stocks in the Qilian Mountains, providing a scientific basis for future ecological projects and carbon stock analysis in the region.

Law of earthquake productivity in the Olami‒Feder‒Christensen‒Zhurkov model

The generalized cellular model based on the of Olami‒Feder‒Christensen cellular automaton model and modified by the allowance for the lifetime of the material on the basis of the kinetic concept for strength of solids by Academician S.N. Zhurkov is used to model and clarify the nature of the statistical law of earthquake productivity. The modified model is named Olami‒Feder‒Christensen‒Zhurkov model (OFCZ). The OFCZ model implements the main statistical regularities of seismicity: the Gutenberg‒Richter and Omori‒Utsu laws, the Bath’s law, fractal geometry of seismicity, and the law of earthquake productivity. It is shown that the clustering of model events (analogs of earthquakes), corresponding to the law of earthquake productivity, is due to the kinetic component of the OFCZ model. The productivity dependences on material strength and medium temperature are obtained. The influence of the Zhurkov parameter and the cell coupling parameter in the cellular model (the dissipativity of the model) on the productivity is considered. It is shown that the revealed dependences of productivity on strength and temperature are consistent with the empirical data.

Spatial effects of two-stage contagion: a Cellular Automata model

Spatial effects of two-stage contagion: a Cellular Automata model

Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

An extended model for evacuation considering pedestrian behavior of emergency avoidance in the earthquake situation

In earthquake situations, emergency avoidance is an important way to ensure the life safety of pedestrians. To study the behavior of emergency avoidance of pedestrians in a disaster situation, an extended cellular automata model for pedestrian evacuation is established according to the real video records. The concepts of “avoidance rate” and “avoidance time” are introduced to describe emergency avoidance behavior. The trampling behavior of pedestrians is also considered in the model, which draws on the concept of repulsive force in the social force model. Taking into account both evacuation time and stampede probability, the total risk index is used to describe the overall evacuation risk. The single-story teaching building was used as an evacuation scene. Parameters affecting the whole evacuation process and overall evacuation risk such as avoidance rate and avoidance time were analyzed. What’s more, seven evacuation strategies were discussed in detail. The study shows that reasonable arrangement of pedestrian evacuation with avoidance rate and avoidance time in a certain range can reduce congestion and improve the safety of evacuation and the evacuation time will not increase obviously. This work can provide theoretical references for evaluating the overall risk of evacuation and also provide technical support for formulating evacuation strategies under earthquakes.

Non-monotonic behavior of jam probability and stretched exponential distribution in pedestrian counterflow

Abstract We adopt a floor field cellular automata model to study the statistical properties of bidirectional pedestrian flow moving in a straight corridor. We introduce a game-theoretic framework to deal with the conflict of multiple pedestrians trying to move to the same target location. By means of computer simulations, we show that the complementary cumulative distribution of the time interval between two consecutive pedestrians leaving the corridor can be fitted by a stretched exponential distribution, and surprisingly, the statistical properties of the two types of pedestrian flows are affected differently by the flow ratio, i.e., the ratio of the pedestrians walking toward different directions. We also find that the jam probability exhibits a non-monotonic behavior with the flow ratio, where the worst performance arises at an intermediate flow ratio of around 0.2. Our simulation results are consistent with some empirical observations, which suggest that the peculiar characteristics of the pedestrians may attributed to the anticipation mechanism of collision avoidance.

An extended cellular automation model for bicycles with group and retrograde behaviors at signalized intersections

The rise of shared bicycles has increased the demand for group riding, integrating bicycles into social groups. Additionally, retrograde riding, where cyclists travel against the designated direction, is a common behavior observed in bicycle flows. The interaction and self-organization phenomenon of group and retrograde behaviors are complex, significantly impacting traffic efficiency. This paper develops a two-dimensional Extended Moore Neighborhood and constructs state-updating rules for regular riding, group riding and retrograde riding. Each rule comprises a psychological decision layer and a physical execution layer, forming a cellular automaton model for group and retrograde bicycles. Field experiments are conducted to calibrate the model parameters and verify the behavioral characteristics. Finally, we execute numerical simulations at a signalized intersection to explore the coupling effects of group and retrograde behaviors on self-organization within the bicycle flow and the traffic capacity. The results indicate that group behavior increases queue length while reducing start wave speed and expansion degree. Retrograde behavior intensifies the negative effects on bicycle flow. These findings provide insights for managing both forward and retrograde bicycle flows.