Cellular Automation Model Research Articles

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.

Prediction of Formal Transformations in City Structure (Kufa as a Model) Based on the Cellular Automation Model and Markov Chains

Prediction of Formal Transformations in City Structure (Kufa as a Model) Based on the Cellular Automation Model and Markov Chains

New energy dominant technology diffusion mechanism based on cellular automation model—The case of the current market and policy environment

New energy dominant technology diffusion mechanism based on cellular automation model—The case of the current market and policy environment

Heterogeneous traffic flow characteristics on the highway with a climbing lane under different truck percentages: The framework of Kerner’s three-phase traffic theory

Nowadays, in order to improve the traffic conditions in mountainous areas, the demand of climbing lanes is increasing. In this paper, car–truck heterogeneous traffic flow characteristics on the highway with a climbing lane are studied up based on an improved Kerner–Klenov–Schreckenberg–Wolf (KKSW) cellular automation (CA) model. To depict the speed characteristics of vehicle’s climbing and lane-changing behaviors, additional evolution rules for trucks are introduced into the KKSW model. Specifically, the motion of trucks climbing on the slope is described as a deceleration process and an equilibrium process. Furthermore, we define the probability of a truck driver using the climbing lane as a function of the observed real-time occupancy on it. We have found that in accordance with Kerner’s theory, traffic breakdown is an F→S transition that occurs in metastable free flow with respect to the F→S transition. When the traffic demand increases, the bottleneck appears due to the merging of trucks from the climbing lane to the inner lane. Localized synchronized flow patterns (LSP) emerge on both the inner and outer lanes. When traffic demand increases further, wide moving jams emerge in synchronized flow and general pattern (GP) appears on the inner lane, whereas synchronized flow patterns remain on the outer lane only. In addition, the macroscopic traffic flow characteristics under varying truck percentages are analyzed. We have found that the improving effect of climbing lane on traffic efficiency is closely related to the truck percentage.

A future intelligent traffic system with mixed autonomous vehicles and human-driven vehicles

Autonomous vehicles (AVs) being an essential component of the future smart city traffic system is a hot topic in recent years, even though it is still in its development stage. It is conceivable that in the near future, AVs and human-driven vehicles (HVs) will have to co-exist in traffic systems. This is the first paper to study a mixed traffic system from a micro perspective based on the cellular automation model. In this system, by the use of sensors or mutual information exchange, each AV will have a ‘foresight’ and will be able to know the speeds and positions of vehicles in front of it. In the circular road scenario, we studied how the traffic capacity is influenced by the degree of foresight, the ratio of AVs to HVs, vehicle density, and the probability of random deceleration of HVs. Then we came up with the below conclusions: (a) The optimal foresight is k=5. An AV only needs to gather the information of the 5 vehicles in front of it. (b) The two critical factors to measure the capacity of a traffic network are the critical vehicle density and the maximum average flow. When the ratio of AVs to HVs is increased, these two critical factors increase at an accelerating rate. (c) Even if a low ratio of HVs is running in the system, it will have an appreciable negative impact. An increased probability of random deceleration can expand the hysteresis loop range and reduce average flow. (d) Within a specific range of vehicle density, there is an optimal ratio of AVs at which the traffic system has the maximum average flow. This has implications in controlling the ratio of AVs. Finally, theoretical solutions of critical vehicle density are obtained by using the mean-field theory in physics. If the vehicle density is larger than these critical values, the traffic system will be in a deadlock state, and all vehicles cannot move. These formulas are crucial to controlling the density and the ratio of AVs and HVs in future intelligent traffic systems and will help to avoid large-scale traffic congestion. The above findings will have practical ramifications for precise traffic management and traffic control in a mixed AV and HVs system.

Assessing the Potential Distribution of Asian Gypsy Moth in Canada: A Comparison of Two Methodological Approaches

Gypsy moth (Lymantria dispar L.) is one of the world’s worst hardwood defoliating invasive alien species. It is currently spreading across North America, damaging forest ecosystems and posing a significant economic threat. Two subspecies L. d. asiatica and L. d. japonica, collectively referred to as Asian gypsy moth (AGM) are of special concern as they have traits that make them better invaders than their European counterpart (e.g. flight capability of females). We assessed the potential distribution of AGM in Canada using two presence-only species distribution models, Maximum Entropy (MaxEnt) and Genetic Algorithm for Rule-set Prediction (GARP). In addition, we mapped AGM potential future distribution under two climate change scenarios (A1B and A2) while implementing dispersal constraints using the cellular automation model MigClim. MaxEnt had higher AUC, pAUC and sensitivity scores (0.82/1.40/1.00) when compared to GARP (0.70/1.26/0.9), indicating better discrimination of suitable versus unsuitable areas for AGM. The models indicated that suitable conditions for AGM were present in the provinces of British Columbia, Ontario, Quebec, Nova Scotia and New Brunswick. The human influence index was the variable found to contribute the most in predicting the distribution of AGM. These model results can be used to identify areas at risk for this pest, to inform strategic and tactical pest management decisions.

Research on on-street temporary parking effects based on cellular automaton model under the framework of Kerner’s three-phase traffic theory

In this paper, the on-street temporary parking behavior is studied under the framework of Kerner’s three-phase traffic theory based on an improved Kerner–Klenov–Schreckeneberg–Wolf (KKSW) cellular automation (CA) model named KKSW-OSTP (on-street temporary parking) model. The complete parking process is divided into four steps and the microscopic effects of these four steps are analyzed. The parking behavior has significant negative impacts on the traffic flow, while having limited positive impacts on the left lane. The congestion pattern caused by on-street temporary parking is analyzed. Localized synchronized flow pattern (LSP) is observed in low-density traffic condition. Moving synchronized flow pattern (MSP) and General Pattern (GP) are observed in high-density traffic condition. The macroscopic effects of on-street temporary parking on road traffic conditions are analyzed by introducing parking rate into the model. It is found that more on-street temporary parking demand results in higher probability of traffic breakdown and higher congestion level. However, with the increase of parking rate and leave-parking rate, the frequency of rear-end conflicts reduces.

A cellular automation model for convoy traffic in Arctic waters

Marine transportation in arctic waters involves risk due to the presence of ice, rapid weather changes, reduced visibility, and remoteness. Icebreaker assistance and ice-convoys are used to facilitate the safe navigation of commercial vessels transiting Arctic waters. Collision between individual ships, or a ship and an icebreaker, during a convoy is a potential danger. To study such risk, this paper presents an updated Nagel-Schrekenberg (NaSch) model of the marine convoy traffic integrated with a Bayesian Network (BN) based probabilistic approach. This approach has been used to predict the maximum waterway density for the safe flow of traffic, and the collision probability during a convoy. The approach is tested here on a convoy in the Vilkitskii strait. The BN model proposed here could assist in assessing the failure probabilities of the causal factors and their contribution to collision likelihood. It may also be useful in developing a collision monitoring system that provides a real-time estimate of collision probability. The estimated probability could be used as an early warning for potential collisions, thus enhancing safe navigation and safety at sea.

From a Flapping Bumblebee to an Evolving Sand Dune: A Reconstruction-Based Algorithm to Feature the Digitally Recorded Objects

A reconstruction-based image processing algorithm is developed to automatically extract feature points of digitalized 2D objects. This algorithm, which is introduced using a bumblebee flight case, is made up of two parts: a four-connected dot chasing rearrangement scheme and an extreme point extraction on a polarized contour. It is then applied to a dune evolution case that is simulated with a cellular automation model. The results show that the proposed algorithm is effective in characterizing individual moving objects. An additional algorithm is developed to categorize the extracted feature points of a bumblebee with translucent wings.

Formation mechanism of large grains inside annealed microstructure of GH4169 superalloy by cellular automation method

In authors’ previous work [Mater. Charact. 141 (2018) 212–222], it was found that the heterogeneous deformed microstructures can be replaced by the relatively homogeneous recrystallized grains through an annealing treatment. However, there are still some relatively large recrystallized grains. To find the reasons for the formation of large grains, some new annealing treatment tests were done, and the cellular automation (CA) simulations were carried out in the present work. The experimental results showed that the microstructural evolution during annealing treatment is significantly affected by the content of δ phase. So, the effects of δ phase on the nucleation and growth of grains are carefully considered in the CA model to accurately simulate the microstructural evolution behavior. By the CA simulation, it is found that the dislocation density rapidly decreases due to the nucleation of static recrystallization (SRX) and the growth of dynamc recrystallization (DRX) nuclei at the early stage of annealing. The high initial dislocation density can provide the high velocity for the growth of DRX nuclei, which is responsible for the formation of coarse grains. However, the growth rate of SRX nuclei is relatively small due to the low dislocation density and pinning effects of δ phase.

Soliton Distribution in the Ball and Box Cellular Automation Model

Solitons are very important waves in Mathematical Physics. They help model many physical situations for instance tsunami waves, application in optical communication systems, application in plasma physics and application in laser guided technology among others. Despite enormous applications, solving the non-linear equations which have soliton solutions is challenging be- cause the nonlinear characteristic of the system abruptly changes due to some slight changes of valid parameters including time. A remedy to this is to use cellular automation models to model soliton dynamics. Cellular automation models do not change in response to slight changes on parameters. In this paper, using computer simulation, an investigation of the distribution of the number of solitons from the Ball and Box cellular automation model was examined. The distribution of the number of solitons from the two and three colour Ball and Box cellular automation was established. Using the online integer sequence, for the two colour Ball and Box cellular automation model, it was found that the distribution of the number of the solitons is indexed by the binomial coefficients. On the Other hand, for the three colour Ball and Box cellular automation model, it was found that only solitons of lengths one to four possesses the distribution while the other soliton lengths do not possess any distribution function.

Application and Evaluation of the Reinforcement Learning Approach to Eco-Driving at Intersections under Infrastructure-to-Vehicle Communications

Eco-driving behavior is able to improve vehicles’ fuel consumption efficiency and minimize exhaust emissions, especially with the presence of infrastructure-to-vehicle (I2V) communications for connected vehicles. Several techniques such as dynamic programming and neural networks have been proposed to study eco-driving behavior. However, most techniques need a complicated problem-solving process and cannot be applied to dynamic traffic conditions. Comparatively, reinforcement learning (RL) presents great potential for self-learning to take actions in a complicated environment to achieve the optimal mapping between traffic conditions and the corresponding optimal control action of a vehicle. In this paper, a vehicle was treated as an agent to select its maneuver, that is, acceleration, cruise speed, and deceleration, according to dynamic conditions while approaching a signalized intersection equipped with I2V communication. An improved cellular automation model was utilized as the simulation platform. Three parameters, including the distance between the vehicle and the intersection, signal status, and instant vehicle speeds, were selected to characterize real-time traffic state. The total CO2 emitted by the vehicle on the approach to the intersection serves as a measure of reward policy that informs the vehicle how good its operation was. The Q-learning algorithm was utilized to optimize vehicle driving behaviors for eco-driving. Vehicle exhaust emissions and traffic performance (travel time, stop duration, and stop rate) were evaluated in two cases: (1) an isolated intersection, and (2) a medium-scale realistic network. Simulation results showed that the eco-driving behavior obtained by RL can not only reduce emissions but also optimize traffic performance.

Modeling the Effects of Bus Stops on Bicycle Traffic Flow by Cellular Automata

Since currently huge traffic demands for public bus and bicycles exist in the majority of cities of China, it is highly likely that the bus stop has undeniable impacts on the bicycle flow that is close to the bus stop. In this paper, we proposed the test in several bicycle paths beside bus stops in Nanjing and aimed at exploring the specific effects of bus stop on the bicycle flow nearby. We assumed there were two such effects: space restriction and pedestrian conflicts. Further, we built up a cellular automation (CA) model to study the feature of these effects and find out not only the outcome of these two effects, but also the inner correlation between them.

Model and application of bidirectional pedestrian flows at signalized crosswalks**Project supported by the National Natural Science Foundation of China (Grant No. 51578149).

This research of bidirectional pedestrian flows at signalized crosswalks is divided into two parts: model and application. In the model part, a mixed survey including the questionnaire investigation and tracking investigation is conducted to gain the basic data about walking tendentiousness of a pedestrian crossing. Then, the forward, right-hand, outstripping, and influential coefficients are outlined to quantize walking tendentiousness of pedestrian crossing and estimate transition probabilities of pedestrians. At last, an improved cellular automation model is proposed to describe walking tendentiousness and crossing behaviors of pedestrians. In the application part, channelization research of bidirectional pedestrian flows is presented for real signalized crosswalk. In this process, the effects of right-side-walking and conformity behaviors on the efficiency of pedestrian crossing are thoroughly analyzed based on simulations and experiments to obtain a final channelization method to raise the efficiency of a pedestrian crossing at the crosswalk.

A cellular automation model accounting for bicycle’s group behavior

Recently, bicycle has become an important traffic tool in China, again. Due to the merits of bicycle, the group behavior widely exists in urban traffic system. However, little effort has been made to explore the impacts of the group behavior on bicycle flow. In this paper, we propose a CA (cellular automaton) model with group behavior to explore the complex traffic phenomena caused by shoulder group behavior and following group behavior on an open road. The numerical results illustrate that the proposed model can qualitatively describe the impacts of the two kinds of group behaviors on bicycle flow and that the effects are related to the mode and size of group behaviors. The results can help us to better understand the impacts of the bicycle’s group behaviors on urban traffic system and effectively control the bicycle’s group behavior.

Effect of Water Flows on Ship Traffic in Narrow Water Channels Based on Cellular Automata

Abstract In narrow water channels, ship traffic may be affected by water flows and ship interactions. Studying their effects can help maritime authorities to establish appropriate management strategies. In this study, a two-lane cellular automation model is proposed. Further, the behavior of ship traffic is analyzed by setting different water flow velocities and considering ship interactions. Numerical experiment results show that the ship traffic density-flux relation is significantly different from the results obtained by classical models. Furthermore, due to ship interactions, the ship lane-change rate is influenced by the water flow to a certain degree.

Continuous-discontinuous cellular automaton method for cohesive crack growth in rock

A group of nonsingular enrichment functions is proposed for an exact representation of the cohesive stresses, which are consistent with a nonsingular asymptotic stress field around a crack tip. An improvement in the adaptive crack tip polar coordinate is proposed for treating the local polar coordinate error. Finally, a discontinuous cellular automation model was developed, including a cell model, cell state, and updating rules. Combined with these developments and the cohesive rock model, a continuous-discontinuous cellular automaton method (CDCA) for a cohesive crack analysis is proposed, through which the calculation is only limited to the cell locality without re-meshing, and therefore, no assembled global stiffness matrix is required. Numerical simulations reveal the accuracy and efficiency of the presented method.

Intelligent safety adjustment of branch airflow volume during ventilation-on-demand changes in coal mines

Abstract In view of the frequent ventilation-on-demand (VOD) changes of branches during intensive production in coal mines, the models for regulating the ventilation resistance of associated branches and ventilator frequency were established. Automated ventilation system adjustment software has been developed by monitoring the air volume of the minimum remainder branch in the network, and all air volumes of the branches in the network can be obtained by inverse calculation of associated branches. When the VOD of branches changes, the best regulating branch in the ventilation network is selected by a cellular automation (CA) model, and the ventilation resistance is determined by ventilation network calculation. It is also possible to calculate the total air volume of the ventilation system, according the VOD of the branch to calculate the ventilator frequency. Advanced simulation regulating results are verified by ventilation network calculation. The ventilation resistance of associated branches, ventilator frequency, and joint regulation are simulated by the automated ventilation system adjustment software. A pipe network ventilation parameter monitoring system is designed and developed based on typical ventilation network models. We confirm the VOD of the branch based on the absolute gas emission volume prediction model and then establish a regulating model for the ventilation resistance of associated branches and the ventilator frequency. Considering the fire and explosion risks associated with gas, CO2 is instead released to simulate gas emission in the experiment; gas concentration is controlled by the ventilation resistance of associated branches and ventilator frequency. These results verify that CO2 concentration is kept below the threshold value by increasing the air volume of the branch, the feasibility of the ventilation system, and regulating the VOD of the branch; additionally, they provide theoretical guidance for adjusting ventilation branch quantity by the association branches of ventilation resistance and ventilator frequency.

Numerical simulation of EBCHM for the large-scale TC4 alloy slab ingot during the solidification process

A mixed Lagrangian and Eulerian algorithm (MILE) is used to simulate the temperature field during the solidification process of the large-scale Ti-6Al-4V alloy slab ingot in electron beam cold hearth melting (EBCHM). Based on the finite element (FE) method coupled with the cellular automation (CA) model, the microstructure evolution of the ingot is obtained in this paper. The simulated results show that with the pulling speed increasing from 1 × 10−4 m/s to 3.5 × 10−4 m/s, the liquidus depth increases from 0.64 cm to 0.70 cm, the length of the transition region increases and the mean grain size is found to increase. With the pouring temperature increasing from 1953 K to 2013 K, the liquidus depth increases from 0.42 cm to 1.34 cm and the length of the transition region increases, whereas the depth of mushy zone decreases. The region near the bottom of the ingot has finer grains for the non-steady solidifying state of TC4 alloy. Moreover, the role of pulling speed is much greater than that of pouring temperature on the microstructure evolution during EBCHM for large-scale TC4 alloy slab ingot.

Spatio-Temporal Analysis and Forecasting of Distributed PV Systems Diffusion: A Case Study of Shanghai Using a Data-Driven Approach

In recent years, distributed photovoltaic (PV) systems have witnessed rapid development worldwide. Nevertheless, the diffusion of distributed PV systems in a specific region is still indefinite and hard to predict, which bring uncertainties to the planning and operation of electricity distribution network. This paper investigates the diffusion tendency and forecasting approach of distributed PV systems from macro- and micro-aspects. Macroscopic analysis includes spatial clustering of PV systems and quantitative analysis of PV adoption drivers in the time-dimension. Shanghai, Pudong in China is studied in this paper to offer some insights. Analysis reveals that the capacity and location of PV systems are clustered. These clusters continuously spread to the surrounding with changes of size and location, under the impact of internal and external factors. This indicates that diffusion of PV systems can be simulated by a cellular automation model. For microscopic analysis, a data-driven forecasting approach of PV diffusion is proposed based on cellular automation. Analysis shows that the developing state of PV cells can be forecasted based on multi-source datasets. Besides, statistical distribution of newly installed PV capacity per cell tends to be stable, so that it can also be considered as a predictor of distributed PV systems diffusion.