Symmetric Two-lane Cellular Automaton Research Articles

Analysis of mixed traffic flow characteristics based on cellular automata model under lane management measures

In the future, mixed traffic flow comprising Human-Driven Vehicles (HDVs), Connected and Autonomous Vehicles (CAVs), and CAV platoons will coexist for an extended period. Exploring the operational characteristics of mixed traffic flow under different lane management measures is essential for effective management and control. Initially, we analyze the motion characteristics of HDVs, CAVs, and CAV platoons and identify the car-following types within mixed traffic. Based on vehicle motion characteristics and the variance in maximum desired speeds among HDV drivers, we establish longitudinal motion rules for HDVs using a safety distance model. For CAVs and their platoons, we develop longitudinal motion rules by considering platoon merging and splitting behaviors, as well as speed and spacing error requirements for platoon driving. Subsequently, we formulate lateral lane-change rules based on the Symmetric Two-lane Cellular Automata (STCA) model, considering differences in reaction times and following distances between HDVs and CAVs. Finally, we conducted simulation experiments on a unidirectional three-lane highway using the multi-lane mixed traffic flow cellular automata model, analyzing the characteristics of mixed traffic under various lane management measures, such as mixed lane, CAV-dedicated lane, and CAV-priority lane. The results show that as the proportion of CAV (denoted as p) increases, the traffic flow capacity, optimal density, and jam density also increase. When p≤0.6, the k−q diagram exhibits a triangular shape, whereas for p>0.6, it assumes a trapezoidal shape. Implementing CAV-dedicated lane can reduce Cooperative Adaptive Cruise Control (CACC) degradation rates but only enhances traffic flow capacity when the CAV proportion reaches a certain threshold. Compared to the mixed lane scheme, when p≤0.3 and k≤0.3, the CAV-priority lane schemes not only meet traffic demands but also reduce CACC degradation rates. The vehicle speed in CAV-priority lane surpasses that in HDV lanes, facilitating improved traffic efficiency for CAVs. The distribution of maximum speeds among HDV drivers affects the fundamental diagram of mixed traffic flow and the performance of CAV-priority lane, with greater impacts observed as the standard deviation of the HDVs' maximum speed increases.

Multi-lane traffic flow model based on cellular automaton fine-scale under cooperative vehicle infrastructure system

Microscopic traffic flow modeling is crucial for predicting future traffic demand and optimizing the design of transportation systems. At the microscopic level, a well-crafted model is capable of describing the interactions and dynamic variations among vehicles with greater accuracy. The cell scale in the classical cellular automaton (CA) multi-lane traffic flow model is a critical parameter to accurately express the location relationship of vehicles. In this paper, a new method based on Symmetric Two-lane Cellular Automaton (STCA), entitled STCA-X, is proposed for the cell scale selection. Firstly, the position, velocity, acceleration, and interaction of vehicle operation in the urban multi-lane environment are analyzed, and a feature model is built based on CA. One important drawback of the existing models is that they do not match the vehicle movement in the actual lane. To handle this problem, a fine-scale CA multi-lane model is developed. Secondly, a new traffic flow model is created by redefining several key behaviors such as road following, lane changing, and blocking in the STCA model. The experimental results are analyzed and compared with several of the state-of-the-art methods. Analysis of the simulation results proves that STCA-X improves vehicle lane change frequency and lane utilization efficiency. However, the enhancements in the model have resulted in increased computational complexity. Consequently, future research will delve into novel approaches to boost computational efficiency, thereby propelling the progress of traffic flow modeling technology.

Investigating model and impacts of lane-changing execution process based on CA model

Lane changing is one of the basic driving behaviors and consists of lane-changing decision-making process and lane-changing execution process. However, most existing traffic simulation models focus on lane-changing decision-making process and assume that drivers complete their lane-changing behaviors instantaneously. Then these models may not reproduce the actual traffic phenomenon. Furthermore, there are several failed lane-changing behaviors, referring to that drivers may go back to the original lane during lane-changing process. And the impacts of these behaviors have not been investigated up to now. In order to characterize the lateral movement process and to investigate its impacts on traffic flow, this study puts forward a symmetric two-lane cellular automaton model with lane-changing execution (STCA-LE model). In this model, the lateral movement rules of lane changing vehicles are formulated and introduced. The results of numerical simulation indicate that there is a positive relationship between the lane-changing duration and traffic density that is consistent with observation data analysis. Based on this model, the lane-changing trajectories and impacts of lane-changing process are investigated by introducing the critical lane-changing duration. The results of this study could be taken as an important reference to the development of traffic measures and the designation of intelligent vehicles.

Cellular automaton model for three-lane urban road considering Internet of Vehicles lane

Although the technology of Internet of Vehicles (IoV) is developing rapidly, it will still take a long time to realize its overall popularization. Aimed at this transition phase, this paper proposes to set up an IoV lane on the urban road, which is specially designed for the connected vehicles, to provide a better driving environment for the connected vehicles. Considering the operation characteristics of traffic flow under traditional and IoV environment, this paper establishes a three-lane cellular automata model for urban road traffic flow considering IoV lane, modified on the basis of the Modified Comfortable Driving (MCD) model and symmetric two-lane cellular automata (STCA) model, and then takes simulation by MATLAB and makes analysis. The result shows that the setting of IoV lane can improve the velocity of networked vehicles to a great extent with no or just a bit decline in the ordinary vehicles’ speed, and it has a great effect on the mixture traffic flow, including the increase in both traffic volume and the average speed. What’s more, when the networking proportion is between 0.2 and 0.76, and the space occupation ranges from 0.18 to 0.56, the traffic benefit of IoV special lane can reach the best.

Dynamic model of high speed following traffic flow

For the physical phenomenon of high-speed car following in the road traffic flow, all the vehicles with small spacing do not run at low speed. The speeds of the vehicles are significantly higher than those they normally should be when they are in this vehicles’s density. There are more than 7% of high-speed following vehicles in the measured data. At present, the traditional traffic flow model cannot simulate the phenomenon of high-speed car following, so a new nonlinear dynamic mathematical model is needed to describe and analyze the physical phenomenon. In order to study the physical phenomenon of high-speed car following in road traffic, a traffic flow dynamics model for simulating the phenomena is proposed, which combines with the prospect theory and takes into account some factors such as driver’s decision-making behavior and randomization. It is called HCCA (high-speed car following celluar automat) model. In the model, the prospect theory is used to analyze the driver’s lane changing behavior under the uncertain conditions. Combined with the characteristics of the radical driver, the dynamic prediction speed is considered for the front car followed by the radical driver, and the HCCA dynamics rules of high-speed following traffic flow mechanics are defined. By the computer numerical simulation, the evolution mechanism and the characteristics of high-speed car-following flow are studied. The results show that compared with the symmetric two-lane cellular automata (STCA) dynamic model, the HCCA dynamic model established in this paper can simulate abundant traffic physical phenomena, and reproduce complex traffic phenomena such as free flow, synchronous flow and wide-range motion congestion. Finally, the phenomenon of high-speed car following is simulated and the results of high-speed car following rate over 7% with small spacing are in good agreement with the measured results. It overcomes the shortage that traditional STCA model cannot simulate the synchronous flow. It is found that the larger the proportion of radical drivers, the larger the high-speed car following rate and traffic flow with small spacing are under the same road density. The high-speed car following traffic flow mechanics model proposed in this paper has a certain reference significance and practical value for analyzing the physical phenomenon of high-speed car following and enriching the traffic flow theory.

An improved two-lane cellular automaton traffic model based on BL-STCA model considering the dynamic lane-changing probability

Based on the brake light (BL) model, Knospe et al. proposed a symmetric two-lane cellular automaton (BL-STCA) model, which could reproduce various empirically observed two-lane phenomena. In real traffic, the effect of brake light on the lane-changing behavior cannot be ignored. Therefore, BL-STCA model is interesting. However, there are two problems with BL-STCA model, too strong exchange of vehicles between lanes and unreasonable phenomenon in some special scenarios, such as a broken-down vehicle parked on one lane due to traffic accident. In order to solve the problems, we introduce the dynamic lane-changing probability and proposed an improved BL-STCA model with modification of lane-changing rules. The simulation results show as below. (1) Our new model effectively solves the above two problems of BL-STCA model. In addition, the lane-changing frequency is consistent with the real traffic data, which means the validity of our new model. (2) Compared with single-lane scenario, the lane-changing behaviors in two-lane scenario can effectively suppress the emergence of wide moving jam. (3) From the microcosmic level, the lane-changing behaviors can well explain the moving blank phenomenon within wide moving jam.

Multi-lane Changing Model with Coupling Driving Intention and Inclination

Considering the impact of drivers’ psychology and behaviour, a multi-lane changing model coupling driving intention and inclination is proposed by introducing two quantitative indices of intention: strength of lane changing and risk factor. According to the psychological and behavioural characteristics of aggressive drivers and conservative drivers, the safety conditions for lane changing are designed respectively. The numerical simulations show that the proposed model is suitable for describing the traffic flow with frequent lane changing, which is more consistent with the driving behaviour of drivers in China. Compared with symmetric two-lane cellular automata (STCA) model, the proposed model can improve the average speed of vehicles by 1.04% under different traffic demands when aggressive drivers are in a higher proportion (the threshold of risk factor is 0.4). When the risk factor increases, the average speed shows the polarization phenomenon with the average speed slowing down in big traffic demand. The proposed model can reflect the relationship among density, flow, and speed, and the risk factor has a significant impact on density and flow.

Cellular Automaton-Based Traffic Flow Simulation Model for Traffic Incidents

AbstractTraffic incidents decrease the capacity of road segments and disturb normal traffic flow. Analyzing the evolution of traffic incident effects by building a simulation model for traffic incidents not only helps in better understanding traffic flow characteristics in abnormal conditions, but also provides a theoretical basis for traffic control strategies. Considering the characteristics of driver behavior, this study proposes an improved model of traditional symmetric two-lane cellular automaton (STCA) based on lane-changing rules. Velocity is used to build a traffic simulation cellular automaton model for traffic incident conditions. The improved model simulated traffic flow better than the traditional STCA model.

A Cellular Automaton Model for Highway: Considering Multi-Lane Traffic Rules

Vehicular flow in highway is inherently complex and development of microscopic models of vehicular flow has been a daunting task for researchers. This paper presents the use of Cellular automata (CA) micro simulation for modeling multi-lane traffic characteristics in highway, as well as considering the average speed difference (ASD) and lane-changing rules (LCR) in the CA model. Firstly, on the base of the Symmetric Two-Lane Cellular Automata (STCA) model, we analyzed the impact on traffic characteristics, through adjusting the maximum speed and lane-changing rules. Secondly, by the micro simulation, the relationships between speed and density in traffic flow were given considering the ASD and the LCR. The simulation results showed that, (a) the more obvious of speed dispersion, the more serious about speed declining, (b) the speed of overall traffic flow slowed down by 163% under extreme conditions, (c) the more reckless of lane-changing rule drivers used, the higher speed of traffic flow can reach.

A new cellular automaton model for traffic flow considering realistic turn signal effect

In real traffic, any vehicle must give lane-changing signal (i.e. the turn signal) before changing lanes; at this time, the vehicles behind the lane-changing vehicle will be hindered and may form “plugs” due to the turn signal effect. However, few studies focus on exploring the effect. In this paper, the turn signal effect was taken into account by proposing a new symmetric two-lane cellular automaton (T-STCA) model, and the new model was set to compare with the STCA, H-STCA and A-STCA models. Numerical results show that using the T-STCA model to describe lane-changing or overtaking, the process appeared in several consecutive time steps; while using the other three models, the process appeared only in one time step. In addition, the T-STCA model could describe the mixed traffic flow more realistically and the turn signal effect could help the plugs to dissolve more quickly.

Segregation effect in symmetric cellular automata model for two-lane mixed traffic

Segregation effects commonly exist in granular mixtures with difference in size, shape or density. In mixed traffic flow, slow vehicle and fast vehicle, as two types of particles, have different desired speed. We investigate the segregation along the road in mixed traffic flow by using a symmetric two-lane cellular automata model. A parameter D, which quantifies the degree of segregation, is defined. We study the density dependency of the parameter at different randomization probability. Simulation results show that segregation is more obviously in free flow region. We argue that the overtaking maneuvers have similar effect as percolation in granular flow.

Honk effect in the two-lane cellular automaton model for traffic flow

In real traffic, when a vehicle is hindered by its preceding vehicle and simultaneously it cannot change lane, it may honk the horn. As a result, the driver of the preceding vehicle may change lane to make way for it. However, this effect has seldom been investigated before. In this paper, we take this issue into account by proposing a new symmetric two-lane cellular automaton model. It is shown that the honk has almost no effect in the homogeneous traffic whereas it enhances the flux in the intermediate density regime for the heterogeneous traffic. We suggest that this is because the occurrence probability and the lifetime of the plug are suppressed due to the introduction of the honk. Finally, we argue that the honk behavior is not encouraged in the asymmetric two-lane model.