Cellular Automaton Traffic Flow Model Research Articles

A cellular automaton traffic model by mechanical restriction and the slow-to-start rules in the framework of Kerner’s three-phase traffic theory

Most of the conventional cellular automaton (CA) traffic flow models have two shortcomings: unlimited deceleration capabilities and incapability of reproducing the synchronized flow in the three-phase traffic flow theory. Based on an original deceleration CA model that emphasizes limited mechanical capabilities and human overreaction as the origin of congested traffic states, this paper proposes a new deceleration CA model where the slow-to-start rules are incorporated. For periodic boundary conditions, one also finds that the present model can reproduce well the three different phases of traffic flow (free flow, synchronized traffic flow, wide moving jam) as well as two first-order phase transitions (the transitions from free flow to synchronized flow and from synchronized flow to wide moving jam) among them. Compared to the original deceleration CA model, one notes that the phase transition from the synchronized flow to wide moving jam becomes distinguishable. Furthermore, the present model can reliably reproduce most empirical findings including synchronized flow with different slopes, the so-called pinch effect, and the time-headway distribution of free flow and so on. Importantly, the synchronized flow with different slopes is supported by spatiotemporal diagrams and the statistical distribution of velocity and so on. For open boundary conditions, the present model can reproduce the spatiotemporal diagrams of well-known five patterns including moving synchronized flow pattern, localized synchronized flow pattern, widening synchronized flow pattern, dissolving general pattern and general pattern.

An Improved Cellular Automata Traffic Flow Model Considering Driving Styles

An improved cellular automata model (CA model) considering driving styles is proposed to analyze traffic flow characteristics and study traffic congestion’s dissipation mechanism. The data were taken from a particular case in the Next Generation Simulation (NGSIM) program, which selected US-101 as the survey location from 7:50 a.m.–8:05 a.m. to investigate vehicle trajectory information. Different driving styles and the differences in vehicle parameters (speed, acceleration, deceleration, etc.) were obtained using principal component analysis and the k-means clustering method. The selected model was proposed for improvement based on analyzing the existing CA models and combining them with the actual road conditions. Considerations of driving styles and two operation mechanisms (over-acceleration and speed adaptation) were introduced in the improved model. The result obtained after the traffic simulation shows that the improved CA model is effective, and the mutual transformation of different traffic flow phases can be simulated. In the improved CA model, dissipating traffic congestion effectively and balancing the overall flow of the road are realized to improve the traffic capacity up to around 115% compared to the NaSch model and meet the demand of all kinds of drivers expecting to drive at the safest distance, which provides a theoretical basis for relieving traffic congestion. The various driving styles in terms of safety, comfort, and effectiveness are performed differently in the improved CA model. An aggressive driving style contributes to increasing traffic capacity up to around 181% compared to a calm driving style, while the calm style contributes to maintaining traffic flow stability.

Effects of Adaptive Random Deceleration on Traffic Flow

Based on the Nagel-Schreckenberg model, an improved cellular automaton traffic flow model is proposed, in which the random deceleration probability of each vehicle is no longer fixed, but is adaptively adjusted according to the local traffic density in its vision and its current velocity. The numerical simulation results show that the maximum traffic capacity of the improved model under the same parameters is greater than that of the Nagel-Schreckenberg model, and is closer to the measured data. In addition, the traffic flow and vehicle velocity under different meteorological conditions are simulated by using the improved model, and the synchronized flow phenomenon consistent with the actual traffic is reproduced. Meanwhile, the results show that under the same parameters, when the traffic density is equal to 0.3, the traffic flow of the improved model increases by about 11% compared with the original model, and when the traffic density increases to 0.6, the traffic flow increases by about 27%.

Analysis of Traffic Flow Characteristics in Intelligent Networking Environment

In an intelligent networked environment, both the car and the road are more intelligent, and the information interaction between the vehicle and the roadside equipment is more frequent, the vehicle has a greater advantage in information acquisition, processing, decision-making and standardized driving compared to ordinary manual driving vehicles. The study of mixed traffic flow between intelligent networked vehicles and ordinary vehicles in the intelligent networked environment is of great significance to improve the operational efficiency of traffic flow and road capacity. In this paper, we build simulation scenarios in Vissim based on the meta cellular automaton traffic flow model and the Wiedemann model, conduct simulation experiments on the mixed traffic flow of intelligent networked vehicles and ordinary vehicles, and study and analyse the influence of the change in the proportion of intelligent networked vehicles on the flow, density, speed and other characteristics of the traffic flow. The results show that when the proportion of intelligent networked vehicles in the traffic flow is 40% to 80%, with the increase in the proportion of intelligent networked vehicles, the operating speed of the traffic flow, the density of the traffic flow has been improved to varying degrees, and the road passable flow has also been improved.

A simulation of traffic noise emissions at a roundabout based on a cellular automaton model

The calculation and evaluation of traffic noise is an important task in urban road design. Roundabouts are a common form of urban road intersection. The complexity of traffic operations makes the calculation of traffic noise near a roundabout challenging. To explore traffic noise at roundabouts, a cellular automaton traffic flow model for a two-lane roundabout is established. Based on this model, a dynamic simulation method for traffic noise at roundabouts is proposed. The traffic operation and noise emissions at a roundabout are simulated. The vehicle speed distribution and traffic noise distribution at the roundabout are analysed, and the relationship between the traffic volume and sound power level of the cells is discussed. Finally, the proposed method is compared with existing traffic noise models, and the accuracy and efficiency of the proposed method are verified. The results of this paper show that the speed distribution and noise emission distribution at the roundabout are not uniform. When the traffic volume increases to saturation, the noise emission on the ring road will not keep increasing, and the sound power level of the cells on the inner ring is approximately 2 dBA higher than that of the outer ring. The methods and results in this paper may be valuable for road traffic design and noise control.

A cellular automation traffic flow model for traffic light signal management in intersection system with and without random driving behaviour

In this study, traffic light management in intersection system was simulated based on cellular automata traffic flow model. Characteristic of vehicles flow is defined according to the NS model. Our model was developed by adding driving behaviours, which are randomly defined in different maximum vehicle speed of mixed flow for two-lane traffic flow without lane-changing. The relation between time duration of traffic light signal and the traffic flow by analyzing vehicle density and averaged speed were investigated. The results show that the time duration of traffic light signal affects the traffic flow. Our model can be used to find the appropriated time duration to handle the traffic and can eventually reduce traffic jam. Additionally. Our result also presents an effect of the driving behaviour on the traffic flow.

A new cellular automata traffic flow model considering asynchronous update of vehicle velocity

This study examines the cellular automata traffic flow model, which considers the asynchronous update of vehicles’ velocities. Computer simulations are used to identify three typical phases: linear free flow phase, nonlinear moving phase and traffic jam phase. Compared to the original NaSch model, the system of the present model can reach the maximum flow when the vehicle density is higher. The influence of the delay probability and the maximum time step in which drivers intend to keep their current velocity on fundamental diagram is discussed.

Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior

In this paper, a two-lane cellular automata traffic flow model based on regular hexagonal cells is proposed. The realistic lane changing maneuvers with the lateral movement process, the lane changing duration, and the unsuccessful lane changing are considered by the new model. The vehicle trajectories from NGSIM dataset are used to extract the realistic lane changing maneuvers and calibrate the lane changing angle. The numerical results showed that the flow in the intermediate density range of the new model is lower than that of the traditional model. While the lane changing frequency in the same density range of the new model is higher than that of the traditional model. The lane changing duration will increase as the density grows. And the unsuccessful lane changing rate grows to large values in high density range. As to the heterogeneous traffic flow, the lane changing frequency is obviously greater than that in homogeneous condition. The trucks are more likely to make unsuccessful lane changing. And the lane changing duration of trucks is longer than that of cars in free flow region, but this is not the case in congested flow.

Study on fuel consumption in the Kerner–Klenov–Wolf three-phase cellular automaton traffic flow model

Based on the Kerner–Klenov–Wolf three-phase cellular automaton traffic model, we study fuel consumption of vehicles on one-way lanes under open boundary conditions. The difference in the appearance of the phase diagram is found to be related to the length of the vehicle. Phase diagram is divided into free flow, congested phase I, congested phase II and maximum current (MC) phase for the 15 cells of vehicle length and lacks the maximum current (MC) phase for 5 cells of vehicle length. By analysis of the cross-correlation between local density and flow and the spatial–temporal patterns, synchronized flow (SF) on the area upstream of the road is determined. The numerical computations for fuel consumption on free flow, congested phase I, synchronous flow (SF) and maximum current (MC) are carried out. The results indicate that the injection rate and the removal rate have great impacts on fuel consumption in the open boundary traffic system. The maximum current (MC) phase and congested phase I maximize fuel consumption. The fuel consumption of synchronized flow (SF) exceeds that of the free flow, but lower than that of the congested phase I and the maximum current (MC).

Review of the cellular automata models for reproducing synchronized traffic flow

Cellular automaton (CA) approach is an important theoretical framework for studying complex system behavior and has been widely applied in various research field. CA traffic flow models have the advantage of flexible evolution rules and high computation efficiency. Therefore, CA develops very quickly and has been widely applied in transportation field. In recent two decades, traffic flow study quickly developed, among which 'synchronized flow' is perhaps one of the most important concepts and findings. Many new CA models have been proposed in this direction. This paper makes a review of the development of CA models, concerning their ability to reproduce synchronized flow as well as traffic breakdown from free flow to synchronized flow. Finally, future directions have been discussed.

Traffic flow cellular automaton model with bi-directional information in an open boundary condition

AbstractWith Connected Vehicle Technologies being popular, drivers not only perceive downstream traffic information but also get upstream information by routinely checking backward traffic conditions, and the backward-looking frequency or probability is usually affected by prevailing traffic conditions. Meanwhile, the bi-directional perception range of drivers is expected to significantly increase, which results in more informed and coordinated driving behaviours. So, we propose a traffic flow bi-directional CA model with two perception ranges, and perform the numerical simulations with the field data collected from a one-lane highway in Richmond, California, USA as the benchmark data. Numerical results show that the CA model can effectively reproduce the oscillation of relatively congested traffic and the traffic hysteresis phenomenon. When adjusting the backward-looking probability and the perception range, the CA model can well simulate the travel times of all vehicles, and the generation and dissolution of traffic jams under various scenarios.

A simple cellular automaton model with dual cruise-control limit in the framework of Kerner’s three-phase traffic theory

In this study, a new cellular automata traffic flow model with dual cruise-control limit, where the vehicles with their velocities v=1,5 are not affected by noise and the slow-to-start rule is also introduced for standing vehicles with just one free cell, is established. Computer simulations are used to identify three typical phases from the fundamental diagram: free flow, synchronized flow, and wide moving jam. However, in the original cruise-control limit cellular automata traffic model, there are only two kinds of traffic phases, namely, free flow and congested traffic flow. Furthermore, in the ”linear” synchronous flow region, the ratio of flow to density depends on the randomization probability. The synchronous flow with scattered data is firstly found under the dense distribution. Compared to the previous models, the rules of our model are simpler, but it can present many features of Kerner’s three-phase theory, in particular, 2Z-characteristic for phase transitions.

One-dimensional cellular automaton traffic flow model based on defensive driving strategy

In a SDNaSch model, vehicles tend to brake sharply, leading to an inherent safety risk cellular automata based on defensive driving, considering the deceleration behavior of the preceding vehicle before the braking process. A numerical simulation is performed to determine the flow–density relationship, average speed–density relationship, emergency brake ratio–density relationship and space–time diagram under different defensive deceleration probabilities, and the safety and stability of the defensive driving model are analyzed. The results show that the use of the defensive driving model improves the traffic flow in the medium–high density area, delays the occurrence of static congestion, considerably reduces the emergency braking behavior of operating vehicles, and improves the stability and safety during transportation. In the medium–high density area, a stable and uniform synchronized flow appears, and the “speed transition” phenomenon consistent with the measured data is observed simultaneously. Compared with the traditional SDNaSch model, the proposed model can better describe the actual running state of the traffic flow.

A cellular automata traffic flow model with spatial variation in the cell width

This paper addresses a practical problem in road transportation regarding how the arrangement of three-dimensional facilities on a road (e.g., toll stations, construction guardrails and safety cones) will affect driving behavior. According to literature research and site investigations, in most cases, such facilities cause drivers to perceive a certain level of variation in the lane width, which will ultimately affect their driving behavior. For example, a driver will control a vehicle more carefully when approaching such a facility and usually reduce the passing speed according to his/her perception and experience. To the best of our knowledge, however, few related numerical studies and corresponding practical test cases have been reported at present. This paper uses a cellular automata traffic flow model to analyze this traffic problem. Notably, the existing models of this kind do not consider the cell width and its effect on vehicle speed. Motivated by the above problems and based on the human eye imaging principle, the dynamic visual field mechanism and test data, the present work first proposes a numerical relationship among the vehicle speed, the distance to the target and the perceived width of a fixed facility alongside the lane. Then, a new cellular automata traffic flow model with spatial variation in the cell width is designed to correlate the cell shape variable with the perceived width of the lane. Simulations based on this new model are conducted to simulate and compare the statuses of vehicles in a single lane with and without fixed facilities. The simulation results show that the speed reduction caused by cell deformation has a significant influence on the flow of traffic and that a “gate” effect occurs in deformed cells, resulting in behavior close to actual traffic phenomena. However, as the traffic density increases, the influence of deformed cells on a vehicle’s movement lessens. The proposed model may be a meaningful extension of cellular automata traffic flow models.

Cellular automaton model considering the effect of brake light and traffic light at the intersection

SummaryTraffic flow modeling of urban traffic on a single‐lane road with one traffic light operating as a bottleneck is the basis of research on urban road network. By introducing the brake light rules into ChSch model, we proposed a new cellular automaton traffic flow model of intersection considering the effect of brake light and traffic light to avoid running a red light and rear‐end collision. The first vehicle of the vehicle queue formed in front of the stop line at red light (FVoQ) is focused, and the evolution characteristics of intersection traffic flow are analyzed. Results show that the proposed model is more realistic than ChSch model in the following aspects: (a) The FVoQ decelerates more smoothly with the proposed model. The behavior of smooth deceleration reflects the driver's desire of comfortable driving in real traffic. (b) The proportion of the FVoQ's great and sudden deceleration to avoid running a red light is greatly reduced with the proposed model. In ChSch model, the proportion is larger than 80%, while in the proposed model, the proportion is less than 1%. The behavior of great and sudden deceleration reflects the aggressive driving pattern of a few drivers in real traffic. (c) The rear‐end collision accident rate caused by FVoQ's aggressive driving behaviors is greatly reduced with the proposed model, which conforms more to the real traffic.

Research on critical characteristics of highway traffic flow based on three phase traffic theory

In this paper, based on further development of the rules of Kerner–Klenov–Wolf (KKW) cellular automaton (CA) traffic flow model, we propose a Cellular Automaton model for single and two lane highway traffic flow in the framework of Kerner’s three-phase traffic theory. This model divides the traffic running process into two parts: speed adaption behavior under safety conditions and vehicle disturbances. The free flow, synchronized flow, wide moving jams and different first-order phases between them can be simulated. The results are verified in accordance with the fundamental hypothesis and results of the three-phase flow theory after analyzing the data measured by the virtual probe. In the lane changing possibility and road density figures, it is found that the lane changing possibility of free flow is always greater than that in the synchronized flow. And the lane changing possibility is randomly distributed in a two dimensional region which is consistent with the synchronized flow features. In the areas of the wide moving jams, the lane changing possibility is gradually reduced to 0. The results show that there is a close correlation between phase change and the significant reduction of the lane changing possibility. The lane changing behavior has the dual effect of maintaining the vehicle’s current driving state and inducing the phase transition.

A Multi-lane Cellular Automata Model Based on Elastic Safe Lane Change Rule

Based on the analysis of STCA model and X-STCA model, combined with the actual situation of vehicle lane-changing, the lane-changing choice probability P and safety parameter λ reflecting driving psychology is introduced, and the concept of lane change risk is redefined. This paper proposes a elastic safety lane-changing rule that is more in line with traffic conditions, and further establishes a multi-lane cellular automaton traffic flow model based on this rule. And the model can reduce the accident rate in terms of safety.

Traffic flow in the vicinity of crosswalks on a provincial highway in China

In this paper, motorized traffic flow in the vicinity of an unsignalized crosswalk on a provincial highway in China was studied. Based on the Kerner–Klenov–Schreckenberg–Wolf (KKSW) three-phase cellular automaton (CA) traffic flow model for single-lane roads, the cars’ moving rules when there were pedestrians walking on the crosswalk was redefined. A series of simulations were carried out to reveal the cars’ travel process and the influence of pedestrians crossing behavior on traffic flow. The simulations results show the redefined model can be used to study not only the characteristics of traffic flow in the vicinity of crosswalks on provincial highways but also some complex traffic problems such as traffic breakdown, synchronized flow, and moving jams. Moreover, some suggestions about signal control for the pedestrians crossing in the future were presented. Thus, this work is helpful for managing traffic flow and improving road safety and capacity in the vicinity of crosswalks on provincial highways in China.

Traffic flow and safety analysis

Traffic rule is a key factor affecting traffic flow and safety. We develop our models, including the cellular automata traffic flow model as well as the linear regression one, aiming at calculating traffic flow and evaluating safety conditions with varied traffic rules. Then, we thoroughly investigate four types of paths in a freeway, namely two straight lanes, three straight lanes, ramps, and roundabouts as case studies and discuss the different traffic rules as comparison. The results demonstrate that “Keep-Right-Except-To-Pass” rule is not as effective as the free rule in promoting traffic flow; however, this rule ensures safety for drivers better than the free rule. Additionally, a new traffic rule, which sets different posted speed limits for adjacent lanes, is proposed to promote better traffic flow with safety requirements satisfied. Furthermore, we apply effective rules and alternatives, left driving norms as well as intelligent system as extension and obtain better results. Finally, model's sensitivity analysis regarding to probability of decelerating and posted speed limits proves the stability of our results.

A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model

ABSTRACTThe purpose of this study is to propose a cellular automata (CA) traffic flow model with high accuracy for lane change decision and name it LCCAM. A driving simulator experiment was conducted to find factors affecting lane changing decisions. A back-propagate (BP) neural network was used to obtain the lane changing rules for the microscopic lane changing decision model (LCDM), and the collected accurate vehicular trajectory data were used to train the BP neural networks for the prediction of lane changes. After comparing different input variable combinations, the most accurate input setting was determined, including the locations and velocities of neighboring vehicles, inner/outer lane indicator, and the speed limits of the corresponding lane. Then, the determined BP neural network was adopted in the LCCAM as the LCDM. Simulation results showed that the LCCAM can capture important characteristics such as the mean velocities and the number of lane changes well, by comparing with observed traffic flow. Meanwhile, the LCCAM illustrates a better performance in replicating the number of lane changes than the other reference CA models. The research results show that the LCCAM proposed in this study will have potential and value for autonomous driving and active safety analysis in the future.