Automaton Traffic Flow Model Research Articles

Comparison of gap-based and flow-based control strategies using a new controlled stochastic cellular automaton model for traffic flow

ABSTRACT Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC.

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

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

On cellular automata model of traffic flow with Lennard-Jones potential

ABSTRACT By introducing the Lennard-Jones potential into the interaction of vehicles, a new cellular automata model of traffic flow is proposed, in which the fixed probability of randomisation of cellular automata is replaced by the state transition probability of cell sites. The proposed model considers that the driver is affected by the interactional potential of vehicles before him/her, and the decision-making process is related to the value of the interactional potential. The model is more suitable for the driver’s random decision-making process which is based on the stochastic behaviour of traffic flow. The fundamental diagram (flow density relationship) of the proposed model is obtained, and the complex traffic phenomenon is reproduced through numerical simulation.

Cellular Automata Model for Traffic Flow with Optimised Stochastic Noise Parameter

Based on the existing safe distance cellular automata model, an improved cellular automata model based on realistic human reactions is proposed in this paper, which aims to reproduce the characteristics of congested traffic flow. In the proposed model, the stochastic noise param-eter is optimised by considering driving behavioural dif-ference. The relative speed, gap and acceleration of the front vehicle are introduced into the optimised stochastic noise parameter oriented to describing the asymmetric acceleration behaviour of drivers in congestion. The sim-ulation results show that an uneven distribution of accel-eration trajectories of vehicles experiencing congestion exhibited on the spatial-temporal diagram of the pro-posed model is reproduced. Based on the analysis of the NGSIM, compared with the model with traditional sto-chastic noise parameter, the vehicles that move accord-ing to the proposed model can follow more easily and more realistically. Then the actual gap of vehicles can be better reflected by the proposed model and the change of vehicle speed is more stable. Additionally, the traffic efficiency from two aspects of flow and speed shows that the proposed model can significantly improve the traffic efficiency in the medium high density region.

A multi-cell Cellular Automata model of traffic flow with emergency vehicles: Effect of a corridor of life and drivers’ behaviour

The intensity of traffic flow is constantly increasing, which causes significant difficulties in the movement of emergency vehicles through the city. Observations conclude that there is a need to pay more attention to drivers for an approaching emergency vehicle by implementing new technologies in cars and road infrastructure and educating drivers. One way to present valuable results is to use mathematical and computer simulation methods. Various macroscopic and microscopic road traffic models allow traffic flow analysis. However, it should be emphasised that standard traffic flow models do not include emergency vehicle traffic. We propose a multi-agent microscopic model for analysing the traffic flow of emergency vehicles in an urban environment, with some limitations to the distance between vehicles and their proper distribution (“corridor of life”) to leave a free passage for a privileged vehicle. Real data was used to calibrate and validate the model. Our simulation studies show the importance of certain aspects of road traffic (distance between cars, “corridor of life”, size and type of roadside, frictional conflict, etc.) to increase/decrease the traffic flow considering the approaching emergency vehicle.

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.

A Divided Two-Lane Cellular Automaton Model of Traffic Flow Considering Driving Tendency

Different types of drivers usually take on different driving tendency on urban road, based on the diversity of driving tendency, a new two-lane cellular automaton model was proposed under condition of periodical boundary. The mean speed, flow volume and lane-changing frequency under different lane-changing and deceleration rules were simulated. In the process of computational simulation, the fundamental diagrams of traffic flow under different rules were obtained by changing traffic flow density. Simulation results show that the proposed new rules can improve traffic flow volume and suppress traffic jam to a certain degree. Finally, the critical points were discussed to show the impact of lane-changing on traffic flow.

Cellular automata model for traffic flow at intersections in internet of vehicles

Considering the effect of the front vehicle’s speed, the influence of the brake light and the conflict of the traffic flow, we established a cellular automata model called CE-NS for traffic flow at the intersection in the non-vehicle networking environment. According to the information interaction of Internet of Vehicles (IoV), introducing parameters describing the congestion and the accurate speed of the front vehicle into the CE-NS model, we improved the rules of acceleration, deceleration and conflict, and finally established a cellular automata model for traffic flow at intersections of IoV. The relationship between traffic parameters such as vehicle speed, flow and average travel time is obtained by numerical simulation of two models. Based on this, we compared the traffic situation of the non-vehicle networking environment with conditions of IoV environment, and analyzed the influence of the different degree of IoV on the traffic flow. The results show that the traffic speed is increased, the travel time is reduced, the flux of intersections is increased and the traffic flow is more smoothly under IoV environment. After the vehicle which achieves IoV reaches a certain proportion, the operation effect of the traffic flow begins to improve obviously.

Exact results of 1D traffic cellular automata: The low-density behavior of the Fukui–Ishibashi model

The maximum entropy states of the cellular automata models for traffic flow in a single-lane with no anticipation are presented and discussed. The exact analytical solutions for the low-density behavior of the stochastic Fukui–Ishibashi traffic model were obtained and compared with computer simulations of the model. An excellent agreement was found.

A cellular automata model for traffic flow based on kinetics theory, vehicles capabilities and driver reactions

In this paper, a reliable cellular automata model oriented to faithfully reproduce deceleration and acceleration according to realistic reactions of drivers, when vehicles with different deceleration capabilities are considered is presented. The model focuses on describing complex traffic phenomena by coding in its rules the basic mechanisms of drivers behavior, vehicles capabilities and kinetics, while preserving simplicity. In particular, vehiclés kinetics is based on uniform accelerated motion, rather than in impulsive accelerated motion as in most existing CA models. Thus, the proposed model calculates in an analytic way three safe preserving distances to determine the best action a follower vehicle can take under a worst case scenario. Besides, the prediction analysis guarantees that under the proper assumptions, collision between vehicles may not happen at any future time. Simulations results indicate that all interactions of heterogeneous vehicles (i.e., car–truck, truck–car, car–car and truck–truck) are properly reproduced by the model. In addition, the model overcomes one of the major limitations of CA models for traffic modeling: the inability to perform smooth approach to slower or stopped vehicles. Moreover, the model is also capable of reproducing most empirical findings including the backward speed of the downstream front of the traffic jam, and different congested traffic patterns induced by a system with open boundary conditions with an on-ramp. Like most CA models, integer values are used to make the model run faster, which makes the proposed model suitable for real time traffic simulation of large networks.

Mechanisms of jamming in the Nagel-Schreckenberg model for traffic flow.

We study the Nagel-Schreckenberg cellular automata model for traffic flow by both simulations and analytical techniques. To better understand the nature of the jamming transition, we analyze the fraction of stopped cars P(v=0) as a function of the mean car density. We present a simple argument that yields an estimate for the free density where jamming occurs, and show satisfying agreement with simulation results. We demonstrate that the fraction of jammed cars P(v∈{0,1}) can be decomposed into the three factors (jamming rate, jam lifetime, and jam size) for which we derive, from random walk arguments, exponents that control their scaling close to the critical density.

Multi-section cellular automata model of traffic flow

It is more common for drivers to pass through multiple sections to reach destinations instead of single road section. Howerver, most of researches concentrate on improving the effect in an independent section. Based on traditional cellular automata traffic model, a multi-section model is proposed by regarding serverl road sections as a traffic system. In this model, different sections of the road might have different lengths, numbers of lanes or maximal speeds. And vehicles travel from one section to another. The main difficulty lies in dealing with the relationships among the traffic flows of different sections. Besides basic rules in NaSch model, the vehicle inflow rule, crossroad randomization brake rule and crossroad inflow rule is added in this paper to enable vehicles to flow between sections. At the beginning of section, to avoid conflicting at crossroads under open boundary condition, the concept of car pool is introduced when new vehicles enter into sections. Before arriving at the end of section, crossroad randomization brake is used to simulate the influences of crossroads. Speed decreases in probability until lower than a maximal crossroad speed. When leaving the section, vehicles go to the next section with a straight ratio. Also, new vehicles may enter according to traffic condition. Therefore, cellular automata of different sections can be connected in series.Finally, numerical simulation is demonstrated to study the influences of important parameters, including traffic inflow probability, maximal crossroad speed and crossroad randomization brake probability. Compared with traditional models, this model focuses on connecting sections. And improvements of basic models can be implanted easily, thereby increasing the accuracy of the whole model in the future. The experimental result are as follows. 1) According to space-time graphs of different inflow probabilities, there is a new kind of traffic flow called mixed flow. Traffic congestion often starts from crossroads, and spreads to the whole section. And traffic jams in previous section might relieve traffic pressure in latter section. 2) With the increase of traffic inflow probability, crossroads tends to have a greater influence on average speed as well as average traffic density. What is more, the moderate increase of vehicle numbers could cause the road capacity to drop rapidly if it exceeds the threshold value.

A new cellular automata model of traffic flow with negative exponential weighted look-ahead potential**Project supported by the National Natural Science Foundation of China (Grant Nos. 11572264, 11172247, 11402214, and 61373009).

With the development of traffic systems, some issues such as traffic jams become more and more serious. Efficient traffic flow theory is needed to guide the overall controlling, organizing and management of traffic systems. On the basis of the cellular automata model and the traffic flow model with look-ahead potential, a new cellular automata traffic flow model with negative exponential weighted look-ahead potential is presented in this paper. By introducing the negative exponential weighting coefficient into the look-ahead potential and endowing the potential of vehicles closer to the driver with a greater coefficient, the modeling process is more suitable for the driver’s random decision-making process which is based on the traffic environment that the driver is facing. The fundamental diagrams for different weighting parameters are obtained by using numerical simulations which show that the negative exponential weighting coefficient has an obvious effect on high density traffic flux. The complex high density non-linear traffic behavior is also reproduced by numerical simulations.

Cellular automata model for urban road traffic flow considering pedestrian crossing street

In order to analyze the effect of pedestrians’ crossing street on vehicle flows, we investigated traffic characteristics of vehicles and pedestrians. Based on that, rules of lane changing, acceleration, deceleration, randomization and update are modified. Then we established two urban two-lane cellular automata models of traffic flow, one of which is about sections with non-signalized crosswalk and the other is on uncontrolled sections with pedestrians crossing street at random. MATLAB is used for numerical simulation of the different traffic conditions; meanwhile space–time diagram and relational graphs of traffic flow parameters are generated and then comparatively analyzed. Simulation results indicate that when vehicle density is lower than around 25 vehs/(km lane), pedestrians have modest impact on traffic flow, whereas when vehicle density is higher than about 60 vehs/(km lane), traffic speed and volume will decrease significantly especially on sections with non-signal-controlled crosswalk. The results illustrate that the proposed models reconstruct the traffic flow’s characteristic with the situation where there are pedestrians crossing and can provide some practical reference for urban traffic management.

The effect of lateral interaction on traffic flow

Abstract We propose an extended cellular automaton model for traffic flow, taking into account lateral interactions with defects and between vehicles. The fundamental diagram for a given defects density on the road is studied. It is found that the plateau size increases linearly with the decreasing road width for little defects densities. Furthermore, the capacity increases linearly with the increasing road width. However, for a fixed road width, the capacity decreases exponentially with the increasing defects density. The lateral effects for non-mutual interactions between lanes and for the same maximal velocity is also investigated. It is found that the lateral effects on one lane are meaningful only when the density on the other lane is above the critical density. However, the lateral effects are always present if fast and slow lanes exist. Little differences have been found for the mutual interactions.

Cellular automaton models for traffic flow considering opposite driving of an emergency vehicle

Aiming at two-lane road, this paper establishes three models to analyze the opposite-overtaking rules of emergency vehicle based on cellular automaton (CCA) model. Based on the simulation of mixed traffic flow for multi-density conditions, the density-speed diagrams have been obtained consequently. According to the analysis, when the traffic density of the opposite lane is low, the opposite driving behavior of emergency vehicle can improve the average speed effectively. At the same time, if the cocurrent lane is in high-density traffic, the traffic in the opposite lane will be disturbed, but the vehicles in the cocurrent lane will not be affected. The paper has further discussed the influence of different emergency vehicle driving behaviors on traffic. The results reveal that as the traffic of the opposite lane is in a low-density range, if emergency vehicle operates overtaking behavior precisely, the greater the density of the cocurrent lane is, the more obviously the speed improve. Meanwhile large random fluctuation of overtaking times will occur. While the risky lane change behavior displays different traffic characteristics, that is when the same direction lane is in high density, the speed increases slightly and the lane change number is changed regularly.

A cellular automata model of traffic flow with variable probability of randomization**Project supported by the National Natural Science Foundation of China (Grant Nos. 11172247, 61273021, 61373009, and 61100118).

Research on the stochastic behavior of traffic flow is important to understand the intrinsic evolution rules of a traffic system. By introducing an interactional potential of vehicles into the randomization step, an improved cellular automata traffic flow model with variable probability of randomization is proposed in this paper. In the proposed model, the driver is affected by the interactional potential of vehicles before him, and his decision-making process is related to the interactional potential. Compared with the traditional cellular automata model, the modeling is more suitable for the driver’s random decision-making process based on the vehicle and traffic situations in front of him in actual traffic. From the improved model, the fundamental diagram (flow–density relationship) is obtained, and the detailed high-density traffic phenomenon is reproduced through numerical simulation.

One-dimensional cellular automaton model of traffic flow considering dynamic headway

Based on the NaSch cellular automaton traffic model, a modified single lane traffic model is proposed by considering the dynamic headway of successive vehicles, in which the complex characteristic and driving behavior difference between drivers are taken into account. The relationship between the flow rate and the traffic density is obtained by the numerical simulation, and it shows a two-dimensional region in the flow density plane. The three traffic phases, i.e., free flow, synchronized flow, and wide moving jams, are exhibited. It indicates that the synchronized flow and traffic jams can appear even if there is no traffic bottleneck. Besides, the high speed car-following phenomenon is indicated when the traffic is in the synchronized flow. The rate of the high speed car-following is in good agreement with the measured result.

Study on the Traffic Capacity of Expressway Based on Cellular Automata Model

Based on the Nagel-Schrekenberg cellular automaton model of traffic flow, this article analyzed the influence that driving on the right side takes to the traffic flow in the condition of light and heavy traffic. With the combination of fluid dynamics and vehicle dynamics, we established the Cellular Automata model for mixed speed two-lane traffic flow on the rule driving on the right side. Then we used the AHP method to find out the large bus share rate, the number of vehicles changing lanes and the influence law of safety factors on traffic flow. We came to a conclusion that the relationship between traffic flow and load are inverted U shape changes in the low load and high load conditions, the accident rate is the maximum when the V/C (the ratio of traffic flow and the ability of corresponding section) is the minimum; with the increase of V/C, the accident rate decreased gradually.

A Cellular Automaton Model for Traffic Flow -Investigating the Effect of Turning

A cellular automaton model is proposed, modeling vehicular traffic flow on a two dimensional lattice in which the vehicles turn at an intersection with a given probability. It is shown that the introduction of turning reduces the long-term average velocity, and can be predicted by a power law depending on the probability of a vehicle turning and the density of cars. The reduction in speed decreases rapidly once the light cycle length surpasses a certain threshold, the value of which can be predicted from the observed power law. Keywords: cellular automaton, traffic flow, traffic light strategy, turning, dynamical systems, power law