CN106652564A - Traffic flow cellular automaton modeling method under car networking environment - Google Patents

Abstract

The present invention proposes a traffic flow cellular automata modeling method in the Internet of Vehicles environment, which is suitable for vehicle operation rules in the Internet of Vehicles environment, and combines cellular automata modeling and simulation to realize the simulation of traffic flow, thereby Analyze traffic flow characteristics. On the basis of the Nasch model, combined with the characteristics of the Internet of Vehicles environment, a single-lane operation rule is established to simulate road operation conditions; and then a two-lane model is established to more realistically reflect road traffic conditions. The novel method proposed by the invention increases the prediction of the speed of the preceding vehicle and the situation of changing lanes, can effectively simulate the traffic flow in the traffic scene, and improves the authenticity of the traffic simulation. The results show that under the Internet of Vehicles environment, the vehicles have a higher operating speed, which greatly improves the road capacity and improves the traffic conditions.

Description

Cellular Automata Modeling Method for Traffic Flow in the Internet of Vehicles Environment

technical field

The invention relates to a traffic flow cellular automaton modeling method under the environment of the Internet of Vehicles.

Background technique

Since the end of the 20th century, the number of automobiles in my country has entered the fast lane of growth. At the same time, the probability of traffic accidents has also increased year by year. According to statistics from the Traffic Management Bureau of the Ministry of Public Security, in 2010, there were 3,906,164 traffic accidents across the country, a year-on-year increase of 35.9%. Among them, there were 219,521 casualties caused by road traffic accidents, and the direct property loss reached 930 million yuan. Safety guarantee is the most basic requirement for vehicle operation. Although there are many reasons for traffic accidents, the advancement of vehicle-mounted active safety systems and the realization of vehicle-road coordination systems can undoubtedly provide more security for vehicle safety. More reliable decision-making information and path guidance schemes can improve the safety of vehicle operation. As a popular background, the Internet of Vehicles is booming; the exploration of Internet of Vehicles technology is the key to the research of various countries and technology companies.

The current research on the Internet of Vehicles is mostly the optimization and improvement of hardware facilities such as lane departure alarms, adaptive cruise control, forward collision warning functions, and other on-board functions or road test systems. There is relatively little research on the definition.

Contents of the invention

Based on the above deficiencies, the present invention provides a traffic flow cellular automata modeling method in the Internet of Vehicles environment, which is suitable for vehicle operation rules in the Internet of Vehicles environment, combined with cellular automata modeling, quantitative and qualitative analysis of traffic flow The parameter relationship increases the prediction of the speed of the vehicle in front and the situation of changing lanes, and improves the authenticity of traffic simulation.

The technology adopted in the present invention is as follows: a traffic flow cellular automaton modeling method under the Internet of Vehicles environment, which defines the road conditions as follows: the vehicle is controlled by the driver; each vehicle cell can communicate with the front to obtain Accurate driving information of the vehicle in front, and communicates its own traffic information with the vehicle behind; the driver makes correct judgments and decisions based on the acquired accurate traffic environment information, the method is as follows:

(1). Modeling rules of single-lane cellular automata

Step 1: Acceleration, v _n (t+1)=min(v _n (t)+1,v _max ) corresponds to the characteristic that the driver expects to drive at the maximum speed in real life;

Step two: slow down,

v _n (t+1)=min(v _n (t+1),d _n +v' _n+1 (t+1))

v' _n+1 (t+1)＝min(v _n+1 (t),v _max -1,d _n+1 (t))

The driver takes deceleration measures in order to avoid a collision with the vehicle in front. Due to the transparency of the vehicle speed and vehicle position in the Internet of Vehicles environment, the minimum distance between the two vehicles becomes the distance between the two vehicles at time t and the speed value of the vehicle in front at time t+1 Sum;

In the formula, v' _n+1 (t+1)——indicates the speed prediction of the vehicle in front at time t+1;

d _n (t)——indicates the number of spaces between the vehicle n and the vehicle in front at time t;

Step 3: Random slowing down,

When the vehicle decelerates due to various uncertain factors (such as bad road conditions, different driver mentality, etc.), when the speed is less than a certain threshold, the vehicle will not decelerate; when it is greater than a certain threshold, deceleration measures will be taken with a certain probability;

In the formula, RD——represents random probability;

p——represents a random number between 0 and 1. When p<RD, random slowing down is performed;

Step 4: Update the position, x _n (t+1)=x _n (t)+v _n (t+1); the vehicle moves forward at the adjusted speed;

(2). Modeling rules of two-lane cellular automata

The movement process of the two-lane changeable model vehicle evolves in the order of "lane change-acceleration-deceleration-random slowing-position update". After the lane change, the vehicles run in their respective lanes according to the single-lane update rule

The rules for changing lanes are as follows:

In the formula, d _nother —indicates the number of spaces between the position of car n in another lane and the car n+1 in front of this lane;

d _nback ——indicates the number of spaces between n cars and n-1 workshops in this lane;

v _i,n+1 (t)——Indicates the speed of n+1 vehicles in lane i at time t;

Vehicles run updates according to the above rules to analyze traffic flow characteristics.

The method proposed by the invention increases the prediction of the speed of the vehicle in front and the situation of changing lanes, can effectively simulate the traffic flow in the traffic scene, and improves the authenticity of the traffic simulation. The results show that the vehicle has a higher running speed under the Internet of Vehicles environment using this method, which greatly improves the road capacity and improves the traffic condition.

Description of drawings

Fig. 1 is the system operation interface under the MATLAB environment-the single-lane system operation environment diagram;

Fig. 2 is the system operating interface-two-lane system operating environment diagram under the MATLAB environment;

Figure 3 is a schematic diagram of a two-lane lane change;

Fig. 4 is p=0.3, the space-time diagram of k=0.05;

Fig. 5 is p=0.3, the space-time diagram of k=0.2;

Fig. 6 is p=0.3, the space-time diagram of k=0.4;

Fig. 7 is p=0.1, the space-time diagram of k=0.02;

Fig. 8 is p=0.1, the space-time diagram of k=0.2;

Fig. 9 is p=0.1, the space-time diagram of k=0.4;

Fig. 10 is p=0.5, the space-time diagram of k=0.02;

Fig. 11 is p=0.5, the space-time diagram of k=0.2;

Fig. 12 is p=0.5, the space-time diagram of k=0.4;

Figure 13 is a flow-density diagram under different moderation conditions;

Figure 14 is a velocity-density diagram under different moderation conditions;

Figure 15 is a diagram of the lane change probability model.

detailed description

Below according to the accompanying drawings of description, the present invention will be further described by way of example:

Example 1

A cellular automata modeling method for traffic flow in the Internet of Vehicles environment. The road conditions are defined as follows: the vehicle is controlled by the driver; each vehicle cell can communicate with the front to obtain accurate driving information of the vehicle in front, and The traffic information of oneself communicates with the vehicle behind; the driver makes correct judgments and decisions based on the acquired accurate traffic environment information, the method is as follows:

(1). Modeling rules of single-lane cellular automata

Step 1: Acceleration, v _n (t+1)=min(v _n (t)+1,v _max ) corresponds to the characteristic that the driver expects to drive at the maximum speed in real life;

Step two: slow down,

v _n (t+1)=min(v _n (t+1),d _n +v' _n+1 (t+1))

v' _n+1 (t+1)＝min(v _n+1 (t),v _max -1,d _n+1 (t))

The driver takes deceleration measures in order to avoid a collision with the vehicle in front. Due to the transparency of the vehicle speed and vehicle position in the Internet of Vehicles environment, the minimum distance between the two vehicles becomes the distance between the two vehicles at time t and the speed value of the vehicle in front at time t+1 Sum;

In the formula, v' _n+1 (t+1)——indicates the speed prediction of the vehicle in front at time t+1;

d _n (t)——indicates the number of spaces between the vehicle n and the vehicle in front at time t;

Step 3: Random slowing down,

When the vehicle decelerates due to various uncertain factors (such as bad road conditions, different driver mentality, etc.), when the speed is less than a certain threshold, the vehicle will not decelerate; when it is greater than a certain threshold, deceleration measures will be taken with a certain probability;

In the formula, RD——represents random probability;

p——represents a random number between 0 and 1. When p<RD, random slowing down is performed;

Step 4: Update the position, x _n (t+1)=x _n (t)+v _n (t+1); the vehicle moves forward at the adjusted speed;

(2). Modeling rules of two-lane cellular automata

The movement process of the two-lane changeable model vehicle evolves in the order of "lane change-acceleration-deceleration-random slowing-position update". After the lane change, the vehicles run in their respective lanes according to the single-lane update rule

The rules for changing lanes are as follows:

In the formula, d _nother —indicates the number of spaces between the position of car n in another lane and the car n+1 in front of this lane;

d _nback ——indicates the number of spaces between n cars and n-1 workshops in this lane;

v _i,n+1 (t)——Indicates the speed of n+1 vehicles in lane i at time t;

Vehicles run updates according to the above rules to analyze traffic flow characteristics.

Example 2

Simulation Analysis of Cellular Automata under the Environment of Internet of Vehicles

(1) Simulation environment setting method

According to the rules introduced above, use MATLAB to build a simulated road section. At the initial moment of the simulation, the system initializes the vehicle's position information and speed information. After the simulation starts, in each simulation step, the vehicle's position and speed are updated according to the model rules until the simulation time ends.

Take the single-lane model as an example. Based on the idea of MATLAB matrix and image processing module, a 1x100 matrix is used to represent a single-lane road cell, and a value of 1 indicates that there is a car, and a value of 0 indicates that there is no car, that is, the simulated road is composed of 100 cells, each cell The cell length is 5m, and the maximum velocity v _max =5. Figure 1 is the system operation interface in the MATLAB environment, where black represents a single lane, and white dots represent vehicles at the position of the cell. The Run button means to start the simulation, the Stop button means to stop the simulation, and the Quit button means to exit the simulation, and the number in the upper left corner records the time of the system simulation.

The following simulations use the refined model to draw conclusions. The so-called cell model is to cellize the road, so that the length represented by each cell becomes shorter, and the number of cells occupied by vehicles increases accordingly.

(2) Simulation result analysis

(1) Analysis of the simulation results of the refined single-lane model----space-time diagram

Simulation environment: The road is composed of 1000 cells, and other parameters are the same as the refined single-lane model.

Figures 4-12 are the time-space diagrams of the refined model in the Internet of Vehicles environment when the densities are 0.2, 0.3, and 0.5 under different moderation probabilities. It can be seen from the figure that as the density increases, the traffic flow enters the metastable region; under the same density condition, as the slowing down probability increases, the traffic phenomenon of stopping and going becomes more obvious. When the density is small, the vehicle is in the state of free driving, and the slowdown probability has no obvious influence on the traffic condition; when the density increases to a certain level, the traffic flow reaches a metastable state, and the vehicle running state is chaotic at this time, and the slowdown probability has a great impact on the vehicle operation. With the further increase of traffic flow density and serious road congestion, the driving of vehicles is greatly hindered, and the slowdown probability has no obvious impact on it.

(2) Analysis of the simulation results of the refined single-lane model--analysis of the flow-density relationship

Figure 13 lists the traffic-density diagrams of the NS model and the improved model in the Internet of Vehicles environment. The initial conditions of the two are exactly the same (the road length is 200, and the maximum speed is 12). The moderation probability of the NS model is p=0.3, and the moderation probabilities of the improved models given in this paper are p=0.1, p=0.3, and p=0.5 respectively.

It can be seen from the figure that under the NS environment, the critical density and the maximum traffic flow are both small. Comparing the curves of the improved model of the Internet of Vehicles environment under different slowing conditions (p=0.1, p=0.3, p=0.5), it can be seen that when the traffic density is low or high, the influence of the slowing probability on the traffic flow Not obvious. This is because when the traffic density is low, there are few vehicles on the road and the distance between the fronts is large, the random slowing of a certain car will not affect the normal driving of other vehicles; when the traffic density is too high, the overall running speed Low, the change of the random slowdown probability has no effect on the change of the overall traffic. When the density is between 0.1 and 0.7, under the same density condition, as the probability of random slowing increases, the flow rate decreases obviously, and the critical density value also decreases from 0.37 to 0.3.

(3) Analysis of the simulation results of the refined single-lane model--analysis of the relationship between speed and density

Figure 14 lists the speed-density diagrams of the NS model and the improved model in the Internet of Vehicles environment. The initial conditions of the two are exactly the same (the road length is 200, and the maximum speed is 12). It can be seen from the figure that the traditional NS environment has a lower speed, and the speed is more sensitive to density changes. Comparing the speed-density curves of the improved model of Internet of Vehicles under different slowing conditions, it can be seen that under the same conditions, the greater the slowing probability, the smaller the speed. When the density is small (less than 0.1) or large (above 0.7), the slowdown probability has negligible influence on it.

(4) Simulation analysis of refined dual-lane model

For the two-lane lane-changing model, the arrival rate of vehicles, that is, the traffic volume of the road section, is constantly changed, and the lane-changing probability is used to represent the lane-changing situation of vehicles in the Internet of Vehicles environment, as shown in Figure 15.

It can be seen from the figure that when the flow rate is relatively small, the distribution of vehicles on the road is very uniform, and the space between vehicles is basically kept at v _max . bike lane. As the flow continues to increase, the orderly distribution of vehicles is gradually suppressed due to the increase in flow. When the spacing distribution is chaotic, the conditions for lane change will be met, and the probability of vehicle lane change will increase accordingly. With the further increase of the traffic flow, the traffic flow will enter a metastable state, and the traffic situation at this time is relatively chaotic, which may lead to the local maximum of the lane change probability. When the traffic continues to increase, the distance between vehicles becomes smaller, the chance of changing lanes also decreases, and the probability of changing lanes gradually decreases.

Due to the relative transparency of road condition information and accurate judgment of road conditions in the Internet of Vehicles environment, as the density increases, there is a greater chance of changing lanes than in ordinary environments.

Claims (1)

1. A traffic flow cellular automata modeling method in the Internet of Vehicles environment, which defines the road conditions as follows: the vehicle is controlled by the driver; each vehicle cell can communicate with the front to obtain accurate driving information of the vehicle in front, And communicate the own traffic information with the vehicle behind; the driver makes correct judgment and decision according to the acquired accurate traffic environment information, characterized in that the method is as follows: (1) Modeling rules of single-lane cellular automata Step 1: Acceleration, v _n (t+1)=min(v _n (t)+1,v _max ) corresponds to the characteristic that the driver expects to drive at the maximum speed in real life; Step two: slow down, v _n (t+1)=min(v _n (t+1),d _n +v' _n+1 (t+1)) v' _n+1 (t+1)＝min(v _n+1 (t),v _max -1,d _n+1 (t)) The driver takes deceleration measures in order to avoid a collision with the vehicle in front. Due to the transparency of the vehicle speed and vehicle position in the Internet of Vehicles environment, the minimum distance between the two vehicles becomes the distance between the two vehicles at time t and the speed value of the vehicle in front at time t+1 Sum; In the formula, v' _n+1 (t+1)——indicates the speed prediction of the vehicle in front at time t+1; d _n (t)——indicates the number of spaces between the vehicle n and the vehicle in front at time t; Step 3: Random slowing down, When the vehicle decelerates due to various uncertain factors (such as bad road conditions, different driver mentality, etc.), when the speed is less than a certain threshold, the vehicle will not decelerate; when it is greater than a certain threshold, deceleration measures will be taken with a certain probability; In the formula, RD——represents random probability; p——represents a random number between 0 and 1. When p<RD, random slowing down is performed; Step 4: Update the position, x _n (t+1)=x _n (t)+v _n (t+1); the vehicle moves forward at the adjusted speed; (2) Modeling rules of two-lane cellular automata The movement process of the two-lane changeable model vehicle evolves in the order of "lane change-acceleration-deceleration-random slowing-position update". After the lane change, the vehicles run in their respective lanes according to the single-lane update rule The rules for changing lanes are as follows: $\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; )</span>\\ {\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ></span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\\ {\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>\end{array}\right.$ In the formula, d _nother —indicates the number of spaces between the position of car n in another lane and the car n+1 in front of this lane; d _nback ——indicates the number of spaces between n cars and n-1 workshops in this lane; v _i,n+1 (t)——Indicates the speed of n+1 vehicles in lane i at time t; The vehicle runs updates according to the above rules to analyze the traffic flow characteristics.