CN106846828A - A kind of opposite pedestrian stream crossing facilities canalization method of lower high density of signal control - Google Patents

Abstract

The invention discloses a channelization method for high-density opposite pedestrian flow crossing street facilities under signal control, aiming at improving the efficiency of high-density opposite pedestrian flow street crossing under signal control. First, through questionnaire survey and related calculations, the forward coefficient, overtaking coefficient, right-leaning coefficient, herd influence coefficient, and the walking transfer probability of pedestrians are obtained. Secondly, a simulation model of high-density opposite pedestrian flow under signal control is established to provide basic data for the channelization of pedestrian flow across the street. Finally, through the analysis of the impact of high-density facing pedestrians walking rightward and conformity under signal control, and the comparative analysis of simulation data and real test data, the channelization measures for high-density facing pedestrian flow under signal control are obtained.

Description

A channelization method for high-density opposite pedestrian flow crossing street facilities under signal control

technical field

The invention relates to a method for channelizing high-density opposite pedestrian flow crossing street facilities under signal control, and belongs to the technical field of traffic engineering.

Background technique

With the continuous advancement of the national new-type urbanization policy, the urbanization speed of cities and towns has been accelerating, and the problem of pedestrian crossings emerging from it has attracted more and more attention. As the most common pedestrian crossing signal system in the city, it is more and more important to analyze the pedestrian crossing problem inside it. The reason is that the pedestrian crossing signal system is part of the road signal control system. Under the "vehicle-oriented" design idea, pedestrian signals are often coordinated with vehicle system signals, resulting in insufficient time for pedestrians to cross the street. Problems such as serious conflicts are becoming more and more serious. In addition to adding means such as overpasses and underpasses, how to improve the efficiency of pedestrian crossing within the scope of existing road resources has become a hot issue in the transportation field in recent years.

The research on channelization of high-density opposite pedestrian flow under signal control is particularly important to our country. On the one hand, with the acceleration of urbanization in my country, a large number of people have poured into cities, but the construction of pedestrian facilities has not kept up with the pace. Pedestrian crosswalks are the basic facilities for pedestrians crossing streets in cities, and the research on their traffic efficiency is of great significance. On the other hand, as a member of developing countries, the density of pedestrian crossing in my country is obviously higher than that in developed countries, which makes pedestrian crossing appear insufficient in terms of traffic efficiency and comfort. Congested crossing behaviors, resulting in traffic risks.

Due to the complexity, dynamics and complexity of pedestrian flow, the research on pedestrian flow theory has great challenges and high academic value, which prompts more and more researchers to study pedestrian traffic with the help of physical and computer means. flow. However, there is a lack of research on channelization modeling of pedestrian flow at home and abroad, and there are biased ideas such as "simulation for simulation's sake" and "channelization for channelization's sake" in the research. Although some studies have studied the simulation and channelization of pedestrian crossings in detail, they lack the background of traffic applications, such as rarely considering the high-density pedestrian crossing.

Contents of the invention

Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a method for channeling high-density opposite pedestrian flow crossing facilities under signal control, and establishes a high-density signal-controlled method based on cellular automata in combination with actual survey data. The opposite pedestrian flow crossing model aims at the low efficiency of traditional pedestrian crossings under high-density pedestrian flow conditions, analyzes the impact of pedestrian walking tendency characteristics (right leaning and herd behavior) on pedestrian crossing efficiency, and proposes a pedestrian crossing model based on comparative experiments. The efficiency of street crossing is better than that of traditional pedestrian crossing channelization. This method can obtain the channelization measures of high-density opposite pedestrian flow crossing facilities under signal control to improve pedestrians' right inclination and conformity.

Technical scheme: in order to achieve the above object, the technical scheme adopted in the present invention is:

A method for channelizing high-density opposite pedestrian flow crossing facilities under signal control, comprising the following steps:

Step 1, pedestrian crossing data collection and recording: collect and record road traffic environment information and pedestrian flow information data; road traffic environment information mainly includes the length L of pedestrian crossings that need to be channelized, and the width W of pedestrian crossings; pedestrian flow information data mainly includes Pedestrian flow S ₁ , S ₂ on both sides of the crosswalk that needs to be channelized, pedestrian speed v, pedestrian crossing probability illegally;

Step 2, the calculation of pedestrian crossing transition probability, includes the following steps:

Step 21, obtaining the forward probability, front left probability, front right probability, left travel probability, and right travel probability under different scenarios considering pedestrian right-leaning and conformity under high-density conditions;

Step 22: According to the advance probability, front left probability, front right probability, left row probability, and right row probability obtained in step 21, calculate the introduction advance coefficient, surpass coefficient, right-leaning coefficient, and herd influence coefficient. , Right tilt coefficient and herd influence coefficient to calculate the transition probability of pedestrians crossing the street without considering the influence of herd and considering the influence of herd;

Step 3, crossing pedestrian flow data prediction: mainly to provide basic data for the determination of the high-density range of pedestrian crossing and the analysis of channelization measures for crossing facilities; select a virtual crosswalk that approximately matches the size of the crosswalk that needs to be channelized, and establish a crosswalk based on cellular automaton The simulation model of high-density crossing pedestrian flow under the control of the signal of , in each time step, each pedestrian updates the following steps synchronously, and at the same time, the illegal crossing probability of pedestrians determined in step 1 determines the Illegal street crossing;

Step 31: If there is no opposite pedestrian within a certain range in front of a pedestrian, go to step 34; otherwise, calculate the transition probabilities in five directions by using a method that does not consider the influence of the herd, and go to step 32;

Step 32: If there is a head-on collision between this pedestrian and the opposite pedestrian, the pedestrian cannot move, and the two pedestrians exchange positions, otherwise the two pedestrians exchange positions with a certain probability; otherwise, go to step 33;

Step 33: Calculate the transition probabilities of the five directions under the influence of herdity by using the method of considering herd influence, and turn to step 34;

Step 34: The speed of the pedestrian moving forward, the front left and the front right is a small value between the distance from the opposite pedestrian and the maximum speed;

Step 4: Feedback on channelization of street crossing facilities: mainly by determining the high-density range of pedestrian crossings, identifying the feasibility of channelization of street crossing facilities, and studying the influence of different degrees of pedestrian walking to the right and conformity on pedestrian crossing efficiency. Comparing the predicted data of density crossing pedestrian flow with the real test data, a channelization measure of high density crossing pedestrian flow under signal control is finally obtained.

Preferably: in the step 1, road traffic environment information is collected through on-the-spot investigation; pedestrian flow information data is obtained through video collection.

Preferably: in the step 21, obtain the forward probability, front left probability, front right probability, left travel probability, and right travel probability in different scenarios considering pedestrian right leaning and conformity under high-density conditions through questionnaire survey.

Preferably: in the step 22, the formulas for calculating the advancing coefficient f, surpassing coefficient s, right-leaning coefficient r and herd influence coefficient are as follows:

In the formula, p _uv represents the probability of pedestrians choosing position v in scene u, u is a, b, c, d, e, f, g, h, i, k or l; v is 1, 2, 3, 4 or 5; cr1 indicates the influence coefficient of right conformity in the two scenarios of e) and f), cl1 indicates the influence coefficient of left conformity in the two scenarios of e) and f), and cr2 indicates the influence coefficient of the two scenarios of g) and h) The influence coefficient of right conformity in , cl2 represents the influence coefficient of left conformity in two scenarios of g) and h), cr3 represents the influence coefficient of right conformity in two scenarios of i) and j), and cl3 represents the influence coefficient of right conformity in i) and j) ) the left-herd influence coefficient in two scenarios, cr4 represents the right-herd influence coefficient in k) and l) two scenarios, cl4 represents the left-herd influence coefficient in k) and l) two scenarios.

Preferably: in the step 22, the formula for calculating the transition probability of the five directions without considering the herd influence is as follows:

The formula for calculating the transition probability considering the five directions of herd influence is as follows:

In the formula, P _i is the transition probability of the pedestrian choosing position i, and p ₀ is the basic transition probability:

In the formula: n is the total number of positions without pedestrians.

Preferably: pedestrians close to the edge of the pedestrian crossing in step 3 have a 20% probability of illegally crossing the street; the range in front of the pedestrian in step 31 is 2.4m*2.4m; 50% probability to exchange positions; in the step 34, the speeds of the pedestrians moving forward, the front left and the front right are small values between the distance from the opposite pedestrian and the maximum speed of 1.2m/s, while the speeds of the left and right is 0.4m/s.

Preferably: in the step 4, the high-density range of pedestrian crossing is determined: different densities are carried out on the designated virtual pedestrian crossing, the opposite pedestrian flow simulation of different directions is divided, and the average crossing time, the average crossing speed and the average crossing delay are analyzed. According to the linear characteristics of the density curve family, the high-density range of the pedestrian crossing L*W is obtained; check whether the pedestrian density corresponding to the pedestrian flow S ₁ and S ₂ on both sides of the pedestrian crossing that needs to be channelized is within the high-density range, and if it is necessary to channelize If the pedestrian flow density of the optimized pedestrian crossing is not within this range, it will be reported that the pedestrian crossing does not need channelization, continue to determine the optimal right-inclined coefficient and herd influence coefficient, and determine the channelization measures for pedestrian crossings; and vice versa.

Preferably: the high density range of 20m*5.6m of the pedestrian crossing is 0.1-0.4.

Preferable: in the said step 4, the optimum right-inclined coefficient and the herd influence coefficient are determined: under different right-inclined coefficients and the herd influence coefficient, the curve family of average crossing time, average street-crossing speed and average street-crossing delay in the crosswalk L*W high-density range is analyzed , to get the optimal right-leaning coefficient and herd influence coefficient when the pedestrian crossing efficiency is the highest.

Preferably: the determination of the channelization measures for pedestrian crossing facilities in the step 4: this part can be divided into two parts, one of which is the acquisition of real test data, which requires the participation of test personnel, and conducts the process of channelizing the crosswalk L*W that needs to be channelized Simulate and record the average crossing time, average crossing speed, the number of pedestrians crossing the street and the proportion of crossing the street; the second is to use the simulation model of high-density opposite pedestrian flow crossing the street under the signal control based on cellular automata to conduct simulation analysis on the first part of the real test; In the case of the same number of pedestrians crossing the street and the same proportion of crossing the street, compared with the average crossing time and average crossing speed recorded in the real test, the channelization measures of high-density opposite pedestrian flow crossing facilities under the control of signals to improve pedestrians' right-leaning and conformity were obtained.

Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects:

The present invention obtains forward coefficient, overtaking coefficient, right-leaning coefficient, herd influence coefficient, and pedestrian walking transition probability through questionnaire survey and related calculation. By establishing a simulation model of high-density opposite pedestrian flow under signal control, basic data are provided for the channelization of pedestrian crossing facilities. Through the analysis of the impact of high-density facing pedestrians walking rightward and conformity under signal control, and the comparative analysis of simulation data and real test data, the channelization measures for high-density facing pedestrian flow under signal control are obtained.

Description of drawings

Figure 1 is a schematic diagram of the channelization method of high-density opposite pedestrian flow crossing street facilities under signal control;

Fig. 2 is a schematic diagram of different survey scenarios of questionnaire survey;

Figure 3 is a schematic diagram of the transition probability of opposite pedestrians crossing the street;

Fig. 4 is a schematic diagram of a virtual pedestrian crossing;

Figure 5 is a simulation effect diagram of high-density opposite pedestrian flow crossing the street under signal control;

Figure 6 is the curve family of average crossing speed, average crossing time, average street crossing delay and different densities under the proportion division of different directions; Figure 6a is the average crossing speed-density curve family under the proportion division of different directions, and Figure 6b is the proportion of different directions The average crossing time-density curve family under the division, Figure 6c is the average crossing delay-density curve family under the division of different directions;

Figure 7 is a cutout photo of different test numbers.

detailed description

Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

A method for channelizing high-density opposite pedestrian flow crossing facilities under signal control, comprising the following steps:

1. Pedestrian crossing data collection and recording:

The road traffic environment information collected through field investigation includes the length L (m) and width W (m) of the crosswalk that needs to be channelized. The pedestrian flow information data obtained through the video collection method include the pedestrian flow S ₁ and S ₂ (person/s) on both sides of the pedestrian crossing that needs to be channelized, and the illegal probability of pedestrian crossing. (The length of the pedestrian crossing that needs to be channelized is L=17m, and the width W=6m).

2. Calculation of pedestrian transition probability:

Obtain the forward probability, front left probability, front right probability, left travel probability, and right travel probability under different scenarios considering the right-deviation and conformity of pedestrians under high-density conditions through questionnaire surveys. and the herd influence coefficient to calculate the transition probabilities of the five directions of pedestrian crossing without considering the herd influence and considering the herd influence. The different scenes of the questionnaire survey are shown in Figure 2. The black triangles and gray triangles represent right- and left-walking pedestrians respectively, the gray squares represent pedestrians themselves, and the black circles represent other pedestrians. ), where scene a) judges the tendency of pedestrians to walk in different directions; scene b) judges the right-inclined and overshooting coefficient of pedestrians; scene c) judges the overshooting coefficient of pedestrians; The next eight scenes (scene e)---scene l)) respectively form a comparison scene, and judge the influence of the number of other pedestrians in the pedestrian's left and right vision and the conformity of pedestrian walking on pedestrian walking. The calculation formulas for advancing coefficient f, surpassing coefficient s, right-leaning coefficient r and herd influence coefficient are as follows:

In the formula: the symbol p _a1 is the probability that the pedestrian chooses position 1 in the scene a), and the symbols p _a2 , p _b2 , p _a3 . . . are defined in a similar way to p _a1 . Symbols cr1 and cl1 are the influence coefficients of right herd and left herd in the two scenarios of e) and f) respectively, and the scoring methods of symbols cr2 and cl2, cr3 and cl3, cr4 and cl4 are similar to cr1 and cl1, corresponding to The scenarios are g) and h), i) and j), k) and l).

The schematic diagram of the transition probability is shown in Fig. 3, P _i is the transition probability of the pedestrian choosing position i, and p ₀ is the basic transition probability.

In the formula: n is the total number of positions without pedestrians

The formula for calculating the transition probability of the five directions without considering the herd influence:

Consider the formula for calculating the transition probability of the five directions of herd influence:

Table 1 and Table 2 are the statistical results of the questionnaire survey (897 valid returns) and the calculation results of the correlation coefficient:

Table 1 Statistical Results of Questionnaire Survey (%)

Table 2 Calculation results of correlation coefficient

3. Prediction of cross-street pedestrian flow data

An introduction to the simulation model of high-density crossing pedestrian flow under signal control based on cellular automata.

3.1 Select a virtual pedestrian crossing that approximately matches the size of the pedestrian crossing that needs to be channelized (see Figure 4), set the length L = 20m, and the width W = 5.6m (matching the pedestrian crossing that needs to be channelized), the purple horizontal line The area between l1 and the black vertical line l2 is the normal pedestrian area, the area between the purple horizontal line l1, the green horizontal line l3 and the black vertical line l2 is the illegal crossing area for pedestrians, the purple horizontal line l1, the blue vertical line l4 and the black vertical line The area between line l2 is the waiting area for pedestrians. Establish a simulation model of high-density crossing pedestrian flow under signal control based on cellular automata.

3.2 In each time step, each pedestrian updates the following steps synchronously. The duration of a time step is 1s. At the same time, pedestrians near the edge of the pedestrian crossing have a 20% probability of illegally crossing the street (walking into the pedestrian crossing area). Figure 5 shows the simulation effect diagram.

1): If there is no opposite pedestrian within the range of 2.4m*2.4m in front of the pedestrian, go to 4), otherwise use the method that does not consider the influence of herd to calculate the transition probability of the five directions, go to 2);

2): If there is a head-on collision between the pedestrian and the opposite pedestrian, the pedestrian cannot move (simulated dead angle), and the two pedestrians exchange positions, otherwise the two pedestrians exchange positions with a 50% probability. Go to step 3);

3): Calculate the transition probabilities of five directions in different scenarios under the influence of herdity by using the method of considering herd influence, and turn to step 4);

4): The speed of pedestrians moving forward, front left and front right is a small value between the distance from the opposite pedestrian and the maximum speed of 1.2m/s, while the speed of left and right is 0.4m/s.

4 Feedback on channelization of street crossing facilities

Mainly by determining the high-density range of pedestrian crossings, identifying the feasibility of channelization of crossing facilities, studying the influence of different degrees of right-leaning and conformity of pedestrians on pedestrian crossing efficiency, and predicting the high-density crossing pedestrian flow under signal control. Comparing the experimental data, a channelization measure for high-density opposite pedestrian flow crossing facilities under signal control is finally obtained.

4.1 The high-density range is determined, and the designated virtual pedestrian crossing is divided into different densities and directions (90/10, 80/20, 70/30, 60/40, 50/50, of which 90/10 is similar to one-way Pedestrian flow, 50/50 is a balanced flow) simulation of opposite pedestrian flow, analysis of the average crossing time, average crossing speed and average crossing delay with different densities of the curve family (see Figure 5) of the linear characteristics, found in the density 0.05-0.4 There is a big change between them (curve slope), the average crossing time, average crossing speed and average crossing delay between 0.05-0.1 are obviously under low-density conditions, while the average crossing time, average crossing speed and average crossing delay above 0.4 The delay in crossing the street is obviously inconsistent with the reality (it is impossible to have such a long time for crossing the street under normal circumstances), and finally the high density range of 20m*5.6m pedestrian crossing is 0.1-0.4. Special Note: The density here refers to the ratio of the total number of pedestrians crossing the street to the number of pedestrians that can be accommodated in the normal pedestrian area, so there is no unit. Check whether the pedestrian density corresponding to the pedestrian flow S1 and S2 (person/s) on both sides of the crosswalk that needs to be channelized is within the high-density range. If the pedestrian flow density of the crosswalk that needs to be channelized is not within this range, feedback this Pedestrian crossings do not need to be channelized, so continue to determine the optimal right-leaning coefficient and herd influence coefficient, and determine the channelization measures for pedestrian crossings; and vice versa. The inspection result here is in the high-density range, so proceed to steps 4.2 and 4.3; otherwise, it means that the secondary pedestrian crossing does not need to be channelized.

4.2 Determination of optimal right-leaning coefficient and herd influence coefficient

By changing the statistical results of the questionnaire survey, the influence of right-deviation and herdity on pedestrian walking can be changed, and different right-difference coefficients and herd influence coefficients can be obtained (as shown in Table 3). 4.8m Curve family of average crossing time, average crossing speed and average crossing delay in different directions within the high-density range of 0.1-0.4. It can be obtained that the pedestrian crossing efficiency is the highest when the right-leaning probability and conformity probability are increased by 20%.

Table 3 Different right-leaning coefficients and herd influence coefficients

4.3 Determination of pedestrian crossing channelization measures

This part can be divided into two parts, one is the acquisition of real test data, which requires the participation of test personnel, simulates the pedestrian crossing L*W that needs to be channelized, and records the average crossing time, average crossing speed, number of pedestrians and The proportion of crossing the street; the second is to use the simulation model of high-density crossing pedestrian flow under the signal control based on cellular automata to simulate and analyze the first part of the real test; in the case of the same number of pedestrian crossings and the same proportion of crossing the street, compare the real test records Down the average street crossing time, average street crossing speed, get the channelization measures of high-density opposite pedestrian flow crossing street facilities under the signal control of improving pedestrians' right inclination and conformity.

To obtain real test data, it should be noted that in the test, in addition to the test participants, normal pedestrians are also considered. The test scenario is as follows:

1) When the green light is on, 40 test personnel start to cross the street from the waiting area on one side of the crosswalk; the test number is 1;

2) When the green light is on, 40 test personnel start to cross the street in the waiting area on each side of the crosswalk; the test number is 2;

3) Under the condition that the experimenters were told to increase the probability of right deviation and conformity by 20%, they carried out experiment 1) and experiment 2). Trial numbers are 3 and 4, respectively.

Each test was carried out 5 times, and a video was taken for each test (some simple cut photos are shown in Figure 6), and relevant street crossing data were recorded.

The comparison of simulation data and real test data is shown in Table 4.

Table 4 Comparison of simulation data and real test numbers

Through the correlation analysis of Figure 6 and Table 4, the channelization measures for high-density opposite pedestrian flow crossing facilities under the signal control of the pedestrian crossing with a length of about 20m and a width of about 5.6m (the crosswalk needs to be channelized within this range) are obtained:

1) Increase the right-leaning probability and conformity probability by 20%

2) Reduce companion behavior and try to walk in places with fewer pedestrians, especially under high-density conditions like one-way pedestrian flow.

A signal-controlled high-density opposite pedestrian flow crossing facility channelization system, including a pedestrian crossing data acquisition and recording unit, a pedestrian crossing transition probability calculation unit, a crossing pedestrian flow data prediction unit, and a street crossing facility channelization feedback unit, wherein:

The pedestrian crossing data acquisition and recording unit is used to collect and record the traffic environment data and pedestrian physical characteristic data when pedestrians cross the street; the road traffic environment information that needs to be recorded mainly includes the length of the pedestrian crossing L (m), the width of the pedestrian crossing W (m), Pedestrian flow S ₁ , S ₂ (person/s), pedestrian speed v(m/s), probability of illegal crossing of pedestrians, etc.

The pedestrian crossing transition probability calculation unit is mainly to provide rational data for the prediction of crossing pedestrian flow; it is used to calculate the normalized forward probability, front left probability, front right probability, left travel probability and right travel probability of each pedestrian when crossing the street. It is the normalized probability that the pedestrian chooses the walking direction. The calculation of pedestrian transition probability can be divided into two parts. One is to obtain the forward probability, front left probability, front right probability, left travel probability, and right travel probability in different scenarios considering the right-handedness and conformity of pedestrians under high-density conditions ( Through questionnaires); the second is to calculate the transition probability of pedestrians crossing the street in five directions without considering the influence of the herd and considering the influence of the herd (Each pedestrian either considers the herd influence or does not consider the herd influence at each time step).

The crossing pedestrian flow data prediction unit mainly provides basic data for the determination of the high-density range of pedestrian crossings and the analysis of channelization measures for crossing facilities; establishes a simulation model of high-density crossing pedestrian flow under signal control based on cellular automata, and in each time step , each pedestrian updates the following four steps synchronously, Note: each time step is 1 second, and pedestrians have a certain probability of crossing the street illegally.

Step 1: If there is no opposite pedestrian within the range of 2.4m*2.4m in front of a pedestrian, use the method that does not consider the influence of the herd to calculate the transition probability of the five directions, and then go to step 4; otherwise, go to step 2,

Step 2: If there is a head-on collision between this pedestrian and the opposite pedestrian, the pedestrian cannot move (simulating a dead angle), and the two pedestrians exchange positions, otherwise the two pedestrians exchange positions with a 50% probability. Otherwise go to step 3;

Step 3: Calculate the transition probabilities of the five directions under the influence of herdity by using the method of considering herd influence, and turn to step 4;

Step 4: The speed of pedestrians moving forward, front left and front right is a small value between the distance from the opposite pedestrian and the maximum speed of 1.2m/s, while the speed of left and right is up to 0.4m/s.

The channelization feedback unit of street crossing facilities is mainly to determine the high-density range of pedestrian crossings, identify the feasibility of channelization of street crossing facilities, and study the influence of different degrees of pedestrian walking to the right and conformity on pedestrian crossing efficiency. Comparing the predicted data of pedestrian crossing with the real test data, a channelization measure of high-density opposite pedestrian crossing facilities under signal control is finally obtained. This part is mainly divided into three parts: the determination of the high-density range of pedestrian crossings, the determination of the optimal right-leaning coefficient and herd influence coefficient, and the determination of pedestrian crossing channelization measures.

Determination of the high-density range of pedestrian crossings: on the virtual pedestrian crossing (L and W are the length and width of the pedestrian crossing respectively), simulate the opposite pedestrian flow with different densities and different directions, and analyze the average crossing time, average crossing speed and average crossing Linear characterization of the family of curves for delays and different densities, resulting in a high-density range of pedestrian crossings L*W. If the pedestrian flow density of the pedestrian crossing that needs to be channelized is not within this range, it will be reported that the pedestrian crossing does not need to be channelized, and continue to determine the optimal right-inclining coefficient and herd influence coefficient, and determine the channelization measures for pedestrian crossings; and vice versa.

Determination of the optimal right-inclined coefficient and herd influence coefficient: analyze the curve family of average crossing time, average crossing speed and average crossing delay in the high-density area of pedestrian crossing L*W under different right-inclined coefficients and herd influence coefficients, and obtain the optimal right-inclined coefficient and herd Influence coefficient.

Determination of pedestrian crossing channelization measures: this part can be divided into two parts, one is the acquisition of real test data, which requires the participation of test personnel, simulates the pedestrian crossing L*W that needs to be channelized, and records the average crossing time, average Crossing speed, number of pedestrians crossing the street and percentage of pedestrians crossing the street. The second is to simulate and analyze the first part of the real test by using the high-density opposite pedestrian flow crossing simulation model based on the signal control of cellular automata; under the same number of pedestrian crossings and the same proportion of crossing the street, compare the real test to record the average crossing Time, average crossing speed, and channelization measures for high-density opposite pedestrian flow crossing facilities under the signal control of improving pedestrians' right inclination and conformity.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made. It should be regarded as the protection scope of the present invention.

Claims (10)

1. A high-density opposite pedestrian flow street-crossing facility channelization method under signal control, is characterized in that, comprises the following steps: Step 1, pedestrian crossing data collection and recording: collect and record road traffic environment information and pedestrian flow information data; road traffic environment information mainly includes the length L of pedestrian crossings that need to be channelized, and the width W of pedestrian crossings; pedestrian flow information data mainly includes Pedestrian flow S ₁ , S ₂ on both sides of the crosswalk that needs to be channelized, pedestrian speed v, pedestrian crossing probability illegally; Step 2, calculation of pedestrian crossing transition probability, including steps 21 and 22: Step 21, obtaining the forward probability, front left probability, front right probability, left travel probability, and right travel probability under different scenarios considering pedestrian right-leaning and conformity under high-density conditions; Step 22: According to the advance probability, front left probability, front right probability, left row probability, and right row probability obtained in step 21, calculate the introduction advance coefficient, surpass coefficient, right-leaning coefficient, and herd influence coefficient. , Right tilt coefficient and herd influence coefficient to calculate the transition probability of pedestrians crossing the street without considering the influence of herd and considering the influence of herd; Step 3, crossing pedestrian flow data prediction: mainly to provide basic data for the determination of the high-density range of pedestrian crossing and the analysis of channelization measures for crossing facilities; select a virtual crosswalk that approximately matches the size of the crosswalk that needs to be channelized, and establish a crosswalk based on cellular automaton The simulation model of high-density opposite pedestrian flow under the signal control of , in each time step, each pedestrian will update the following steps 31 to 34 synchronously. Illegal crossing behavior of pedestrians on the edge; Step 31: If there is no opposite pedestrian within a certain range in front of a pedestrian, go to step 34; otherwise, calculate the transition probabilities in five directions by using a method that does not consider the influence of the herd, and go to step 32; Step 32: If there is a head-on collision between this pedestrian and the opposite pedestrian, the pedestrian cannot move, and the two pedestrians exchange positions, otherwise the two pedestrians exchange positions with a certain probability; otherwise, go to step 33; Step 33: Calculate the transition probabilities of the five directions under the influence of herdity by using the method of considering herd influence, and turn to step 34; Step 34: The speed of the pedestrian moving forward, the front left and the front right is a small value between the distance from the opposite pedestrian and the maximum speed; Step 4: Feedback on channelization of street crossing facilities: mainly by determining the high-density range of pedestrian crossings, identifying the feasibility of channelization of street crossing facilities, and studying the influence of different degrees of pedestrian walking to the right and conformity on pedestrian crossing efficiency. Comparing the predicted data of density crossing pedestrian flow with the real test data, a channelization measure of high density crossing pedestrian flow under signal control is finally obtained. 2. The method for channelizing high-density opposing pedestrian flow across the street under signal control according to claim 1, characterized in that: in the step 1, the road traffic environment information is collected through on-the-spot investigation; the pedestrian flow information data is obtained by the video collection method . 3. The method for channelizing high-density facing pedestrian flow under the control of signals according to claim 1, characterized in that: in the step 21, the difference between the right-leaning and conformity of pedestrians under high-density conditions is considered through questionnaire surveys. Forward probability, front left probability, front right probability, left row probability, and right row probability in the scene. 4. The method for channelizing high-density opposite pedestrian flow crossing facilities under signal control according to claim 1, characterized in that: in the step 22, the calculation formulas of forward coefficient f, overtaking coefficient s, right-inclining coefficient r and herd influence coefficient are as follows Shown: $\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 4</span>}\\ \mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}\\ \mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}\end{array}\right.<span\; class="notranslate">\; ;</span>$ $\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span>\end{array}\right.<span\; class="notranslate">\; ;</span>$ In the formula, p _uv represents the probability of pedestrians choosing position v in scene u, u is a, b, c, d, e, f, g, h, i, k or l; v is 1, 2, 3, 4 or 5; cr1 indicates the influence coefficient of right conformity in the two scenarios of e) and f), cl1 indicates the influence coefficient of left conformity in the two scenarios of e) and f), and cr2 indicates the influence coefficient of the two scenarios of g) and h) The influence coefficient of right conformity in , cl2 represents the influence coefficient of left conformity in two scenarios of g) and h), cr3 represents the influence coefficient of right conformity in two scenarios of i) and j), and cl3 represents the influence coefficient of right conformity in i) and j) ) the left-herd influence coefficient in two scenarios, cr4 represents the right-herd influence coefficient in k) and l) two scenarios, cl4 represents the left-herd influence coefficient in k) and l) two scenarios. 5. The method for channelizing high-density opposing pedestrian flow crossing facilities under signal control according to claim 1, characterized in that: in the step 22, the calculation formula of the transition probability of the five directions without considering the herd influence is as follows: $\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\end{array}\right.$ The formula for calculating the transition probability considering the five directions of herd influence is as follows: $\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ \mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 4</span>}\end{array}\right.<span\; class="notranslate">\; ;</span>$ In the formula, P _i is the transition probability of the pedestrian choosing position i, and p ₀ is the basic transition probability: In the formula: n is the total number of positions without pedestrians. 6. The method for channelizing high-density opposing pedestrian crossing facilities under signal control according to claim 1, characterized in that: in said step 3, pedestrians near the edge of the pedestrian crossing have a 20% probability of crossing the street illegally; said step The range in front of the pedestrian in 31 is 2.4m*2.4m; in the step 32, the two pedestrians exchange positions with a probability of 50%; in the step 34, the speed of the pedestrian forward, front left and front right is and Small value between pedestrian distance and maximum speed 1.2m/s, and 0.4m/s for left and right travel. 7. The method for channelizing high-density opposite pedestrian crossing facilities under signal control according to claim 1, characterized in that: in the step 4, the high-density range of pedestrian crossing is determined: different density is carried out on the designated virtual pedestrian crossing. , opposite direction pedestrian flow simulation divided in different directions, analyze the average crossing time, average crossing speed and average crossing delay and the linear characteristics of curve families with different densities, and obtain the high density range of pedestrian crossing L*W; check the pedestrian crossing that needs to be channelized Whether the pedestrian density corresponding to the pedestrian flow S ₁ and S ₂ on both sides is within the high-density range, if the pedestrian flow density of the pedestrian crossing that needs to be channelized is not within this range, then feedback that the pedestrian crossing does not need to be channelized, and continue to optimize the right leaning The coefficient and the herd influence coefficient are determined, and the pedestrian crossing channelization measures are determined; and vice versa. 8 . The method for channelizing high-density facing pedestrian crossing facilities under signal control according to claim 7 , wherein the high-density range of 20m*5.6m of the pedestrian crossing is 0.1-0.4. 9. The method for channelizing high-density opposing pedestrian flow under the signal control according to claim 1, characterized in that: in the step 4, the optimal right-dipping coefficient and the herd influence coefficient are determined: analyzing different right-dipping coefficients and herd influence coefficients Under the curve family of average crossing time, average crossing speed and average crossing delay in the high-density area of pedestrian crossing L*W, the optimal right-leaning coefficient and herd influence coefficient are obtained when the pedestrian crossing efficiency is the highest. 10. The method for channelizing high-density facing pedestrian crossing facilities under signal control according to claim 1, characterized in that: in the step 4, the channelization measures for pedestrian crossing facilities are determined: this part can be divided into two parts, which One is the acquisition of real test data, which requires the participation of test personnel, and simulates the pedestrian crossing L*W that needs to be channelized, and records the average crossing time, average crossing speed, number of pedestrians crossing the street and the proportion of crossing the street; the other is to use cell-based The simulation model of high-density crossing pedestrian flow under the signal control of the automaton is used to simulate and analyze the first part of the real test; in the case of the same number of pedestrians crossing the street and the same proportion of crossing the street, the average crossing time and average crossing speed are recorded by comparing the real test, and the obtained Channelization measures for high-density opposite pedestrian flow crossing facilities under signal control to improve pedestrian right-leaning and conformity.