CN108256155B - Passenger getting-off point selection method for T-junction passenger car - Google Patents
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Abstract
The invention relates to a passenger point selection method for a passenger car at a T-junction, which comprises the following steps: 1) establishing an Aw-Rascle crowd dynamics model; 2) introducing an influence matrix of a cross junction area on the basis of the Aw-Rascle crowd dynamics model, and constructing a T-junction crowd evacuation dynamics model; 3) constructing a Gaussian distribution model of the population at the T-shaped intersection based on the population evacuation dynamics model at the T-shaped intersection; 4) and changing the Gaussian distribution center position of the Gaussian distribution model in the T-shaped intersection area by setting the step length, and simulating to obtain the passenger point with the lowest trampling risk. Compared with the prior art, the invention has the advantages of quickly obtaining the best passenger point of the large bus at the T-shaped intersection, more comprehensive consideration factors and the like.
Description
Technical Field
The invention relates to a method for selecting a passenger getting-off point of a large bus, in particular to a method for selecting a passenger getting-off point of a bus at a T-shaped intersection.
Background
The T-shaped intersection is a high-incidence section of accidents caused by trampling of people, and the reasons for the accidents are that new people are unreasonably introduced in many cases. A high-risk area of trampling accidents exists at the intersection of the T-shaped intersection, the crowd density of the area is the highest, and the key for reducing the crowd density of the area is to avoid the trampling accidents or reduce the casualties of the trampling accidents. Until now, no relevant technology for accurately calibrating the high risk areas of the population exists, and the high risk areas can only be roughly judged through experience. Passengers on a large bus on a branch road of the T-shaped intersection can be regarded as people who are about to enter the intersection, so that the selection of the passenger getting-off point of the large bus can influence the crowd density of a high-risk area of the T-shaped intersection, and further influence whether a trampling accident or the casualty degree of the trampling accident can occur.
To date, there are several shortcomings to the research on the selection method of the passenger point of the large bus at the t-junction: 1) related technologies for accurately calibrating high risk areas of evacuated people are not available. 2) No relevant method for adjusting the passenger point of a large bus to reduce the number of people in high risk areas is available.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a passenger point selection method for a passenger car at a T-junction.
The purpose of the invention can be realized by the following technical scheme:
a passenger point selection method for a passenger car at a T-junction comprises the following steps:
1) establishing an Aw-Rascle crowd dynamics model;
2) introducing an influence matrix of a cross junction area on the basis of the Aw-Rascle crowd dynamics model, and constructing a T-junction crowd evacuation dynamics model;
3) constructing a Gaussian distribution model of the population at the T-shaped intersection;
4) and changing the Gaussian distribution center position of the Gaussian distribution model in the T-shaped intersection area by setting the step length, and simulating to obtain the passenger point with the lowest trampling risk.
The Aw-Rascle population dynamics model is expressed as:
ρt+(ρv)x+(ρu)y＝0
(v+Ph)t+v(v+Ph)x+u(u+Ph)y＝s1
(v+Pv)t+v(v+Pv)x+u(u+Pv)y＝s2
where ρ represents the population density, v represents the horizontal velocity, u represents the vertical velocity, and PhRepresenting a pressure term in the horizontal direction, PvRepresenting the pressure term, s, in the vertical direction1And s2Indicating the relaxation term factor, subscript t indicating the partial derivative over time, subscript x indicating the partial derivative over distance x, and subscript y indicating the partial derivative over distance y.
The relaxation term factor s1And s2Are respectively:
where τ is the relaxation time, the velocity V (ρ) is the maximum velocity in the horizontal direction, and U (ρ) is the maximum velocity in the vertical direction.
The impact matrix of the intersection region is represented as:
wherein M isimpC is the maximum influence coefficient, i and j are respectively the abscissa and the ordinate of the evacuated individual in the intersection region, and E is a matrix with elements of 1.
The T-junction crowd evacuation dynamics model is expressed as:
ρt+(ρv)x+(ρu)y＝0
ρ(v+Ph(ρ,v,u))t+ρv(v+Ph(ρ,v,u))x
+ρu(u+Ph(ρ,v,u))y＝ρs1
ρ(v+Pv(ρ,v,u))t+ρv(v+Pv(ρ,v,u))x
+ρu(u+Pv(ρ,v,u))y＝ρs2
wherein, Ph(ρ, v, u) and Pv(ρ, v, u) are the horizontal direction pressure term and the vertical direction pressure term, respectively, after considering the influence matrix of the intersection region.
The gaussian distribution model is represented as:
wherein ρ (x, y,0) is the crowd density at coordinate (x, y,0) determined by the T-junction crowd evacuation dynamics model, DmaxThe density value of the largest population, a and b are the horizontal and vertical coordinates of the density distribution center in the vertical and horizontal directions, respectively, and sigma is the relaxation amount.
The step 4) is specifically as follows:
401) setting a set step length d;
402) setting an initial position W of a Gaussian distribution center position0＝0；
403) Initializing the crowd density by using a Gaussian distribution model, and carrying out crowd evacuation simulation based on the T-shaped intersection crowd evacuation dynamics model to obtain a high risk area Pi；
404) Recording the number of people M in high-risk areasiAnd maximum population density ρmax；
405) Setting a new position Wi+1＝Wi+d；
406) Judgment of Wi+1Whether the branch is located, if yes, jumping to step 403), and if not, executing step 407);
407) obtaining a lower-passenger-point position W with a minimum number of high-risk-area people based on maximum crowd density at a plurality of positionsxI.e. the lowest stepping risk passenger point.
In the step 403), a high risk area is obtained through the population evacuation thermodynamic diagram at the T-junction.
In the step 404), the crowd number and the maximum crowd density in the high risk area are obtained through the T-shaped intersection crowd evacuation density contour map.
In the step 404), the maximum crowd density at a plurality of positions is collected, a relation graph between the maximum crowd density and the passenger drop point position of the high risk area is drawn, and the passenger drop point position with the minimum crowd number in the high risk area is obtained.
Compared with the prior art, the invention has the following beneficial effects:
1. can realize the optimized goal of avoiding treading accidents or reducing treading casualties
The passenger getting-off point of the passenger car is continuously adjusted at a certain distance (namely step length), and the passenger getting-off point with the minimum number of people in the high risk area is obtained through an automatic optimization algorithm, so that the optimization target is achieved.
2. Visualization and trend judgment of simulation result
The prior art focuses on theoretical research on crowd evacuation, theoretically analyzes trend change in the crowd evacuation process, and clearly and definitely shows fewer simulation results. According to the invention, by utilizing the proposed T-junction crowd evacuation dynamics model and using MATLAB R2017a to write a program, the critical value of density can be rapidly identified and the positioning of a high-risk area can be completed through a crowd density contour map and a thermal distribution map. Meanwhile, the passenger leaving point of the large bus is continuously adjusted by a certain step length, a relation graph between the maximum crowd density and the passenger leaving point position of a high risk area is obtained, and the trend that the maximum crowd density changes along with the passenger leaving point can be judged, so that the optimal passenger leaving point is found. Therefore, the visualization and trend judgment of the simulation result are another advantage of the invention.
3. Accurately simulating motion state of pedestrian
The invention adopts Gaussian distribution, and can better simulate the motion state of the pedestrian through the maximum density value of the pedestrian and the centers of the density distribution in the vertical and horizontal directions.
4. Comprehensive consideration of
The T-shaped intersection crowd evacuation dynamics model constructed by the invention has anisotropy, can reflect the dynamics characteristics of all directions in the crowd evacuation process, and has more comprehensive consideration factors.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic view of a drop-off optimization process of the present invention;
FIG. 3 is a schematic diagram showing the location of the trample event in McJohn's wort during 2015 in the example;
FIG. 4 is a thermodynamic diagram of population evacuation at a T-junction;
FIG. 5 is a contour plot of crowd evacuation density at a T-junction;
FIG. 6 is a graph of high risk area crowd density versus drop-off location.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 1, the invention realizes a passenger point selection method for a passenger car at a t-junction, comprising the following steps: 1) establishing an Aw-Rascle crowd dynamics model; 2) introducing an influence matrix of a cross junction area on the basis of the Aw-Rascle crowd dynamics model, and constructing a T-junction crowd evacuation dynamics model; 3) constructing a Gaussian distribution model of the population at the T-shaped intersection based on the population evacuation dynamics model at the T-shaped intersection; 4) and changing the Gaussian distribution center position of the Gaussian distribution model in the T-shaped intersection area by setting the step length, and simulating to obtain the passenger point with the lowest trampling risk. According to the method, the passenger getting-off point of the passenger car is continuously changed through simulation of the established T-shaped intersection crowd evacuation dynamics model, the minimum crowd number of the high-risk area under the step length can be quickly obtained, the optimal passenger getting-off point is determined, and therefore the optimization target is achieved.
1. Aw-Rascle population dynamics model
A.Aw and M.rascle provide a two-dimensional space crowd evacuation model (hereinafter referred to as Aw-Rascle model) based on a one-dimensional flow model. The classical conservation of mass equation is determined by the partial differential equation of conservation of mass:
ρt+(ρv)x＝0 (1)
where ρ and v represent population density and velocity, ρtIs the partial derivative of time, (ρ v)xIs the partial derivative of the distance x.
To reflect the anisotropy of the evacuated population with the anisotropy of the fluid motion, a "pressure" term is defined. Assuming ρ and v are independent, the fluid mechanics navier-stokes equation is introduced and the pressure term is changed to:
wherein C is0Representing the driver's expected coefficient of response to density. The relationship of ρ and v is reflected with a partial differential equation:
(v+P(ρ))t+v(v+P(ρ))x＝0 (2)
where P (ρ) represents the "pressure" term in the model, v represents the horizontal velocity, and u represents the vertical velocity, the model is applied to a two-dimensional space. The one-dimensional Aw-Racle population dynamics model is a model formed by two nonlinear hyperbolic Partial Differential Equations (PDE) in formula (1) and formula (2).
Converting a one-dimensional mass conservation partial differential equation into a two-dimensional partial differential equation shown as a formula (3), and converting a one-dimensional Aw-Rascle crowd dynamics model into a two-dimensional Aw-Rascle crowd dynamics model (consisting of the formulas (3), (4) and (5)):
ρt+(ρv)x+(ρu)y＝0 (3)
(v+Ph)t+v(v+Ph)x+u(u+Ph)y＝s1 (4)
(v+Pv)t+v(v+Pv)x+u(u+Pv)y＝s2 (5)
where ρ represents the population density, v represents the horizontal velocity, u represents the vertical velocity, and PhRepresenting a pressure term in the horizontal direction, PvRepresenting the pressure term, s, in the vertical direction1And s2To representThe relaxation term factor, subscript t denotes the partial derivative over time, subscript x denotes the partial derivative over distance x, and subscript y denotes the partial derivative over distance y.
Relaxation term factor s1And s2The pedestrian adjusts the actual speed to the expected speed V (rho) and U (rho) according to the current density of the stream of people, which are respectively expressed as:
where τ is the relaxation time spent close to the desired speed, the speed V (ρ) is the maximum speed in the horizontal direction, and U (ρ) is the maximum speed in the vertical direction, i.e., the desired speed.
In the Aw-Rascle model, PhIs a function of P and v and can be expressed as Ph(p，v)，PvIs a function of rho and u and can be expressed as Pv(ρ, v). The initial conditions are that rho (x, y,0) is more than or equal to 0, and v (x, 0) is more than or equal to | vf1| v is less than or equal to | and u (y, 0) < | vf2||，vf1And vf2Is the maximum speed at which individuals are evacuated in both directions. The function is given by equation (8) and equation (9):
wherein, both beta and gamma are constants.
2. T-shaped intersection crowd evacuation dynamics model
On the basis of the Aw-Rascle crowd evacuation model, the T-junction crowd evacuation dynamics model increases the influence of bidirectional superposition of vectors in the T-junction area in the vertical direction u and the horizontal direction v of the vectors, so that the crowd at the intersection position is enabled to beThe density distribution is more reasonable. Influence matrix M for introducing intersection regionimpAnd the problem of bidirectional superposition is solved:
wherein M isimpC is the maximum influence coefficient, i and j are respectively the abscissa and the ordinate of the evacuated individual in the intersection region, and E is a matrix with elements of 1.
In the central intersection, the moving directions of the main road and the branch road crowd are horizontally and vertically overlapped, the main road is taken as a main reference direction by the model, and the central position of the intersection is determined by modifying a pressure item P. Horizontal pressure term P taking into account the influence matrix of the intersection regionh(ρ, v, u) and the pressure term P in the vertical directionv(ρ, v, u) are respectively expressed as:
wherein, tau1、τ2Are different parameter impact matrices.
Equations (13) and (14) are obtained by substituting equations (11) and (12) into equations (4) and (5) and multiplying ρ. Constructing a crowd evacuation dynamics model (consisting of formulas (3), (13) and (14)) of the T-junction area:
ρt+(ρv)x+(ρu)y＝0 (3)
3. gaussian distribution model of T-junction crowd
The traditional model of crowd evacuation dynamics uses a gaussian distribution given by a two-dimensional space, as shown in equation (15):
where ρ (x, y,0) is the population density at coordinate (x, y,0), DmaxThe a and b are the horizontal and vertical coordinates of the density distribution center in the vertical and horizontal directions, respectively, for the maximum population density value. Crowd density values refer to the number of rows per unit area.
In the T-junction crowd evacuation dynamics model of the invention, the improved Gaussian distribution is used, as shown in formula (16). And adjusting the Gaussian function into a higher-order smoothing function, introducing an aspect ratio 1/a & ltb & gt at the intersection point as a factor of Gaussian distribution, and taking the value of sigma as a relaxation quantity so as to obtain an overall distribution state closer to the actual population.
4. Optimized passenger car passenger point
The passenger getting-off position of the large bus is defined as the central area position of Gaussian distribution, and the influence of the change of the central area position of Gaussian distribution on the trampling condition at the intersection of the trunk road and the branch road is simulated. The passenger getting-off point of the passenger car is continuously changed through a certain distance (namely step length), so that the position of the Gaussian distribution center area is adjusted, and a series of key parameters such as the position of a high-risk area and the crowd density of the high-risk area can be obtained through simulation and visualization output. Through comparison, the getting-off point with the minimum number of people in the high-density area, namely the getting-off point with the lowest trampling risk is found out, so that the optimization goal of minimizing trampling accident casualties or avoiding trampling accidents is achieved.
As shown in fig. 2, the optimization process for optimizing passenger point acquisition in a passenger car specifically includes:
401) setting a set step length d;
402) setting an initial position W of a Gaussian distribution center position0＝0；
403) Initializing the crowd density by using a Gaussian distribution model, and carrying out crowd evacuation simulation based on the T-shaped intersection crowd evacuation dynamics model to obtain a high risk area PiThe high risk area refers to the crowd density larger than the set maximum crowd density value DmaxThe area of (a);
404) recording the crowd number M of the high-risk area based on the crowd evacuation density contour map at the T-shaped intersectioniAnd maximum population density ρmax；
405) Setting a new position Wi+1＝Wi+d；
406) Judgment of Wi+1Whether the branch is located, if yes, jumping to step 403), and if not, executing step 407);
407) acquiring the maximum crowd density at a plurality of positions and drawing a relation graph between the maximum crowd density and the passenger drop point position of the high risk area, thereby obtaining the passenger drop point position W with the minimum crowd number in the high risk areaxI.e. the lowest stepping risk passenger point.
The T-junction crowd evacuation dynamics model was used to study the trampling event in 2015, and the position of the trampling event was shown in FIG. 3 by reproducing the trampling event at the T-junction between 204 street and 223 street (204-223 junction) through simulation. At 06 hours (09 minutes at 09 Greenwich mean time) in 2015, the stepping event occurred at the T-shaped intersection at 204 and 223 streets. The initial population is distributed primarily on the 204 streets and moves forward in sequence to presenting a pilgrim. The bus on the 223 street downloads a batch of passengers, when the part of people enters the intersection of the 223 and 204 streets, the people in two directions collide with each other, the people fall down due to collision and trample, however, the people behind the bus cannot know the jam condition in the front and continuously move forward, and large-area trampling events among the pilgrims are caused.
For the purpose of realizing the simulation example, in the present embodiment, the maximum density value D is setmaxIs 7.0p/m2(people/per square meter), horizontal and vertical pedestriansSpeeds vf1 and vf2, it was assumed according to the relevant study that vf 1-vf 2-1.36 ms-1。
2000 evacuated persons were set on the street as initial conditions according to the actual parameters of the treading events at the intersection of 204 street and 223 street. Based on the mathematical model provided by the invention, an MATLAB program is compiled, and a visual simulation result, namely a population evacuation thermodynamic diagram at the T-junction, is calculated and obtained, as shown in FIG. 4. The chart shows the movement direction of people, and accurately positions the high-density area of people, namely the high-risk area of treading accidents. Meanwhile, a contour map of crowd evacuation density at the T-junction can also be obtained through simulation, as shown in fig. 5. Through the density contour map, the maximum crowd density rho of the high risk area corresponding to each passenger point can be foundmax. By collecting rho of a plurality of passenger pointsmaxThe maximum population density rho of the high risk area can be drawnmaxAnd the position of the next guest point, as shown in FIG. 6. By analyzing FIG. 6, the best point of presence on street 223 can be found to achieve the optimization goal.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (7)
1. A passenger point selection method for a passenger car at a T-junction is characterized by comprising the following steps:
1) establishing an Aw-Rascle crowd dynamics model, wherein the Aw-Rascle crowd dynamics model is expressed as follows:
ρt+(ρv)x+(ρu)y＝0
(v+Ph)t+v(v+Ph)x+u(u+Ph)y＝s1
(v+Pv)t+v(v+Pv)x+u(u+Pv)y＝s2
where ρ represents the population density, v represents the horizontal velocity, u represents the vertical velocity, and PhRepresenting a pressure term in the horizontal direction, PvRepresenting the pressure term, s, in the vertical direction1And s2Denotes the relaxation term factor, subscript t denotes the partial derivative over time, subscript x denotes the partial derivative over distance x, subscript y denotes the partial derivative over distance y;
2) on the basis of the Aw-Rascle crowd dynamics model, introducing an influence matrix of a cross junction area to construct a T-junction crowd evacuation dynamics model, wherein the influence matrix of the cross junction area is expressed as:
wherein M isimpThe matrix is an influence matrix, C is a maximum influence coefficient, i and j are respectively an abscissa and an ordinate of the evacuation individual in the intersection region, and E is a matrix with elements of 1;
the T-junction crowd evacuation dynamics model is expressed as:
ρt+(ρv)x+(ρu)y＝0
ρ(v+Ph(ρ,v,u))t+ρv(v+Ph(ρ,v,u))x+ρu(u+Ph(ρ,v,u))y＝ρs1
ρ(v+Pv(ρ,v,u))t+ρv(v+Pv(ρ,v,u))x+ρu(u+Pv(ρ,v,u))y＝ρs2
wherein, Ph(ρ, v, u) and Pv(ρ, v, u) are the horizontal and vertical pressure terms, respectively, after considering the influence matrix of the intersection region;
3) constructing a Gaussian distribution model of the population at the T-shaped intersection;
4) changing the Gaussian distribution center position of the Gaussian distribution model in the T-shaped intersection area by setting the step length, and obtaining a passenger point with the lowest stepping risk based on the simulation of the T-shaped intersection crowd evacuation dynamics model.
2. The method of claim 1, wherein the slack term factor s is a number of factors1And s2Are respectively:
where τ is the relaxation time, the velocity V (ρ) is the maximum velocity in the horizontal direction, and U (ρ) is the maximum velocity in the vertical direction.
3. The method of claim 1, wherein the gaussian distribution model is expressed as:
wherein ρ (x, y,0) is the crowd density at coordinate (x, y,0) determined by the T-junction crowd evacuation dynamics model, DmaxThe density value of the largest population, a and b are the horizontal and vertical coordinates of the density distribution center in the vertical and horizontal directions, respectively, and sigma is the relaxation amount.
4. The method for selecting a passenger point at a T-junction according to claim 1, wherein the step 4) is specifically:
401) setting a set step length d;
402) setting an initial position W of a Gaussian distribution center position0＝0；
403) Initializing crowd density by a Gaussian distribution model, and evacuating the crowd at the T-shaped intersection based on the crowd densityCarrying out crowd evacuation simulation by using the mechanical model to obtain a high risk area Pi；
404) Recording the number of people M in high-risk areasiAnd maximum population density ρmax；
405) Setting a new position Wi+1＝Wi+d；
406) Judgment of Wi+1Whether the branch is located, if yes, jumping to step 403), and if not, executing step 407);
407) obtaining a lower-passenger-point position W with a minimum number of high-risk-area people based on maximum crowd density at a plurality of positionsxI.e. the lowest stepping risk passenger point.
5. The method for selecting passenger getting-off points at a T-junction according to claim 4, wherein in step 403), the high risk area is obtained through a thermodynamic diagram of population evacuation at the T-junction.
6. The method for selecting a passenger getting-off point at a T-junction according to claim 4, wherein in the step 404), the number of people in the high risk area and the maximum crowd density are obtained through a T-junction crowd evacuation density contour map.
7. The method for selecting a passenger drop-off point at a T-junction according to claim 4, wherein in the step 404), the maximum crowd density at a plurality of positions is collected, a relation graph between the maximum crowd density and the passenger drop-off point position in the high risk area is drawn, and the passenger drop-off point position with the minimum crowd number in the high risk area is obtained.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101714185A (en) * | 2009-12-22 | 2010-05-26 | 中国建筑科学研究院建筑防火研究所 | Automatic simulation method for building disaster evacuation |
| WO2016094040A1 (en) * | 2014-12-09 | 2016-06-16 | Intel Corporation | Coordinated crowd dispersal navigation |
| CN106096072A (en) * | 2016-05-17 | 2016-11-09 | 北京交通大学 | Dense crowd emulation mode based on intelligent body |
| CN106205042A (en) * | 2016-08-26 | 2016-12-07 | 王起全 | Subway crowded trampling accident early warning linkage system and early warning linkage method |
| CN106250677A (en) * | 2016-07-21 | 2016-12-21 | 同济大学 | Under hazardous condition based on kinesiology bead model, crowd panic propagates modeling method |
| CN106485327A (en) * | 2016-09-08 | 2017-03-08 | 同济大学 | Under a kind of hazardous condition, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution |
| CN107464021A (en) * | 2017-08-07 | 2017-12-12 | 山东师范大学 | A kind of crowd evacuation emulation method based on intensified learning, device |
-
2017
- 2017-12-20 CN CN201711384954.7A patent/CN108256155B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101714185A (en) * | 2009-12-22 | 2010-05-26 | 中国建筑科学研究院建筑防火研究所 | Automatic simulation method for building disaster evacuation |
| WO2016094040A1 (en) * | 2014-12-09 | 2016-06-16 | Intel Corporation | Coordinated crowd dispersal navigation |
| CN106096072A (en) * | 2016-05-17 | 2016-11-09 | 北京交通大学 | Dense crowd emulation mode based on intelligent body |
| CN106250677A (en) * | 2016-07-21 | 2016-12-21 | 同济大学 | Under hazardous condition based on kinesiology bead model, crowd panic propagates modeling method |
| CN106205042A (en) * | 2016-08-26 | 2016-12-07 | 王起全 | Subway crowded trampling accident early warning linkage system and early warning linkage method |
| CN106485327A (en) * | 2016-09-08 | 2017-03-08 | 同济大学 | Under a kind of hazardous condition, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution |
| CN107464021A (en) * | 2017-08-07 | 2017-12-12 | 山东师范大学 | A kind of crowd evacuation emulation method based on intensified learning, device |
Non-Patent Citations (4)
| Title |
|---|
| Linda Rothman，et al.Dangerous student car drop-off behaviours andchild pedestrian-motor vehicle collisions: an observational study.《Traffic Injury Prevention》.2016,正文第1-24页. * |
| ZHANG Liguo，et al.Calibration of the Aw一Rascle Traffic Flow Model via.《Proceedings of the 35th Chinese Control Conference》.2016,正文第9234-9239页. * |
| 何理.地铁车站大客流疏运风险形成机理及行为特征研究.《中国博士学位论文全文数据库 工程科技Ⅱ辑》.2016,C033-16. * |
| 秦伟 等.大型活动拥挤踩踏事故人群疏散研究分析.《三峡大学学报(人文社会科学版)》.2017,第30卷正文第2073-2079页. * |
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