LU501032B1 - Method of prediction for passenger evacuation in subway carriage of urban rail transit - Google Patents
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Abstract
The present invention providing a method of prediction for passenger evacuation in subway carriage of urban rail transit, comprising the steps of: 1) constructing a target door selection model based on fuzzy logic theory when passengers evacuate out of a subway carriage, and calculating a probability value of passengers selecting each door; 2) calculating a probability value of passengers' movement in real time based on a meta-cellular automata model, combined with passengers' own psychological behavior characteristics, and establishing a movement model of passengers evacuating out of the subway carriage; and 3) carrying out a simulation experiment by using the passenger target door selection model and movement model, simulating the evacuation of passengers in the subway carriage, and predicting impacts of a location of an accident point, an effective width of the carriage doors and a way of seat arrangement in the subway carriage on the evacuation of passengers when an accident occurs. The present invention effectively predicts the evacuation behavior of passengers in the subway carriage in case of unexpected accidents, and provides a certain auxiliary decision support for the emergency and rapid evacuation of passengers in the carriage.
Description
-1- LU501032
TECHNICAL FIELD The present invention belongs to the field of pedestrian safety evacuation technology, and relates to a pedestrian prediction and evacuation, especially a method of prediction for passenger evacuation in subway carriage of urban rail transit.
BACKGROUND At present, China's economy is developing rapidly, the population is increasing, and the demand for public transportation is getting bigger and bigger. In the new urbanization speed, the traditional ground transportation mode can no longer meet the needs of people, and urban rail transit, as an important supplement to the traditional ground transportation system, has become an essential demand in the national urbanization process. Urban rail transit has become the backbone of urban public transportation with its high capacity, high speed, safe and pollution-free technical characteristics, and its development is of great significance to improve the public transportation sharing rate and alleviate urban traffic congestion, therefore, metro has also become a popular transportation mode. According to statistics, as of May 1, 2020, a total of 47 cities in China have opened urban rail transit, including 41 in Mainland China and 6 in Hong Kong, Macao and Taiwan of China. By the end of 2019, the total length of opened urban rail transit lines in mainland China is 6,730.27 kilometers.
Along with the continuous expansion of the scale of metro lines, the rapid increase in metro passenger traffic has also brought considerable pressure on metro operations. The total passenger volume in mainland China in 2019 was 23.814 billion, up 11.8% from 21.3 billion in 2018, and according to statistics, the single-day passenger volume of Qingdao Metro Line 3 was 427,800 on May 2, 2019, which set the highest daily passenger volume record since the operation of Qingdao Metro. Usually, subway vehicles travel in underground tunnels, and once an unexpected accident occurs, it is necessary to find a nearby subway station for emergency stop and then evacuate passengers, which is a complicated situation. Especially during the peak commuting period or
-2- holidays, the carriages are crowded, and if passengers are not guided to evacuate in time, the 010% bottleneck effect will easily occur at the doors of the subway carriages during the evacuation process, prolonging the evacuation time and making it more difficult to evacuate people. How to improve the evacuation efficiency of passengers in subway stations to reduce casualties and property damage is an urgent problem to be solved.
However, it is difficult to simulate the evacuation of passengers in subway carriages in case of an accident. The prediction of passenger evacuation will enable staff to monitor passenger flow and guide passengers to evacuate in time in case of unexpected accidents.
SUMMARY In view of the above-mentioned problems in the existing technology, the purpose of the present invention is to propose a method of prediction for passenger evacuation in subway carriage of urban rail transit for predicting the selection and movement process of evacuation doors for passengers, which can provide certain auxiliary decision support for emergency and rapid evacuation of passengers in the carriages.
The purpose of the present invention can be achieved by the following technical solution: a method of prediction for passenger evacuation in subway carriage of urban rail transit, comprising the following steps: S1) constructing a target door selection model based on fuzzy logic theory when passengers evacuate out of a subway carriage, and calculating a probability value of passengers selecting each door.
S2) calculating a probability value of passengers’ movement in real time based on a meta-cellular automata model, combined with passengers’ own psychological behavior characteristics, and establishing a movement model of passengers evacuating out of the subway carriage; and S3) carrying out a simulation experiment by using the passenger target door selection model and movement model, simulating the evacuation of passengers in the subway carriage, and predicting impacts of a location of an accident point, an effective width of the carriage doors and a way of seat arrangement in the subway carriage on the evacuation of passengers when an accident occurs.
In the aforementioned method of prediction for passenger evacuation in subway carriage of
-3- . . . . . LU501032 urban rail transit, the step S1) includes the following specific steps: a) fuzzification: fuzzifying the probability p of the target carriage door selection affectin ) ying the p yp g g g passenger evacuation as an input quantity of fuzzy logic and establishing fuzzy set is M Lt specifically establishing three fuzzy sets: {S,.M,.L,}, {S,.M, La} $M LL wherein, S,, M . L respectively denote the probability p of selecting a certain door as low, medium and high; d, denotes a distance between the door and the passenger; S. + M,. L, respectively denote the distance between the door and the passenger as short, medium d a> Fa TESP y p g and long; d, denotes a distance between the door and the accident point, S. ~ M, L, respectively denote the distance between the door accident point as short, medium d, a, Fa, TESP y pP and long; p denotes a density of the passenger around the door, S,, M, L respectively denote the density of the passenger around the door as small, medium and large.
b) setting the affiliation function of fuzzy input variables and fuzzy output variables respectively: selecting a Gaussian-type affiliation function, and the affiliation functions of the three fuzzy quantities affecting the target door selection when passengers evacuate and the fuzzy output y q g 8 p g y outp quantity of passengers selecting the target door are: » Ach S38 0<d <10 er? | 0<d,<8 L420, ld 162 wd)=1 e? 0", 10<d <20 ud, )=) e* 8 , 8<d,<16 4-20, (827162 e 2 | 20<d <6326 eV 162d, <6\17 Loy +&” e23 0<p<3 e 7, 0<p<03 1,2762 Ab» u(p)=< e ? 3, 3<p<6 H(p)= ec 7 > 03<p<l Le, 127052 e2 67. 6<p<10 e2 015° 015<p<05 wherein, the ranges of values of d, and d, are determined according to a actual physical size of the B-type subway train, and the range of value p is determined according to the average value of daily practice.
„4 - c) fuzzy inference: setting fuzzy rules for target door selection of passengers during evacuation 092 according to the average value of practice, and inferring the probability of target door selection of passengers according to the fuzzy rules.
d) defuzzification: converting the fuzzy value of the passenger's target door selection probability obtained by said inference into an explicit output value, and the conversion is done by means of centroid defuzzification as follows: De f, xuCxdx [xy wherein, X is the fuzzy set of output variable Pand # (x) is the affiliation function of P- In the aforementioned method of prediction for passenger evacuation in subway carriage of urban rail transit, the step S2) includes the following specific steps.
a) introducing the quantities affecting the psychology and behavior of passengers when they move into the meta-cellular automata model and establishing a static field model and a dynamic field model of the passenger's next moveable position.
b) the static field model of the passenger's moveable position is: S, == py + (j=) —Ji=m,)} +(j =n.) (k e[1.24]) wherein, (7, J) denotes each cell coordinates of passenger moveable position, (p,q) and (m,n) denotes the position coordinates of the accident point and the position coordinates of each subway carriage door, respectively, to build a simulation environment to start a simulation test with B-type subway vehicle as an example, therefore, Æ denotes the number of carriage doors of B-type subway train, there are 24 doors, k [1,24]; the dynamic field model of the movable position of passengers is: D, = MG. jm 2 MG. jm wherein, M(i, j,n) denotes the cumulative number of passengers passing through the passenger neighbor cell, n denotes the next moveable cell position of passengers, and the evacuation of passengers is simulated by the Moore-type cellular automaton model, and the next moveable cell position of passengers has 8 in total, son =8;
-5- Le . . LU501032 c) based on establishing the static field model and dynamic field model of the passenger's moveable position, the probability of the passenger's next move is calculated as: P, = N exp(K,S,)exp(K,D,)0-n,)a, N = {Eexp(K,S,)exp(K,D,)0—n,)a,}" wherein, S,and D, respectively denotes the static field value and dynamic field value of each cell, K,and K, respectively are the scale factors of static field and dynamic field, 7, denotes whether the cell is occupied by an obstacle, when the cell is occupied by an obstacle, n, =0, otherwise, 7, =1,«, denotes whether the cell is occupied by a passenger, when the cell is occupied by a passenger, œ, =0, otherwise, œ, =1.
d) real-time update using the movement probability of passengers to calculate the movement position of passengers at each step.
In the aforementioned method of prediction for passenger evacuation in subway carriage of urban rail transit, the step a) of step S2), the quantities affecting the psychological and behavioral aspects of the passenger when moving include the distance of the passenger to the accident point, the distance of the passenger to the door of the carriage.
In the above method of prediction for passenger evacuation in subway carriage of urban rail transit, in the step c) of said step S2), the said obstacle in the simulation experiment mainly refers to fire source or seat.
In the aforementioned method of prediction for passenger evacuation in subway carriage of urban rail transit, the step S3) specifically comprises: combining the target door selection model for passenger evacuation based on the fuzzy logic theory and the evacuation movement model for passengers based on the meta-cellular automata model, using the simulation experiment to further predict the evacuation of passengers in case of an unexpected accident in the subway carriage.
Compared with the prior art, the present prediction method for evacuation of passengers in subway cars of urban rail transit has the following beneficial effects: Taking into account the distance from the door to the passenger, the distance from the door to
-6- the accident point, the density of passengers around the door, the distance from the passenger to the 092 accident point, as well as passengers' tendency to avoid harm and herd mentality, this solution establishes a prediction method for the evacuation of passengers inside the subway car of urban rail transit based on an improved metric automaton model and fuzzy logic theory, which fully combines the small computational effort of the metric automaton model and the fuzzy It can predict the evacuation door selection and movement process of passengers in the subway carriage in case of unexpected accidents, effectively predict the evacuation behavior of passengers in the subway carriage in case of unexpected accidents, and provide some auxiliary decision support for the emergency and rapid evacuation of passengers in the carriage, with strong innovation and practicality.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart of the present invention; FIG. 2 is a schematic diagram of a Moore-type metric automaton model of the present invention; FIG. 3 is a schematic diagram of a simulation environment in a subway carriage of the present invention; FIG. 4 is a schematic diagram of the relationship between the passenger evacuation time and the location of the accident point of the present invention; FIG. 5 is a schematic diagram of the relationship between the passenger evacuation time and the effective utilization width of the doors of the subway carriage of the present invention; FIG. 6 is a schematic diagram of the relationship between the evacuation time of passengers of the present invention and the seat arrangement square trial in the subway car of the present invention.
DETAILED DESCRIPTION Embodiments of the present invention will be further described with reference to the accompanying drawings and specific embodiments. As shown in FIGs.1 and 2, the present method of prediction for passenger evacuation in subway carriage of urban rail transit, comprising the following steps. S1) constructing a target door selection model based on fuzzy logic theory when passengers evacuate out of the subway carriage, and calculating the probability value of passengers selecting
-7- LU501032 each door.
S2) calculating a probability value of passengers movement in real time based on a meta-cellular automata model, combined with passengers’ own psychological behavior characteristics, and establishing a movement model of passengers evacuating out of the subway carriage; and S3) carrying out a simulation experiment by using the passenger target door selection model and movement model, simulating the evacuation of passengers in the subway carriage, and predicting impacts of a location of an accident point, an effective width of the carriage doors and a way of seat arrangement in the subway carriage on the evacuation of passengers when an accident occurs.
Step S1) includes the following specific steps: a) fuzzification: fuzzifying the probability p of the target carriage door selection affecting passenger evacuation as an input quantity of fuzzy logic and establishing fuzzy set is , M »L,}; specifically establishing three fuzzy sets: {S, Ma La}, Sa Ma La, }, 1, M, L,}- wherein, S,, M, L,respectively denote the probability p of selecting a certain door as low, medium and high; dj denotes a distance between the door and the passenger; Sa M,~ La respectively denote the distance between the door and the passenger as short, medium and long; d, denotes a distance between the door and the accident point; Sa My.» La, respectively denote the distance between the door accident point as short, medium and long; p denotes a density of the passenger around the door, S,, M, L respectively denote the density of the passenger around the door as small, medium and large.
b) setting the affiliation function of fuzzy input variables and fuzzy output variables respectively: selecting a Gaussian-type affiliation function, and the affiliation functions of the three fuzzy quantities affecting the target door selection when passengers evacuate and the fuzzy output quantity of passengers selecting the target door are:
-8- La LU501032
1.d fay e200 0<d <10 e?8 | 0<d,<8 L420, ld 162 wd)=1 e? 0", 10<d <20 ud, )=) e* 8 , 8<d,<16 4-20, (827162 e Ts, 20<d <6426 ee? 16<d, <6J17
1.22 LP? e23 0<p<3 e 203, 0<p<03 1,2762 Lely wp)={e? 3 , 3<p<6 up)=4 ee?” , 03<p<l Le, 127052 e2 67. 6<p<10 e2 015° 015<p<05 wherein, the ranges of values of d, and d, are determined according to a actual physical size of the B-type subway train, and the range of value p is determined according to the average value of daily practice.
c) fuzzy inference: setting fuzzy rules for target door selection of passengers during evacuation according to the average value of practice, and inferring the probability of target door selection of passengers according to the fuzzy rules.
d) defuzzification: converting the fuzzy value of the passenger's target door selection probability obtained by said inference into an explicit output value, and the conversion is done by means of centroid defuzzification as follows: |. xu(x)dx P=
ZO wherein, X is the fuzzy set of output variable Pand 4 (x) is the affiliation function of 2 - Step S2) includes the following specific steps: a) introducing the quantities affecting the psychology and behavior of passengers when they move into the meta-cellular automata model and establishing a static field model and a dynamic field model of the passenger's next moveable position.
b) the static field model of the passenger's moveable position is: Sy ==) +(=q)" =i =m)" +(-n)",(k €[1.24]) wherein, (J) (p,q) and (m, > nm)
-9- . . . . . . . . LU501032 subway carriage door, respectively, to build a simulation environment to start a simulation test wıth B-type subway vehicle as an example, therefore, Æ denotes the number of carriage doors of B-type subway train, there are 24 doors, k [1,24]; the dynamic field model of the movable position of passengers is: D, = M1) D> Mi. j,n) n=l wherein, M(i, j,n) denotes the cumulative number of passengers passing through the passenger neighbor cell, n denotes the next moveable cell position of passengers, and the evacuation of passengers is simulated by the Moore-type cellular automaton model, and the next moveable cell position of passengers has 8 in total, son =8; c) based on establishing the static field model and dynamic field model of the passenger's moveable position, the probability of the passenger's next move is calculated as: P, = N exp(K,S,)exp(K,D,)0-n,)a, N = {Eexp(K,S,)exp(K,D,)0—n,)a,}" wherein, S,and D, respectively denotes the static field value and dynamic field value of each cell, K,and K, respectively are the scale factors of static field and dynamic field, 7, denotes whether the cell is occupied by an obstacle, when the cell is occupied by an obstacle, n, =0, otherwise, 7, =1,«, denotes whether the cell is occupied by a passenger, when the cell is occupied by a passenger, œ, =0, otherwise, œ, =1. d) real-time update using the movement probability of passengers to calculate the movement position of passengers at each step.
In step a) of step S2), the quantities affecting the psychological and behavioral aspects of the passenger when moving include the distance of the passenger to the accident point, the distance of the passenger to the door of the carriage.
In step c) of step S2), the obstacle in the simulation experiment mainly refers to the fire source or seat. The step S3) specifically includes:
-10- . . . LU501032 combining the target door selection model for passenger evacuation based on the fuzzy logic theory and the evacuation movement model for passengers based on the meta-cellular automata model, using the simulation experiment to further predict the evacuation of passengers in case of an unexpected accident in the subway carriage.
As shown in FIG. 3, the simulation carriage diagram of the evacuation of passengers from the subway car 1s identified, and the corresponding simulation environment of the subway carriage 1s built by MATLAB software based on the actual physical dimensions of three B-type subway train carriages. To ensure the accuracy of the invention, each influencing factor was repeated 20 times in the simulation experiments. As shown in FIGs. 4 to 6, the relationship between the evacuation time of passengers and the location of the accident point, the effective width of the carriage door and the seating arrangement in the carriage 1s shown.
The following is a further explanation of the invention by setting up a specific simulation scenario. The simulation experiment is conducted for the impact of the accident point location, the effective width of the carriage doors and the way of seat arrangement on the evacuation of passengers in the event of an accident in the subway car. The following simulation scenarios were set up and the corresponding simulation results were obtained. Table 1 Simulation scenarios and results Scenario Scenario setting Total number | Evacuation number of passengers time(s The fire source is set on the left side of the 1 i 100 people 12.31s carriage The fire source is set on the middle of the 2 i 100 people 9.20s carriage The fire source is set on the right side of the 3 . 100 people 12.50s carriage Effective use of the door width of Im 100 people 10.18s The effective width of the door is 1.5m 100 people 6 Seat arrangement is single row longitudinal 100 people Seating arrangement for single row of horizontal 100 people Seat layout is a combination of single-row 2 100 people 10.54s longitudinal and double-row lateral Seating arrangement for double row of 100 people 12.23s transverse Wherein, the scenario 2 is significantly more efficient than the scenarios 1 and 3 in evacuating
-11- passengers; the scenario 4 is more conducive to evacuating passengers than the scenario 5 when the 092 effective width of the carriage doors is increased; the scenarios 6, 7, 8 and 9 simulate the effects of four different seating arrangements in the carriage including single row longitudinal, single row transverse, single row longitudinal combined with double row transverse and double row transverse on the evacuation time of passengers. The seat arrangement under the four scenarios is not only different in the number of seats, but also different in the effective utilization area of evacuation routes. According to the simulation results of this solution, it can be obtained that scenario 6, i.e. single-row longitudinal seat arrangement, is more conducive to improving evacuation efficiency.
In summary, the invention discloses a method of prediction for passenger evacuation in subway carriage of urban rail transit based on the meta-cellular automata model, and its innovation mainly lies in the incorporation of fuzzy logic theory. This model can be used to predict the door selection and movement of passengers in the subway carriage in case of emergency, and then guide the rapid evacuation of passengers in emergency situations. It provides certain auxiliary decision support for guiding the emergency and rapid evacuation of passengers in the carriage and reducing casualties and in-process losses.
The foregoing descriptions are merely preferred embodiments of the present invention, but not intended to limit the present invention. A person skilled in the art may make various alterations and variations to the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (6)
1. A method of prediction for passenger evacuation in subway carriage of urban rail transit, comprising the steps of: S1) constructing a target door selection model based on fuzzy logic theory when passengers evacuate out of a subway carriage, and calculating a probability value of passenger selecting each door; S2) calculating a probability value of passenger movement in real-time based on a meta-cellular automata model, combined with own psychological behavior characteristics of passengers, and establishing a movement model of passengers evacuating out of the subway carriage; and S3) carrying out a simulation experiment by using the target door selection model of passenger and the movement model, simulating a situation of passenger evacuation in the subway carriage, and predicting impacts of a location of an accident point, an effective width of the carriage doors and a way of seat arrangement in the subway carriage on the evacuation of passengers when an accident occurs.
2. The method according to claim 1, wherein the step S1) comprising the specific steps of: a) fuzzification: fuzzifying the probability p of the target carriage door selection affecting passenger evacuation as an input quantity of fuzzy logic and establishing fuzzy set is , M »L,}; specifically establishing three fuzzy sets: {S, Ma La}, Sa Ma La, }, SM LA}; wherein, S,, M, L, respectively denote the probability p of selecting a certain door as low, medium and high; d, denotes a distance between the door and the passenger; Sa M,~ La respectively denote the distance between the door and the passenger as short, medium and long; d, denotes a distance between the door and the accident point; Sa My.» La, respectively denote the distance between the door accident point as short, medium and long; p denotes a density of the passenger around the door, S,, M, L respectively denote the density of the passenger around the door as small, medium and large; b) setting affiliation functions of fuzzy input variables and fuzzy output variables respectively:
-13- . . Lo . Lo . LU501032 selecting a Gaussian-type affiliation function, and the affiliation functions of the three fuzzy quantities affecting the target door selection when passengers evacuate and the fuzzy output quantity of passengers selecting the target door are: da y oT 0<d <10 e?3 > 0<d, <8 _L(4720 2 A262 uld)={ ee? 09, 10<d<20 > —(d,)=, e? * , 8<d,<16 > (6720, Ledley, e 2% 20<d <6326 es | 16<d,<6J17 1 po LP eu 0<p<3 e 203 , 0<p<03 _ 26y Lely #(p)=<e* 3 | 3<p<6 > Hp) = e 2093 03<p<1l > Leno Lp 03y e? “ , 6<p<10 e ? 015, 0.15<p<05 wherein, the ranges of values of d, and d, are determined according to an actual physical size of the B-type subway train, and the range of value p is determined according to the average value of daily practice; c) fuzzy inference: setting fuzzy rules for target door selection of passengers during evacuation according to the average value of practice, and inferring the probability of target door selection of passengers according to the fuzzy rules; and d) defuzzification: converting the fuzzy value of the probability of the target door selection of passengers obtained by the inference into an explicit output value, and the conversion being done by means of centroid defuzzification as follows: | xu (x)dx p= J no wherein, X 1s the fuzzy set of output variable Pand # (x) is the affiliation function of 2 .
3. The method according to claim 1, wherein the step S2) comprising the specific steps of: a) introducing the quantities affecting the psychology and behavior of passengers when they move into the meta-cellular automata model and establishing a static field model and a dynamic field model of the passenger's next moveable position; b) a static field model of the passenger's moveable position is:
-14 - LU501032 Sy =NG=p) +(j=q) —JG-m}*+G= n°, € [1.24 , wherein, (i,j) denotes each cell coordinates of passenger moveable position, (p,q) and (m,n) denotes the position coordinates of the accident point and the position coordinates of each subway carriage door, respectively, to build a simulation environment to start a simulation test with B-type subway vehicle as an example, therefore, Æ denotes the number of carriage doors of B-type subway train, there are 24 doors, k [1,24]; a dynamic field model of the movable position of passengers is: D, = MG. jm DM, j.n) n=l > wherein, M(i, j,n) denotes the cumulative number of passengers passing through the passenger neighbor cell, n denotes the next moveable cell position of passengers, and the evacuation of passengers is simulated by the Moore-type cellular automaton model, and the next moveable cell position of passengers has 8 in total, son =8; c) based on establishing the static field model and dynamic field model of the passenger's moveable position, the probability of the passenger's next move is calculated as: P, = N exp(K,S,)exp(K,D,)0-n,)a, N = {Xexp(K,S,)exp(K,D,)(=n,),}", wherein, S,and D, respectively denotes the static field value and dynamic field value of each cell, K,and K, respectively are the scale factors of static field and dynamic field, 7, denotes whether the cell is occupied by an obstacle, when the cell is occupied by an obstacle, n, =0, otherwise, n, =1;a, denotes whether the cell is occupied by a passenger, when the cell is occupied by a passenger, «, =0, otherwise, œ, =1; and d) real-time update using the movement probability of passengers to calculate the movement position of passengers at each step.
4. The method according to claim 3, wherein in the step a) of the step S2), a quantity affecting the psychological and behavioral aspects of the passenger when moving, comprising: a distance of
-15- the passenger to the accident point, and a distance of the passenger to the door of the carriage. 17501058
5. The method according to claim 3, wherein in the step c) of the step S2), the obstacle in the simulation experiment mainly refers to the fire source or seat.
6. The method according to claim 3, wherein the step S3) specifically comprising: combining the target door selection model for passenger evacuation based on the fuzzy logic theory and the evacuation movement model for passengers based on the meta-cellular automata model, using the simulation experiment to further predict the evacuation of passengers in case of an unexpected accident in the subway carriage.
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