CN113762644A - Congestion state prediction method and device based on Markov chain - Google Patents
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Abstract
The invention discloses a congestion state prediction method and a congestion state prediction device based on a Markov chain, which relate to the technical field of communication and are used for predicting the probability of the congestion state of passengers in each carriage of a subway and providing reference information for riding of a user, and the method comprises the following steps: obtaining the carriage congestion states of N trains corresponding to the target station within the historical time, and determining a congestion state transition probability matrix P according to the carriage congestion states of the N trains; the congestion state of the carriage comprises at least two states, wherein N is a positive integer, and P is a positive number; and calculating the probability M that the carriage congestion state of each train corresponding to the target station in the future is a target state according to the congestion state transition probability matrix P, wherein the target state is any one of at least two states, and M is greater than or equal to 0 and less than or equal to 1. The embodiment of the invention is applied to the scene of predicting the passenger crowding state in the subway carriage.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a congestion state prediction method and device based on a Markov chain.
Background
With the continuous development of modern transportation, users have become more and more common to travel through public transportation, wherein, the long-distance public transportation mainly includes: trains, high-speed rails, airplanes and the like, and the short-distance public transportation mainly comprises the following components: buses, subways, taxis, and the like. The subway is the most special public transport and is most concerned about traveling in cities.
However, in the current state, when a passenger goes out and takes a subway, because the subway has no fixed seat, and the number of carriages of the subway is large, when the passenger takes a bus, the door selected by the passenger to take a bus has blindness and randomness, the passenger in a certain carriage is full, and the passengers in other carriages are less, so that the difference of the crowded states in different doors on the subway is large, thereby the waste of public resources to a certain extent is caused, and the riding experience of part of the passengers is poor.
Disclosure of Invention
The embodiment of the invention provides a congestion state prediction method and device based on a Markov chain, which are used for predicting the probability of the congestion state of passengers in each carriage of a subway and providing reference information for a user to take a bus.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
in a first aspect, a method for predicting a congestion state based on a markov chain is provided, and the method includes: obtaining the carriage congestion states of N trains corresponding to the target station within the historical time, and determining a congestion state transition probability matrix P according to the carriage congestion states of the N trains; the congestion state of the carriage comprises at least two states, wherein N is a positive integer, and P is a positive number; and calculating the probability M that the carriage congestion state of each train corresponding to the target station in the future is a target state according to the congestion state transition probability matrix P, wherein the target state is any one of at least two states, and M is greater than or equal to 0 and less than or equal to 1.
In a possible implementation manner, acquiring the carriage congestion states of N trains corresponding to the target station within the historical time includes: obtaining the seat number and the passenger number in the carriage of each train in N trains corresponding to the target station in the historical time, and determining the seat proportion corresponding to the carriage according to the seat number and the passenger number; and determining the carriage congestion state of each train according to the value range met by the seat proportion, and corresponding to different carriage congestion states under the condition that the seat proportion meets different value ranges.
In one possible implementation, determining the congestion state transition probability matrix P according to the carriage congestion states of N trains includes: determining the congestion state transition probability of transitioning from a first target state to a second target state according to the carriage congestion states of N-1 groups of trains corresponding to N trains, wherein the first target state and the second target state are any one of at least two states; any one group of trains in the N-1 groups of trains is two adjacent trains in the N trains; a congestion state transition probability matrix P is determined based on the determined plurality of congestion state transition probabilities.
In a possible implementation manner, calculating a probability M that the congestion status of each train corresponding to the target station in the future is the target status according to the congestion status transition probability matrix P includes: calculating the probability M that the carriage congestion state of the first train corresponding to the target station in the future is in the target state by a first algorithm according to each congestion state transition probability included in the congestion state transition probability matrix P; wherein the first algorithm is as follows:
In a possible implementation manner, calculating a probability M that the congestion status of each train corresponding to the target station in the future is the target status according to the congestion status transition probability matrix P includes: calculating the probability M that the carriage congestion state of the first train corresponding to the target station is in the target state in the future time through a second algorithm according to the congestion state transition probability matrix P and the initial congestion state probability vector M (0); wherein the second algorithm is: m (t) -M (t-1) P-M (t-2) P2＝M(0)Pt，M(0)＝[M1(0)，M2(0)，M3(0)，...，Ma(0)]And a is a positive integer, and M (0) is a congestion state probability vector of a carriage of the last train corresponding to the target station in the historical time.
In a second aspect, there is provided a markov chain-based congestion state prediction apparatus comprising: the device comprises an acquisition unit, a determination unit and a calculation unit; the acquisition unit is used for acquiring the carriage congestion states of N trains corresponding to the target station within historical time; the determining unit is used for determining a congestion state transition probability matrix P according to the carriage congestion states of the N trains; the congestion state of the carriage comprises at least two states, wherein N is a positive integer, and P is a positive number; and the calculating unit is used for calculating the probability M that the carriage congestion state of each train corresponding to the target station in the future is a target state according to the congestion state transition probability matrix P, wherein the target state is any one of at least two states, and M is greater than or equal to 0 and less than or equal to 1.
In a third aspect, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computer, cause the computer to perform a method of markov chain based congestion status prediction as in the first aspect.
In a fourth aspect, an electronic device includes: a processor and a memory; wherein the memory is configured to store one or more programs, the one or more programs comprising computer executable instructions that, when executed by the processor, cause the electronic device to perform a markov chain based congestion status prediction method according to the first aspect.
The embodiment of the invention provides a congestion state prediction method and a congestion state prediction device based on a Markov chain, which are applied to a scene of predicting the congestion state of passengers in subway carriages, wherein the carriage congestion states of N trains corresponding to a target station in historical time are obtained, so that a congestion state transition probability matrix P of the target station can be determined according to the carriage congestion states of N trains passed by the target station in historical time, and further, the probability M that the congestion state of each carriage of each train to be reached by the target station in future time is a target state is calculated based on the congestion state transition probability matrix P of the target station, so that the possibility that the congestion state of each carriage of each train to be reached by the target station in future time is each of at least two congestion states is determined. According to the scheme, the passenger crowding state probability in each carriage of the subway can be predicted, and reference information is provided for the user to take the subway.
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Fig. 1 is a schematic structural diagram of a congestion status prediction system according to an embodiment of the present invention;
fig. 2 is a first schematic flowchart of a congestion status prediction method based on a markov chain according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a congestion status prediction method based on a markov chain according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of a congestion status prediction method based on a markov chain according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a congestion status prediction apparatus based on a markov chain according to an embodiment of the present invention;
fig. 6 is a first schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.
In the description of the present invention, "/" means "or" unless otherwise specified, for example, a/B may mean a or B. "and/or" herein is merely an association describing an associated object, and means that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. Further, "at least one" or "a plurality" means two or more. The terms "first", "second", and the like do not necessarily limit the number and execution order, and the terms "first", "second", and the like do not necessarily limit the difference.
The method for predicting the congestion state based on the Markov chain can be applied to a system for predicting the congestion state. Fig. 1 is a schematic diagram showing a configuration of the congestion status prediction system. As shown in fig. 1, the congestion status prediction system 10 includes a data collection device 11, a data analysis device 12, and a data calculation device 13. The data acquisition device 11 is connected with the data analysis device 12, and the data analysis device 12 is connected with the data calculation device 13. The data acquisition device 11, the data analysis device 12, and the data calculation device 13 may be connected in a wired manner or in a wireless manner, which is not limited in the embodiment of the present invention.
The congestion status prediction system 10 may be used in the internet of things, and the congestion status prediction system 10 may include hardware such as a plurality of Central Processing Units (CPUs), a plurality of memories, and a storage device storing a plurality of operating systems.
Data acquisition equipment 11 can be used for the thing networking, sets up in the environment that needs the data collection, for example, data acquisition equipment 11 can be for people flow sensor or object recognition sensor etc. can set up in the scene that needs monitoring people flow, for example the station is imported and exported, train door or carriage in, etc to data transmission to data analysis equipment 12 that will gather.
The data analysis device 12 may also be used in the internet of things, and is configured to receive the real-time data sent by the data acquisition device 11, and may store the received data for future use, and the further data analysis device 12 may further perform specific analysis on the received data to perform processing such as labeling or data classification.
The data calculation device 13 may also be used in the internet of things, and is configured to calculate data analyzed and processed by the data analysis device 12, so as to calculate predicted data in a future time for reference by a user.
It should be noted that the data acquisition device 11, the data analysis device 12, and the data calculation device 13 may be independent devices or may be integrated in the same device, and the present invention is not limited in this respect.
When the data acquisition device 11, the data analysis device 12 and the data calculation device 13 are integrated in the same device, the communication mode among the data acquisition device 11, the data analysis device 12 and the data calculation device 13 is the communication among the internal modules of the device. In this case, the communication flow between the two is the same as "the communication flow between the data acquisition device 11, the data analysis device 12, and the data calculation device 13 in the case where they are independent of each other".
In the following embodiments provided by the present invention, the present invention is described by taking an example in which the data acquisition device 11, the data analysis device 12, and the data calculation device 13 are provided independently of each other.
A method for predicting a congestion state based on a markov chain according to an embodiment of the present invention is described below with reference to the accompanying drawings.
As shown in fig. 2, the method for predicting a congestion state based on a markov chain according to an embodiment of the present invention is applied to a device for predicting a congestion state based on a markov chain, which includes a plurality of memories and a plurality of CPUs, and includes steps S201 to S202:
s201, obtaining the carriage congestion states of N trains corresponding to the target station in the historical time, and determining a congestion state transition probability matrix P according to the carriage congestion states of the N trains.
The traffic congestion state comprises at least two states, wherein N is a positive integer, and P is a positive number.
As a possible implementation manner, when the congestion status of the cars of the trains passing through the target station within the next time needs to be predicted, the congestion status of the cars of a plurality of trains passing through the target station before the current time can be acquired, and the corresponding congestion status transition probability matrix P can be constructed according to the acquired congestion status of the cars of the plurality of trains.
It should be noted that the train may be a subway, and the congestion state of the train may be understood as a congestion state in any one car on the subway, and because the doors selected by passengers to take the train have blindness and randomness, the congestion state difference in different cars on the subway is large.
As one possible implementation manner, the congestion state transition probability matrix P includes a plurality of congestion state transition probabilities, and each congestion state transition probability indicates a congestion state change situation from a previous train to a next train in a car corresponding to two adjacent trains.
It should be noted that the two carriages corresponding to the two adjacent trains can be understood as two carriages formed by two corresponding trains when different trains corresponding to the same door of the platform stop.
As one possible implementation, the congestion status of the car may include at least two statuses: an extremely comfortable state (first state), a comfortable state (second state), a general state (third state), a crowded state (fourth state), and an extremely crowded state (fifth state).
S202, calculating the probability M that the carriage congestion state of each train corresponding to the target station is in the target state in the future according to the congestion state transition probability matrix P.
Wherein the target state is any one of at least two states, and M is greater than or equal to 0 and less than or equal to 1.
As one possible implementation, the congestion state transition probability matrix P is used to indicate a probability that, among a plurality of trains passing through the destination station in the past, the car congestion state of two adjacent trains is changed from a certain state to another congestion state.
Illustratively, the probability of the carriage congestion state of two adjacent trains being converted from the extreme comfort state to the extreme comfort state, the probability of the carriage congestion state being converted from the extreme comfort state to the general state, the probability of the carriage congestion state being converted from the extreme comfort state to the congestion state, the probability of the carriage congestion state being converted from the extreme comfort state to the extreme congestion state, and the like can be indicated by the congestion state transition probability matrix P.
As a possible implementation, in the congestion state transition probability matrix P, by PijIndicating the probability of transitioning from the ith state to the jth state.
In the embodiment of the invention, the congestion state in the carriages corresponding to all doors in the target station is determined, a congestion state transition probability matrix P is obtained through the congestion state transition probability in the carriages corresponding to multiple subways passing through the target station before the current time, so that the congestion state probability of the train passing through the target station in the t-th time after the current time is deduced according to the congestion state transition probability matrix P, an initial state probability vector is determined according to the congestion state of the carriage at the time when t is 0 (namely the congestion state of the train in the last train before the current time), and the congestion state probability prediction result of the train in the t-th time after the current time is finally calculated.
In one design, in order to obtain the car congestion states of N trains corresponding to the destination station within the historical time, as shown in fig. 3, the "obtaining the car congestion states of N trains corresponding to the destination station within the historical time" in S201 according to the embodiment of the present invention may specifically include the following S301 to S302.
S301, seat numbers and passenger numbers in carriages of each train in N trains corresponding to the target station in the historical time are obtained, and the people-seat ratio corresponding to the carriages is determined according to the seat numbers and the passenger numbers.
As a possible implementation mode, the seat number and the passenger number in each carriage of a plurality of trains passing through the target station before the current time are obtained, and the corresponding seat ratio is obtained by calculating the ratio of the passenger number and the seat number in each carriage.
And S302, determining the carriage congestion state of each train according to the value range met by the people-seat ratio.
Under the condition that the seat proportion meets different value ranges, the device corresponds to different carriage crowding states.
As a possible implementation manner, the congestion state in each car can be determined according to the corresponding relationship between the proportion of seats in each car and a plurality of preset value ranges.
For example, when the number of seats in the vehicle compartment is denoted as S and the number of passengers in the vehicle compartment is denoted as T, the proportion of seats in the vehicle compartment can be denoted as: r is T/S; and a plurality of value ranges can be preset: specifically, when R ∈ (0, 1/2), the congestion state in the vehicle compartment is "extremely comfortable state (first state)", when R ∈ (1/2, 1) ", the congestion state in the vehicle compartment is" comfortable state (second state) ", when R ∈ (1, 3/2), the congestion state in the vehicle compartment is" general state (third state) ", when R ∈ (3/2, 5/2), the congestion state in the vehicle compartment is" congestion state (fourth state) ", when R ∈ (5/2, + ∞), the congestion state in the vehicle interior is "extremely congested state (fifth state)", and S and T are positive integers.
In the embodiment of the invention, a method for predicting the congestion state based on a Markov chain is provided, and specifically, the method can predict the probability of the congestion state in all the numbers of subway cars of a train after the current time of a target station, and firstly, the congestion state in a carriage is determined according to the proportion of seats in the carriage; obtaining a congestion state transition probability matrix P according to the carriage congestion state transition probability of a plurality of trains passing through the target station before the current moment; and then further deducing a carriage congestion state probability formula of the train which is about to pass through the target station after the current time, and determining and calculating a prediction result of the carriage congestion state probability of the train which is about to reach the target station according to the congestion state of the last train which passes through the target station before the current time.
In one design, in order to determine the congestion state transition probability matrix P according to the congestion states of the cars of N trains, as shown in fig. 4, "determining the congestion state transition probability matrix P according to the congestion states of the cars of N trains" in S201 according to the embodiment of the present invention may specifically include the following steps S401 to S402.
S401, according to the carriage congestion states of the N-1 groups of trains corresponding to the N trains, determining the congestion state transition probability of the train from the first target state to the second target state.
The first target state and the second target state are both any one of at least two states; any one train in the N-1 trains is two adjacent trains in the N trains.
S402, determining a congestion state transition probability matrix P based on the plurality of determined congestion state transition probabilities.
As one possible implementation, based on N trains that pass the target site before the current time, N-1 groups of trains can be determined, thereby determining N-1 congestion state transition probabilities.
It should be noted that the above-mentioned extreme comfort state (first state) can be represented by E1, the comfort state (second state) can be represented by E2, the general state (third state) can be represented by E3, the congestion state (fourth state) can be represented by E4, and the extreme congestion state (fifth state) can be represented by E5.
Illustratively, a process of transitioning from one of the above five states to another state is referred to as a congestion state transition process, and a state transition probability of transitioning from the i-th state to the j-th state is PijI.e. P (E)i-Ej)＝P(Ej/Ei)＝PijTherefore, the congestion state transition probability matrix P is obtained as:
wherein, the conditions need to be satisfied:
specifically, if the congestion status at the target station of 40 trains passing through the target station before the current time is as follows:
| Number of |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Status of state | E1 | E1 | E2 | E1 | E2 | E2 | E3 | E2 | E3 | E3 |
| Number of shifts | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| Status of state | E4 | E4 | E3 | E3 | E4 | E5 | E5 | E4 | E5 | E4 |
| Number of shifts | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Status of state | E2 | E3 | E2 | E3 | E3 | E4 | E3 | E2 | E2 | E1 |
| Number of shifts | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 |
| Status of state | E1 | E2 | E2 | E3 | E2 | E4 | E3 | E4 | E3 | E4 |
As can be seen from the table, among the 5 transitions from the E1 state to other states included in the above table, 2 transitions from the E1 state to the E1 state, and 3 transitions from the E1 state to the E2 state, and therefore,
among the 11 transitions from the E2 state to other states included in the above table, 2 are transitions from the E2 state to the E1 state, 3 are transitions from the E2 state to the E2 state, 5 are transitions from the E2 state to the E3 state, and 1 is transition from the E2 state to the E4 state, and therefore,
among the 12 transitions from the E3 state to other states included in the above table, 4 are transitions from the E3 state to the E2 state, 3 are transitions from the E3 state to the E3 state, and 5 are transitions from the E3 state to the E4 state, and therefore,
among the 4 transitions from the E4 state to other states included in the above table, 1 is a transition from the E4 state to the E2 state, 4 is a transition from the E4 state to the E3 state, 1 is a transition from the E4 state to the E4 state, and 2 is a transition from the E4 state to the E5 state, and therefore,
among the 3 transitions from the E5 state to other states included in the above table, 2 are transitions from the E5 state to the E4 state, and 1 is transition from the E5 state to the E5 state, and thus,
therefore, the congestion state transition probability matrix P can be obtained as follows:
in the embodiment of the invention, the congestion state transition probability of transition from any one of at least two states to another state can be determined according to the carriage congestion states of N-1 groups of trains corresponding to N trains passing through the target station before the current time, so that a congestion state transition probability matrix P is determined according to a plurality of congestion state transition probabilities and is used for calculating the probability M that the carriage congestion state of the t-th train corresponding to the target station is the target state in the future time.
In one design, in order to calculate the probability M that the congestion status of the train of each time corresponding to the destination station is the destination status in the future time according to the congestion status transition probability matrix P, S202 provided in the embodiment of the present invention may specifically include the following S501.
S501, according to each congestion state transition probability included in the congestion state transition probability matrix P, calculating the probability M that the congestion state of the train in the first time corresponding to the target station is in the target state in the future through a first algorithm.
Wherein the first algorithm is as follows:
i and j are used for indicating the ith state or the jth state in the at least two states, a is used for indicating the number of the states included in the at least two states, and a, i and j are positive integers.
As one possible implementation manner, in order to predict the probability of the congestion state of the passing train in the process of the target station developing over time, the probability of the congestion state of the train in the first time after the current time is denoted as Mj(t)。
Therefore, after t congestion state transitions have elapsed from the time when t is 0 (i.e., the last train that passed the destination stop before the current time), that is, when t trains reach the destination stop, there is a time when t trains arrive at the destination stop
The congestion state of the train reaching the target station after t-1 times of congestion state transition can be regarded as EjAfter a congestion status transition, it is Ej+1Then there is
In one design, in order to calculate the probability M that the congestion status of the train of each time corresponding to the destination station is the destination status in the future time according to the congestion status transition probability matrix P, S202 provided in the embodiment of the present invention may specifically include the following S601.
S601, calculating the probability M that the carriage congestion state of the first train corresponding to the target station in the future is in the target state by a second algorithm according to the congestion state transition probability matrix P and the initial congestion state probability vector M (0).
Wherein the second algorithm is: m (t) -M (t-1) P-M (t-2) P2＝M(0)Pt，M(0)＝[M1(0)，M2(0)，M3(0)，...，Ma(0)]And a is a positive integer, and M (0) is a congestion state probability vector of a carriage of the last train corresponding to the target station in the historical time.
As a possible implementation manner, it may be determined that the initial state probability vector is M (0) ═ M by the congestion state corresponding to the last train passing through the target station before the current time1(0),M2(0),M3(0),M4(0),M5(0)]Then, the probability m (t) of each congestion state in at least two states of the tth train passing the target station after the current time can be obtained.
Thus, it is possible to obtain:
for example, according to the specific data in the above table one, in the case that the congestion status of the train in the last train before the current time is E4, it may be determined that the initial state probability vector is M (0) ═ 0,0,0,1,0, and the state probability matrices P and M (0) are substituted into formula nine, so that the probability that the congestion status corresponding to the train passing through the destination station after the current time is in each of at least two states may be calculated.
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The embodiment of the invention provides a congestion state prediction method and a congestion state prediction device based on a Markov chain, which are applied to a scene of predicting the congestion state of passengers in subway carriages, wherein the carriage congestion states of N trains corresponding to a target station in historical time are obtained, so that a congestion state transition probability matrix P of the target station can be determined according to the carriage congestion states of N trains passed by the target station in historical time, and further, the probability M that the congestion state of each carriage of each train to be reached by the target station in future time is a target state is calculated based on the congestion state transition probability matrix P of the target station, so that the possibility that the congestion state of each carriage of each train to be reached by the target station in future time is each of at least two congestion states is determined. According to the scheme, the passenger crowding state probability in each carriage of the subway can be predicted, and reference information is provided for the user to take the subway.
The scheme provided by the embodiment of the invention is mainly introduced from the perspective of a method. To implement the above functions, it includes hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiment of the present invention, a congestion status prediction apparatus based on a markov chain may be divided into function modules according to the above method, for example, each function module may be divided according to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. Optionally, the division of the modules in the embodiment of the present invention is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 5 is a schematic structural diagram of a congestion status prediction apparatus based on a markov chain according to an embodiment of the present invention. As shown in fig. 5, a congestion status prediction apparatus 50 based on a markov chain is used for predicting a probability of a congestion status of a passenger in each car of a subway, and providing reference information for a user to take a car, for example, for performing a congestion status prediction method based on a markov chain as shown in fig. 2. The Markov chain-based congestion state prediction device 50 includes: an acquisition unit 501, a determination unit 502, and a calculation unit 503.
An obtaining unit 501, configured to obtain a carriage congestion state of N trains corresponding to a target station within a historical time.
A determining unit 502, configured to determine a congestion state transition probability matrix P according to the carriage congestion states of the N trains; the traffic congestion state includes at least two states, N is a positive integer, and P is a positive number.
A calculating unit 503, configured to calculate, according to the congestion state transition probability matrix P, a probability M that the congestion state of the train of each time corresponding to the target station in the future is a target state, where the target state is any one of at least two states, and M is greater than or equal to 0 and less than or equal to 1.
Optionally, as shown in fig. 5, the obtaining unit 501 provided in the embodiment of the present invention is specifically configured to obtain the number of seats and the number of passengers in each train of N trains corresponding to the target station in the historical time.
The determining unit 502 is further configured to determine a seat proportion corresponding to the compartment according to the seat number and the number of passengers.
The determining unit 502 is further configured to determine the carriage congestion state of each train according to the value range that the seat-to-seat ratio satisfies, and correspond to different carriage congestion states under the condition that the seat-to-seat ratio satisfies different value ranges.
Optionally, as shown in fig. 5, the determining unit 502 provided in the embodiment of the present invention is specifically configured to determine, according to the congestion states of the cars of N-1 groups of trains corresponding to N trains, a congestion state transition probability of transitioning from the first target state to the second target state, where the first target state and the second target state are both any one of at least two states; any one train in the N-1 trains is two adjacent trains in the N trains.
The determining unit 502 is further configured to determine a congestion state transition probability matrix P according to the determined multiple congestion state transition probabilities.
Optionally, as shown in fig. 5, the calculating unit 503 is specifically configured to calculate, according to each congestion state transition probability included in the congestion state transition probability matrix P, a probability M that the congestion state of the train in the first time t corresponding to the target station is a target state by using a first algorithm; wherein the first algorithm is as follows:
i and j are used for indicating the ith state or the jth state in the at least two states, a is used for indicating the number of the states included in the at least two states, and a, i and j are positive integers.
Optionally, as shown in fig. 5, the calculating unit 503 is specifically configured to calculate, by using a second algorithm, a probability M that the congestion state of the train in the first time corresponding to the target station is the target state in the future according to the congestion state transition probability matrix P and the initial congestion state probability vector M (0); wherein the second algorithm is: m (t) -M (t-1) P-M (t-2) P2＝M(0)Pt,M(0)＝[M1(0)，M2(0)，M3(0)，…，Ma(0)]a is a positive integer, and M (0) is a congestion state probability vector of a carriage of the last train corresponding to the target station in the historical time.
In the case of implementing the functions of the integrated modules in the form of hardware, the embodiment of the present invention provides another possible structural schematic diagram of the electronic device related to the above embodiment. As shown in fig. 6, an electronic device 60 is used for predicting the probability of the congestion state of passengers in each car of the subway and providing reference information for the passengers to take a train, for example, for executing a method for predicting the congestion state based on the markov chain shown in fig. 2. The electronic device 60 includes a processor 601, a memory 602, and a bus 603. The processor 601 and the memory 602 may be connected by a bus 603.
The processor 601 is a control center of the communication apparatus, and may be a single processor or a collective term for a plurality of processing elements. For example, the processor 601 may be a Central Processing Unit (CPU), other general-purpose processors, or the like. Wherein a general purpose processor may be a microprocessor or any conventional processor or the like.
For one embodiment, processor 601 may include one or more CPUs, such as CPU 0 and CPU 1 shown in FIG. 6.
The memory 602 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
As a possible implementation, the memory 602 may be present separately from the processor 601, and the memory 602 may be connected to the processor 601 via a bus 603 for storing instructions or program code. The processor 601 can implement a method for predicting a congestion state based on a markov chain according to an embodiment of the present invention when it calls and executes instructions or program codes stored in the memory 602.
In another possible implementation, the memory 602 may also be integrated with the processor 601.
The bus 603 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
It is to be noted that the structure shown in fig. 6 does not constitute a limitation of the electronic apparatus 60. In addition to the components shown in fig. 6, the electronic device 60 may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
As an example, in conjunction with fig. 5, the functions implemented by the acquisition unit 501, the determination unit 502, and the calculation unit 503 in the electronic device are the same as those of the processor 601 in fig. 6.
Optionally, as shown in fig. 6, the electronic device 60 provided in the embodiment of the present invention may further include a communication interface 604.
A communication interface 604 for connecting with other devices via a communication network. The communication network may be an ethernet network, a radio access network, a Wireless Local Area Network (WLAN), etc. The communication interface 604 may include a receiving unit for receiving data and a transmitting unit for transmitting data.
In one design, in the electronic device provided by the embodiment of the present invention, the communication interface may be further integrated in the processor.
Fig. 7 shows another hardware configuration of the electronic apparatus in the embodiment of the present invention. As shown in fig. 7, the electronic device 80 may include a processor 801, a communication interface 802, a memory 803, and a bus 804. The processor 801 is coupled to a communication interface 802 and a memory 803.
The functions of the processor 801 may refer to the description of the processor 601 above. The processor 801 also has a memory function, and the function of the memory 602 can be referred to.
The communication interface 802 is used to provide data to the processor 801. The communication interface 802 may be an internal interface of the communication device, or may be an external interface of the communication device (corresponding to the communication interface 604).
It is noted that the configuration shown in fig. 7 does not constitute a limitation of the electronic device 80, and that the electronic device 80 may include more or less components than those shown in fig. 7, or combine some components, or a different arrangement of components, in addition to the components shown in fig. 7.
Through the above description of the embodiments, it is clear for a person skilled in the art that, for convenience and simplicity of description, only the division of the above functional units is illustrated. In practical applications, the above function allocation can be performed by different functional units according to needs, that is, the internal structure of the device is divided into different functional units to perform all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
The embodiment of the present invention further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed by a computer, the computer executes each step in the method flow shown in the above method embodiment.
Embodiments of the present invention provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform one of the above method embodiments based on a markov chain congestion status prediction method.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, and a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM), registers, a hard disk, an optical fiber, a portable Compact disk Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any other form of computer-readable storage medium, in any suitable combination, or as appropriate in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Since the electronic device, the computer-readable storage medium, and the computer program product in the embodiments of the present invention may be applied to the method described above, for technical effects obtained by the method, reference may also be made to the method embodiments described above, and details of the embodiments of the present invention are not repeated herein.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions within the technical scope of the present invention are intended to be covered by the scope of the present invention.
Claims (12)
1. A Markov chain-based congestion state prediction method is applied to a Markov chain-based congestion state prediction device, and is characterized by comprising the following steps:
obtaining the carriage congestion states of N trains corresponding to a target station within historical time, and determining a congestion state transition probability matrix P according to the carriage congestion states of the N trains; the congestion state of the carriage comprises at least two states, wherein N is a positive integer, and P is a positive number;
and calculating the probability M that the carriage congestion state of each train corresponding to the target station in the future is a target state according to the congestion state transition probability matrix P, wherein the target state is any one of the at least two states, and M is greater than or equal to 0 and less than or equal to 1.
2. The method of claim 1, wherein the obtaining of the carriage congestion status of the N trains corresponding to the destination station within the historical time comprises:
the method comprises the steps of obtaining the seat number and the passenger number in a carriage of each train in N trains corresponding to a target station in historical time, and determining the seat proportion corresponding to the carriage according to the seat number and the passenger number;
and determining the carriage congestion state of each train according to the value range met by the seat proportion, and corresponding to different carriage congestion states under the condition that the seat proportion meets different value ranges.
3. The method according to claim 1 or 2, wherein the determining a congestion state transition probability matrix P according to the carriage congestion states of the N trains comprises:
determining the congestion state transition probability of transitioning from a first target state to a second target state according to the carriage congestion states of N-1 groups of trains corresponding to the N trains, wherein the first target state and the second target state are any one of the at least two states; any one group of trains in the N-1 groups of trains is two adjacent trains in the N trains;
and determining the congestion state transition probability matrix P according to the plurality of determined congestion state transition probabilities.
4. The method according to claim 1 or 2, wherein the calculating the probability M that the congestion status of the train of each time corresponding to the target station in the future is the target status according to the congestion status transition probability matrix P comprises:
calculating the probability M that the carriage congestion state of the first train corresponding to the target station in the future is in the target state by a first algorithm according to each congestion state transition probability included in the congestion state transition probability matrix P;
wherein the first algorithm is:
i and j are used for indicating the ith state or the jth state in the at least two states, a is used for indicating the number of the states included in the at least two states, and a, i and j are positive integers.
5. The method according to claim 1 or 2, wherein the calculating the probability M that the congestion status of the train of each time corresponding to the target station in the future is the target status according to the congestion status transition probability matrix P comprises:
calculating the probability M that the carriage congestion state of the first train corresponding to the target station is in the target state in the future time through a second algorithm according to the congestion state transition probability matrix P and the initial congestion state probability vector M (0);
wherein the second algorithm is: m (t) -M (t-1) P-M (t-2) P2＝M(0)Pt，M(0)＝[M1(0)，M2(0)，M3(0)，...，Ma(0)]And a is a positive integer, and M (0) is a congestion state probability vector of a carriage of the last train corresponding to the target station in the historical time.
6. A Markov chain-based congestion state prediction device is characterized by comprising: the device comprises an acquisition unit, a determination unit and a calculation unit;
the acquisition unit is used for acquiring the carriage congestion states of N trains corresponding to the target station within the historical time;
the determining unit is used for determining a congestion state transition probability matrix P according to the carriage congestion states of the N trains; the congestion state of the carriage comprises at least two states, wherein N is a positive integer, and P is a positive number;
the calculating unit is configured to calculate, according to the congestion state transition probability matrix P, a probability M that a congestion state of a carriage of each train corresponding to a target station in future time is a target state, where the target state is any one of the at least two states, and M is greater than or equal to 0 and less than or equal to 1.
7. The Markov chain-based congestion status prediction device of claim 6, wherein the obtaining unit is specifically configured to obtain the number of seats and passengers in the car of each train of the N trains corresponding to the target station within the historical time;
the determining unit is further used for determining the seat proportion corresponding to the compartment according to the seat number and the passenger number;
the determining unit is further configured to determine the carriage congestion state of each train according to the value range that the seat-to-seat ratio satisfies, and correspond to different carriage congestion states under the condition that the seat-to-seat ratio satisfies different value ranges.
8. The Markov chain-based congestion status prediction device of claim 6 or 7, wherein the determining unit is specifically configured to determine a congestion status transition probability of transitioning from a first target status to a second target status according to the congestion status of the carriages of the N-1 groups of trains corresponding to the N trains, and the first target status and the second target status are both any one status of the at least two statuses; any one group of trains in the N-1 groups of trains is two adjacent trains in the N trains;
the determining unit is further configured to determine the congestion state transition probability matrix P according to the determined multiple congestion state transition probabilities.
9. The Markov chain-based congestion status prediction device of claim 6 or 7, wherein the calculating unit is specifically configured to calculate, according to each congestion status transition probability included in the congestion status transition probability matrix P, a probability M that the congestion status of the train in the t-th time corresponding to the target station is a target status in a future time by using a first algorithm;
wherein the first algorithm is:
i and j are used for indicating the ith state or the jth state in the at least two states, a is used for indicating the number of the states included in the at least two states, and a, i and j are positive integers.
10. The Markov chain-based congestion status prediction device of claim 6 or 7, wherein the calculating unit is specifically configured to calculate, according to the congestion status transition probability matrix P and the initial congestion status probability vector M (0), a probability M that the congestion status of the train in the first time corresponding to the target station is a target status in a future time by a second algorithm;
wherein the second algorithm is: m (t) -M (t-1) P-M (t-2) P2＝M(0)Pt，M(0)＝[M1(0)，M2(0)，M3(0)，…，Ma(0)]And a is a positive integer, and M (0) is a congestion state probability vector of a carriage of the last train corresponding to the target station in the historical time.
11. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computer, cause the computer to perform a markov chain based congestion status prediction method of any one of claims 1 to 5.
12. An electronic device, comprising: a processor and a memory; wherein the memory is configured to store one or more programs, the one or more programs including computer-executable instructions, which when executed by the processor, cause the electronic device to perform a Markov chain-based congestion status prediction method of any one of claims 1-5.
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