CN108132913B

CN108132913B - Rail transit passenger flow movement estimation method and system and electronic equipment

Info

Publication number: CN108132913B
Application number: CN201711316746.3A
Authority: CN
Inventors: 赵娟娟; 须成忠; 张帆; 赵宝新
Original assignee: Shenzhen Institute of Advanced Technology of CAS
Current assignee: Shenzhen Institute of Advanced Technology of CAS
Priority date: 2017-12-11
Filing date: 2017-12-11
Publication date: 2021-10-29
Anticipated expiration: 2037-12-11
Also published as: CN108132913A

Abstract

The present disclosure relates to the field of public transportation technologies, and in particular, to a method, a system, and an electronic device for estimating passenger flow movement in rail transit. The rail transit passenger flow movement estimation method comprises the following steps: step a: dividing passenger states according to positions of passengers in the station; step b: according to the passenger state, carrying out queue division on passengers in the station; step c: analyzing the conversion process between the passenger states according to the station types, and analyzing the transfer process between the queues according to the conversion process between the passenger states; step d: and respectively counting the number of passengers in each queue according to the transfer process among the queues, and carrying out rail transit passenger flow movement estimation. Through the method and the device, the number of passengers waiting for the train in each station, the number of passengers entering the station, the number of passengers leaving the station, the number of passengers transferring the train and the number of passengers on the train can be known in real time, real-time online movement estimation of subway passenger flow is realized, and the dynamic operation management requirements of a subway system are met.

Description

Rail transit passenger flow movement estimation method and system and electronic equipment

Technical Field

The present disclosure relates to the field of public transportation technologies, and in particular, to a method, a system, and an electronic device for estimating passenger flow movement in rail transit.

Background

In recent years, the rail transit construction of China exceeds that of any country in the world, and the situation of rapid development is presented. Subway traffic is an important component of public traffic, has the characteristics of high speed, accurate time, large traffic volume, long distance, high comfort level, small influence by the outside and the like, plays an important role in the problems of large urban public traffic flow, road congestion and the like, and increasingly becomes a preferred traffic mode for citizens to go out.

With the rapid increase of passenger flow, high-density people are gathered in the underground closed space, so that the safety operation of the subway faces huge challenges, the service quality is reduced and the like. At present, many front-line cities, particularly many trains and subway stations face an over-saturation state in peak hours, and particularly, platforms, station entering channels and transfer channels of some large stations are subjected to over-saturation to different degrees. Besides, traffic accidents, weather conditions, train faults and the like can all influence the passenger flow conditions of the subway. However, the early warning and evacuation scheme for emergency in subway stations is difficult to be formulated effectively due to lack of detailed passenger flow distribution monitoring in subway traffic networks at present. Therefore, it is more and more important to accurately estimate the passenger flow conditions of each station (platform, access way, transfer way), each online train and each section with fine granularity in real time.

The subway traffic system online passenger flow movement real-time estimation has remarkable effects on better understanding of characteristics of traffic flow such as dynamic property, transmissibility, crowdedness and the like, and analyzing aspects of dynamic relation between traffic demand and supply, generation and dissipation of congestion queuing, traffic policy effect evaluation and the like. However, the existing subway passenger flow movement analysis methods are to simulate the movement of passengers in a subway network by only using historical data, and cannot meet the requirement of dynamic operation management of subway traffic. When dynamic operation management strategy formulation and train on-line control are carried out, current passenger flow demand distribution is required to be taken as a reference, so that subway passenger flow on-line movement needs to be estimated in real time, and movement of on-line passengers (entering a subway network and not leaving the subway network) in the subway network is estimated.

Disclosure of Invention

The application provides a method, a system and an electronic device for estimating passenger flow movement of rail transit, which aim to solve at least one of the technical problems in the prior art to a certain extent.

In order to solve the above problems, the present application provides the following technical solutions:

a rail transit passenger flow movement estimation method comprises the following steps:

step a: dividing passenger states according to positions of passengers in the station, wherein the passenger states comprise an entering state, a train waiting state, an on-train state, a transfer state and/or an exiting state;

step b: according to the passenger states, carrying out queue division on passengers in the station, wherein the queues comprise an inbound queue, a waiting queue, a train queue, a transfer queue and/or an outbound queue;

step c: analyzing the conversion process between the passenger states according to the station types, and analyzing the transfer process between the queues according to the conversion process between the passenger states; the station types comprise common stations and/or transfer stations;

step d: and respectively counting the number of passengers in each queue according to the transfer process among the queues, and carrying out rail transit passenger flow movement estimation.

The technical scheme adopted by the embodiment of the application further comprises the following steps: in step a, the dividing the passenger states according to the positions of the passengers in the stations further comprises: respectively refining the inbound state, the waiting train state, the on-train state, the transfer state and/or the outbound state into WI_a,i、WT_a,i、OT_i,sq、TR_a,i,jAnd/or WO_a,i，WI_a,iRepresenting a slave site s_aThe gate of the station gate walks to the line l_iThe station, WT_a,iIs shown at site s_aLine l_iThe station to which it belongs waiting for the train, OT_i,sqIs shown on line l_iOn-train, TR, numbered sq_a,i,jIndicated at transfer station s_cSlave line l_iWhere station walks to line l_jThe station, WO_a,iIs shown at site s_aLine l_iThe platform is walked to the gate of leaving.

The technical scheme adopted by the embodiment of the application further comprises the following steps: in step c, the analyzing the transition process between the passenger states according to the station types specifically includes:

step c 1: when a passenger is in an inbound state, selecting a line or a platform after the passenger enters the station as a first random event, calculating the occurrence probability of the first random event, and converting the inbound state of the passenger into a waiting state according to the probability calculation result and the walking time of the passenger;

step c 2: when the passengers are in a waiting state, the passengers are converted from the waiting state to an on-train state according to the arrival time of the train and the train capacity;

step c 3: when the passenger is in the on-train state, the action selected by the passenger when the train arrives at the next station is taken as a second random event, the occurrence probability of the second random event is calculated, and the passenger state to be converted is analyzed according to the probability calculation result: if the passenger selects to get off the train and the next station is a common station, the passenger is converted from the on-train state to the out-station state, and if the next station is a transfer station, the passenger is converted from the on-train state to the out-station state or the transfer state; if the passenger selects transfer, the passenger is converted from the on-train state to the transfer state; if the passenger selects to be on the train, the passenger continues to be in the on-train state;

step c 4: when the passenger is in the transfer state, the passenger is converted from the transfer state to the waiting state according to the walking time of the passenger.

The technical scheme adopted by the embodiment of the application further comprises the following steps: in step c, the analyzing the transition process between the passenger states according to the station types further includes: obtaining the relationship between a passenger and a passing site according to an entering site, entering time and a travel path, wherein the relationship between the passenger and the passing site comprises entering, passing, getting off and getting out and/or transfer; and respectively counting the number of passengers entering the station, passing the station, getting off the station and/or transferring in different time periods according to the passenger states, and respectively using the identifiers_#Bd(k,i,a)、_#Gtf(k,i,a)、_#Tr (k, i, a, j) and_#ps (k, i, a) represents the statistics of the number of passengers entering, passing, alighting, and/or transferring.

The technical scheme adopted by the embodiment of the application further comprises the following steps: in step c1, the calculating the occurrence probability of the first random event specifically includes: set via site s_aThe line number set of (a) is: lb_a＝{b₁,b₂,...,b_nN is the number of lines in the path, in I_kTime slot slave site s_aInbound passenger heading route

Subject to station overviewThe formula for calculating the ratio is:

the technical scheme adopted by the embodiment of the application further comprises the following steps: in step c3, the calculating the occurrence probability of the second random event specifically includes: at station s_aSlave line l_iThe replaceable route number set is LC_a＝{c₁,c₂,c₃,...,c_mGet the line l_iPassengers with train number sq, when the train arrives at s_aWhen the train is stopped, the probability of the passenger respectively switching to the transfer state, the exit state or the state continuously on the train from the state on the train is calculated by the following formula:

the technical scheme adopted by the embodiment of the application further comprises the following steps: in step c, the analyzing the transition process between the queues according to the transition process between the passenger states specifically includes:

step c 5: when the passengers are in the station-entering state, constructing a first random number generator, estimating a selected line or station after the passengers enter the station through the first random number generator, and adding the passengers into the station-entering queue;

step c 6: for passengers in an inbound queue, ETT while walking_i,a,kAfter time, transfer from inbound queueMoving to waiting queue, ETT_i,a,kIndicating passenger slave-stations s_aRoute l for gate to walk to_iThe time of the station;

step c 7: for passengers in the waiting queue, when a train arrives and enough passenger carrying space exists, the passengers are transferred from the waiting queue to the train queue;

step c 8: constructing a second random number generator when the passenger is in an on-train state, and simulating a state transition result of the passenger in the on-train state using the second random number generator when the train arrives at a next station, determining a queue transition result of the passenger according to the state transition result of the passenger in the on-train state: if the passenger is converted into the outbound state from the on-train state, the passenger is transferred to the outbound queue from the train queue; if the passenger state is converted from the on-train state to the transfer state, the passenger is transferred from the train queue to the transfer queue; if the passenger continues to be in the on-train state, the passenger continues to be in the train queue;

step c 9: for the passenger who is converted from the on-train state to the transfer state, the TFT is used for walking_i,j,c,kAfter a time, transfer from train queue to waiting queue, TFT_i,j,c,kIndicating passenger presence at transfer station s_cSlave line l_iWhere station walks to line l_jThe time of the station;

step c 10: for the passenger who is converted from the on-train state to the out-station state, the EXT when walking_i,a,kAfter time, transfer from train queue to outbound queue, EXT_i,a,kIndicating passenger presence at station s_aSlave line l_iThe time the platform walks to the outbound gate.

The embodiment of the application adopts another technical scheme that: a rail transit passenger flow movement estimation system comprising a passenger state transition analysis module and a passenger online movement estimation module, the passenger state transition analysis module comprising:

a passenger state dividing unit: the system comprises a passenger state division module, a passenger state division module and a passenger state division module, wherein the passenger state division module is used for dividing passenger states according to positions of passengers in stations, and the passenger states comprise an inbound state, a waiting train state, an on-train state, a transfer state and/or an outbound state;

a state transition analysis unit: analyzing a transition process between the passenger states according to station types, wherein the station types comprise common stations and/or transfer stations;

the passenger online movement estimation module includes:

a queue dividing unit: the queue division is used for dividing the queues of passengers in the station according to the passenger states, wherein the queues comprise an inbound queue, a waiting queue, a train queue, a transfer queue and/or an outbound queue;

a queue transfer analysis unit: analyzing the transfer process between the queues according to the conversion process between the passenger states;

a quantity counting unit: and the system is used for respectively counting the number of passengers in each queue according to the transfer process among the queues and carrying out rail transit passenger flow movement estimation.

The technical scheme adopted by the embodiment of the application further comprises the following steps: the passenger state dividing unit further includes: respectively refining the inbound state, the waiting train state, the on-train state, the transfer state and/or the outbound state into WI_a,i、WT_a,i、OT_i,sq、TR_a,i,jAnd/or WO_a,i，WI_a,iRepresenting a slave site s_aThe gate of the station gate walks to the line l_iThe station, WT_a,iIs shown at site s_aLine l_iThe station to which it belongs waiting for the train, OT_i,sqIs shown on line l_iOn-train, TR, numbered sq_a,i,jIndicated at transfer station s_cSlave line l_iWhere station walks to line l_jThe station, WO_a,iIs shown at site s_aLine l_iThe platform is walked to the gate of leaving.

The technical scheme adopted by the embodiment of the application further comprises the following steps: the state transition analysis unit includes:

a first state transition subunit: the system comprises a bus station, a bus waiting time and a bus waiting time, wherein the bus station is used for selecting a line or a platform after a passenger enters the bus station as a first random event when the passenger is in an entering state, calculating the occurrence probability of the first random event, and converting the passenger from the entering state to the waiting state according to the probability calculation result and the passenger walking time;

a second state transition subunit: the system is used for converting the passenger waiting state into the on-train state according to the train arrival time and the train capacity when the passenger is in the waiting state;

a third state transition subunit: when the passenger is in the on-train state, the action selected by the passenger when the train arrives at the next station is taken as a second random event, the occurrence probability of the second random event is calculated, and the passenger state to be converted is analyzed according to the probability calculation result: if the passenger selects to get off the train and the next station is a common station, the passenger is converted from the on-train state to the out-station state, and if the next station is a transfer station, the passenger is converted from the on-train state to the out-station state or the transfer state; if the passenger selects transfer, the passenger is converted from the on-train state to the transfer state; if the passenger selects to be on the train, the passenger continues to be in the on-train state;

a fourth state transition subunit: and the passenger switching device is used for switching the passenger from the transfer state to the waiting state according to the walking time of the passenger when the passenger is in the transfer state.

The technical scheme adopted by the embodiment of the application further comprises the following steps: the state transition analysis unit further includes:

a site relation statistics subunit: the system comprises a passenger obtaining unit, a passenger obtaining unit and a passenger obtaining unit, wherein the passenger obtaining unit is used for obtaining the relation type of the passenger and the passing station according to the entering station, the entering time and the traveling path, and the relation type of the passenger and the passing station comprises entering, passing, getting-off and/or transferring; respectively counting the number of passengers entering the station, passing the station, getting off the station and/or transferring in different time periods according to the passenger states, and respectively using the identifiers_#Bd(k,i,a)、_#Gtf(k,i,a)、_#Tr (k, i, a, j) and/or_#Ps (k, i, a) represents the statistics of the number of passengers arriving, passing, alighting, and transferring.

The technical scheme adopted by the embodiment of the application further comprises the following steps: the calculating, by the first state transition subunit, the occurrence probability of the first random event specifically includes: establishing via sitess_aThe line number set of (a) is: lb_a＝{b₁,b₂,...,b_nN is the number of lines in the path, in I_kTime slot slave site s_aInbound passenger heading route

The probability of the station is calculated by the following formula:

the technical scheme adopted by the embodiment of the application further comprises the following steps: the third state transition subunit calculates the occurrence probability of the second random event specifically as follows: at station s_aSlave line l_iThe replaceable route number set is LC_a＝{c₁,c₂,c₃,...,c_mGet the line l_iPassengers with train number sq, when the train arrives at s_aWhen the train is stopped, the probability of the passenger respectively switching to the transfer state, the exit state or the state continuously on the train from the state on the train is calculated by the following formula:

the technical scheme adopted by the embodiment of the application further comprises the following steps: the queue transfer analysis unit specifically includes:

a first random number generation subunit: when the passengers are in the inbound state, the method is used for constructing a first random number generator, estimating a selected line or platform after the passengers enter the station through the first random number generator, and adding the passengers into an inbound queue;

the first queue transfer subunit: for ETT on foot_i,a,kAfter a time, the passengers in the inbound queue are transferred to a waiting queue, ETT_i,a,kIndicating passenger slave-stations s_aRoute l for gate to walk to_iThe time of the station;

a second queue transfer subunit: the passenger transfer system is used for transferring passengers in the waiting queue to the train queue when the train arrives and enough passenger carrying space exists;

a second random number generation subunit: and a second random number generator for constructing the second random number generator when the passenger is in the on-train state, and simulating the state transition result of the passenger in the on-train state using the second random number generator when the train arrives at the next station, and determining the queue transition result of the passenger according to the state transition result of the passenger in the on-train state: if the passenger is converted into the outbound state from the on-train state, the passenger is transferred to the outbound queue from the train queue; if the passenger state is converted from the on-train state to the transfer state, the passenger is transferred from the train queue to the transfer queue; if the passenger continues to be in the on-train state, the passenger continues to be in the train queue;

a third queue transfer subunit: passenger walking TFT for converting from on-train state to transfer state_i,j,c,kAfter a time, transfer from train queue to waiting queue, TFT_i,j,c,kIndicating passenger presence at transfer station s_cSlave line l_iWhere station walks to line l_jThe time of the station;

a fourth queue transfer subunit: walk EXT for passenger converted from on-train state to outbound state_i,a,kAfter time, transfer from train queue to outbound queue, EXT_i,a,kIndicating passenger presence at station s_aSlave line l_iThe time the platform walks to the outbound gate.

The embodiment of the application adopts another technical scheme that: an electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the one processor to cause the at least one processor to perform the following operations of the rail transit passenger flow movement estimation method described above:

Compared with the prior art, the embodiment of the application has the advantages that: the rail transit passenger flow movement estimation method, the rail transit passenger flow movement estimation system and the rail transit passenger flow movement estimation electronic equipment are based on intelligent traffic card swiping data and train operation data as data sources, the passenger states are divided according to the positions of passengers in a subway network, the conversion conditions among the passenger states are analyzed according to the station types of stations where the passengers are located, the passengers are divided into queues according to the passenger states, the transfer results among the passenger queues are determined according to the conversion among the passenger states, the number of passengers in each queue is counted finally, the number of passengers waiting for trains in each station, the number of passengers entering the station, the number of passengers leaving the station, the number of passengers transferring the station and the number of passengers on the train are obtained, real-time online movement estimation of subway passenger flow is achieved, and dynamic operation management requirements of a subway system are met.

Drawings

Fig. 1 is an overall flowchart of a rail transit passenger flow movement estimation method according to an embodiment of the present application;

FIG. 2 is a flow chart of a passenger state transition analysis method of an embodiment of the present application;

FIG. 3 is a stochastic process of interconversion between passenger states;

FIG. 4 is a flow chart of a passenger online movement estimation method according to an embodiment of the present application;

FIG. 5 is a diagram illustrating the correspondence between passenger queues and passenger status;

fig. 6 is a schematic structural diagram of a rail transit passenger flow movement estimation system according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of hardware equipment of a rail transit passenger flow movement estimation method according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

Please refer to fig. 1, which is a flowchart illustrating an overall method for estimating a passenger flow movement in rail transit according to an embodiment of the present application. The rail transit passenger flow movement estimation method comprises the following steps:

The rail transit passenger flow movement estimation method in the embodiment of the application is based on intelligent traffic card swiping data and train operation data as data sources, and movement of subway passengers in a subway network is estimated on line in real time. Through real-time movement estimation of subway passengers, the real-time distribution conditions of the passengers at each station, each on-line train and each section, such as the number of passengers on a certain running train, the number of waiting passengers on each platform in a certain subway station, the number of transfer passengers in a certain subway station and the like, are known, and the requirement of subway traffic dynamic operation management is met.

The data source used in the embodiment of the application comprises intelligent transportation card swiping data and train operation data. The card swiping data of the intelligent transportation card comprises card id (unique identification of the intelligent transportation card), StationId (identification of subway stations), TrnsctTime (transaction (card swiping) time) and TrnsctyType (transaction type (in and out)). The train operation data mainly records the arrival and departure times of trains at each station, including the train number sq, the line L, the station S, the time t, and the type (arrival or departure).

Based on the above, the rail transit passenger flow movement estimation method in the embodiment of the application comprises two parts, namely passenger state transition analysis and passenger online movement estimation. The passenger state transition analysis and the passenger online movement estimation will be described in detail below, respectively.

Specifically, please refer to fig. 2, which is a flowchart illustrating a passenger state transition analysis method according to an embodiment of the present application. The passenger state transition analysis method comprises the following steps:

step 100: analyzing the station of each line in the subway network, and dividing the station of each line into a common station and/or a transfer station respectively;

in step 100, a subway network has a plurality of lines L ═ L₁,l₂,…,l_MEach line consists of a plurality of stations S ═ S₁,s₂,…,s_NAnd the stations of each line comprise ordinary stations and/or transfer stations. The line referred to in the present application is directional, for example, the Shenzhen subway line I includes two lines from Luo lake to airport east and from airport east to Luo lake. For a common station, passengers entering the station from each station swipe cards through an entrance gate and enter the station, walk through an entrance channel to reach a platform to wait for a train; or when the passenger takes the train to reach the destination station, the passenger gets off the station and walks to the outbound gate through the outbound channel. For the transfer station, passengers can transfer between two or more lines of the transfer station, and the transfer passengers walk to the 'platform' after the transfer to wait for the train through the 'transfer passage' after getting off the train before the transfer.

Step 110: dividing passenger states according to positions of passengers in the subway network;

in step 110, in order to facilitate real-time estimation of passenger movement in the subway network, the present application first divides passenger states into five categories, i.e. inbound (WI), Waiting Train (WT), On Train (OT), Transfer (TR) and/or outbound (WO), according to the position of the passenger; the WI state is an initial state, the WO state is an end state, the WI, WT, TR and WO states are all in a subway station, and the OT state is related to an online train. Then, according to the specific position of the passenger in the subway network, the states of the five classes of passengers are further refined into WI_a,i、WT_a,i、OT_i,sq、TR_a,i,j、WO_a,iSpecifically, the detailed description of the five passenger states is shown in table 1 below:

table 1 passenger status details

Status of state	Explanation of the invention
		WI_a,i	From s_aThe gate of the station walking to the line l_iBelonging station
WT_a,i	At s_aStation I_iPlatform waiting train to which line belongs
		OT_i,sq	On the line l_iOn-train with number sq
TR_a,i,j	At transfer stations s_cStation slave line l_iThe station moves to_jBelonging station
		WO_a,i	At s_aStation I_iGate for walking to and from platform to which line belongs

Step 120: analyzing the conversion process among the five passenger states according to the station type of the station where the passenger is located;

in step 120, in order to estimate the movement of the passenger online in real time, the embodiment of the present application regards the passenger online movement process as a random process for mutual transition between the above five passenger states, and specifically, as shown in fig. 3, is a schematic diagram of the random process for mutual transition between the passenger states. For the passengers arriving from the station gate of the station S, assuming that the station type of the station S is a transfer station or the number of lines passing through the station S is multiple, the passenger possibly goes to at least two stations, namely the corresponding WI_a,iAt least two of them are included. Specifically, the transition between five passenger types is analyzed according to the station type of the station where the passenger is locatedThe process comprises the following steps:

1. the initial state WI of the passenger after the passenger is swiped and arrives at the station is taken as a first random event, and after the initial state WI of the passenger is determined, the state to be transferred from the WI state to the next step is determined, namely the WT state. At this time, it is necessary to determine the time when the passenger transits from the WI state to the WT state, which is the passenger walking time from the entry gate to the platform.

Further, the passenger walking time is related to the passenger walking speed, the layout of pedestrian paths in the stations, the passenger flow density, and other factors, and the passenger walking time may vary from station to station in different time periods of the day. Since the walking time of the passengers only accounts for a small part of the total time taken by the passengers, the influence of these factors on the walking time of the passengers is ignored in the embodiment of the present application, and it is assumed that the walking time from the gate to the platform of all the passengers at the same station is the same, and the transfer time is the same, and the specific calculation method of the walking time of the passengers is specifically described in patent 201410596972.1, which will not be described again in this application. Specifically, the passenger walking time is divided into an inbound walking time, an outbound walking time, and a transfer walking time, and the identification ETT is used respectively_i,a,k、EXT_i,a,kAnd TFT_i,j,c,kThe passenger walking time distribution is shown in table 2 below:

TABLE 2 passenger walk time distribution representation

Identification	Description of the invention
		ETT_i,a	Passenger slave s_aGate walking to line l_iTime of station
EXT_i,a	Passenger slave line l_iStation s_aTime of platform walking to gate of leaving station
		TFT_i,j,c	Passengers at transfer stations s_cFrom l_iPlatform walking to l of line_jTime of line station

2. When the passenger is in the WT state, the state to be transitioned to from the WT state next is also determined, i.e., the OT state; at this time, it is also necessary to determine the time to transition from the WT state to the OT state, which is primarily related to the arrival time of the train and the train capacity (if there is a train capacity that cannot accommodate all waiting passenger needs, the passenger may need to wait for the next or multiple trains, i.e., there is a hysteresis). In the embodiment of the present application, the train capacity uses the maximum capacity of the train, and it can be understood that other capacity data may be used according to the train operation time, the train type, and other factors.

3. When the passenger is in the OT state, the state to be switched next is uncertain. The concrete expression is as follows: when the train reaches the next stop, the passenger may Get Off (Get-Off) or leave the stop (Pass) on the train. If the passenger gets off at the station and the station belongs to a general station, the state to which the passenger is transferred next should be the WO state; if the station is a Transfer station, the next state to which the passenger transfers may be the WO state or the TR state, i.e., Transfer to another line, and there may be more than one line that can be transferred. Thus, as shown in fig. 3, the present embodiment regards the action selected by the passenger in the OT state when the train arrives at the next stop as a second random event. When the passenger selects three actions of getting off, getting on or transferring, the corresponding state transitions of the three actions are OT → WO, OT → OT and OT → TR respectively.

4. When the passenger is in the TR state, the state to which he or she is next transitioned can be uniquely determined as the WT state; at this time, it is also necessary to determine the time for transition from the TR state to the WT state, which is related to the transfer walking time in table 2.

Step 130: calculating the occurrence probability of random events;

in step 130, it can be known from the passenger state transition analysis in step 120 that the random events include a first random event and a second random event, and the first random event represents the selection of multiple lines or stations after the passenger arrives at the station; the second random event appears as the action selected by the passenger when the train arrives at the next stop. First, it is assumed that the occurrence probabilities of the first random event and the second random event are stable for a given time period δ. Then, the day is divided into a plurality of time periods, for example, divided by half an hour, and the time period I ═ I that can be divided in one day is set to { I }₁,I₂,I₃,...,I₄₈And respectively calculating the occurrence probability of the first random event and the second random event in each time period.

For convenience of explanation, for a trip of a passenger, in the embodiment of the present application, relationship types between the passenger and all passing stops S in the trip process are divided into four types, namely, entering stop (E), passing stop (P), getting-off stop (L), and/or transfer (T), where the four types of relationship types are specifically defined as shown in table 3 below:

TABLE 3 relationship types of passenger's trip and passing site

Relationships between	Explanation of the invention
		Entering station (E)	Passengers get on from s station (passenger's departure station)
Through (P)	Passenger train passing s station
		Lower vehicle out station (L)	Passengers get off from s station
Transfer (T)	Passenger transfer from one line to another at station s

Respectively counting the number of passengers with four types of relation types in different time periods, and respectively using the identifiers_#Bd(k,i,a)、_#Gtf(k,i,a)、_#Tr(k,i,a,j)、_#Ps (k, i, a) represents the statistical results of the number of passengers of four relationship types, which are shown in table 4 below:

TABLE 4 statistics of passenger number for four relationship types

For the statistics of the number of passengers in table 4, the travel record of the passengers in the historical same period, the travel route and the time spent between two stops can be combined to obtain the statistics. Given the arrival station, the arrival time and the selection path of a certain passenger, the relationship type of the passenger and the passing station can be obtained, and the time of arriving at each station can be estimated. The details are shown in table 5 below:

TABLE 5 passenger arrival times at various via sites

Line	Site	Relationships between	Time of arrival
				4down	MZ	E	07:01
4down*3down	SZB	T	07:03
				3down	BSL	P	07:04
3down	ML	P	07:06
				3down	SML	P	07:09
3down	LHB	P	07:11
				3dwon*2up	SNG	T	07:13
2up	LHC	L	07:13

Table 5 shows that a passenger arrives at mz (minzhi) at 07:01 to lhc (lianhuacun), the type of relationship between the passenger and the passing site, and the arrival time at each site, and finally the number of passengers for each relationship type is summarized by passenger status and time period.

For the first random event, i.e. passenger slave s_aAnd the station is in an initial state WI after swiping the card into the station. Hypothesis pathway s_aThe line number set of the station is as follows: lb_a＝{b₁,b₂,...,b_nAt I, then_kTime period from s_aPassenger who arrives at a stop by swiping card goes to

The probability of the station where the line is located can be calculated by formula (1):

in formula (1), n is the number of lanes of the path.

For the second random event, i.e. for the passenger in the OT state, the action selected when the train arrives at the next stop, assume at s_aStation slave line l_iThe replaceable route number set is LC_a＝{c₁,c₂,c₃,...,c_mThen the ride line l can be calculated using equation (2)_iPassengers of the train numbered sq, when the train arrives at s_aWhen standing, passengers from OT shapeProbability of state transition to TR, WO or continued in OT state, respectively:

please refer to fig. 4, which is a flowchart illustrating a method for estimating online movement of a passenger according to an embodiment of the present application. The passenger online movement estimation method comprises the following steps:

step 200: dividing the queues of the passengers in each station according to the passenger states;

in step 200, in each station, according to the passenger state, the passengers in the station are divided into five queues, which are an inbound queue, a waiting queue, a train queue, a transfer queue and/or an outbound queue, respectively, and the moving process of the passengers between different queues corresponds to the transition process of the passengers between different states, specifically, as shown in fig. 5, a corresponding relationship diagram between the passenger queue and the passenger state is shown. Wherein the inbound queue corresponds to the WI state, the outbound queue corresponds to the WO state, the transfer queue corresponds to the TR state, the waiting queue corresponds to the WT state, and the train queue corresponds to the OT state.

Step 210: when the passenger is in an initial state (WI state), constructing a first random number generator, estimating a station to which the passenger enters the station in real time through the first random number generator, and adding the passenger into an inbound queue;

in step 210, the first random number generator is the station generator for passengers to go to after arriving at the station.

Step 220: for passengers in an inbound queue, ETT while walking_i,a,kAfter the time, the passengers are transferred from the queue of entering the station to the queue of waiting, and at the moment, the passengers are converted into a WT state from a WI state;

step 230: for passengers in the waiting queue, when a train arrives and enough passenger carrying space exists, the passengers are transferred from the waiting queue to the train queue, and at the moment, the passengers are switched from a WT state to an OT state;

step 240: when the passengers are in OT states, a second random number generator is constructed, when the train arrives at the next station, the second random number generator is used for simulating state conversion results of the passengers on the train, and queue transfer results of the passengers are determined according to the state conversion results of the passengers;

in step 240, the second random number generator, that is, the passenger state transition generator when the train arrives at each station, and according to the state transition result obtained by simulation of the second random number generator, the queue transition result of the passenger is specifically: when the passenger state is changed from OT state to WO state, the passenger is transferred from train queue to outbound queue, when the passenger state is changed from OT state to TR state, the passenger is transferred from train queue to transfer queue, when the passenger is continuously in OT state, the passenger is continuously in train queue.

Step 250: for passengers who switch from OT state to TR state when walking TFT_i,j,c,kAfter the time, transferring from the train queue to a waiting queue;

step 260: for passengers who change from OT state to WO state, EXT when walking_i,a,kTransferring the train queue to an outbound queue (leaving the subway system) after time;

step 270: and respectively counting the number of passengers in each queue in each station, and realizing real-time online movement estimation of subway passenger flow.

In step 270, the number of passengers waiting for trains in each station, the number of passengers entering the station, the number of passengers leaving the station, the number of passengers transferring the trains, and the number of passengers on the trains can be obtained through counting the number of passengers in each queue, so that the distribution of passengers in the subway network is obtained, and the real-time online movement estimation of subway passenger flow with high calculation accuracy and high efficiency is realized.

Please refer to fig. 6, which is a schematic structural diagram of a rail transit passenger flow movement estimation system according to an embodiment of the present application. The rail transit passenger flow movement estimation system comprises a passenger state conversion analysis module and a passenger online movement estimation module. The passenger state conversion analysis module is used for dividing the passenger states according to the positions of the passengers in the subway network and analyzing the conversion conditions among the passenger states according to the station types of the stations where the passengers are located. The passenger online movement estimation module is used for dividing the passenger queues according to the passenger states, determining the transfer result among the passenger queues according to the conversion among the passenger states, and finally counting the number of passengers in each queue to realize the real-time online movement estimation of subway passenger flow.

Specifically, the passenger state conversion analysis module comprises a station analysis unit, a passenger state division unit and a state conversion analysis unit;

a site analysis unit: the system comprises a plurality of lines, a plurality of stations and a plurality of transfer stations, wherein the stations are used for analyzing stations of each line in the subway network and dividing the stations in each line into common stations and/or transfer stations respectively; wherein a subway network has a plurality of lines L ═ L₁,l₂,…,l_MEach line consists of a plurality of stations S ═ S₁,s₂,…,s_NAnd the stations of each line comprise ordinary stations and/or transfer stations. The line referred to in the present application is directional, for example, the Shenzhen subway line I includes two lines from Luo lake to airport east and from airport east to Luo lake. For a common station, passengers entering the station from each station swipe cards through an entrance gate and enter the station, walk through an entrance channel to reach a platform to wait for a train; or when the passenger takes the train to reach the destination station, the passenger gets off the station and walks to the outbound gate through the outbound channel. For the transfer station, passengers can transfer between two or more lines of the transfer station, and the transfer passengers walk to the 'platform' after the transfer to wait for the train through the 'transfer passage' after getting off the train before the transfer.

A passenger state dividing unit: for passenger-by-passenger presence in subway networksDividing the passenger states by the positions of the passengers; in order to estimate the movement of passengers in the subway network in real time, the method comprises the steps of firstly dividing the passenger states into five categories of entering station (WI), Waiting Train (WT), on-train (OT), Transfer (TR) and exiting station (WO) according to the positions of the passengers, wherein the WI state is an initial state, the WO state is an end state, the WI, WT, TR and WO states are all in the subway station, and the OT state is related to an on-line train. Then, according to the specific position of the passenger in the subway network, the states of the five classes of passengers are further refined into WI_a,i、WT_a,i、OT_i,sq、TR_a,i,j、WO_a,iSpecifically, the detailed description of the five passenger states is shown in table 1 below:

table 1 passenger status details

A state transition analysis unit: the system is used for analyzing the conversion process among the five passenger states according to the station type of the station where the passenger is located; in order to estimate the movement of the passenger online in real time, the passenger online movement process is regarded as a random process for mutual conversion among the five passenger states. For the passengers arriving from the station gate of the station S, assuming that the station type of the station S is a transfer station or the number of lines passing through the station S is multiple, the passenger possibly goes to at least two stations, namely the corresponding WI_a,iAt least two of them are included. Specifically, the state transition analysis unit includes:

a first state transition subunit: the system is used for taking an initial state WI where a passenger is in after swiping a card and entering a station as a first random event, calculating the occurrence probability of the first random event, and converting the WI state of the passenger into a WT state according to the probability calculation result and the walking time of the passenger; when the initial state WI of the passenger is determined, the state to which the next step from the WI state is to be transferred is determined, i.e., the WT state. At this time, it is necessary to determine the time when the passenger transits from the WI state to the WT state, which is the passenger walking time from the entry gate to the platform.

TABLE 2 passenger walk time distribution representation

A second state transition subunit: when the passenger is in the WT state, the passenger is converted from the WT state to the OT state according to the train arrival time and the train capacity; at this time, it is also necessary to determine the time to transition from the WT state to the OT state, which is primarily related to the arrival time of the train and the train capacity (if there is a train capacity that cannot accommodate all waiting passenger needs, the passenger may need to wait for the next or multiple trains, i.e., there is a hysteresis). In the embodiment of the present application, the train capacity uses the maximum capacity of the train, and it can be understood that other capacity data may be used according to the train operation time, the train type, and other factors.

A third state transition subunit: the passenger state switching method comprises the steps that when a passenger is in an OT state, the action selected by the passenger when the train arrives at the next station is used as a second random event, the occurrence probability of the second random event is calculated, and the passenger state to be switched is analyzed according to the probability calculation result; when the passenger is in the OT state, the state to be converted next is uncertain, which is represented by: when the train reaches the next stop, the passenger may Get Off (Get-Off) or leave the stop (Pass) on the train. If the passenger gets off at the station and the station belongs to a general station, the state to which the passenger is transferred next should be the WO state; if the station is a Transfer station, the next state to which the passenger transfers may be the WO state or the TR state, i.e., Transfer to another line, and there may be more than one line that can be transferred. Therefore, the present embodiment regards the passenger in the OT state, the action selected when the train arrives at the next stop, as the second random event. When the passenger selects three actions of getting off, getting on or transferring, the corresponding state transitions of the three actions are OT → WO, OT → OT and OT → TR respectively.

A fourth state transition subunit: for switching the passenger from the TR state to the WT state according to the passenger walking time when the passenger is in the TR state; at this time, it is also necessary to determine the time for transition from the TR state to the WT state, which is related to the transfer walking time in table 2.

A site relation statistics subunit: the method is used for obtaining the relationship types of the passengers and the passing stations according to the station entering stations, the station entering time and the traveling paths, and respectively counting the number of the passengers in each relationship type in different time periods. For convenience of explanation, for a trip of a passenger, the embodiment of the present application divides the relationship between the passenger and all passing stations S in the trip process into four types, namely, entering station (E), passing station (P), getting-off station (L), and transfer station (T), and the four types of relationship are specifically defined as shown in table 3 below:

TABLE 3 relationship between passenger's trip and passing site

Respectively counting the number of the four classes of passengers in each time period and the passengers passing through the station, and respectively using the identifiers_#Bd(k,i,a)、_#Gtf(k,i,a)、_#Tr(k,i,a,j)、_#Ps (k, i, a) represents the statistical results of the number of passengers in four relations, and the statistical results of the number of passengers are shown in table 4 below:

TABLE 4 statistical results of passenger number

For the statistics of the number of passengers in table 4, the travel record of the passengers in the historical same period, the travel route and the time spent between two stops can be combined to obtain the statistics. Given the arrival station, the arrival time and the travel path of a certain passenger, the relationship between the passenger and the passing station can be obtained, and the time of arriving at each station can be estimated. The details are shown in table 5 below:

TABLE 5 passenger arrival times at various via sites

Table 5 shows that a passenger arrives at mz (minzhi) at 07:01 to lhc (lianhuacun), the relationship between the passenger and the passing site, and the time period for arriving at each site, and finally the number of each type of passenger is summarized according to the passenger status and time period.

The first state conversion subunit and the third state conversion subunit respectively calculate the occurrence probability of the first random event and the second random event according to the statistical result of the number of passengers; as for the manner of calculating the occurrence probability of the first random event and the second random event, first, it is assumed that the occurrence probability of the first random event and the second random event is stable for a given time period δ. Then, the day is divided into a plurality of time periods, for example, divided by half an hour, and the time period I ═ I that can be divided in one day is set to { I }₁,I₂,I₃,...,I₄₈And respectively calculating the occurrence probability of the first random event and the second random event in each time period.

For the first random event, i.e. passenger slave s_aAnd the station is in an initial state WI after swiping the card into the station. Hypothesis pathway s_aThe line number set of the station is as follows: lb_a＝{b₁,b₂,...,b_nN is the number of paths, then at I_kTime period from s_aPassenger who punches card and enters station goes to

for the second random event, i.e. for the passenger in the OT state, the action selected when the train arrives at the next stop, assume at s_aStation slave line l_iThe replaceable route number set is LC_a＝{c₁,c₂,c₃,...,c_mThen the ride line l can be calculated using equation (2)_iPassengers of the train numbered sq, when the train arrives at s_aProbability of a passenger transitioning from the OT state to TR, WO, or continuing to be in the OT state, respectively, while standing:

the passenger online movement estimation module comprises a queue dividing unit, a queue transfer analysis unit and a quantity statistical unit;

a queue dividing unit: the passenger queue dividing device is used for dividing the passenger queue in each station according to the passenger state; in each station, according to the passenger state, the passengers in the station are divided into five queues, namely an inbound queue, an outbound queue, a transfer queue, a waiting queue and a train queue, respectively, and the moving process of the passengers between different queues corresponds to the switching process of the passengers between different states, specifically, as shown in fig. 5, a corresponding relationship diagram of the passenger queue and the passenger state is shown. Wherein the inbound queue corresponds to the WI state, the outbound queue corresponds to the WO state, the transfer queue corresponds to the TR state, the waiting queue corresponds to the WT state, and the train queue corresponds to the OT state.

A queue transfer analysis unit: the system is used for analyzing the transfer process among the queues according to the conversion process among the passenger states; specifically, the queue transfer analysis unit includes:

a first random number generation subunit: when the passenger is in an initial state (WI state), the system is used for constructing a first random number generator, estimating a station which the passenger arrives at after arriving at the station in real time through the first random number generator, and adding the passenger into the queue of arriving at the station; the first random number generator is the station generator for passengers to go to after arriving at the station.

The first queue transfer subunit: for ETT while walking_i,a,kAfter time, the passengers in the inbound queue are transferred to a waiting queue, and at the moment, the passengers are converted into a WT state from a WI state;

a second queue transfer subunit: the passenger waiting queue is used for transferring passengers in the waiting queue to the train queue when the train arrives and enough passenger carrying space exists, and the passengers are converted into an OT state from a WT state;

a second random number generation subunit: the second random number generator is used for constructing a second random number generator when the passenger is in an OT state, simulating the state conversion result of the passenger on the train by using the second random number generator when the train arrives at the next station, and determining the queue transfer result of the passenger according to the state conversion result of the passenger; specifically, the second random number generator is a passenger state conversion generator when the train arrives at each station, and according to a state conversion result obtained by simulation of the second random number generator, the queue transfer result of the passenger is specifically: when the passenger state is changed from OT state to WO state, the passenger is transferred from train queue to outbound queue, when the passenger state is changed from OT state to TR state, the passenger is transferred from train queue to transfer queue, when the passenger is continuously in OT state, the passenger is continuously in train queue.

A third queue transfer subunit: passenger walking TFT for converting OT state into TR state_i,j,c,kAfter time, transfer from train queue toWaiting for a queue;

a fourth queue transfer subunit: passenger walking EXT for converting OT state into WO state_i,a,kAfter time (leaving the subway system), transferring from the train queue to the outbound queue;

a quantity counting unit: the method is used for counting the number of passengers in each queue in the station respectively and realizing real-time online movement estimation of subway passenger flow. In the embodiment of the application, the number of passengers waiting for the train at each station, the number of passengers entering the train, the number of passengers leaving the train, the number of passengers transferring the train and the number of passengers on the train can be obtained through counting the number of passengers in each queue, so that the distribution of the passengers in a subway network is obtained, and the real-time online movement estimation of the subway passenger flow with high calculation precision and high efficiency is realized.

Please refer to fig. 7, which is a schematic structural diagram of a hardware device of a method for estimating passenger flow movement in rail transit according to an embodiment of the present application, and as shown in fig. 7, the device includes one or more processors and a memory. Taking a processor as an example, the apparatus may further include: an input device and an output device.

The processor, memory, input devices, and output devices may be connected by a bus or other means, as exemplified by the bus connection in fig. 7.

The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor executes various functional applications and data processing of the electronic device, i.e., implements the processing method of the above-described method embodiment, by executing the non-transitory software program, instructions and modules stored in the memory.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The input device may receive input numeric or character information and generate a signal input. The output device may include a display device such as a display screen.

The one or more modules are stored in the memory and, when executed by the one or more processors, perform the following for any of the above method embodiments:

The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in the embodiments of the present application.

Embodiments of the present application provide a non-transitory (non-volatile) computer storage medium having stored thereon computer-executable instructions that may perform the following operations:

Embodiments of the present application provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the following:

The rail transit passenger flow movement estimation method, the rail transit passenger flow movement estimation system and the rail transit passenger flow movement estimation electronic equipment are based on intelligent traffic card swiping data and train operation data as data sources, the passenger states are divided according to the positions of passengers in a subway network, the conversion conditions among the passenger states are analyzed according to the station types of stations where the passengers are located, the passengers are divided into queues according to the passenger states, the transfer results among the passenger queues are determined according to the conversion among the passenger states, the number of passengers in each queue is counted finally, the number of passengers waiting for trains in each station, the number of passengers entering the station, the number of passengers leaving the station, the number of passengers transferring the station and the number of passengers on the train are obtained, real-time online movement estimation of subway passenger flow is achieved, and dynamic operation management requirements of a subway system are met.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A rail transit passenger flow movement estimation method is characterized by comprising the following steps:

step d: respectively counting the number of passengers in each queue according to the transfer process among the queues, and carrying out rail transit passenger flow movement estimation;

the analyzing of the transition process between the passenger states according to the station types specifically includes:

step c 4: when the passenger is in the transfer state, the passenger is converted into a waiting state from the transfer state according to the walking time of the passenger;

in step c, the analyzing the transition process between the passenger states according to the station types further includes: obtaining the relationship between a passenger and a passing site according to an entering site, entering time and a travel path, wherein the relationship between the passenger and the passing site comprises entering, passing, getting off and getting out and/or transfer; counting the number of passengers entering the station, passing the station, getting off the station and/or transferring in different time periods according to the passenger states, and respectively representing the counting result of the number of passengers entering the station, passing the station, getting off the station and/or transferring by using identifiers # Bd (k, i, a), # Gtf (k, i, a), # Tr (k, i, a, j) and/or # Ps (k, i, a);

further comprising:

step c 6: for passengers in an inbound queue, ETT while walking_i,a,kTransferring from inbound queue to waiting queue, ETT, after time_i,a,kIndicating passenger slave-stations s_aRoute l for gate to walk to_iThe time of the station;

2. Method for estimating the passenger flow movement in rail transit according to claim 1, characterized in thatIn step a, the dividing the passenger states according to the positions of the passengers in the station further includes: respectively refining the inbound state, the waiting train state, the on-train state, the transfer state and/or the outbound state into WI_a,i、WT_a,i、OT_i,sq、TR_a,i,jAnd/or WO_a,i，WI_a,iRepresenting a slave site s_aThe gate of the station gate walks to the line l_iThe station, WT_a,iIs shown at site s_aLine l_iThe station to which it belongs waiting for the train, OT_i,sqIs shown on line l_iOn-train, TR, numbered sq_a,i,jIndicated at transfer station s_aSlave line l_iWhere station walks to line l_jThe station, WO_a,iIs shown at site s_aLine l_iThe platform is walked to the gate of leaving.

3. The method for estimating passenger flow movement in rail transit according to claim 1, wherein in the step c1, the calculating the probability of occurrence of the first random event is specifically: set via site s_aThe line number set of (a) is: lb_a＝{b₁,b₂,...,b_nN is the number of lines in the path, in I_kTime slot slave site s_aInbound passengers travel to route l_biThe probability of the station is calculated by the following formula:

4. the method according to claim 3, wherein in the step c3, the calculating the occurrence probability of the second random event is specifically: at station s_aSlave line l_iThe replaceable route number set is LC_a＝{c₁,c₂,c₃,...,c_mGet the line l_iPassengers with train number sq, when the train arrives at s_aWhen standingThe calculation formula of the probability of the passenger to respectively transit to the transfer, the exit or the continuous on-train state from the on-train state is as follows:

5. a rail transit passenger flow movement estimation system is characterized by comprising a passenger state transition analysis module and a passenger online movement estimation module, wherein the passenger state transition analysis module comprises:

the passenger online movement estimation module includes:

a quantity counting unit: the system is used for respectively counting the number of passengers in each queue according to the transfer process among the queues and carrying out rail transit passenger flow movement estimation;

the state transition analysis unit includes:

a fourth state transition subunit: the system is used for converting the transfer state of the passenger into a waiting state according to the walking time of the passenger when the passenger is in the transfer state;

the state transition analysis unit further includes:

a site relation statistics subunit: the system comprises a passenger obtaining unit, a passenger obtaining unit and a passenger obtaining unit, wherein the passenger obtaining unit is used for obtaining the relation type of the passenger and the passing station according to the entering station, the entering time and the traveling path, and the relation type of the passenger and the passing station comprises entering, passing, getting-off and/or transferring; respectively counting the number of passengers getting on, passing, getting off and/or transferring in different time periods according to the passenger states, and respectively representing the counting results of the number of the passengers getting on, passing, getting off and/or transferring by using identifications # Bd (k, i, a), # Gtf (k, i, a), # Tr (k, i, a, j) and # Ps (k, i, a);

the first queue transfer subunit: for ETT on foot_i,a,kAfter the time, the passengers in the inbound queue are transferred to the waiting queue,ETT_i,a,kindicating passenger slave-stations s_aRoute l for gate to walk to_iThe time of the station;

6. The rail transit passenger flow movement estimation system of claim 5, wherein the passenger state classification unit classifying the passenger state according to the position of the passenger within the station further comprises: respectively refining the inbound state, the waiting train state, the on-train state, the transfer state and/or the outbound state into WI_a,i、WT_a,i、OT_i,sq、TR_a,i,jAnd/or WO_a,i，WI_a,iRepresenting a slave site s_aThe gate of the station gate walks to the line l_iThe station, WT_a,iIs shown at site s_aLine l_iThe station to which it belongs waiting for the train, OT_i,sqIs shown on line l_iOn-train, TR, numbered sq_a,i,jIndicated at transfer station s_aSlave line l_iWhere station walks to line l_jThe station, WO_a,iIs shown at site s_aLine l_iThe platform is walked to the gate of leaving.

7. The system according to claim 5, wherein the first state transition subunit calculates the probability of occurrence of the first random event specifically as: set via site s_aThe line number set of (a) is: lb_a＝{b₁,b₂,...,b_nN is the number of lines in the path, in I_kTime slot slave site s_aInbound passenger heading route

The probability of the station is calculated by the following formula:

8. the system according to claim 7, wherein the third state transition subunit calculates the probability of occurrence of the second random event specifically as: at station s_aSlave line l_iThe replaceable route number set is LC_a＝{c₁,c₂,c₃,...,c_mGet the line l_iPassengers with train number sq, when the train arrives at s_aWhen the train is stopped, the probability of the passenger respectively switching to the transfer state, the exit state or the state continuously on the train from the state on the train is calculated by the following formula:

9. an electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the following operations of the method of estimating rail transit passenger flow movement described in any of 1 to 4 above: