CN106709609B - A Method of Forecasting and Controlling the Incoming Quantity of Subway Stations - Google Patents

Abstract

The invention discloses a method for predicting and controlling the amount of subway entering a station, which includes the following steps: constructing a linear function formula between the amount of entering a station and the flow rate of a section section; The parameters to be estimated are determined; based on the section flow of a given interval, the target inbound volume of the corresponding station is calculated; the passenger flow control strategy is determined based on the target inbound volume. The invention is applied to the organization and management of passenger flow in urban rail transit, limits the amount of incoming subway stations through predictive control, controls the full load rate of subway sections, and alleviates passenger flow congestion, thereby avoiding excessive pressure on lines or network caused by large passenger flow. Furthermore, through the grading of the passenger flow in the section section, the subway station is guided to adopt hierarchical control measures for the inbound volume in different periods according to the situation in the actual operation and management, so as to make the inbound volume control more reasonable.

Description

A Method of Forecasting and Controlling the Incoming Quantity of Subway Stations

technical field

The invention relates to the technical field of urban rail transit. More specifically, it relates to a method for predicting and controlling the incoming volume of a subway station.

Background technique

With the rapid development of economy and society, many domestic cities have built or plan to build urban rail transit to solve the increasingly congested traffic problems in the process of urbanization. However, with the gradual increase of passenger flow, the passenger flow control of urban rail transit stations is becoming more and more difficult. The contradiction between the rapidly growing passenger flow demand and transportation capacity of urban rail transit is becoming more and more prominent. The feasible way to alleviate the congestion problem is from the perspective of passenger flow control. Manage requirements. Passenger flow control, also known as flow limitation, refers to the safety measures taken to limit the speed of passengers entering the station to ensure the safety needs of passenger transport organizations, so as to achieve the purpose of reducing the flow of passengers entering the station within a unit time. Station current restriction mainly includes normal current restriction and temporary current restriction: normal current restriction refers to adopting the same current restriction measure at a specific time within a certain period of time, which is mainly used in morning and evening peak hours; temporary current restriction refers to short-term current restriction on stations Uncertain flow limit, mainly affected by unexpected events, large-scale activities and bad weather.

At present, in the actual operation and management, there is still a lack of appropriate theoretical basis and calculation methods for the selection of current-limiting stations, the determination of current-limiting time periods, and the determination of current-limiting intensity, which mainly rely on the experience of managers. Theoretical studies on the above problems mainly include: In terms of control measures, Liu Lianhua et al. first proposed that passenger flow control should be implemented from three levels of control modes: station level, line level, and network level, and analyzed the applicable conditions and principles of disposal measures for each level of control mode ; Zhao Peng et al. used the linear programming method to build a station passenger flow collaborative control model from the line level, and took Beijing Rail Transit Line 5 as an example to verify the model; Liu Xiaohua et al. constructed a joint control strategy between stations, by reducing the upstream station The speed of passenger flow entering the station is to reserve the train transportation capacity for this station to balance the capacity of trains on the line; Zhang Zheng et al. constructed a coordinated current limiting method for passenger flow at a single point of the station and on the line according to the principle of flow balance; Tian Xujing et al. proposed The principles of prior prediction, system integration, enhanced communication, and hierarchical responsibility to deal with sudden large passenger flow incidents, focused on the design of traffic organization and station passenger flow control methods under the situation of large passenger flow, and proposed self-organized and other-organized large passenger flow safety control measures; Taking Shanghai Rail Transit Line 6 and Line 8 as the background, Li Jianlin analyzed the contradiction between demand and transport capacity during morning peak hours, put forward suggestions for improvement on flow limiting measures, and analyzed the operational effects of different flow control measures.

The publication number in the existing patent literature is CN103661501A, which discloses an automatic flow limiting method based on multi-point passenger flow detection information feedback, including the steps of: real-time detection and calculation of the number of passengers in the station hall and the number of passengers on the platform, and analyzing the increase in the number of passengers on the platform Trend; calculate the remaining number of passengers that can be accommodated in the station hall and the remaining amount of passengers that can be accommodated on the platform; adjust the number of passengers entering and leaving the station hall in real time according to the remaining amount of passengers that can be accommodated in the station hall; adjust the number of passengers on the platform according to the remaining amount of passengers that can be accommodated in the platform ; According to the increase in the number of passengers in the station hall caused by the adjustment of the number of passengers entering and leaving the platform, further adjust the number of passengers entering and leaving the station hall to achieve the purpose of limiting the flow. This technical solution belongs to post-regulation, that is, measures are taken when or after passenger flow congestion occurs at the station, and the flow limiting effect is not good, and this solution is a local control strategy, and its shortcoming is mainly that it can only alleviate the congestion state of some stations, but not To achieve the purpose of coordinating and implementing the overall current limiting measures of the line network.

Therefore, it is necessary to provide a method for predicting and controlling the amount of incoming subway stations.

Contents of the invention

In order to overcome the deficiencies of the prior art, the present invention proposes a method for predicting and controlling the amount of incoming subway stations. This method is applied to the organization and management of passenger flow in urban rail transit. The main purpose is to limit the amount of incoming subway stations through predictive control, control the full load rate of subway sections, and alleviate passenger flow congestion, thereby avoiding excessive pressure on lines or network caused by large passenger flow.

To achieve the above object, the present invention adopts the following technical solutions:

A method for predicting and controlling the amount of subway entering a station, the method comprising the following steps:

S1: According to the analysis of the relationship between the station inbound passenger flow and the section flow in the subway network, the linear function formula between the station inbound volume and the section section flow in the subway network is constructed:

in,

x _i is the total inbound volume of the i-th station, i=1,2,...,n;

y _j is the total flow rate of the section on the jth interval, j=1,2,...,m;

α _ji is the ratio of the passenger flow entering from the i-th station and passing through the j-th section to the total inbound volume of the i-th station;

β _ij is the ratio of the passenger flow from the i-th station to the total passenger flow of the j-th section in the passenger flow of the j-th section;

Δt _ji is the time required for the passenger flow to reach the j-th section from the i-th station. Considering the punctuality of urban rail transit, it can be considered that for the determined i and j, Δt _ji is a fixed value constant;

x _i (t-Δt _ji ) is the total inbound volume of the i-th station in the (t-Δt _ji )th time period;

y _j (t) is the total flow of the section on the jth interval in the tth period;

m and n are natural numbers, n is the total number of stations, and m is the total number of intervals.

S2: Carry out linear fitting based on the historical data values of station inbound volume and section flow, and determine the parameters to be estimated;

S3: Based on the cross-sectional flow in a given interval, calculate the target inbound volume of the corresponding station;

S4: Determine the passenger flow control strategy based on the target inbound volume.

Preferably, in step S2, the following steps are specifically included:

S21: Determine that the jth interval has the following relationship

y _j (t)＝α _j1 x ₁ (t-Δt _j1 )+α _j2 x ₂ (t-Δt _j2 )+α _j3 x ₃ (t-Δt _j3 )+...+α _jn x _n (t- Δt _jn );

S22: From the historical data, select the total flow y _j (t) of the upper section of the j-th interval in the time period t of the day and the inbound volume x ₁ , x ₂ , x ₃ ... x _n of n stations as a set of inputs data;

S23: As in step S22, multiple sets of input data of different days are selected from historical data;

S24: Substituting multiple sets of input data into the relational expression in step S21 to determine the parameters to be estimated α _j1 , α _j2 , α _j3 ... α _jn , where j takes the values 1, 2, ..., m in turn;

S25: Determine that the i-th station has the following relationship

x _i (t-Δt _ji )=β _i1 y ₁ (t)+β _i2 y ₂ (t)+β _i3 y ₃ (t)+...+β _in y _n (t);

S26: From the historical data, select the total inbound volume y _j (t) of the i-th station in the t time period of the day and the total flow y ₁ , y ₂ , y ₃ ... y _n of the upper sections of the m intervals as a group Input data;

S27: As in step S26, multiple sets of input data of different days are selected from historical data;

S28: Substituting multiple sets of input data into the relational formula in step S25 to determine the parameters to be estimated β _i1 , β _i2 , β _i3 ... β _mj , where i takes values 1, 2, ..., n in turn.

Preferably, step S3 specifically includes the following steps:

S31: Based on the fact that the passenger flow of the i-th station entering the station and passing through the j-th section is equal to the passenger flow of the i-th station entering the station in the section passenger flow of the j-th section, the following equation is obtained:

α _ji x _i (t-Δt _ji ) = β _ij y _j (t);

S32: Determine the maximum cross-sectional flow y _j (t) of the j-th interval based on the maximum full load rate of the interval section;

S33: Let i=1, j take the value of 1, 2, ..., m in sequence, and obtain the m incoming quantities x ₁ (t-Δt ₁₁ ) of the first station in the (t-Δt _j1 )th time period , x ₁ (t-Δt ₂₁ ), x ₁ (t-Δt ₃₁ ), ..., x ₁ (t-Δt _m1 );

S34: Select the minimum value among the above m inbound quantities x ₁ (t-Δt ₁₁ ), x ₁ (t-Δt ₂₁ ), x ₁ (t-Δt ₃₁ ), ..., x ₁ (t-Δt _m1 ) min x ₁ (t-Δt _j1 ) is used as the control value of the inbound volume of the first station in the (t-Δt _j1 ) period;

S35: Let i take the value of 2,...,n in turn, repeat the above steps, and obtain the control value of the inbound volume of each station in the (t-Δt _j1 )th time period, which is the target inbound volume of the corresponding station.

The accuracy of the parameter values to be estimated increases with the increase of the historical database.

Further preferably, the maximum full load rate of the section section is 140%.

Preferably, in step S4, since a large amount of historical data is used to determine the regression parameters α _ji and β _ij , according to the above calculation, determining the passenger flow control strategy based on the target inbound volume includes:

Forecast and estimate the time period and corresponding passenger flow control value of each station that needs to be limited in the future, and give suggestions for forecasting traffic limit at the station; and/or

By comparing the inbound volume of the station in the current period with the target inbound volume, it is determined whether the station currently needs to limit the flow and implement measures to control the inbound volume.

Preferably, the passenger flow control strategy also includes hierarchical control of station flow restriction measures.

Preferably, different levels of full-load ratios are set for different sections, and the flow-limiting time periods and passenger flow control values corresponding to different levels of full-load ratios are calculated to realize hierarchical control of station flow-limiting measures.

Further preferably, the different levels of full load ratios include three levels, which are 120%, 130% and 140% respectively.

The beneficial effects of the present invention are as follows:

A method for predicting and controlling the amount of incoming subway stations in the present invention is applied to the organization and management of passenger flow in urban rail transit, through predictive control to limit the amount of incoming subway stations, to control the full load rate of subway sections, and to alleviate passenger flow congestion, thereby avoiding large passenger flow Excessive stress on wiring or wire mesh. Furthermore, through the grading of the passenger flow in the section section, the subway station is guided to adopt hierarchical control measures for the inbound volume in different periods according to the situation in the actual operation and management, so as to make the inbound volume control more reasonable.

Description of drawings

The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

FIG. 1 shows a step diagram of a method for predicting and controlling the amount of subway entering a station.

Fig. 2 shows a flow chart of a method for predicting and controlling the amount of subway entering a station.

Fig. 3 shows a schematic diagram of the relationship between stations and sections in Embodiment 1.

Detailed ways

In order to illustrate the present invention more clearly, the present invention will be further described below in conjunction with preferred embodiments and accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not limit the protection scope of the present invention.

As shown in Figure 1, a method for predicting and controlling the amount of subway entering a station, the method includes the following steps:

S1: According to the analysis of the relationship between the station inbound passenger flow and section section flow in the subway line network, construct the linear function formula between the station entry volume and section section flow in the subway line network;

S2: Carry out linear fitting based on the historical data values of station inbound volume and section flow, and determine the parameters to be estimated;

S3: Based on the cross-sectional flow in a given interval, calculate the target inbound volume of the corresponding station;

S4: Determine the passenger flow control strategy based on the target inbound volume.

As shown in Figure 2, the specific method steps are as follows:

Step S1: According to the analysis of the relationship between the station inbound passenger flow and section section flow in the subway network, construct the linear functional expression between the station entry volume and section section flow in the subway line network:

Considering the state transition and hysteresis of inbound passenger flow propagation at the station, time information is added to the analysis process, and the linear functional relationship between the two is constructed as follows:

Among them, x _i is the total inbound volume of the i-th station, i=1,2,...,n; y _j is the total flow of the section on the j-th section, j=1,2,...,m; α _ji is the proportion of the passenger flow entering from the i-th station and passing through the j-th section to the total inbound volume of the i-th station; β _ij is the passenger flow entering the station from the i-th station in the j-th section Δt _ji is the time required for the passenger flow to reach the j-th section from the i-th station. Considering the punctuality of urban rail transit, it can be considered that for a certain i and j, Δt _ji is a fixed value constant; x _i (t-Δt _ji ) is the total inbound volume of the i-th station in the (t-Δt _ji )th time period; y _j (t) is the t-th time period The total flow of the section on the jth interval; m and n are natural numbers, n is the total number of stations, and m is the total number of intervals.

Step S2: Carry out linear fitting based on the historical data values of station inbound volume and section flow, and determine the parameters to be estimated, specifically including the following steps:

S21: Determine that the jth interval has the following relationship

y _j (t)＝α _j1 x ₁ (t-Δt _j1 )+α _j2 x ₂ (t-Δt _j2 )+α _j3 x ₃ (t-Δt _j3 )+...+α _jn x _n (t- Δt _jn );

S22: From the historical data, select the total flow y _j (t) of the upper section of the j-th interval in the time period t of the day and the inbound volume x ₁ , x ₂ , x ₃ ... x _n of n stations as a set of inputs data;

S23: As in step S22, multiple sets of input data of different days are selected from historical data;

S24: Substituting multiple sets of input data into the relational expression in step S21 to determine the parameters to be estimated α _j1 , α _j2 , α _j3 ... α _jn , where j takes the values 1, 2, ..., m in sequence;

S25: Determine that the i-th station has the following relationship

x _i (t-Δt _ji )=β _i1 y ₁ (t)+β _i2 y ₂ (t)+β _i3 y ₃ (t)+...+β _in y _n (t);

S26: From the historical data, select the total inbound volume y _j (t) of the i-th station in the t time period of the day and the total flow y ₁ , y ₂ , y ₃ ... y _n of the upper sections of the m intervals as a group Input data;

S27: As in step S26, multiple sets of input data of different days are selected from historical data;

S28: Substituting multiple sets of input data into the relational formula in step S25 to determine the parameters to be estimated β _i1 , β _i2 , β _i3 ... β _mj , where i takes values 1, 2, ..., n in turn.

Step S3: Based on the cross-sectional flow of a given section, calculate the target inbound volume of the corresponding station.

S31: Based on the fact that the passenger flow of the i-th station entering the station and passing through the j-th section is equal to the passenger flow of the i-th station entering the station in the section passenger flow of the j-th section, the following equation is obtained:

α _ji x _i (t-Δt _ji ) = β _ij y _j (t);

S32: Determine the maximum cross-sectional flow y _j (t) of the j-th interval based on the maximum full load rate of the interval section;

S33: Let i=1, j take the value of 1, 2, ..., m in sequence, and obtain the m incoming quantities x ₁ (t-Δt ₁₁ ) of the first station in the (t-Δt _j1 )th time period , x ₁ (t-Δt ₂₁ ), x ₁ (t-Δt ₃₁ ), ..., x ₁ (t-Δt _m1 );

S34: Select the minimum value among the above m inbound quantities x ₁ (t-Δt ₁₁ ), x ₁ (t-Δt ₂₁ ), x ₁ (t-Δt ₃₁ ), ..., x ₁ (t-Δt _m1 ) min x ₁ (t-Δt _j1 ) is used as the control value of the inbound volume of the first station in the (t-Δt _j1 ) period;

S35: Let i take the value of 2,...,n in turn, repeat the above steps, and obtain the control value of the inbound volume of each station in the (t-Δt _j1 )th time period, which is the target inbound volume of the corresponding station.

It should be noted that the accuracy of the parameter values to be estimated increases with the increase of the historical database.

Step S4: Determine the passenger flow control strategy based on the target inbound volume.

Since the determination of the regression parameters α _ji and β _ij uses a large amount of historical data, according to the above calculation, determining the passenger flow control strategy based on the target inbound volume includes: predicting and estimating the time period and corresponding According to the passenger flow control value of the station, it will give the station flow limit prediction suggestion; and/or by comparing the current period of the station with the target inflow, determine whether the station currently needs to limit the flow and implement measures to control the inflow.

Furthermore, the passenger flow control strategy also includes hierarchical control of station flow limitation measures: different sections are set with different levels of full load rate, and the time period of flow limit and passenger flow control value corresponding to different levels of full load rate are calculated to realize station traffic control. Hierarchical control of current limiting measures. In the present invention, the full load ratios of different levels include three levels, which are 120%, 130% and 140% respectively.

Example 1

As shown in FIG. 3 , this embodiment includes three stations (1), (2) (3) and two sections (section 1 and section 2).

According to the analysis of the relationship between the station inbound passenger flow and the section flow in the subway network, the linear function formula between the station inflow and the section flow in the subway network is constructed:

Among them, x _i is the total inbound volume of the i-th station, i=1,2,...,n; y _j is the total flow of the section on the j-th section, j=1,2,...,m; α _ji is the proportion of the passenger flow entering from the i-th station and passing through the j-th section to the total inbound volume of the i-th station; β _ij is the passenger flow entering the station from the i-th station in the j-th section Δt _ji is the time required for the passenger flow to reach the j-th section from the i-th station. Considering the punctuality of urban rail transit, it can be considered that for a certain i and j, Δt _ji is a fixed value constant; x _i (t-Δt _ji ) is the total inbound volume of the i-th station in the (t-Δt _ji )th time period; y _j (t) is the t-th time period The total flow of the section on the jth interval; m and n are natural numbers, n is the total number of stations, and m is the total number of intervals.

In this embodiment, assuming that the current time period is the tth time period, under the premise of considering the state transition and time lag, the formulas for interval 1 and interval 2 are as follows:

For station ①:

For station ②:

For the station ③:

Through the collection and linear fitting of the historical data of the inbound volume of the three stations and the cross-section flow of the two intervals, the values of the parameters α _ji and β _ij are to be determined. The specific process is as follows:

Step 1: Determine that the jth interval has the following relationship

y _j (t)＝α _j1 x ₁ (t-Δt _j1 )+α _j2 x ₂ (t-Δt _j2 )+α _j3 x ₃ (t-Δt _j3 )+...+α _jn x _n (t- Δt _jn );

Step 2: From the historical data, select the total flow y _j (t) of the upper section of the j-th interval in the time period t of the day and the inbound volume x ₁ , x ₂ , x ₃ ... x _n of n stations as a group Input data;

Step 3: As in Step 2, select multiple sets of input data of different days from the historical data;

Step 4: Substitute multiple sets of input data into the relational formula in step 1, and determine the parameters to be estimated α _j1 , α _j2 , α _j3 ... α _jn , where j takes the values 1 and 2 in turn;

Step 5: Determine that the i-th station has the following relationship

x _i (t-Δt _ji )=β _i1 y ₁ (t)+β _i2 y ₂ (t)+β _i3 y ₃ (t)+...+β _in y _n (t);

Step 6: Select the total inbound volume y _j (t) of the i-th station in the time period t of a day and the total flow y ₁ , y ₂ , y ₃ ... y _n of the upper sections of m sections from the historical data as a group input data;

Step 7: As in step 6, select multiple sets of input data of different days from the historical data;

Step 8: Substitute multiple sets of input data into the relational formula in step 5, and determine the parameters to be estimated β _i1 , β _i2 , β _i3 ... β _mj , where i takes the values 1, 2 and 3 in sequence.

Step 9: Obtain undetermined parameters α ₁₁ , α ₁₂ , α ₁₃ , α ₂₁ , α ₂₂ , α ₂₃ , β ₁₁ , β ₁₂ , β ₂₁ , β ₂₂ , β ₃₁ and β ₃₂ .

According to the analysis of the relationship between inbound volume and section flow, the following equation holds true for station ①:

α ₁₁ x ₁ (t-Δt ₁₁ )=β ₁₁ y ₁ (t) (6)

α ₂₁ x ₁ (t-Δt ₂₁ )=β ₁₂ y ₂ (t) (7)

At this time, if the full load rate of a given section is 140%, that is, y ₁ (t) and y ₂ (t) are known, two different inbound values of station ① can be calculated according to formula 6 and formula 7, respectively x ₁ (t-Δt ₁₁ ), x ₁ (t-Δt ₂₁ ), take min[x ₁ (t-Δt ₁₁ ), x ₁ (t-Δt ₂₁ )] as the controlled inbound quantity of station 1, assuming x ₁ (t-Δt ₁₁ ) is the minimum value, then the maximum inbound volume of station 1 in (t-Δt ₁₁ ) time periods shall not exceed x ₁ (t-Δt ₁₁ ).

In future station passenger flow management, this result can be used to guide the station to monitor the inbound volume in (t-Δt ₁₁ ) time periods in advance to confirm whether the current inbound volume exceeds the calculated controlled inbound volume x ₁ ( t-Δt ₁₁ ), take corresponding flow-limiting measures in advance, so as to achieve the purpose of predicting and controlling the inbound volume.

Similarly, the following equation holds for station ②:

α ₁₂ x ₂ (t-Δt ₁₂ )=β ₂₁ y ₁ (t) (8)

α ₂₂ x ₂ (t-Δt ₂₂ )=β ₂₂ y ₂ (t) (9)

Also take min[x ₂ (t-Δt ₁₂ ),x ₂ (t-Δt ₂₂ )] as the controlled inbound quantity of station ② according to the above method.

Similarly, the following equation holds true for station ③:

α ₁₃ x ₃ (t-Δt ₁₃ )=β ₃₁ y ₁ (t) (10)

α ₂₃ x ₃ (t-Δt ₂₃ )=β ₃₂ y ₂ (t) (11)

Also take min[x ₃ (t-Δt ₁₃ ), x ₂ (t-Δt ₂₃ )] as the controlled inbound quantity of station ③ according to the above method.

Example 2

On the basis of Example 1, assuming that the full load rate of the section section is given different set values such as: 120%, 130%, and 140% three levels, the time to reach the full load rate of these three levels is obviously also different, in turn Set as t ₁ , t ₂ , t ₃ , then the flow rate at the section section can be obtained: y(t ₁ ) ^c=120% , y(t ₂ ) ^c=130% , y(t ₃ ) ^c=140% , put The three levels of cross-section flow are used as input data, and the corresponding inbound volume control value and time period can be output by using the aforementioned method. Here still take the station ① as an example for illustration:

Assuming that when y(t ₁ ) ^c=120% , the control value of station ①’s inbound volume is x ₁ (t ₁ -Δt ₁₁ ) ^c=120% , which means that in (t ₁ -Δt ₁₁ ) time periods, intervals 1 There may be slight congestion, and the station should pay attention to take measures to reduce the number of incoming stations.

Assuming that when y(t ₂ ) ^c=130% , the control value of station ①’s inbound volume is x ₁ (t ₂ -Δt ₁₁ ) ^c=130% , which means that in (t ₂ -Δt ₁₁ ) time periods, intervals 1 There may be moderate congestion, and the station should further reduce the amount of incoming traffic on the basis of the original restrictions.

Assuming that when y(t ₃ ) ^c=140% , the control value of station ①’s inbound volume is x ₁ (t ₃ -Δt ₁₁ ) ^c=140% , which means that in (t ₃ -Δt ₁₁ ) time periods, intervals 1 There may be serious congestion, and the station should adopt stricter measures to limit the amount of incoming traffic on the basis of the original restrictions.

In this embodiment, by grading the passenger flow (or full load rate) of the section section, it can guide the subway station to take grading control measures for the inbound volume in different periods according to the situation in the actual operation and management, so that the inbound volume control is more reasonable.

Apparently, the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those of ordinary skill in the art can also make It is impossible to exhaustively list all the implementation modes here, and any obvious changes or changes derived from the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims (7)

1. A method for predicting and controlling the subway station entering amount is characterized by comprising the following steps:

s1: constructing a linear function formula between station entering amount and zone discontinuous surface flow in a subway network:

wherein,

x_ithe total station entering amount of the ith station is i-1, 2, …, n;

y_jis the jth zoneTotal flow rate of the cross section at intervals, j is 1,2, …, m;

α_jithe proportion of the passenger flow volume which enters the station from the ith station and passes through the section of the jth interval to the total station entering volume of the ith station is shown;

β_ijthe proportion of the passenger flow entering from the ith station in the j section passenger flow is the total passenger flow of the j section;

△t_jithe time required for the passenger flow to reach the jth section from the ith station;

x_i(t-△t_ji) Is the (t- Δ t)_ji) The total station entering amount of the ith station in each time interval;

y_j(t) is the total flow of the cross section in the jth interval in the tth time period;

n is the total number of stations, and m is the total number of intervals;

s2: performing linear fitting based on historical data values of station arrival amount and zone discontinuous flow to determine parameters to be estimated;

s3: calculating the target station-entering amount of the corresponding station based on the section flow of the given interval;

in step S3, the method specifically includes the following steps:

s31: based on the fact that the passenger flow volume of the ith station entering the station and passing through the jth interval is equal to the passenger flow volume of the ith station entering the station in the section passenger flow of the jth interval, the following equation is obtained:

α_jix_i(t-△t_ji)＝β_ijy_j(t)；

s32: determining the maximum section flow y of the jth section based on the section maximum full load rate_j(t)；

S33: let i equal to 1, j take the values 1,2, …, m in turn, to obtain the (t-. DELTA.t)_j1) M station-entering amounts x of 1 st station in each time period₁(t-△t₁₁)、x₁(t-△t₂₁)、x₁(t-△t₃₁)、…、x₁(t-△t_m1)；

S34: selecting the m inbound traffic x₁(t-△t₁₁)、x₁(t-△t₂₁)、x₁(t-△t₃₁)、…、x₁(t-△t_m1) Minimum value minx of (1)₁(t-△t_j1) The 1 st station is at the (t-Deltat) th_j1) A control value of the station entering amount in each time interval;

s35: sequentially taking the value of i as 2, …, n, and repeating the steps S33 and S34 to obtain the (t-delta t) th station of each station_j1) The control value of the station entering amount in each time interval is the target station entering amount of the corresponding station;

s4: and determining a passenger flow control strategy based on the target arrival amount.

2. A method for predicting and controlling a subway approach amount as claimed in claim 1, wherein said step S2 specifically includes the following steps:

s21: determining that the jth interval has the following relation

y_j(t)＝α_j1x₁(t-△t_j1)+α_j2x₂(t-△t_j2)+α_j3x₃(t-△t_j3)+...+α_jnx_n(t-△t_jn)；

S22: selecting total flow y of the upper section of the jth interval in the t time period in one day from historical data_j(t) and the amount of arrival x at n stations₁、x₂、x₃…x_nAs a set of input data;

s23: selecting sets of input data for different days from the historical data as described in step S22;

s24: substituting the multiple sets of input data selected in S23 into the relational expression in step S21 to determine the parameter alpha to be estimated_j1、α_j2、α_j3…α_jnWherein j is 1,2, …, m in sequence;

s25: determining that the ith station has the following relational expression

x_i(t-△t_ji)＝β_i1y₁(t)+β_i2y₂(t)+β_i3y₃(t)+...+β_iny_n(t)；

S26: selecting the time period t in one day from the historical dataTotal station entering amount x of ith station_i(t) and total flow y of cross-section in m intervals₁、y₂、y₃…y_mAs a set of input data;

s27: selecting sets of input data for different days from the historical data as described in step S26;

s28: substituting the multiple sets of input data selected in S27 into the relational expression in step S25 to determine the parameter beta to be estimated_i1、β_i2、β_i3…β_mjWherein i is 1,2, …, n in sequence.

3. A method for predicting and controlling the arrival amount of a subway according to claim 1, wherein said maximum loading rate of section is 140%.

4. A method for predictively controlling a subway approach traffic as claimed in claim 1, wherein in said step S4, determining a passenger flow control strategy based on a target approach traffic comprises:

forecasting and estimating the future time period of current limiting of each station every day and the corresponding passenger flow control value, and giving a station current limiting forecasting suggestion; and/or

And determining whether the current station needs to carry out current limiting and implementing measures for controlling the station entering amount by comparing the station entering amount in the current time period with the target station entering amount.

5. A method for predictively controlling the arrival volume of a subway as defined in claim 2, wherein said passenger flow control strategy further comprises a hierarchical control of station current limiting measures.

6. The method for predicting and controlling the subway station entering quantity according to claim 1, wherein full load rates of different levels are set for different intervals, and current limiting time periods and passenger flow volume control values corresponding to the full load rates of different levels are calculated to realize hierarchical control of station current limiting measures.

7. A method for predicatively controlling the arrival amount of a subway according to claim 6, wherein said different levels of full load rates comprise three levels of 120%, 130% and 140%, respectively.