CN111367900A - AFC data-based urban rail transit network normal state current limiting intensity calculation method - Google Patents

AFC data-based urban rail transit network normal state current limiting intensity calculation method Download PDF

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CN111367900A
CN111367900A CN202010119502.1A CN202010119502A CN111367900A CN 111367900 A CN111367900 A CN 111367900A CN 202010119502 A CN202010119502 A CN 202010119502A CN 111367900 A CN111367900 A CN 111367900A
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孙会君
袁富亚
高自友
吴建军
康柳江
尹浩东
杨欣
屈云超
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Abstract

The invention provides a method for calculating normal current limiting intensity of an urban rail transit network based on AFC data. The method comprises the following steps: collecting AFC data of urban rail transit to obtain the number of passengers entering the station and the number of passengers leaving the station in each time period; according to the AFC data of the urban rail transit, the train operation schedule information and the topological structure of the urban rail transit network, the number of transfer passengers of each transfer station in each time period is obtained through simulation calculation; and calculating the minimum passenger number, the maximum passenger number and the total passenger number index of each station in each time interval according to the number of passengers getting in the station, the number of passengers getting out of the station and the number of passengers to be transferred in each time interval of each station, and calculating the current limiting strength and the current limiting rate of each time interval of each station. The invention can be used for cooperatively optimizing the urban rail transit train schedule and the current limiting measures, reasonably setting the passenger flow management measures and the schedule operation scheme, and improving the operation safety and the passenger safety in the urban rail transit system.

Description

AFC data-based urban rail transit network normal state current limiting intensity calculation method
Technical Field
The invention relates to the technical field of urban rail transit current-limiting optimization, in particular to a method for calculating normal current-limiting intensity of an urban rail transit network based on AFC data.
Background
With the continuous expansion of the urban scale and the continuous increase of the traffic demand, a plurality of cities form a large urban disease taking traffic jam as a core. The urban rail transit called "green transit" is gradually becoming the main transportation mode of people, especially office workers on commuting trips, due to its characteristics of large capacity, fast speed, low energy consumption, etc. Urban rail transit gradually becomes the backbone of urban public transport, and urban rail transit networked operation is further expanded. However, due to the imbalance between the huge demand and the limited passenger capacity and the irregularity of the space-time distribution of the travel demand, the development of urban rail transit is facing tremendous passenger traffic pressure, especially in the early-late rush hour. In fact, oversaturated passenger flow demands of urban rail transit necessarily have a serious impact on both passenger and train operation. On one hand, the train with the overlarge passenger occupancy rate is difficult to meet the boarding requirements of all waiting passengers on the platform, so that a large number of passengers are detained on the platform for waiting for a long time, and the accumulated waiting passengers on the platform are increased. It is statistically found that overcrowding at a platform will pose a significant safety threat to passenger safety and train operation. On the other hand, the oversized getting-on and getting-off requirements require more time, so that the stop time of the train is directly increased, and the train delay is caused. Moreover, in the case of severe traveling pressure, the delay of a part of waiting trains is not only difficult to eliminate, but also can be extended continuously so as to affect the normal operation of the following trains. In order to reduce the pressure of the large passenger flow on the lines and stations, a safer and more effective passenger flow organization and management method is urgently needed.
At present, a method widely adopted and effective in China for relieving the pressure of the mass traffic of urban rail transit is to perform passenger flow control, also called flow limiting, and mainly comprises control measures of reducing the station entering speed of passengers, surrounding and blocking the stations, shunting, intercepting and the like. With the increase of urban rail transit road network passenger flow, current limiting control measures become normal in early and late peak periods, especially in super-large cities such as Beijing, Shanghai and the like. The current limiting measures include three elements: flow limiting station, flow limiting time and flow limiting intensity. However, in the actual passenger flow organization, the manager usually determines the current limiting strength according to subjective experience, and there is no strong theory or data support. At present, although some students have studied on the current limiting strategy in the case of large traffic, they usually take one route and one or several stations as research objects, and lack a traffic organization method considering transfer behavior and cooperatively limiting current for multiple routes and stations. Moreover, a current limiting method for mining urban rail transit networks based on a large amount of passenger trip data is lacked.
One urban rail transit current limiting scheme in the prior art is as follows: based on the network passenger flow demand and distribution characteristics, a multi-objective mathematical programming model with maximized matching degree of the passenger flow demand and the conveying capacity and minimized delayed passenger flow is established from a network level, and empirical analysis is carried out by taking the Beijing urban rail transit network as an object. The disadvantages of this solution are: the incoming line demand traffic is not derived from actual data collection.
Another urban rail transit current limiting scheme in the prior art is as follows: by utilizing the definition of equal time intervals, aiming at the commuting line of the urban rail transit, the limit of the station gate entering machine, namely the train capacity is considered, and an urban rail transit passenger flow control optimization model based on one line is provided. The method comprises the steps of identifying key current-limiting stations and current-limiting time periods by constructing state evaluation indexes (full-load rate distribution entropy, high-full-load rate interval proportion, station platform congestion degree and average full-load rate of arriving trains) of the urban rail transit road network and stations, and establishing a road network cooperative current-limiting model on the basis to determine specific current-limiting stations and current-limiting intensity. The disadvantages of this solution are: the calculation process for obtaining the current limiting strength is complicated.
Disclosure of Invention
The embodiment of the invention provides a method for calculating normal current limiting intensity of an urban rail transit network based on AFC data, which aims to overcome the problems in the prior art.
A method for calculating normal current limiting intensity of an urban rail transit network based on AFC data preferably comprises the following steps:
collecting AFC data of urban rail transit, processing and counting the AFC data to obtain the number of passengers entering the station and the number of passengers leaving the station in each time period;
according to the AFC data of the urban rail transit, the train operation schedule information and the topological structure of the urban rail transit network, the number of transfer passengers of each transfer station in each time period is obtained through simulation calculation;
and calculating the minimum passenger number, the maximum passenger number and the total passenger number index of each station in each time period according to the number of passengers getting in and out of each station in each time period and the number of passengers getting in and out of each station in each time period, and further calculating the current limiting strength and the current limiting rate of each station in each time period.
Preferably, the collecting AFC data of urban rail transit, processing and counting the AFC data to obtain the number of passengers entering the station and the number of passengers leaving the station in each time period at each station includes:
according to the urban rail transit system, intelligent card toll collection devices are arranged at entrances and exits of each station, when passengers pass through the intelligent card toll collection devices, the intelligent card toll collection devices record AFC data of the passengers, the AFC data comprise information such as passenger ID numbers, passenger entrance stations, passenger entrance time, passenger exit stations and passenger exit time, all AFC data of all passengers at each station are collected, and the number of passengers entering the station and the number of passengers exiting the station at each time interval at each station are obtained.
Preferably, the obtaining of the number of transfer passengers at each time period at each transfer station through simulation calculation according to the AFC data of the urban rail transit, the train operation schedule information and the topological structure of the urban rail transit network includes:
and according to the number of passengers entering the station and the number of passengers leaving the station in each time period of each station, passenger travel OD data, train operation schedule information and urban rail transit network topological structure in each time period of each station, the number of passengers to be transferred in each time period of each transfer station is obtained through simulation calculation.
Preferably, the calculating the minimum passenger number, the maximum passenger number and the total passenger number index of each station in each time period according to the number of passengers getting in and out of each station in each time period and the number of passengers getting in and out of each transfer station in each time period, and further calculating the current limiting strength and the current limiting rate of each station in each time period includes:
calculating the minimum passenger number, the maximum passenger number and the total passenger number index of each station in each time interval by using a minimum-maximum value equation, and performing normalization processing on the minimum passenger number, the maximum passenger number and the total passenger number index, wherein specific parameters and signs are defined as follows:
·xlstrepresents the number of passengers entering station s on line l during time t;
·
Figure BDA0002392525030000031
representing the minimum number of passengers entering a certain station of urban rail transit in the time period t;
·
Figure BDA0002392525030000032
representing the maximum number of passengers entering a certain station of urban rail transit in the time period t;
·xt，sumrepresenting the total passenger number entering the urban rail transit station in the time period t;
·
Figure BDA0002392525030000033
representing the minimum total passenger number entering the urban rail transit network in the time interval dimension;
·
Figure BDA0002392525030000034
representing the maximum total passenger number entering the urban rail transit network in the time interval dimension;
·
Figure BDA0002392525030000035
a weight index representing the number of passengers arriving at the station in the t period;
·ylstrepresents the number of passengers leaving the on-board station s on the line l during the time period t;
·Yt minrepresenting the minimum passenger number leaving a certain station of urban rail transit in the time period t;
·Yt maxrepresenting the maximum number of passengers leaving a certain station of urban rail transit in the time period t;
·yt，sumrepresenting the total passenger number leaving the urban rail transit station in the time period t;
·
Figure BDA0002392525030000036
representing the minimum total passenger number leaving the urban rail transit network in the time period dimension;
·
Figure BDA0002392525030000037
representing the maximum total passenger number leaving the urban rail transit network in the time interval dimension;
·
Figure BDA0002392525030000038
a weight index representing the number of passengers outbound during the t period;
·zlstrepresents the number of transfer passengers at station s on route l during time t;
·
Figure BDA0002392525030000039
representing the minimum number of transfer passengers at a certain station of the urban rail transit in the period of t;
·
Figure BDA00023925250300000310
representing the maximum number of transfer passengers at a certain station of the urban rail transit in the time period t;
·zt，sumrepresenting the total number of transfer passengers at the urban rail transit station in the period of t;
·
Figure BDA0002392525030000041
representing the minimum total passenger transfer number of the urban rail transit network in the time interval dimension;
·
Figure BDA0002392525030000042
representing the maximum total passenger transfer number of the urban rail transit network in the time interval dimension;
·
Figure BDA0002392525030000043
a weight index representing the number of passengers transferred in the t period;
·θstrepresenting the current limiting strength value of the station s in the t period;
delta is a preset current limiting intensity threshold value, represents the minimum current limiting intensity value when the urban rail transit implements current limiting measures, and takes a value of 0.02;
·ρstindicating the rate of flow restriction for station s during time t.
Calculating a time interval weight index of each station by using a minimum-maximum value equation:
Figure BDA0002392525030000044
Figure BDA0002392525030000045
Figure BDA0002392525030000046
and (3) carrying out normalization processing on three indexes of the number of passengers arriving at the station, the number of passengers arriving at the station and the number of passengers transferring the station by using a minimum-maximum value equation:
Figure BDA0002392525030000047
Figure BDA0002392525030000048
Figure BDA0002392525030000049
according to
Figure BDA00023925250300000410
And
Figure BDA00023925250300000411
and the weighted sum of the normalized indexes is used for calculating the current limiting intensity of each station in each time period:
Figure BDA00023925250300000412
wherein, w1,w2,w3Is a weighting coefficient of the three indexes;
and calculating the flow limiting rate of each station in each time period according to the flow limiting intensity:
Figure BDA0002392525030000051
δ is a preset threshold of the current limiting intensity.
It can be seen from the technical solutions provided by the embodiments of the present invention that, in the embodiments of the present invention, after cleaning, preprocessing and counting AFC data, influence factors of the current limiting intensity are determined, and a suitable index is selected from the influence factors to represent current limiting measures, and finally, corresponding current limiting intensity and current limiting rate calculation methods are designed. The method can be used for daily passenger flow management and control of the urban rail transit network.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic diagram illustrating an implementation principle of a method for calculating normal current limiting strength of an urban rail transit network based on AFC data according to an embodiment of the present invention.
Detailed Description
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Example one
Based on the background technologies, the embodiment of the invention discloses an AFC data-based method for calculating normal current limiting intensity of an urban rail transit network through in-and-out data of passengers traveling by taking urban rail transit, which is collected by an AFC (automatic Fare Collection) system, and provides normal current limiting measures of the urban rail transit network from the levels of the number of passengers getting in the station, the number of passengers transferring the station, the number of passengers getting out of the station and the like. The number of transfer passengers is calculated by using a multi-agent simulation method adopted by Yang et al (2019) for reference. The method provides technical support and decision basis for the urban rail transit management department to deal with the large passenger flow and obtain normal state current limiting measures.
An implementation principle schematic diagram of the method for calculating normal current limiting strength of the urban rail transit network based on AFC data, provided by the embodiment of the invention, is shown in FIG. 1, and comprises the following processing steps:
step 1, collecting AFC (Automatic Fare Collection System) data of urban rail transit, processing and counting the data, and obtaining the number of passengers entering the station and the number of passengers leaving the station in each time period.
The urban rail transit system is provided with intelligent card toll collection equipment at an entrance and an exit of each station, passengers can record AFC data of the passengers when passing through the equipment, the AFC data comprise information such as passenger ID numbers, passenger entrance stations, passenger entrance time, passenger exit stations, passenger exit time and the like, all AFC data of the passengers at each station are collected, and the number of the passengers entering the station and the number of the passengers exiting the station at each time period at each station are obtained.
And 2, according to the AFC data of the urban rail transit, the train operation schedule information and the topological structure of the urban rail transit network, carrying out simulation calculation to obtain the number of transfer passengers of each transfer station in each time period.
Because the data of the intelligent card only comprises the position and time information of the station where passengers get on or off the train, the number of passengers getting on the train and the number of passengers getting off the train at each station and each time interval can be simply obtained. However, in consideration of the uncertainty of the passenger transfer process, it is difficult to directly obtain the number of transfer passengers per transfer station from the data. In the invention, by using a multi-agent-based simulation process and combining the OD (Origin to Destination) data of passenger trips, the information of the train running schedule and the topological structure of the urban rail transit network, the number of transfer passengers at each time interval at each transfer station is obtained through simulation calculation.
Since each line in the urban rail transit network has directivity, N is defined to represent a finite set of stops and (O, D) is defined to represent an OD pair from the O stop to the D stop, where O, D ∈ N, further, a passenger is defined by pWaiting for an area, arriving at a platform waiting area, taking a train, getting off at a transfer station for preparing transfer, getting off at a platform of a destination station and checking tickets out. But passengers may not have a transfer in the urban rail transit network or more than one transfer has occurred, so not all passengers contain these seven states. Discretizing the state of the passenger into a simulation step Δ T, T using a simulation clock TbAnd teRespectively representing the time of the start and the end of the simulation, K representing the number of simulation cycles, Tc representing the length of each simulation cycle, wherein K is (t)e-tb)/Tc. And matching the movement state of the passengers in the urban rail transit based on the OD data of the passengers through the collected AFC data to obtain the paths of all the passengers, and counting the station entering amount, the station exiting amount and the transfer amount of the stations in each simulation period.
The simulation calculation can approximately simulate the selection behavior of passengers in the urban rail transit network, and the proposed simulation parameters are verified through actual measurement data and a large amount of historical data.
And 3, carrying out normalization processing on the three indexes of each station in each time interval, and determining a reasonable and effective current limiting intensity calculation method.
The time period may be divided into a peak period, an off-peak period, or a fixed time period, such as 1-2 hours. The three indexes comprise a minimum passenger number, a maximum passenger number and a total passenger number, because the three indexes have different influence degrees, the three indexes of each station in each time period are normalized by using a minimum-maximum value equation, and specific parameters and signs are defined as follows:
·xlstrepresents the number of passengers entering station s on line l during time t;
·
Figure BDA0002392525030000071
representing the minimum number of passengers entering a certain station of urban rail transit in the time period t;
·
Figure BDA0002392525030000072
representing the maximum number of passengers entering a certain station of urban rail transit in the time period t;
·xt，sumrepresenting the total passenger number entering the urban rail transit station in the time period t;
·
Figure BDA0002392525030000073
representing the minimum total passenger number entering the urban rail transit network in the time interval dimension;
·
Figure BDA0002392525030000074
representing the maximum total passenger number entering the urban rail transit network in the time interval dimension;
·
Figure BDA0002392525030000075
a weight index representing the number of passengers arriving at the station in the t period;
·ylstrepresents the number of passengers leaving the on-board station s on the line l during the time period t;
·Yt minrepresenting the minimum passenger number leaving a certain station of urban rail transit in the time period t;
·Yt maxrepresenting the maximum number of passengers leaving a certain station of urban rail transit in the time period t;
·yt，sumrepresenting the total passenger number leaving the urban rail transit station in the time period t;
·
Figure BDA0002392525030000076
representing the minimum total passenger number leaving the urban rail transit network in the time period dimension;
·
Figure BDA0002392525030000077
representing the maximum total passenger number leaving the urban rail transit network in the time interval dimension;
·
Figure BDA0002392525030000078
a weight index representing the number of passengers outbound during the t period;
·zlstrepresents the number of transfer passengers at station s on route l during time t;
·
Figure BDA0002392525030000079
representing the minimum number of transfer passengers at a certain station of the urban rail transit in the period of t;
·
Figure BDA00023925250300000710
representing the maximum number of transfer passengers at a certain station of the urban rail transit in the time period t;
·zt，sumrepresenting the total number of transfer passengers at the urban rail transit station in the period of t;
·
Figure BDA00023925250300000711
representing the minimum total passenger transfer number of the urban rail transit network in the time interval dimension;
·
Figure BDA00023925250300000712
representing the maximum total passenger transfer number of the urban rail transit network in the time interval dimension;
·
Figure BDA00023925250300000713
a weight index representing the number of passengers transferred in the t period;
·θstrepresenting the current limiting strength value of the station s in the t period;
delta represents the minimum current limiting strength value when the urban rail transit implements current limiting measures, and the value is 0.02;
·ρstindicating the rate of flow restriction for station s during time t.
Calculating a time interval weight index of each station by using a minimum-maximum value equation:
Figure BDA0002392525030000081
Figure BDA0002392525030000082
Figure BDA0002392525030000083
and (3) carrying out normalization processing on three indexes of the number of passengers arriving at the station, the number of passengers arriving at the station and the number of passengers transferring the station by using a minimum-maximum value equation:
Figure BDA0002392525030000084
Figure BDA0002392525030000085
Figure BDA0002392525030000086
and 4, calculating the current limiting intensity of each station in each time period.
According to
Figure BDA0002392525030000087
And
Figure BDA0002392525030000088
and the weighted sum of the normalized indexes is used for calculating the current limiting intensity of each station in each time period:
Figure BDA0002392525030000089
wherein, w1,w2,w3Are the weighting coefficients of the three indexes.
And 5, calculating the limiting flow rate of each station in each time period.
Figure BDA00023925250300000810
The current limiting strength of urban rail transit refers to the strength of implementing current limiting measures. The current limiting intensity of each station of the urban rail transit network is calculated based on AFC data and is calculated according to the current limiting intensity value thetastWhether current limiting measures need to be taken at the time t of the station s can be intuitively found: when theta isstWhen the delta is less than or equal to delta, no flow limiting measure is needed, and the flow limiting rate is 0; when theta isstWhen the flow rate is larger than delta, the flow limiting measures need to be taken, and the flow limiting rate is thetast- δ. δ is a preset threshold of the current limiting intensity.
Example two
The current limiting conditions of 238 stations in the Beijing urban rail transit network on the working day are analyzed and calculated based on the smart card data collected by the AFC system of the Beijing urban rail transit network. The Beijing urban rail transit network topology and passenger transport data used by the invention are 2 months and 20 days (Thursday) in 2014. The present invention is described by calculating the current limiting intensity of each station for each time period (hour) by the calculation method proposed by the present invention, but is not limited thereto.
Step 1, cleaning and preprocessing collected AFC data of passengers to obtain the number of passengers entering a station and the number of passengers leaving the station in each time period;
the time dimension is divided by taking the hour as the time step, and the number of passengers entering the station and the number of passengers leaving the station per hour can be obtained. Table 1 below lists the number of passengers entering the station and the number of passengers leaving the station at 5 stations of urban rail transit in beijing.
TABLE 1 number of passengers inbound and outbound passenger number example
Figure BDA0002392525030000091
Step 2, obtaining the number of passengers to be transferred per station per hour;
the number of transfer passengers per hour at each station is calculated based on a multi-agent simulation process, and the following table 2 lists the number of transfer passengers at 10 stations of urban rail transit in Beijing.
TABLE 2 hourly transfer passenger flow per station
Figure BDA0002392525030000092
Figure BDA0002392525030000101
Step 3, normalizing the three indexes of the minimum passenger number, the maximum passenger number and the total passenger number of each station in each time interval to determine a reasonable and effective calculation method of the flow limiting intensity;
Figure BDA0002392525030000102
Figure BDA0002392525030000103
Figure BDA0002392525030000104
wherein the content of the first and second substances,
Figure BDA0002392525030000105
and 4, calculating the current limiting intensity of each station in each time period.
Figure BDA0002392525030000106
Wherein, w1,w2,w3Is a weighting coefficient of three indexes, set as w1＝0.7,w2＝0.1,w3＝0.2。
And 5, calculating the limiting flow rate of each station in each time period.
Figure BDA0002392525030000107
Some of the results are shown in tables 3 and 4.
TABLE 3 intensity, flow rate and ranking of flow restrictions for 20 stations in Beijing City during early peak hours
Figure BDA0002392525030000108
Figure BDA0002392525030000111
TABLE 4 intensity, flow rate and ranking of flow restrictions for late rush hour, 20 stations in Beijing City
Figure BDA0002392525030000112
The calculation result shows that the distribution of the current limiting intensity of the station is different between the early peak and the late peak, and the current limiting intensity between 7:00 and 9:00 and 17:00 and 19:00 is the maximum.
In early peak period, 61 stations between 7:00 and 8:00 need to implement current limiting measures, 57 stations before 8:00 to 9:00 need to implement current limiting measures, and stations with high current limiting strength are mainly concentrated in suburbs (such as Tiantonyuan, Tiantonyuan north, apple orchard and the like). Further, as time goes by, stations having a large current limiting strength gradually shift to the city center. It is observed that the stations where the current limiting is first implemented are the north and south aster stations, which also end the current limiting at the latest. The station with large current limiting intensity in the period of 7:00-8:00 is more than that in the period of 8:00-9: 00.
In the late peak period, 59 stations between 17:00 and 18:00 need to implement current limiting measures, 33 stations before 18:00 to 19:00 need to implement current limiting measures, and stations with high current limiting strength are mainly concentrated in CBDs (such as national trade, national gate building and the like) and throat areas (Xidi flag, Dawang road and the like) connecting city centers and suburban large houses. As time goes by, the station with a high current limiting strength gradually shifts from the city center to the suburban area. It is noted that the current limiting intensity value of the west two flag stations is relatively large in the morning and evening rush hour, because the station is not only a transfer station (located at the urban and rural junction), but also the station is close to the software park of the middle guan village, and many commuters exist.
Overall, the early peak times rail transit systems have greater current limiting intensity than late peak times, which is closely related to commuters being on-demand (typically 9:00 commutes) early.
The AFC data-based calculation method provided by the invention is used for carrying out example verification on the Beijing urban rail transit network to calculate and analyze the current-limiting intensity of the urban rail transit network. The result shows that the magnitude and the distribution rule of the current limiting intensity accord with the tide travel rule of urban commuters. In early peak period, stations with high flow limiting intensity are mainly concentrated in suburbs (such as Tiantong yuan, Tiantong yuan north, apple orchard and the like); during late peak periods, stations with high current limiting strength are mainly concentrated on CBDs (such as national trade) and throat areas (such as Xidi flag, Dawang road and the like) connecting city centers and large-scale residences in suburbs. The above conclusions are consistent with the actual situation, which means that the proposed method is feasible and reasonable.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
In summary, in the embodiments of the present invention, after AFC data is cleaned, preprocessed, and counted, influence factors of the current limiting intensity are determined, and a suitable index is selected from the influence factors to represent the current limiting measure, and finally, a corresponding current limiting intensity calculation method is designed. The method can be used for daily passenger flow management and control of the urban rail transit network.
The method provided by the embodiment of the invention can be used for cooperatively optimizing the urban rail transit train schedule and the current limiting measures, reasonably setting the passenger flow management measures and the schedule operation scheme, improving the operation safety and passenger safety in the urban rail transit system, reducing the long-time detention of partial station passengers and improving the fairness of each station.
By applying the method of the embodiment of the invention, reasonable and effective peak current limiting measures can be obtained, the safety inside passenger rail traffic (mainly on platforms and trains) can be improved, and potential risks caused by a passenger flow supersaturation state are reduced; train schedules and current limiting measures can be considered at the same time, and coordination of train operation and passenger flow management can be improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. A method for calculating normal current limiting intensity of an urban rail transit network based on AFC data is characterized by comprising the following steps:
collecting AFC data of urban rail transit, processing and counting the AFC data to obtain the number of passengers entering the station and the number of passengers leaving the station in each time period;
according to the AFC data of the urban rail transit, the train operation schedule information and the topological structure of the urban rail transit network, the number of transfer passengers of each transfer station in each time period is obtained through simulation calculation;
and calculating the minimum passenger number, the maximum passenger number and the total passenger number index of each station in each time period according to the number of passengers getting in and out of each station in each time period and the number of passengers getting in and out of each station in each time period, and further calculating the current limiting strength and the current limiting rate of each station in each time period.
2. The method of claim 1, wherein the collecting AFC data of urban rail transit, and processing and counting the AFC data to obtain the number of passengers entering the station and the number of passengers leaving the station in each time period comprises:
according to the urban rail transit system, intelligent card toll collection devices are arranged at entrances and exits of each station, when passengers pass through the intelligent card toll collection devices, the intelligent card toll collection devices record AFC data of the passengers, the AFC data comprise information such as passenger ID numbers, passenger entrance stations, passenger entrance time, passenger exit stations and passenger exit time, all AFC data of all passengers at each station are collected, and the number of passengers entering the station and the number of passengers exiting the station at each time interval at each station are obtained.
3. The method as claimed in claim 1, wherein the step of obtaining the number of transfer passengers per time slot at each transfer station through simulation calculation according to the AFC data of urban rail transit, the train operation schedule information and the topological structure of the urban rail transit network comprises the following steps:
and according to the number of passengers entering the station and the number of passengers leaving the station in each time period of each station, passenger travel OD data, train operation schedule information and urban rail transit network topological structure in each time period of each station, the number of passengers to be transferred in each time period of each transfer station is obtained through simulation calculation.
4. The method as claimed in claim 1, 2 or 3, wherein the step of calculating the minimum passenger number, the maximum passenger number and the total passenger number index of each station in each time interval according to the number of passengers entering the station and the number of passengers leaving the station in each time interval of each station and the number of passengers transferring in each time interval of each transfer station, and further calculating the current limiting strength and the current limiting rate of each station in each time interval comprises the following steps:
calculating the minimum passenger number, the maximum passenger number and the total passenger number index of each station in each time interval by using a minimum-maximum value equation, and performing normalization processing on the minimum passenger number, the maximum passenger number and the total passenger number index, wherein specific parameters and signs are defined as follows:
·xlstrepresents the number of passengers entering station s on line l during time t;
·
Figure FDA0002392525020000022
representing the minimum number of passengers entering a certain station of urban rail transit in the time period t;
·
Figure FDA0002392525020000023
representing the maximum number of passengers entering a certain station of urban rail transit in the time period t;
·xt，sumrepresenting the total passenger number entering the urban rail transit station in the time period t;
·
Figure FDA0002392525020000024
representing the minimum total passenger number entering the urban rail transit network in the time interval dimension;
·
Figure FDA0002392525020000025
representing the maximum total passenger number entering the urban rail transit network in the time interval dimension;
·
Figure FDA0002392525020000026
a weight index representing the number of passengers arriving at the station in the t period;
·ylstrepresents the number of passengers leaving the on-board station s on the line l during the time period t;
·Yt minrepresenting the minimum passenger number leaving a certain station of urban rail transit in the time period t;
·Yt maxrepresenting the maximum number of passengers leaving a certain station of urban rail transit in the time period t;
·yt，sumindicating departure during a period of tThe total passenger number of the urban rail transit station;
·
Figure FDA0002392525020000027
representing the minimum total passenger number leaving the urban rail transit network in the time period dimension;
·
Figure FDA0002392525020000028
representing the maximum total passenger number leaving the urban rail transit network in the time interval dimension;
·
Figure FDA0002392525020000029
a weight index representing the number of passengers outbound during the t period;
·zlstrepresents the number of transfer passengers at station s on route l during time t;
·
Figure FDA00023925250200000210
representing the minimum number of transfer passengers at a certain station of the urban rail transit in the period of t;
·
Figure FDA00023925250200000211
representing the maximum number of transfer passengers at a certain station of the urban rail transit in the time period t;
·zt，sumrepresenting the total number of transfer passengers at the urban rail transit station in the period of t;
·
Figure FDA00023925250200000212
representing the minimum total passenger transfer number of the urban rail transit network in the time interval dimension;
·
Figure FDA00023925250200000213
representing maximum gross transfer of urban rail transit networks in a time period dimensionThe number of passengers;
·
Figure FDA00023925250200000214
a weight index representing the number of passengers transferred in the t period;
·θstrepresenting the current limiting strength value of the station s in the t period;
delta is a preset current limiting intensity threshold value which represents the minimum current limiting intensity value when the urban rail transit implements current limiting measures,
the value is 0.02;
·ρstindicating the rate of flow restriction for station s during time t.
Calculating a time interval weight index of each station by using a minimum-maximum value equation:
Figure FDA0002392525020000021
Figure FDA0002392525020000031
Figure FDA0002392525020000032
and (3) carrying out normalization processing on three indexes of the number of passengers arriving at the station, the number of passengers arriving at the station and the number of passengers transferring the station by using a minimum-maximum value equation:
Figure FDA0002392525020000033
Figure FDA0002392525020000034
Figure FDA0002392525020000035
according to
Figure FDA0002392525020000038
And
Figure FDA0002392525020000039
and the weighted sum of the normalized indexes is used for calculating the current limiting intensity of each station in each time period:
Figure FDA0002392525020000036
wherein, w1,w2,w3Is a weighting coefficient of the three indexes;
and calculating the flow limiting rate of each station in each time period according to the flow limiting intensity:
Figure FDA0002392525020000037
δ is a preset threshold of the current limiting intensity.
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