CN111861219A - A method and system for identifying global structural risk bottlenecks in regional rail transit - Google Patents

Abstract

The invention relates to a method for identifying global structural risk bottlenecks of regional rail transit, comprising the following steps: a data acquisition step: acquiring road network structure risk data and passage capacity data; sensitivity data processing steps; risk bottleneck result output: outputting optimization sensitivity and failure Sensitivity, that is, to complete the identification of risk bottlenecks. The beneficial effect of the present invention is to provide a method and system for identifying global structural risk bottlenecks of regional rail transit based on sensitivity analysis. After establishing the global structural risk assessment index of the road network, through sensitivity analysis, the influence of the failure and optimization of the in-station passage/section of the road network on the global structural risk of the road network is evaluated, so as to provide decision support for the protection and optimization of the road network risk bottleneck. And the regional rail transit in Chengdu-Chongqing area is taken as an example to verify the effectiveness of the method.

Description

A method and system for identifying global structural risk bottlenecks in regional rail transit

technical field

The invention belongs to the field of computer systems based on specific calculation models, and particularly relates to a method and system for identifying global structural risk bottlenecks of regional rail transit.

Background technique

Regional rail transit refers to a comprehensive rail transit system that includes high-speed railways, intercity trains, monorails, subways and other rail transit systems formed for the needs of regional economic integration. With the rapid development of regional economy and the formation of urban circles, regional rail transit is facing the development requirements of safer, more efficient and more comfortable, reflecting the development characteristics of heterogeneity, integrity, interaction and synergy.

The coexistence of multiple modes of regional rail transit not only meets the needs of the integrated development of the regional economy and improves the convenience of residents' travel, but it also brings more risks. At the same time, the interaction between the multiple modes and the integrity of the road network Therefore, the identification of risk bottlenecks is of great significance for the realization of targeted protection and optimization of key risk points in the regional rail transit system, and the effective reduction of overall risks. However, many existing researches on global risk assessment of rail transit network use the method of index fusion. Although risk factors can be evaluated and ranked, they cannot reflect the overall structure of the road network from the perspective of transportation capacity due to the influence of many subjective factors. risks, and cannot effectively identify the global risk bottlenecks of the road network. Each node/interval in the road network has a different impact on the overall risk of the road network. The failure of some nodes/intervals will greatly increase the risk of the road network, while the optimization of some parts will greatly reduce the risk of the road network.

The existing research on the global risk assessment of rail transit is greatly affected by subjective factors. At present, there are few evaluations of the global structural risk of the rail transit road network, and there are few literatures evaluating the impact of the optimization of the road network structure on the global risk; in the identification of risk bottlenecks On the one hand, there is no report on the combination of sensitivity analysis and identification of rail transit risk bottlenecks.

SUMMARY OF THE INVENTION

On the basis of evaluating the global structural risk of the road network, the present invention uses the sensitivity analysis method to evaluate the failure of passages/sections in each station of the road network and the influence of optimization on the global structural risk of the road network, thereby providing decision support for the protection and optimization of the road network risk bottleneck. .

On the one hand, the present invention provides a method for identifying global structural risk bottlenecks of regional rail transit, including the following steps:

Data acquisition steps: acquire road network structure risk data and channel traffic capacity data;

Sensitivity data processing steps: Calculate the optimal sensitivity according to Equation 1, and calculate the failure sensitivity according to Equation 2,

表示通道

的优化灵敏度，

表示通 道

的通行能力；where S ^* is the risk of road network structure,

Indicates the channel

optimized sensitivity,

Indicates the channel

capacity;

为通道

的失效灵敏度，S^*'为通道通行能力变化后路网结 构风险；in

for the channel

The failure sensitivity of , S ^* ' is the road network structure risk after the passage capacity changes;

Risk bottleneck result output: Output optimization sensitivity and failure sensitivity, that is, complete risk bottleneck identification.

Another aspect of the present invention also provides a regional rail transit global structural risk bottleneck identification system, the system comprising at least one processor; and a memory storing instructions that, when executed by the at least one processor, implement the present invention The method provided by the invention. The beneficial effect of the present invention is to provide a method and system for identifying global structural risk bottlenecks of regional rail transit based on sensitivity analysis. After establishing the global structural risk assessment index of the road network, through sensitivity analysis, the impact of the failure and optimization of the in-station passage/section of the road network on the global structural risk of the road network is evaluated, so as to provide decision support for the protection and optimization of road network risk bottlenecks. And the regional rail transit in Chengdu-Chongqing area is taken as an example to verify the effectiveness of the method.

Description of drawings

Figure 1. The determinants of the global transport capacity risk of the regional rail transit network;

Figure 2. Topological map of Chongqing regional rail transit lines;

Figure 3. Schematic diagram of the calculation process;

Detailed ways

The method for identifying the global structural risk bottleneck of regional rail transit in some embodiments of the present invention includes the following steps:

Data acquisition steps: acquire road network structure risk data and channel traffic capacity data;

Sensitivity data processing steps: Calculate the optimal sensitivity according to Equation 1, and calculate the failure sensitivity according to Equation 2,

表示通道

的优化灵敏度，

表示通 道

的通行能力；where S ^* is the risk of road network structure,

Indicates the channel

optimized sensitivity,

Indicates the channel

capacity;

为通道

的失效灵敏度，S^*'为通道通行能力变化后路网结 构风险；in

for the channel

The failure sensitivity of , S ^* ' is the road network structure risk after the passage capacity changes;

Risk bottleneck result output: Output optimization sensitivity and failure sensitivity, that is, complete risk bottleneck identification.

The optimization sensitivity of the present invention represents the change degree of the risk of the global structure of the road network with the increase of the capacity of a certain channel (a negative value indicates that the risk is reduced), and the smaller the value (that is, the larger the absolute value), the better the corresponding channel. The greater the contribution of risk.

The failure sensitivity of the present invention represents the degree of change of the global structural risk of the road network with the failure of a certain channel capacity (a value greater than one indicates that the risk increases), and the larger the value, the greater the contribution of the failure of the corresponding channel to the increase of the global structural risk. big.

In these specific examples, the optimal sensitivity is calculated according to Equation 2,

为

的微小变化量，优选的

为

的十分之一。in,

for

A small amount of variation, the preferred

for

one-tenth of .

In these specific embodiments, the step of generating the road network structure risk data includes:

Use the optimization algorithm to optimize the objective function that takes the lowest risk of the global transportation capacity of the road network as the optimization objective, and the objective function is shown in Equation 4:

Among them, x _i (t) represents the passenger flow demand of station (or section) i at time t (unit: person/hour), and c _i (t) represents the passenger flow capacity of station (or section) i at time t (unit: is: person/hour), x _i (t)/c _i (t) represents the passenger flow demand load of the station (or section) i at time t; f(x) is the transport capacity risk probability function, which is used to calculate the passenger flow demand load It is mapped to the possibility of the occurrence of transport capacity risk, and w _i (t) represents the transport capacity risk consequence of the station or section numbered i at time t.

In these specific embodiments, the risk consequence w _i (t) is shown in Equation 5:

w _i (t)=min( _xi (t), _ci (t)) Eq.

In these specific embodiments, the capacity risk probability function is shown in Equation 6:

In some specific embodiments, the objective function is shown in formula 7:

In some other specific embodiments, the objective function is shown in Formula 8:

表示通道

的OD需求，

表示通道

的通行能力，

表示通道

的运能风险的风险后果；

表示通道

的OD需求，

表 示通道

的通行能力，

表示通道

的运能风险的风险后果；

表 示通道

的OD需求，

表示通道

的通行能力，

表示通道

的 运能风险的风险后果；q_k表示区间e_k的OD需求，L_k表示区间e_k的通行 能力，W_k表示区间k的运能风险的风险后果。Among them, i=1,...S is the station number, k=1,...T is the section number; a is the station entrance, b is the station exit; c, d are the pick-up and drop-off locations for each rail transit system; f(x ) represents the transport capacity risk probability function;

Indicates the channel

OD requirements,

Indicates the channel

capacity,

Indicates the channel

the risk consequences of the capacity risk;

Indicates the channel

OD requirements,

Indicates the channel

capacity,

Indicates the channel

the risk consequences of the capacity risk;

Indicates the channel

OD requirements,

Indicates the channel

capacity,

Indicates the channel

q _k represents the OD demand of the interval _ek , L _k represents the traffic capacity of the interval _ek , and W _k represents the risk consequence of the transportation capacity risk of the interval k.

In some specific embodiments, the constraints of the objective function include Equation 9 used to calculate the inbound passenger flow of the station; Equation 10 used to calculate the outbound passenger flow of the station, and Equation 11 used to calculate the station exchange passenger flow, using Equation 12 to calculate the passenger flow in the interval, and Equation 13 and Equation 14 representing the passenger flow distribution constraints:

为决策变量，表示分配到路径p_ij ^m的OD需求，

表示站 v_i内从a点到c点的通道，p_ij ^m表示从站i到站j的第m条简单路径，g(a,p)表示a是路网内的某个通道或区间，p是一条简单路径，如果a 在路径p上，则g(a,p)＝1，否则(a,p)＝0；需要指出的是，这里g(a,p) 只是并不是具体的函数，只是判断路网内的某个通道或区间a是不是 在路径p上。in,

is the decision variable, representing the OD demand assigned to the path p _ij ^m ,

represents the channel from point a to point c in station v _i , p _ij ^m represents the mth simple path from station i to station j, g(a, p) represents that a is a certain channel or interval in the road network, p is a simple path, if a is on the path p, then g(a,p)=1, otherwise (a,p)=0; it should be pointed out that g(a,p) here is just not a specific function , just to determine whether a certain channel or interval a in the road network is on the path p.

The judgment result is given according to the actual situation of the road network.

Among them, x _ji ^m is a decision variable, representing the OD requirement assigned to the path p _ji ^m ;

Among them, x _nj ^m represents the decision variable, which represents the OD demand allocated to the path p _nj ^m ;

为决策变量，表示分配到路径p_ij ^m的OD需求，e_k表示第k 个区间，p_ij ^m表示从站i到站j的第m条简单路径；in,

is a decision variable, representing the OD demand assigned to the path p _ij ^m , e _k represents the kth interval, and p _ij ^m represents the mth simple path from station i to station j;

Among them, q _ij represents the OD demand from station i to station j.

In some specific embodiments, the model of the objective function to minimize the global transport capacity risk is as follows: an undirected graph G(V, E) represents the regional rail transit road network, where V is composed of all stations in the road network set, E is the set composed of all the intervals in the road network; there are S stations and T intervals in the road network, v _i represents the i-th station, and e _k is the k-th interval, where i=1,... S is the station number, k=1,...T is the section number.

In other embodiments of the present invention, there is provided a system for identifying global structural risk bottlenecks in regional rail transit, the system including at least one processor; and a memory storing instructions that when executed by the at least one processor , the method of the present invention is implemented.

Specific embodiments of the present invention will be further described below.

1. The assessment method of global capacity risk of regional rail transit (here, "global capacity risk" is not "global structural risk", and "global capacity risk" is the basis for assessing "global structural risk").

The overall risk of regional rail transit is divided into two aspects: the risk of transportation capacity and the risk of loss of personnel and equipment. The capacity risk depends on a variety of factors, which will be described in detail in this example. The factors of people/objects/environment/management in stations and areas will cause single-point risks, which will eventually lead to the risk of loss of personnel and equipment in the road network. There is a link between capacity risk and single-point risk: single-point risk affects capacity risk by reducing the capacity of stations and sections, while higher capacity risk (which indicates a higher passenger flow demand load, will Examples for details) may introduce new single point risks. Since the relationship between single point risk and global structure is not tight, embodiments will focus on capacity risk to assess global structural risk.

When the transportation capacity of the station/section of the road network cannot meet the travel needs of passengers, it will inevitably cause congestion at the station/section, which will bring risks. Here, the ratio of passenger flow demand to road network passenger flow capacity is called the passenger flow demand load index, which is the core element for calculating transport capacity risk. With the increase of the demand load index of passenger flow, the risk of transportation capacity also increases accordingly. The overall transport capacity risk of the road network can be obtained by summing the transport capacity risks of each station and section. The global transport capacity risk of the road network is mainly related to the OD demand, the passenger flow capacity of each part of the road network (including the impact of single-point risk), the passenger flow distribution strategy and other factors: OD demand is constrained by the passenger flow capacity of the road network. The passenger flow (demand) load distribution of stations and sections determines the transport capacity risk of the entire road network.

Based on the above description, in some embodiments of the present invention, the global (transport capacity) risk calculation of the road network is shown in Equation 4.

Among them, x _i (t) represents the passenger flow demand of station (or section) i at time t (unit: person/hour), and c _i (t) represents the passenger flow capacity of station (or section) i at time t (unit: is: person/hour), x _i (t)/ _ci (t) represents the passenger flow demand load of station (or section) i at time t. f(x) is the transport capacity risk probability function, which is used to map the passenger flow demand load to the possibility of transport capacity risk, and w _i (t) represents the transport capacity risk consequence of the station or section numbered i at time t.

作为风险概率函数。车 站和区间发生风险后，其风险后果与其客流量及其容量有关，在此取 值为车站/区间的容量与其客流需求的较小值，即w_i(t)＝min(x_i(t),c_i(t))。The transport capacity risk probability function takes the passenger flow demand load as the input, and considers that as the passenger flow demand load increases, the risk probability should first keep a low level and increase slowly, and then enter a stage of rapid growth, after which the risk probability reaches a very high level and increases. rapidly approaching 1. In some embodiments, after parameter calibration, select

as a risk probability function. After a risk occurs in a station or section, the risk consequence is related to its passenger flow and its capacity. The value here is the smaller value of the capacity of the station/section and its passenger flow demand, that is, w _i (t)=min(x _i (t) , _ci (t)).

2. Global structural risk assessment method for regional rail transit

The global capacity risk of regional rail transit network is determined by three factors (as shown in Figure 1): ①OD demand, ②passenger flow capacity of road network (station/intersection), ③passenger flow allocation strategy, where ①is external conditions, ② It is the inherent structural factor of the road network, and ③ is the non-structural factor (dynamic scheduling strategy). To realize the global structural risk assessment of the road network, in order to make the assessment result truly reflect the influence of the inherent structural factors of the road network, it is necessary to remove the non-structural factors. Based on this idea, in this embodiment, factor ③ is taken as the optimization variable, and the global transport capacity risk of the road network is minimized through the optimal flow distribution scheme, thereby removing the influence of the non-structural factor of the passenger flow distribution strategy. In this embodiment, the minimum value of the global transport capacity risk of the road network is defined as the global structural risk of regional rail transit, as shown in Equation 7.

In some embodiments of the present invention, the model for minimizing the global capacity risk is as follows. The regional rail transit road network is represented by an undirected graph G(V, E), where V is the set composed of all stations in the road network, and E is the set composed of all sections in the road network. There are a total of S stations and T sections in the road network, v _i represents the i-th station, and e _k is the k-th section, where i=1,...S is the station number, k=1,...T number for the interval.

其中v_i表示路网中第i个车站，a 为车站入口，b为车站出口，将车站等效为只有一个入口和一个出口； c，d为各轨道交通制式的上下车地点，即站台，

表示站i内从a入 口到站台c的进站通道，

表示站i内从站台c到出口b的出站通道，

表示站i内从站台c换乘到站台d的换乘通道，为简化处理，认为 站内每对端点之间只存在两个通道，这两个通道方向相反，且站内各 个通路之间均相互独立。模型符号如表1所示。In some specific embodiments, the station passage is divided into three parts: inbound passage, transfer passage, and outbound passage:

where v _i represents the i-th station in the road network, a is the station entrance, b is the station exit, and the station is equivalent to only one entrance and one exit; c, d are the alighting and boarding locations of each rail transit system, that is, the platform,

Indicates the inbound passage from entrance a to platform c in station i,

represents the outbound channel from platform c to exit b in station i,

Indicates the transfer channel from platform c to platform d in station i. In order to simplify the processing, it is considered that there are only two channels between each pair of endpoints in the station, these two channels are in opposite directions, and each channel in the station is independent of each other . Model symbols are shown in Table 1.

Table 1 Relevant symbols of the global capacity risk optimization model

Since there are many loops in the regional rail transit road network, one OD pair may correspond to many feasible paths. In some specific embodiments, only the first K shortest simple paths are considered. In this example, K=5 is taken. The K shortest paths are obtained by graphical tools in python. The objective function is to minimize the global capacity risk:

The constraints are:

(Equation 9) is used to calculate the inbound passenger flow at the station, (Equation 10) is used to calculate the station exit passenger flow, (Equation 11) is used to calculate the station exchange passenger flow, (Equation 12) is used to calculate the section passenger flow, (Equation 13) (Equation 14) represents the passenger flow distribution constraint. The model is solved by a genetic algorithm.

3. Structural risk bottleneck identification method based on sensitivity analysis

On the one hand, the purpose of risk assessment is to understand the overall risk level of the system, and on the other hand, the more important purpose is to identify where the system risk bottleneck is, so as to prevent or optimize the risk bottleneck point. Risk identification should take different approaches for different purposes. The first type of method is based on the need for optimization of risk bottleneck points. It is necessary to find the interval or station transfer channel that reduces the overall risk of the system under the same optimization level, which is the safety optimization bottleneck point of the road network, and needs to be optimized. The first type of method needs to be optimized. In response to the demand for protection of risk bottleneck points, it is necessary to find the interval or station transfer channel that increases the global risk of the system the most under the condition of failure of the bottleneck point.

Type 1 Sensitivity Analysis: Optimizing Sensitivity

After taking optimization measures to improve the traffic capacity of the road network structure, the risk of road network structure will change; and after the same optimization of different road network structures, the degree of risk reduction of the road network structure will be different. Taking the partial derivation of the road network structure risk to the traffic capacity of each channel, the optimal sensitivity of each channel is obtained:

表示通道

的优化灵敏度，

表示通 道

的通行能力。通道优化灵敏度能够直观地表现出此通道的通行能 力得到提升后，路网全局结构风险的降低效果。where S ^* is the risk of road network structure,

Indicates the channel

optimized sensitivity,

Indicates the channel

of traffic capacity. The channel optimization sensitivity can intuitively show the reduction effect of the overall structural risk of the road network after the traffic capacity of this channel is improved.

In this embodiment, the partial derivative is approximately solved by the difference method, that is:

为

的微小变化量，本实施例取为

的十分之一。in

for

The slight variation of , this embodiment is taken as

one-tenth of .

Type II Sensitivity Analysis: Failure Sensitivity

When each channel in the road network fails, the road network will redistribute the passenger flow. At this time, the global structural risk of the road network is calculated to obtain the ratio of the global structural risk of the road network before and after the change of the channel capacity, so as to measure the global structural risk of the road network caused by the channel failure. Impact. Since the failure of some passages will cause some locations to be unreachable, considering that non-rail traffic still has a certain capacity, this embodiment adjusts the capacity of these passages to a smaller value (instead of setting it to 0), so as to avoid Infinity occurs in the calculation.

为通道

的失效风险灵敏度，S^*'为通道通行能力变化后路 网全局结构风险，S^*为通道通行能力变化之前路网全局结构风险。in

for the channel

The failure risk sensitivity of , S ^* ' is the global structural risk of the road network after the change of the passage capacity, and S ^* is the global structural risk of the road network before the change of the passage capacity.

Case Analysis

The data source in the example of this embodiment is: Chongqing Rail Transit Group official website route information (https://www.cqmetro.cn/search-way.html), which is led by the Chongqing Municipal Planning Bureau and compiled by the Municipal Transportation Planning and Research Institute "2018 Annual Report on Traffic Development in Chongqing's Main Urban Area" (http://ghzrzyj.cq.gov.cn/zwxx_186/bmdt/201912/t20191225_2992986.html).

Example scenario

In this embodiment, the Chengdu-Chongqing area is taken as an example to conduct a case study. Figure 2 shows the topology of rail transit lines in Chongqing. In the Chongqing regional rail transit line topology map, only the starting station, the terminal station and the transfer station of each regional rail transit line are retained, including a total of 10 lines, 42 stations, 55 sections, and 63 entry channels. There are 63 outbound channels and 56 transfer channels, covering four types of rail transit systems: high-speed railway, intercity railway, monorail and subway.

data set

This embodiment mainly provides the data for calculating the passenger flow capacity of the road network and the OD demand data.

1) Calculation basis of station passenger flow capacity

In this embodiment, the sites are divided into three types: large-scale sites, medium-sized sites, and small-scale sites. The division is based on: By querying the line planning of each station of Chongqing Rail Transit, the stations with three or more lines are large stations, those with two lines are medium-sized stations, and the rest are small stations. The capacity of the internal channels of the three stations is shown in Table 2:

Table 2 Channel capacity within the station

stand large station medium station small station Passage capacity of pit lane (person/hour) 12800 9600 6400 Outbound channel capacity (person/hour) 12800 9600 6400 Transfer channel capacity (person/hour) 9600 6400 none

2) Calculation basis of passenger flow capacity in the interval

The transportation capacity of each line in the road network is obtained from the type of carriages, the maximum number of carriages, and the minimum departure interval of each line. The information of Chongqing rail transit routes is shown in Table 3. The Chengdu-Chongqing passenger train model is CRH380D, with a capacity of 1,328 people, and the departure interval is 20 minutes; the train model of the Yu-Wan Railway is CRH2A, with a capacity of 623 people, and the departure interval is 50 minutes.

Table 3 Line Information

3) OD demand data

The typical OD requirements of regional rail transit in the Chengdu-Chongqing area selected in this embodiment are shown in Table 4.

Table 4 Typical OD demand of regional rail transit in Chengdu-Chongqing area

Calculation process

Based on the road network capacity and OD requirements, this embodiment adopts the optimal passenger flow distribution method to remove the influence of passenger flow distribution on the calculation of the global transport capacity risk of the road network, thereby obtaining the calculation result of the global structural risk of the road network. The value of the global structural risk is the sum of the risks of all inbound corridors, outbound corridors, transfer corridors and sections in the road network under the optimal passenger flow distribution condition. When solving the failure sensitivity, the passage capacity after failure is taken as one tenth of the original capacity. After each change of the passage capacity, the road network will re-distribute the optimal passenger flow and solve it by genetic algorithm. The setting parameters of the genetic algorithm in this embodiment are: the number of iterations is 1500, and the population size is 2000. The calculation flow chart is shown in Figure 3. The parameters used in the calculation process are shown in Table 5.

Table 5 Parameter table

Result analysis

Two types of sensitivity analysis are carried out on the transfer channels in the road network, and the optimization sensitivity and failure risk sensitivity of each transfer channel are obtained. Among them, the top ten transfer channels with greater influence are shown in Table 6 and Table 7.

Table 6. Sensitivity of the optimization of the transfer channel of the road network

The site where the channel is located The first and last lines of the channel Optimize sensitivity Chongqing North and South Line 3 - Ring Line -12.295 Hongqihegou Line 3-Line 6 -8.014 Chongqing Beibei Intercity-Line 4 -6.711 Chongqing North and South Line 3 - Line 10 -6.112 Wulidian Ring Line - Line 6 -5.368 Shangxin Street Line 6 - Ring Line -3.267 Daping Line 2-Line 1 -3.243 Chongqing Beibei Intercity-Line 10 -1.923 Min'an Avenue Ring Line - Line 4 -1.87 Chongqing North and South Line 10-Line 3 -1.277

Table 7 Failure Sensitivity of Transfer Channels in the Road Network

The site where the channel is located The first and last lines of the channel failure sensitivity Chongqing North and South Line 3 - Line 10 1.353 Min'an Avenue Ring Line - Line 4 1.178 Chongqing Beibei Line 10 - High Speed Rail 1.158 Small Shizi Line 1-Line 6 1.155 Shangxin Street Ring Line - Line 6 1.135 Chongqing Beibei Intercity-Line 10 1.134 Hongqihegou Line 6-Line 3 1.125 Chongqing Beibei High Speed Rail-Line 10 1.122 Shangxin Street Line 6 - Ring Line 1.121 Chongqing Beibei Line 10 - Intercity 1.116

It can be seen from the results that the passage from Line 3 to Huan Line in Chongqing North Station South Square needs to be optimized, and the passage from Chongqing North Station South Square from Line 3 to Line 10 needs to be protected. In addition, Chongqing North Railway Station North Square, Chongqing North Railway Station South Square, Shangxin Street, Hongqihegou and other stations all have multiple transfer channels with high optimization sensitivity/failure risk, and optimization or protective measures need to be taken.

The implementations and functional operations of the subject matter described in this specification can be implemented in digital electronic circuits, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in A combination of one or more of . Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, ie, one or more modules of computer program instructions encoded on one or more tangible non-transitory program carriers, for processing by data The device performs or controls the operation of the data processing device.

Alternatively or additionally, the program instructions may be encoded on artificially generated propagated signals, eg, machine-generated electrical, optical or electromagnetic signals, which are generated as encoded information for communication to an appropriate device for execution by a data processing device. receiver device. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of the foregoing.

The term "data processing apparatus" includes all kinds of apparatus, apparatus, and machines for processing data, including, by way of example, programmable processors, computers, or multiple processors or multiple computers. A device may include special purpose logic circuitry, for example, an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). In addition to hardware, an apparatus may also include code that creates an execution environment for the associated computer program, such as code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these.

A computer program (which may also be called or described as a program, software, software application, module, software module, script, or code) may be written in any form of programming language, including compiled or interpreted or declarative or A procedural language, and a computer program may be developed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. Programs may be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document; in a single file dedicated to the associated program; or in multiple collaborative In a file, for example, a file that stores one or more modules, subroutines, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers, which are located at one site, or distributed over multiple sites and interconnected by a communication network.

The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and devices can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

A computer suitable for the execution of a computer program includes, and by way of example may be based on a general purpose microprocessor or a special purpose microprocessor or both, or any other kind of central processing unit. Typically, the central processing unit will receive instructions and data from read-only memory or random access memory, or both. The main elements of a computer are a central processing unit for running or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operably coupled to receive data from, or transfer data to, one or more mass storage devices for storing data, or both, the The capacity storage is, for example, a magnetic disk, a magneto-optical disk or an optical disk. However, the computer need not have such a device. Additionally, the computer may be embedded in another device, such as a mobile phone, personal digital assistant (PDA), mobile audio or video player, game console, global positioning system (GPS) receiver, or removable storage device, For example, Universal Serial Bus (USB) flash drives, etc.

Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices including, by way of example: semiconductor memory devices such as EPROM, EEPROM and flash memory devices; magnetic disks such as , built-in hard disk or removable disk; magneto-optical disk; CD-ROM and DVD-ROM disks. The processor and memory may be supplemented by or incorporated into special purpose logic circuitry.

Claims (10)

1. A regional rail transit global structure risk bottleneck identification method is characterized by comprising the following steps:

a data acquisition step: acquiring road network structure risk data and channel traffic capacity data;

and a sensitivity data processing step: the optimum sensitivity is calculated according to equation 1, and the failure sensitivity is calculated according to equation 2,

wherein S^*In order to solve the risk of the road network structure,

indicating a channel

The sensitivity of the light source is optimized,

indicating a channel

(ii) a traffic capacity;

wherein

Is a channel

Sensitivity to failure, S^*'The risk of the road network structure after the passage traffic capacity is changed;

and (4) outputting a risk bottleneck result: and outputting the optimized sensitivity and the failure sensitivity, namely completing risk bottleneck identification.

2. The method of claim 1, wherein the optimal sensitivity is calculated according to equation 2,

Wherein,

is composed of

A slight variation of (2), preferably

Is composed of

One tenth of the total.

3. The method of claim 2, wherein said step of generating said road network structure risk data comprises:

optimizing an objective function taking the lowest global operation energy risk of the road network as an optimization target by using an optimization algorithm, wherein the objective function is shown as a formula 4:

wherein x is_i(t) represents the passenger flow demand (in units of: man/hour) at station (or section) i at time t, c_i(t) represents the traffic capacity (in: man/hour) of station (or section) i at time t, x_i(t)/c_i(t) represents the passenger flow demand load of a station (or section) i at time t; (x) is a capacity risk probability function for mapping the passenger flow demand load to the probability of capacity risk occurrence, w_i(t) represents the performance risk consequence of the station or section with time t being number i.

4. The method of claim 3, wherein the risk consequence w_i(t) is represented by formula 5:

w_i(t)＝min(x_i(t),c_i(t)) formula 5.

5. The method of claim 4, wherein the performance risk probability function is as shown in equation 6:

6. the method of claim 3, wherein the objective function is as shown in equation 7:

7. the method of claim 3, wherein the objective function is as shown in equation 8:

Wherein, i is 1, … S is station number, k is 1, … T is section number; a is a station entrance, and b is a station exit; c and d are the getting-on and getting-off points of each rail transit system; (x) a function representing the probability of performance risk;

indicating a channel

The OD requirement of (a) is,

indicating a channel

The capacity of the vehicle to pass through,

indicating a channel

Risk consequences of the performance risk;

indicating a channel

The OD requirement of (a) is,

indicating a channel

The capacity of the vehicle to pass through,

indicating a channel

Risk consequences of the performance risk;

indicating a channel

The OD requirement of (a) is,

indicating a channel

The capacity of the vehicle to pass through,

indicating a channel

Risk consequences of the performance risk; q. q.s_kRepresents a section e_kOD requirement of (1), L_kRepresents a section e_kTraffic capacity of W_kRepresenting the risk consequences of the capacity risk of the interval k.

8. The method of claim 7, wherein the constraints of the objective function include equation 9 for calculating station inbound traffic; equation 10 for calculating station outbound passenger flow, equation 11 for calculating station transfer passenger flow, equation 12 for calculating block passenger flow, equations 13 and 14 representing passenger flow distribution constraints:

wherein,

for decision variables, the representation is assigned to path p_ij ^mThe OD requirement of (a) is,

representing station v_iPath from point a to point c, p _ij ^mRepresenting the mth simple path from station i to station j, g (a, p) representing that a is a certain channel or section in the network, and p is a simple path, if a is on the path p, g (a, p) is 1, otherwise (a, p) is 0;

wherein x is_ji ^mFor decision variables, the representation is assigned to path p_ji ^mThe OD requirement of (1);

wherein x is_nj ^mRepresenting decision variables, representing assignment to paths p_nj ^mThe OD requirement of (1);

wherein,

for decision variables, the representation is assigned to path p_ij ^mOD requirement of (e)_kDenotes the k-th interval, p_ij ^mRepresents the mth simple path from station i to station j;

wherein q is_ijIndicating the OD requirements of station i to station j.

9. The method of claim 1, wherein the model of the objective function that minimizes global operational risk is: an undirected graph G (V, E) represents a regional rail transit road network, wherein V is a set formed by all stations in the road network, and E is a set formed by all intervals in the road network; there are S stations and T intervals altogether in the road network, v_iIndicates the ith station, e_kIs the kth section, where i 1.. S is the station number, k 1.. T is the section number.

10. A regional rail transit global structure risk bottleneck identification system, the system comprising at least one processor; and

A memory storing instructions that, when executed by the at least one processor, perform the method according to any one of claims 1-8.

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