CN111861219A - Regional rail transit global structure risk bottleneck identification method and system - Google Patents
Regional rail transit global structure risk bottleneck identification method and system Download PDFInfo
- Publication number
- CN111861219A CN111861219A CN202010711200.3A CN202010711200A CN111861219A CN 111861219 A CN111861219 A CN 111861219A CN 202010711200 A CN202010711200 A CN 202010711200A CN 111861219 A CN111861219 A CN 111861219A
- Authority
- CN
- China
- Prior art keywords
- risk
- station
- road network
- channel
- sensitivity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000035945 sensitivity Effects 0.000 claims abstract description 38
- 238000005457 optimization Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 12
- 230000006870 function Effects 0.000 claims description 24
- 238000012546 transfer Methods 0.000 claims description 18
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 3
- 238000010206 sensitivity analysis Methods 0.000 abstract description 12
- 238000012502 risk assessment Methods 0.000 abstract description 7
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 10
- 238000004590 computer program Methods 0.000 description 9
- 238000011161 development Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000002068 genetic effect Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000013515 script Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a regional rail transit global structure risk bottleneck identification method, which comprises the following steps: a data acquisition step: acquiring road network structure risk data and channel traffic capacity data; a sensitivity data processing step; and (4) outputting a risk bottleneck result: and outputting the optimized sensitivity and the failure sensitivity, namely completing risk bottleneck identification. The method and the system for identifying the regional rail transit global structure risk bottleneck based on sensitivity analysis have the beneficial effects that the method and the system for identifying the regional rail transit global structure risk bottleneck based on sensitivity analysis are provided. After the global structure risk assessment index of the road network is established, the influence of the failure and optimization of the intra-station channels/intervals of the road network on the global structure risk of the road network is assessed through sensitivity analysis, and therefore decision support is provided for protection and optimization of the risk bottleneck of the road network. And the effectiveness of the method is verified by taking the rail transit in the formed Yu region as an example.
Description
Technical Field
The invention belongs to the field of computer systems based on specific calculation models, and particularly relates to a method and a system for identifying global structure risk bottlenecks of regional rail transit.
Background
The regional rail transit is a comprehensive rail transit system which is formed by aiming at regional economic integration requirements and comprises a plurality of rail transit systems such as a high-speed railway, an intercity train, a single rail and a subway. With the rapid development of regional economy and the formation of urban circles, regional rail transit is facing development requirements of being safer, more efficient and more comfortable, and the development characteristics of isomerism, integrity, interactivity and synergy are reflected.
The characteristic that regional rail transit multisystem coexists had both adapted to the demand of regional economy integration development, had promoted the convenience of resident's trip, had nevertheless also brought more risks, and the interdynamic between the multisystem and the wholeness of road network make the risk influence face increase, the risk consequence increase simultaneously, and consequently, risk bottleneck discernment has important meaning to regional rail transit system realization targeted key risk point protection and optimization, effectively reduce global risk. However, in the related research of the existing rail transit network global risk assessment, many index fusion methods are adopted, and although risk factors can be assessed and sorted, due to the influence of more subjective factors, the global structure risk of the network cannot be reflected from the aspect of transport capacity, and the global risk bottleneck of the network cannot be effectively identified. Each node/interval in the road network has different influences on the global risk of the road network, the road network risk can be greatly increased after some nodes/intervals fail, and the road network risk can be greatly reduced after some nodes/intervals are optimized.
The existing research on rail transit global risk assessment is greatly influenced by subjective factors, at present, the assessment on the rail transit road network global structure risk is less, and documents for assessing the influence of road network structure optimization on the global risk are less; in the aspect of risk bottleneck identification, no report combining sensitivity analysis with rail transit risk bottleneck identification exists at present.
Disclosure of Invention
According to the method, on the basis of evaluating the global structure risk of the road network, a sensitivity analysis method is used for evaluating the failure of channels/intervals in each station of the road network and optimizing the influence on the global structure risk of the road network, so that decision support is provided for the protection and optimization of the risk bottleneck of the road network.
The invention provides a regional rail transit global structure risk bottleneck identification method on the one hand, which comprises 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 the passage
(ii) a traffic capacity;
wherein
Is a channel
Sensitivity to failure, S*' is the road network structure risk 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.
In another aspect, the present invention provides a system for identifying a risk bottleneck of a global structure of regional rail transit, the system including at least one processor; and a memory storing instructions that, when executed by the at least one processor, perform the methods provided by the present invention. The method and the system for identifying the regional rail transit global structure risk bottleneck based on sensitivity analysis have the beneficial effects that the method and the system for identifying the regional rail transit global structure risk bottleneck based on sensitivity analysis are provided. After the global structure risk assessment index of the road network is established, the influence of the failure of the intra-station channels/intervals of the road network and the optimization on the global structure risk of the road network is assessed through sensitivity analysis, so that decision support is provided for the protection and optimization of the risk bottleneck of the road network. And the effectiveness of the method is verified by taking the rail transit in the formed Yu region as an example.
Drawings
FIG. 1 is a global operational risk determining factor of a regional rail transit network;
FIG. 2 is a topology diagram of track traffic lines in a Chongqing area;
FIG. 3 is a schematic view of a calculation flow;
Detailed Description
The method for identifying the regional rail transit global structure risk bottleneck of some embodiments of the invention comprises 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 the passage
(ii) a traffic capacity;
wherein
Is a channel
Sensitivity to failure, S*' is the road network structure risk 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.
The optimization sensitivity of the invention indicates the change degree of the global structure risk of the road network (negative value indicates that the risk is reduced) with the increase of the capacity of a certain channel, and the smaller value (namely, the larger absolute value) indicates that the optimization of the corresponding channel contributes more to reducing the global structure risk.
The failure sensitivity of the invention indicates the change degree of the global structure risk of the road network (the value greater than one indicates that the risk is increased) along with the failure of a certain channel capacity, and the larger the value is, the larger the contribution of the failure of the corresponding channel to the increase of the global structure risk is.
In these particular embodiments, 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.
In these specific embodiments, the step of generating the road network structure risk data includes:
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 isi(t) represents the passenger flow demand (in units of: man/hour) at station (or section) i at time t, ci(t) represents the traffic capacity (in: man/hour) of station (or section) i at time t, xi(t)/ci(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, wi(t) represents the performance risk consequence of the station or section with time t being number i.
In these particular embodiments, the risk consequence wi(t) is represented by formula 5:
wi(t)＝min(xi(t),ci(t)) formula 5.
In these specific embodiments, the performance risk probability function is shown in equation 6:
in some specific embodiments, the objective function is as shown in equation 7:
in some other specific embodiments, the objective function is as shown in equation 8:
wherein, i is 1, S is the station number, k is 1, … T is the 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 risk of fortune; 
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,
representing a channel
The capacity of the vehicle to pass through,
indicating a channel
Risk consequences of the performance risk;
representing 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.skRepresents a section ekOD requirement of (1), LkRepresents a section ekTraffic capacity of WkRepresenting the risk consequences of the capacity risk of the interval k.
In some specific embodiments, the constraint conditions of the objective function include formula 9 used for calculating station arrival passenger flow; 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 pij mThe OD requirement of (a) is,
representing station viPath from point a to point c, pij mRepresents the m-th simple path from station i to station j, and g (a, p) represents that a is a certain channel in the networkA lane or section, p is a simple path, if a is on path p, g (a, p) is 1, otherwise (a, p) is 0; it should be noted that g (a, p) is not a specific function, and only determines whether a certain path or section a in the circuit network is on the path p.
The judgment result is given according to the actual situation of the road network.
Wherein x isji mFor decision variables, the representation is assigned to path pji mThe OD requirement of (1);
wherein x isnj mRepresenting decision variables, representing assignment to paths pnj mThe OD requirement of (1);
wherein,
for decision variables, the representation is assigned to path pij mOD requirement of (e)kDenotes the k-th interval, pij mRepresents the mth simple path from station i to station j;
wherein q isijIndicating the OD requirements of station i to station j.
In some specific embodiments, the model of the objective function that minimizes global operational riskComprises the following steps: 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, viIndicates the ith station, ekIs the kth section, where i ═ 1.. S is the station number, k ═ 1, … T is the section number.
In still other embodiments of the present invention, a system for identifying a risk bottleneck of a global structure of regional rail transit is provided, the system comprising at least one processor; and a memory storing instructions that, when executed by the at least one processor, perform the method of the present invention.
Specific examples of the present invention are further described below.
1. Regional rail transit global transport risk assessment method (here, "global transport risk" is not "global structure risk", and "global transport risk" is the basis for assessing "global structure risk").
The global risk of regional rail transit is divided into two aspects: transport capacity risk and personnel and equipment loss risk. The performance risk depends on a number of factors, and this embodiment will be described in detail. The people/things/ring/pipe factors at stations and in zones create a single point of risk, eventually creating a risk of personnel and equipment loss in the road network. There is a link between the capacity risk and the single point risk: the single point risk affects the capacity risk by reducing the capacity of the stations and the sections, while a higher capacity risk (which means a higher traffic demand load, as will be described in detail in this embodiment) may cause a new single point risk. Since the relationship between the single point risk and the global structure is not tight, the embodiment focuses on the performance risk, thereby evaluating the global structure risk.
When the transportation capacity of a road network station/interval cannot meet the travel demand of passengers, congestion of the station/interval is caused, and risks are brought. The ratio of the traffic demand to the traffic capacity of the road network is referred to herein as a traffic demand load index, which is a core element for calculating the transportation risk. With the increase of the passenger flow demand load index, the transport capacity risk is correspondingly increased. And the transport energy risks of each station and each section are summed to obtain the global transport energy risk of the road network. The global transport capacity risk of the road network is mainly related to OD requirements, the passenger flow capacity of each part of the road network (including the influence of single-point risk), the passenger flow distribution strategy and other factors: and the OD demands form passenger flow (demand) load distribution of stations and intervals through passenger flow distribution under the constraint of the passenger flow capacity of the road network, and further determine the transport capacity risk of the whole road network.
Based on the above description, the calculation of global (operational) risk of road network in some embodiments of the present invention is shown in equation 4.
Wherein x isi(t) represents the passenger flow demand (in units of: man/hour) at station (or section) i at time t, ci(t) represents the traffic capacity (in: man/hour) of station (or section) i at time t, xi(t)/ci(t) represents the traffic demand load at 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, wi(t) represents the performance risk consequence of the station or section with time t being number i.
The capacity risk probability function takes the passenger demand load as input and considers that as the passenger demand load increases, the risk probability should first remain at a low level and increase slowly, then enter a phase of rapid increase, after which the risk probability reaches a high level and approaches 1 rapidly. In some embodiments, after the parameter calibration, the selection is performed
As a function of risk probability. After the station and the section are in risk, the risk consequence is related to the passenger flow and the capacity thereof, wherein the value is the smaller value of the capacity of the station/the section and the passenger flow demand thereof, namely wi(t)＝min(xi(t),ci(t))。
2. Regional rail transit global structure risk assessment method
The global operational risk of the regional rail transit network is determined by three factors (as shown in fig. 1): the method comprises the steps of OD requirements, traffic capacity of a road network (stations/intervals) and a traffic distribution strategy, wherein the OD requirements are external conditions, the traffic capacity is inherent structural factors of the road network, the traffic distribution strategy is non-structural factors (dynamic scheduling strategy), the global structural risk of the road network needs to be evaluated, and the non-structural factors need to be removed in order to enable the evaluation result to truly reflect the influence of the inherent structural factors of the road network. Based on the thought, in the embodiment, the factor (c) is used as an optimization variable, and the optimal distribution scheme is used to enable the global transport capacity risk of the road network to reach the minimum value, so that the influence of the non-structural factor of the passenger flow distribution strategy is removed. In this embodiment, the minimum value of the global operational capacity risk of the road network is defined as the global structure risk of the regional rail transit, as shown in formula 7.
In some embodiments of the invention, a model that minimizes global operational risk is as follows. And (5) representing the regional rail transit road network by using an undirected graph G (V, E), wherein V is a set formed by all stations in the road network, and E is a set formed by all sections in the road network. There are S stations and T intervals altogether in the road network, v iIndicates the ith station, ekIs the k-th section, where i 1.. S is the station number, k 1.. T is the section number.
In some embodiments, the station passage is divided into three parts, namely an entrance passage, a transfer passage and an exit passage:
wherein v isiThe method comprises the steps that the ith station in a road network is shown, a is a station inlet, b is a station outlet, and the station is equivalent to only one inlet and one outlet; c, d are the getting-on and getting-off places of each rail transit system, namely the platform,
representing the inbound path from entry a to station c in station i,
indicating an outbound path from station c to egress b within station i,
representing the transfer path from station c to station d in station i, for simplicity of processing, it is assumed that there are only two paths between each pair of endpoints in station, the two paths are in opposite directions, and the paths in station are independent of each other. The model symbols are shown in table 1.
TABLE 1 Global Performance Risk optimization model correlation notation
Since there are many loops in the regional rail transit network, one OD pair may correspond to many feasible paths, and only the first K shortest simple paths are considered in some embodiments, where K is 5 in this example. These K shortest paths are found by the graphics tool in python. Taking the minimization of global operational risk as an objective function:
The constraint conditions are as follows:
the method comprises the following steps of (formula 9) calculating station arrival passenger flow, (formula 10) calculating station exit passenger flow, (formula 11) calculating station transfer passenger flow, (formula 12) calculating block passenger flow, and (formula 13) (formula 14) representing passenger flow distribution constraint. The model is solved by a genetic algorithm.
3. Structural risk bottleneck identification method based on sensitivity analysis
The purpose of risk assessment is on the one hand to understand the overall risk level of the system and on the other hand, more important to identify where the risk bottleneck of the system is located in order to safeguard or optimize the risk bottleneck. Risk identification should take different approaches for different purposes. Aiming at the requirement of risk bottleneck point optimization, the first method needs to find an interval or a station transfer channel which reduces the global risk of the system most under the same optimization level, namely the safety optimization bottleneck point of the road network, and needs to optimize in a key way; the first method aims at the requirement of risk bottleneck point protection, an interval or a station transfer channel which enables the global risk of a system to be increased most under the condition that a bottleneck point is invalid needs to be found, namely the safety protection bottleneck point of a road network, and important protection is needed.
First type of sensitivity analysis: optimizing sensitivity
After the road network structure is optimized to improve the traffic capacity, the risk of the road network structure is changed; and after different road network structures are optimized identically, the reduction degree of the road network structure risk can be different. And (3) solving the traffic capacity of each channel by the road network structure risk to obtain the optimal sensitivity of each channel:
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 the passage
The traffic capacity of (c). The sensitivity of the channel optimization can intuitively show the reduction effect of the global structure risk of the road network after the traffic capacity of the channel is improved.
This embodiment approximately solves this partial derivative by a difference method in the calculation, namely:
wherein
Is composed of
Is taken as
One tenth of the total.
The second type of sensitivity analysis: sensitivity to failure
And when each channel in the road network fails, the road network redistributes the passenger flow, and at the moment, the global structure risk of the road network is calculated to obtain the ratio of the global structure risk of the road network before and after the capacity of the channel changes, so that the influence of the channel failure on the global structure risk of the road network is measured. Since the failure of some channels will cause some places to be unreachable, considering that non-rail traffic has certain traffic capacity, the embodiment adjusts the traffic capacity of the channels to a smaller value (instead of setting the traffic capacity to 0), thereby avoiding infinite situations in calculation.
Wherein
Is a channel
Sensitivity to failure risk of S*' Global structural Risk of road network after passage traffic capacity change, S*The global structure risk of the road network before the change of the passage capacity is solved.
Example analysis
The data sources in the example of this embodiment are: chongqing rail transit group official network line information (https:// www.cqmetro.cn/search-way. html), and '2018 Chongqing city main city traffic development annual report' compiled by Chongqing city planning bureau lead and city traffic planning research institute (http:// ghzrzyj. cq. gov. cn/zwxx _186/bmdt/201912/t20191225_2992986. html).
Example scenarios
In the present embodiment, the research on the examples is performed by taking the Yu-forming area as an example. Fig. 2 is a topology diagram of track traffic lines in a Chongqing area. In a topology diagram of track traffic lines in a Chongqing area, only an initial station, a terminal station and a transfer station of each line of the track traffic in the area are reserved, and the topology diagram totally comprises 10 lines, 42 stations, 55 intervals, 63 station-entering channels, 63 station-exiting channels and 56 transfer channels, and covers four track traffic systems of a high-speed railway, an intercity railway, a single track and a subway.
Data set
The embodiment mainly provides data for calculating the traffic volume of the road network and OD demand data.
1) Basis for calculating station passenger flow capacity
The present embodiment divides the station into three types, a large station, a medium station, and a small station. The basis of the division is as follows: by inquiring route plans of each station of the Chongqing rail transit, the station comprises three or more routes, namely a large station, two routes, namely a medium station, and the rest are small stations. The traffic capacity of the internal passage of the three stations is shown in table 2:
TABLE 2 intra-station aisle traffic capacity
| Station | Large station | Medium-sized station | Small-sized station |
| Station entrance passage capacity (man/hour) | 12800 | 9600 | 6400 |
| Outbound passage traffic capacity (people/hours) | 12800 | 9600 | 6400 |
| Transfer lane traffic capacity (people/hours) | 9600 | 6400 | Is free of |
2) Basis for calculating interval passenger flow capacity
The transport capacity of each line in the road network is obtained through the carriage model, the maximum grouping number of the carriages and the minimum departure interval of each line. The information of the Chongqing rail transit lines is shown in Table 3. The vehicle type of the special Yuke train is CRH380D, the number of the fixed members is 1328, the train dispatching interval is 20min, the vehicle type of the Yuwan train is CRH2A, the number of the fixed members is 623, and the train dispatching interval is 50 min.
TABLE 3 line data
3) OD demand data
The typical OD requirements for track traffic in the selected Chongqing district are shown in table 4.
TABLE 4 typical OD requirements of regional rail transit in Chongqing areas
Calculation process
Based on the capacity and the OD requirement of the road network, the optimal passenger flow distribution method is adopted in the embodiment to remove the influence of the passenger flow distribution on the global transportation energy risk calculation of the road network, so that the global structure risk calculation result of the road network is obtained. The value of the global structure risk is the sum of the risks of all inbound channels, outbound channels, transfer channels and intervals in the road network under the optimal passenger flow distribution condition. When the failure sensitivity is solved, the passage capacity of the failed channel is one tenth of the original passage capacity. After the passage capacity is changed every time, the road network performs optimal passenger flow distribution again and solves the optimal passenger flow distribution through a genetic algorithm. The setting parameters of the genetic algorithm of the embodiment are as follows: number of iterations 1500, population size 2000. The calculation flow chart is shown in fig. 3. The parameters used in the calculation are shown in table 5.
TABLE 5 parameter Table
Analysis of results
And (3) carrying out two types of sensitivity analysis on the transfer channels in the road network to obtain the optimization sensitivity and the failure risk sensitivity of each transfer channel, wherein the first ten transfer channels with larger influence are shown in tables 6 and 7.
TABLE 6 optimal sensitivity of road network transfer channels
| Station of channel | Channel head and tail line | Optimizing sensitivity |
| Chongqing north and south | No. 3 line-loop line | -12.295 |
| Hongqi river ditch | No. 3 line-No. 6 line | -8.014 |
| North Chongqing | Intercity line No. 4 | -6.711 |
| Chongqing north and south | No. 3 line-No. 10 line | -6.112 |
| Wuli shop | Loop line No. 6 | -5.368 |
| New street | No. 6 line-loop line | -3.267 |
| Terrace | No. 2 line-No. 1 line | -3.243 |
| North Chongqing | Intercity No. 10 line | -1.923 |
| Min' an Dadao | Loop line No. 4 line | -1.87 |
| Chongqing north and south | No. 10 line-No. 3 line | -1.277 |
TABLE 7 road network transfer channel failure sensitivity
| Station of channel | Channel head and tail line | Sensitivity to failure |
| Chongqing north and south | No. 3 line-No. 10 line | 1.353 |
| Min' an Dadao | Loop line No. 4 line | 1.178 |
| North Chongqing | No. 10 line-high-speed rail | 1.158 |
| Small assorted Chinese character | No. 1 line-No. 6 line | 1.155 |
| New street | Loop line No. 6 | 1.135 |
| North Chongqing | Intercity No. 10 line | 1.134 |
| Hongqi river ditch | No. 6 line-No. 3 line | 1.125 |
| North Chongqing | High-speed rail-10 line | 1.122 |
| New street | No. 6 line-loop line | 1.121 |
| North Chongqing | Number 10Line-intercity | 1.116 |
The result shows that the channel of the Chongqing North station south square transferred from the No. 3 line to the ring line needs to be optimized in key point, and the channel of the Chongqing North station south square transferred from the No. 3 line to the No. 10 line needs to be protected in key point. And stations such as the north square of the north station of Chongqing, the south square of the north station of Chongqing, new streets, red flag river ditches and the like have a plurality of transfer channels, so that the optimization sensitivity/failure risk is high, and optimization or protection measures need to be taken.
Implementations and functional operations of the subject matter described in this specification can be implemented in: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware, including the structures disclosed in this specification and their structural equivalents, or combinations of more than one of the foregoing. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more tangible, non-transitory program carriers, for execution by, or to control the operation of, data processing apparatus.
Alternatively or in addition, the program instructions may be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution with a data processing apparatus. The computer storage medium may 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" encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or multiple computers. An apparatus can comprise special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can include, in addition to hardware, code that creates an execution environment for the associated computer program, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
A computer program (which may also be referred to or described as a program, software application, module, software module, script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed 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. A program can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in: in a markup language document; in a single file dedicated to the relevant program; or in multiple coordinated files, such as files that store one or more modules, sub programs, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across 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 apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Computers suitable for carrying out computer programs include, and illustratively may be based on, general purpose or special purpose microprocessors, or both, or any other kind of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for executing or carrying out instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such a device. Further, the computer may be embedded in another apparatus, e.g., a mobile telephone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a removable storage device, e.g., a Universal Serial Bus (USB) flash drive, or the like.
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, e.g., internal hard disks or removable disks; magneto-optical disks; CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, 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 isi(t) represents the passenger flow demand (in units of: man/hour) at station (or section) i at time t, ci(t) represents the traffic capacity (in: man/hour) of station (or section) i at time t, xi(t)/ci(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, wi(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 wi(t) is represented by formula 5:
wi(t)＝min(xi(t),ci(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.skRepresents a section ekOD requirement of (1), LkRepresents a section ekTraffic capacity of WkRepresenting 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 pij mThe OD requirement of (a) is,
representing station viPath 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 isji mFor decision variables, the representation is assigned to path pji mThe OD requirement of (1);
wherein x isnj mRepresenting decision variables, representing assignment to paths pnj mThe OD requirement of (1);
wherein,
for decision variables, the representation is assigned to path pij mOD requirement of (e)kDenotes the k-th interval, pij mRepresents the mth simple path from station i to station j;
wherein q isijIndicating 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, viIndicates the ith station, ekIs 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010711200.3A CN111861219B (en) | 2020-07-22 | 2020-07-22 | Regional rail transit global structure risk bottleneck identification method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010711200.3A CN111861219B (en) | 2020-07-22 | 2020-07-22 | Regional rail transit global structure risk bottleneck identification method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111861219A true CN111861219A (en) | 2020-10-30 |
| CN111861219B CN111861219B (en) | 2024-09-24 |
Family
ID=72949204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010711200.3A Active CN111861219B (en) | 2020-07-22 | 2020-07-22 | Regional rail transit global structure risk bottleneck identification method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111861219B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112488568A (en) * | 2020-12-14 | 2021-03-12 | 上海工程技术大学 | Method for evaluating large passenger flow operation risk of subway station and application thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140019194A1 (en) * | 2012-07-12 | 2014-01-16 | Bank Of America | Predictive Key Risk Indicator Identification Process Using Quantitative Methods |
| CN108345983A (en) * | 2018-01-04 | 2018-07-31 | 北京轨道交通路网管理有限公司 | The appraisal procedure of road network operation security situation and risk, device and processor |
| CN109872008A (en) * | 2019-03-13 | 2019-06-11 | 清华大学 | Urban Rail Transit safety evaluation and Enhancement Method based on bus traveler assignment |
| CN110879575A (en) * | 2018-08-29 | 2020-03-13 | 西门子科技与服务私人有限公司 | Computer-implemented method for sensitivity analysis and sensitivity analysis system |
-
2020
- 2020-07-22 CN CN202010711200.3A patent/CN111861219B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140019194A1 (en) * | 2012-07-12 | 2014-01-16 | Bank Of America | Predictive Key Risk Indicator Identification Process Using Quantitative Methods |
| CN108345983A (en) * | 2018-01-04 | 2018-07-31 | 北京轨道交通路网管理有限公司 | The appraisal procedure of road network operation security situation and risk, device and processor |
| CN110879575A (en) * | 2018-08-29 | 2020-03-13 | 西门子科技与服务私人有限公司 | Computer-implemented method for sensitivity analysis and sensitivity analysis system |
| CN109872008A (en) * | 2019-03-13 | 2019-06-11 | 清华大学 | Urban Rail Transit safety evaluation and Enhancement Method based on bus traveler assignment |
Non-Patent Citations (3)
| Title |
|---|
| 俞礼军;杨灿杰;: "随机路网中风险爱好出行者的路径选择分析", 华南理工大学学报(自然科学版), no. 12, 15 December 2015 (2015-12-15), pages 133 - 138 * |
| 孙家臣;王海燕;任瑶;杨家其;: "西非铝矾土进口海运供应链风险评估", 上海船舶运输科学研究所学报, no. 01, 30 March 2020 (2020-03-30), pages 74 - 80 * |
| 陈洪波;王宁;王建西;: "基于三角模糊云贝叶斯网络的盾构下穿段施工风险评估", 国防交通工程与技术, no. 03, 19 May 2020 (2020-05-19), pages 17 - 22 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112488568A (en) * | 2020-12-14 | 2021-03-12 | 上海工程技术大学 | Method for evaluating large passenger flow operation risk of subway station and application thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111861219B (en) | 2024-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111260221B (en) | Traffic facility bearing capacity evaluation method based on dynamic model and oriented to city global situation | |
| CN109543934B (en) | Method for evaluating comprehensive index of urban public transport network | |
| CN107766969B (en) | Large station fast line layout method based on subway service capacity bottleneck section identification | |
| CN104318758B (en) | Based on multi-level multimodal Public transport network planning method | |
| US20130125779A1 (en) | Metropolitan transport system and method | |
| CN109360420B (en) | Public traffic big data processing system and method | |
| Hong et al. | Spatiotemporal vulnerability analysis of railway systems with heterogeneous train flows | |
| CN113345259A (en) | Ground bus route adjusting method based on data driving | |
| CN111861219A (en) | Regional rail transit global structure risk bottleneck identification method and system | |
| Jin et al. | Exploring the robustness of public transportation system on augmented network: A case from Nanjing China | |
| Zhou et al. | Dispatching design for customized bus of hybrid vehicles based on reservation data | |
| CN111723991A (en) | Method for predicting and evaluating impact influence of large-scale movable scattered field passenger flow on subway station | |
| Novikov et al. | Methodology of passenger public transport organization within the context of long-term territorial development of a city | |
| CN111754071A (en) | Regional rail transit global security enhancing method | |
| Aftabuzzaman et al. | Modeling the spatial impacts of public transport on traffic congestion relief in Melbourne, Australia | |
| CN116452026A (en) | Comprehensive traffic-based traffic junction function evaluation system | |
| Sañudo et al. | Optimal stopping location of a high speed train using GIS and multicriteria decision‐making | |
| CN113850436A (en) | Urban rail transit passenger flow random probability distribution model optimization method | |
| Xiaoliang et al. | Discussion on Optimization of Public Transportation Network Setting considering Three‐State Reliability | |
| Kitagawa et al. | Multiagent-based sustainable bus route optimization in disaster | |
| Xue et al. | Cooperative Optimization of Bus-Subway-Shared Bicycle Based on Uncertainty Theory | |
| CN114564809B (en) | Bus network optimization method considering current network structure | |
| CN118365092B (en) | Subway congestion relieving method and system based on prediction data | |
| Li et al. | Method for identifying global structural risk bottleneck of regional rail transit based on sensitivity analysis | |
| Botian et al. | Multi School Bus Routing Problem with Road Safety Factor via A Heuristic Algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |