CN108364093B - Road network passenger flow cooperative control optimization method based on capacity bottleneck untwining strategy - Google Patents

Road network passenger flow cooperative control optimization method based on capacity bottleneck untwining strategy Download PDF

Info

Publication number
CN108364093B
CN108364093B CN201810103968.5A CN201810103968A CN108364093B CN 108364093 B CN108364093 B CN 108364093B CN 201810103968 A CN201810103968 A CN 201810103968A CN 108364093 B CN108364093 B CN 108364093B
Authority
CN
China
Prior art keywords
station
control
passenger flow
interval
flow
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.)
Active
Application number
CN201810103968.5A
Other languages
Chinese (zh)
Other versions
CN108364093A (en
Inventor
豆飞
姚向明
张文强
张通利
黄悦
赵鹏
刘浩然
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Subway Operation Corp
Original Assignee
Beijing Subway Operation Corp
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Subway Operation Corp filed Critical Beijing Subway Operation Corp
Priority to CN201810103968.5A priority Critical patent/CN108364093B/en
Publication of CN108364093A publication Critical patent/CN108364093A/en
Application granted granted Critical
Publication of CN108364093B publication Critical patent/CN108364093B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q50/40—Business processes related to the transportation industry

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a road network passenger flow cooperative control optimization method based on a capacity bottleneck untwining strategy. The method comprises the steps of firstly establishing an internal relation between station passenger flow and interval transport energy occupation based on incapability constraint passenger flow distribution, obtaining a road network section passenger flow distribution state, then identifying an ability bottleneck interval by combining the interval passenger flow transport ability, and solving the ability bottleneck based on a feedback control strategy, thereby reversely determining a control station, control time and control intensity. The method is suitable for the condition that the passenger flow rule is obvious and relatively stable, has good applicability to the peak time, and provides a theoretical basis for the normal current limiting scheme compilation method in the peak time.

Description

Road network passenger flow cooperative control optimization method based on capacity bottleneck untwining strategy
Technical Field
The invention relates to a road network passenger flow control method, in particular to an urban rail transit road network passenger flow cooperative control optimization method based on a capacity bottleneck untwining strategy.
Background
The expansion of the scale of the subway network causes the continuous increase of the passenger flow demand, the contradiction between the passenger flow demand and the transport capacity is gradually highlighted, particularly in the early and late peak periods, the passenger flow directivity is obvious, part of sections operate with high load for a long time, the phenomenon of crowding and detention of the passenger flow in stations and sections is prominent, and the operation safety of the urban rail transit network is seriously influenced. Except for normal peak time, the time-interval passenger flow volume is increased rapidly due to large-scale activities such as holidays, meeting exhibitions, sports events and the like, and an operation department has to adjust an operation plan in time to meet the passenger flow demand. In addition, sudden events such as severe weather, equipment failure and the like cause train operation interruption or train operation delay, short-time passenger flow steady state sudden change is caused, if effective measures are not taken timely, due to ripple reaction, the large passenger flow can be spread on a rail traffic network, urban rail traffic network congestion is easily caused, casualties and property loss can be caused seriously, and negative influence is caused on operation safety of a rail traffic system.
The root cause of passenger traffic congestion is the mismatch between traffic capacity and passenger traffic demand. The measures for relieving the passenger flow congestion mainly comprise two aspects: firstly, the transportation capacity is improved, and the passenger transportation conveying capacity is improved from the supply perspective; secondly, the demand management is enhanced, and the passenger flow space-time distribution is adjusted from the demand perspective. Because the network infrastructure equipment has a fixed capacity for transportation over a period of time, regulation from the perspective of passenger demand management is a major approach to alleviating congestion. Demand management includes many aspects, such as: floating fare, passenger flow control (flow limiting), passenger flow inducement, congestion payment, etc. In the field of road traffic, congestion charging (fare strategy) becomes an important measure for relieving congestion, however, a new fare system reduces the initial passenger flow to some extent, but the passenger flow returns to the existing level quickly and the growth trend is very strong; the reason why the fare strategy is invalid or the fare strategy is not obvious is that rigid commuting passenger flow occupies a large proportion of subway travelling passenger flow, and the sensitivity of the rigid commuting passenger flow to the fare is low. At present, current limiting becomes an important demand management measure for relieving passenger flow congestion in each big city, and compared with other measures such as fare and induction, the current limiting effect is rapid and effective.
Disclosure of Invention
The current limiting scheme in the prior art has great defects, mainly reflects in strong subjectivity and is difficult to be applied to a large-scale actual road network. Therefore, the invention starts from the congestion source of the transport capacity bottleneck, and constructs the feedback type control scheme generation method by taking the evacuation capacity bottleneck as a basic means. According to the method, a road network passenger flow cooperative control scheme generation algorithm based on an interval capacity bottleneck untwining strategy is constructed according to a road network current limiting organization principle from the perspective of compiling a normal passenger flow control scheme of an urban rail transit road network at a peak time, and a calculation method is provided for compiling the normal passenger flow control scheme at the peak time. The following technical scheme is adopted specifically:
the method comprises the following steps:
(1) constructing a road network feasible path set, and establishing a passenger flow distribution model to determine a passenger flow distribution state;
(2) constructing a station-interval traffic relation through the station passenger flow interval passing rate and the interval transport energy occupancy;
(3) calculating the difference value between the interval throughput and the passenger transport capacity, and judging that a transport capacity bottleneck is formed when the interval throughput is greater than the transport capacity;
(4) if the situation that the transport capacity bottleneck is formed is judged, determining a target control station, calculating weight coefficients of all stations, performing cooperative control on a plurality of stations to obtain effective control flow required by the target control station, and formulating a capacity bottleneck relief strategy;
(5) and correcting the effective flow control rate by using the passage rate of the station passenger flow interval to obtain the station flow control rate, and generating a control scheme according to the station flow control rate.
Preferably, the calculation method of the passenger traffic flow inter-area passing rate of the station in the step (2) is as follows:
Figure BDA0001566545010000021
Figure BDA0001566545010000022
the passenger flow passing rate of the station i relative to the section m represents the ratio of the passenger flow passing through the section m in all the inbound passenger flows of the station i to the total inbound passenger flow,
Figure BDA0001566545010000023
the passenger flow volume of the section m from the station i to the station j, dijThe total passenger flow from station i to station j;
the calculation mode of the interval operation energy occupancy is as follows:
Figure BDA0001566545010000024
in the formula (I), the compound is shown in the specification,
Figure BDA0001566545010000025
the section capacity occupancy rate corresponding to the time period t represents the proportion of the passenger flow passing through the section m from the station i,
Figure BDA0001566545010000026
the passenger flow rate of the section m from the station i to the station j in the time period t, qm(t) is the passenger flow volume in the interval m within the time period t.
Preferably, the specific way of determining the target control station in step (4) is as follows:
step 1) determining the number K of response control station sets according to the congestion load of the bottleneck interval;
step 2) occupying rate of interval transportation energy
Figure BDA0001566545010000027
Based on the above, selecting K stations occupying the interval before energy transportation as the initially selected response control station set
Figure BDA0001566545010000031
Step 3) determination in sequence
Figure BDA0001566545010000032
Whether the middle station has the out-station control implementation condition and whether the distance between the station and the bottleneck section meets the set range requirement, if all the conditions are met, the station is put into a response control station set N'm；
Step 4) if aggregate N'mIf the number of the included stations is less than K, turning to the step 2, selecting the subsequent K stations for further judgment until the number of the stations is in the set N'mThe system comprises K response control stations.
Preferably, the specific way of calculating the weight coefficient of each station in step (4) is as follows:
Figure BDA0001566545010000033
Figure BDA0001566545010000034
in the formula
Figure BDA0001566545010000035
Is a weighting coefficient of a station, and is,
Figure BDA0001566545010000036
the occupation rate of the interval operation energy is,
Figure BDA0001566545010000037
in order to be responsive to the time of the response,
Figure BDA0001566545010000038
is the area of the square outside the station, ViThe number of the outdoor operation public traffic lines,
Figure BDA0001566545010000039
the area of a platform in a certain direction of a station i, mu1-μ5Is an importance parameter.
Preferably, the effective flow control calculation method in step (4) is as follows:
Figure BDA00015665450100000310
Δdi(t) represents an effective control flow rate of the control station i,
Figure BDA00015665450100000311
and controlling the station weight coefficient for the target.
Preferably, the method for correcting the effective flow control rate by the station passenger flow inter-zone passing rate in the step (5) is as follows:
Figure BDA00015665450100000312
calculating the flow control rate of the station
Figure BDA00015665450100000313
βi(t) the station control flow rate for the corresponding time period, diAnd (t) is the actual demand of the station i in the time period t.
The invention has the following beneficial effects: (1) the physical meaning is clear, and the calculation is simple and convenient; (2) influence factors involved in the current limiting process can be fully considered, and the expandability is strong; (3) the method can meet the application requirements of large-scale actual road networks and is convenient for computer implementation.
Drawings
Fig. 1 is a schematic diagram of interval capability bottleneck fluffing.
Fig. 2 is a flow chart of generation of a traffic control scheme based on capability bottleneck mediation.
Fig. 3 is a schematic illustration of a single bottleneck interval fluffing.
Fig. 4 is a schematic illustration of a multiple bottleneck interval fluffing.
FIG. 5 is a diagram of a multi-bottleneck interval fluffing algorithm.
Fig. 6 is a flow chart of inter-line cooperative passenger flow control.
Fig. 7 is a flow chart of network traffic control scheme generation.
Detailed Description
Fig. 1 shows a block traffic distribution diagram of an exemplary line, assuming that the block 3-4 traffic exceeds the maximum capacity of the line, a capacity bottleneck is formed. In practice, this capacity bottleneck is expressed in the form of passengers getting stuck at the platform 3 of the station. In order to relieve the bottleneck section, the passenger flow can be controlled by the front stations (1, 2 and 3). How to control the stations 1,2 or 3 is then the key to solve the problem. Different control strategies can be adopted according to the degree of passenger flow congestion, and if the degree of passenger flow congestion is smaller, the station 3 can be controlled only; when the passenger flow is crowded seriously, the stations 1,2 and 3 can be cooperatively controlled. Therefore, the basic idea of the method constructed by the invention is as follows: firstly, identifying the capacity bottleneck of a road network; secondly, the corresponding bottleneck is dredged, so that the corresponding control strategy is reversely determined; however, there may be multiple capacity bottlenecks in the road network, and the problem of untwining the multi-capacity bottleneck interval under the network condition needs to be solved.
When constructing a network-level passenger flow control scheme, the following key problems are involved:
■, clearing the passenger flow distribution state of road network;
■ accurately calculating the wire mesh passenger transport capacity;
■ identifying road network capacity bottlenecks;
■ constructing an appropriate capability bottleneck unwarping algorithm;
■ making a passenger flow control scheme;
fig. 2 shows a passenger flow control scheme generation flow based on a capacity bottleneck fluffing algorithm.
In the following, the main contents related to the control policy generation process will be described one by one, and mainly include: determining a passenger flow distribution state, constructing a station-interval flow relation, identifying a transport capacity bottleneck, and generating a capacity bottleneck relief strategy and a control scheme.
(1) Passenger flow distribution status determination
Defining an urban rail transit network G ═ (N, E), wherein N is a station set, N ═ {1,2 … i, j }, and adopting virtual station separate description for transfer stations; e is a set of intervals, E ═ {1,2 … m, n }, including intervals and transfer lanes; discretizing a research range (such as a peak period) into equal-length periods, wherein T is a research period set, T is {1,2 … T }, and delta T is a period length; cm(t) the transport capacity of interval m within time period t; q. q.sm(t) is the passenger flow passing through the interval m in the time period t; dij(t) is the traffic demand between the OD pair (i, j) in the time period t; rijSet of feasible paths, r, between OD pairs (i, j)ijkIs the kth path in the set, rijk∈Rij。
(1.1) feasible Path set construction
The path cost is described by adopting the path comprehensive impedance, and the expression is as follows:
Wrs k＝Trs k+Ers k
Figure BDA0001566545010000051
Figure BDA0001566545010000052
in the formula Wrs kIs the integrated impedance value of the k path between the O-D pair (r, s); t isrs kThe impedance value of the train operation section in the kth path between the O-D pair (r, s); ers kIs the impedance value of the transfer arc in the kth path between the O-D pair (r, s); t is tijThe train interval running time between the node i and the node j (the station nodes on the same line); siThe stop time of the train at the node i; t is tij WALKThe average transfer time between node i and node j (station nodes on different lines); alpha is a transfer time amplification factor; t is tj WAITFor the average waiting time of passengers at node j, adopt
Figure BDA0001566545010000053
The calculation is carried out according to the calculation,
Figure BDA0001566545010000054
the departure interval time of the line q where the j node is located is obtained; rrsIs the set of feasible paths between the O-D pair (r, s).
And searching any K short path between O-D through a K short path searching algorithm, wherein in order to ensure the complete construction of a feasible path set, K is generally more than or equal to 5. The k short path search algorithm is relatively mature and will not be described here. In k short paths obtained by the path search algorithm, unreasonable paths can be considered that passengers cannot select, and the reasonability of the k paths needs to be judged, so that an effective feasible path set is generated. The path rationality condition judgment conditions are as follows:
Wrs max＝min(Wrs min(1+θ),Wrs min+U)
Figure BDA0001566545010000055
in the formula, theta is a proportionality coefficient and describes the relative offset proportion of the feasible path impedance value and the minimum path impedance value; u is a constant describing the absolute offset of the feasible path impedance value from the minimum path impedance value.
(1.2) passenger flow distribution model
The method for clearing the passenger flow distribution characteristics of the road network is the first step of constructing a control scheme, and road network passenger flow distribution is carried out by utilizing a widely-applied Logit model, so that the internal relation between station passenger flow requirements and interval capacity occupation is established.
In order to truly reflect the distribution characteristics of the demand, the interval operation energy constraint is not considered in the distribution process. In practice, the traffic congestion due to the transportation capacity limitation is represented by the retention of passengers at the platform, and when the section transportation capacity constraint is not considered, the section traffic is represented as being larger than the section transportation capacity.
Assuming that the traffic demand (OD table) is known in a certain time t, the construction process of the traffic relation is described in detail by taking a certain OD pair (i, j) as an example. Probability p of passenger selecting k path between OD (i, j) based on random user balance theoryijkAs shown in the following formula:
Figure BDA0001566545010000061
in the formula WrijkIs the combined impedance of the kth path between OD (i, j);
Figure BDA0001566545010000062
centralizing the average impedance of the paths for the feasible paths; the synthetic impedance is a weighted value of the section and the node impedance passing through the path.
Then the k path traffic between ODs (i, j) is:
qrijk(t)＝dij(t)·pijk
the flow distribution of all feasible paths in the OD is completed to obtain the passenger flow passing through the interval m
Figure BDA0001566545010000063
See formula:
Figure BDA0001566545010000064
in the formula
Figure BDA0001566545010000065
Represents the amount from the OD pair (i, j) in the flow of passengers through the interval m during the time period t;
Figure BDA0001566545010000066
is a variable from 0 to 1, with 1 indicating that the section m is located on the path rijkAnd the opposite is 0.
Further carrying out passenger flow distribution on all OD pairs in the road network to obtain the total passenger flow q of the interval mm(t), see formula:
Figure BDA0001566545010000067
(2) station-interval traffic relation construction
The station passenger flow interval passing rate and the interval transport energy occupancy are key elements for constructing a passenger flow control scheme.
(2.1) station passenger flow interval passing rate
The station passenger flow section passing rate indicates which sections the passenger flow entering from the station flows through and what proportion accounts for the total station entering amount.
Assuming that the passenger flow of the station A in the unit time is 1000 people, wherein the passenger flow passing through the A-B \ B-C \ C-D \ D-F intervals is 1000, 800, 600 and 100 people respectively, the passing rates of the passenger flow intervals of the corresponding stations are 100%, 80%, 60% and 10% respectively. It can be easily understood that after the passenger flow control is performed on the station A, the effects of the station A on relieving the congestion of the passenger flow in different sections in front are greatly different. The bigger the passenger flow interval passing rate of the station is, the more obvious the dismissal effect is.
Definition of
Figure BDA0001566545010000071
The passenger flow passing rate of the section m for the station i represents the passing of all the passenger flows entering the station iCalculating the ratio of the passenger flow of the interval m to the total inbound passenger flow, and calculating an expression formula:
Figure BDA0001566545010000072
(2.2) Interval energy occupancy
The section transportation energy occupancy rate is a relation between the section transportation energy occupancy rate and the station passenger flow section passage rate from the section perspective. The section transportation energy occupancy rate represents the proportion of the passenger flow from a specific station in all the passenger flows flowing through the section, and is used for describing the relation of the passenger flow demand from the station occupying the section transportation energy.
Assuming that the total passenger flow volume between the intervals C-D in the unit time is 600 persons, wherein 100 persons come from the station A, 300 persons come from the station B, and 200 persons come from the station C, the corresponding interval transport capacity occupancy rates are respectively as follows: 16.67%, 50.00%, 33.33%. It is understood that when the C-D section is in a congested state, if the congestion needs to be relieved, the station with the best control effect is station B, because the traffic flow from station B occupies the largest capacity of the section.
Definition of
Figure BDA0001566545010000073
The section capacity occupation rate is the proportion of the passenger flow passing through the section m from the station i, and is used for describing the internal relation between the section capacity occupation and the station passenger flow demand, and the larger the section capacity occupation rate is, the tighter the relation between the section capacity occupation rate and the station passenger flow demand is. The coefficient is an important parameter for constructing a passenger flow control scheme, and an expression is calculated as follows:
Figure BDA0001566545010000074
in the formula
Figure BDA0001566545010000075
The occupation ratio of the corresponding interval operation energy in the time period t is changed along with the distribution characteristics of the demands in different time periods.
(3) Transport capacity bottleneck identification
The transport capacity of an urban rail transit system may be generally defined as: the total number of passengers that can be transported within one hour in a certain direction on a certain route. The transport capacity can generally be divided into a usable capacity and a design capacity.
Available capacity: on urban rail transit networks, the transport capacity calculation must also take into account changes in passenger demand. Due to the imbalance in passenger arrival, it is not practically guaranteed that all design capacity is occupied by passengers, and especially in off-peak hours, the traffic imbalance factor is generally used for depiction. The passenger flow imbalance coefficient is generally between 0.70 and 0.95. Available capacity is the design capacity x the passenger flow imbalance factor.
Design capability: the amount of passenger space that passes a point in one hour in a direction on a line. The design capacity is equivalent to the maximum capacity, the theoretical capacity or the theoretical maximum capacity and is difficult to realize in the actual transportation and production process. There are two main factors that affect the design ability: firstly, the line capacity, secondly the train capacity, promptly: design capacity-line capacity x train capacity.
The line capacity refers to the number of trains that each fixed device of the urban rail transit system line can pass in a unit time (usually, peak hour) under the condition of adopting a certain vehicle type, signal equipment and driving organization method. The line capacity is the reflection of the comprehensive capacity of the system and mainly depends on the minimum train interval and station stop time, and the calculation formula is as follows:
nmax＝3600/tworkshop
In the formula nmaxRepresents the maximum number of trains/train that the line can pass through in one hour; t is tWorkshopRepresenting the minimum inter-train time/s of the link.
The train capacity is the product of the number of passengers per train and the number of trains marshalling, as shown in the following equation: the train capacity (number of passengers/train) is the number of trains grouped by the number of passengers per train.
The conveying capacity of the urban rail transit line is the number of passengers capable of being conveyed in unit time, and is mainly determined by the number of train formation vehicles and the number of fixed members of the vehicles under the condition of a certain line capacity.
The main basis for determining the number of train formation vehicles is the predicted maximum section passenger flow in the planning annual peak hour, and the calculation formula is as follows:
m＝Pmax/(nPeak ×Pvehicle with wheels)
In the formula: m represents the number of train formation vehicles/vehicle; pmaxRepresenting the predicted maximum cross-sectional passenger flow volume/person at the peak hour of the planning year; n isPeak Representing the number/pair of trains driven at peak hours of the predicted planned year; pVehicle with wheelsRepresenting the number of vehicle occupants/person.
In addition, the following constraint factors are also fully considered when determining the number of train formation vehicles:
platform length limitation. On most lines, when the train consists of up to 8 trains, the train length will be equal to the platform length.
② influence on the line capability. When the train length is close to the platform length, the train is required to stop accurately at the designated position of the station, the stopping time is usually added, and the train length is also an influencing variable as can be known from analysis and calculation for tracking the train interval time.
And economic rationality. With long consist trains, the vehicle load factor is generally lower during off-peak hours.
The number of train formation vehicles in the near term and the far term of the urban rail transit system is determined according to the predicted near term and the far term passenger flow and the number of vehicle members respectively.
The number of the vehicle members refers to the rated passenger capacity of the urban rail transit train, consists of the number of the seats and the number of the standing passengers of the train, and is the sum of the number of the seats of the carriage and the number of the standing passengers on the vacant area; the standing area is the vacant area of the carriage, and the area of the seat is subtracted from the area of the carriage, and the standing area is generally calculated according to 6-8 passengers standing per square meter, and the calculation formula is as follows: the number of vehicle occupants is the number of seats + standing area × the specified standing density.
Transfer lane passing capacity can be calculated by checking the station design specifications and lane width. And will not be described in detail herein.
When the throughput of the interval is greater than the conveying capacityCapacity bottlenecks (capacity constraints are not considered in the distribution, but there is a strict upper limit on the inter-zone throughput in practice) are formed, which in practice appear as congestion of the traffic flow in the immediate front stations. Definition of Δ qmIn order to make the difference between demand and transportation capacity larger, the contradiction between supply and demand becomes more prominent, and the passenger flow congestion at the corresponding station becomes more serious, see formula (4-18):
Δqm(t)＝qm(t)-Cm(t)
in the formula,. DELTA.qmAnd (t) the difference between the throughput and the capacity of the interval m in the time period t, wherein the larger the difference is, the larger the congestion pressure is.
(4) Capability bottleneck mediation strategy
On the basis of determining the capacity bottleneck, how to perform capacity bottleneck fluffing through passenger flow control becomes a key. The present invention will describe the bottleneck untwining process from both single and multi-neck perspectives.
(4.1) Single bottleneck fluffing strategy
Fig. 3 shows a schematic view of the fluffing of a single bottleneck region. Let e3Is the capacity bottleneck interval, then station n3Passenger retention will occur. Relieving station n3The scheme of passenger flow pressure comprises two types: (i) to station n3Carrying out passenger flow control, namely local station control; (ii) for a plurality of stations (e.g. n)1，n2And n3) Control, i.e. cooperative control, is performed. Here, a control station (which may be single or multiple) selected to mitigate a certain section by a target control station value is defined; the effective control flow is defined as an effective flow (different from an actual entering flow) required to be controlled by a control station, and the effective flow means that the partial flow can really play a role in relieving congestion, namely the partial flow can flow through a bottleneck interval.
(i) Control of local station, i.e. to station n3Control is carried out, and the target control station is n3To alleviate interval e3The traffic is effectively controlled as shown in the following formula:
Figure BDA0001566545010000091
in the formula,. DELTA.di(t) represents an effective control flow rate of the control station i,
Figure BDA0001566545010000092
and the weight of the station i in the bottleneck section m is expressed, the effect is larger when the weight is larger, and the corresponding effective control flow is larger. In the example of fig. 3, the target control station is n3，
Figure BDA0001566545010000101
(ii) And (4) performing cooperative control. Suppose a station n1、n2And n3And performing cooperative control, wherein the effective control flow required by the station control should satisfy the following relation:
Figure BDA0001566545010000102
then, the control weight coefficients should satisfy the following constraints:
Figure BDA0001566545010000103
n 'in the formula'mThe control station set selected for the bottleneck interval m is defined as the target control station, i.e. station n in this example1、n2And n3(ii) a At this time, the effective flow control rate of the station i is as follows:
Figure BDA0001566545010000104
it can be seen that the rational determination of the target control station and the weighting factor is the key to the cooperative passenger flow control, and the determination process will be described in detail below.
(4.1.1) target control station determination
In the bottleneck untwining process, if the selection of the response control station is less, the corresponding control strength is higher, and the flow control pressure is higher; if the more the response control station is selected, the greater the influence on the trip of the passenger is. The following describes the determination process of the response control station set in combination with the research results and actual conditions of the earlier projects.
Number of target control stations: the number K of the response control station sets needs to be determined according to the passenger flow congestion pressure of the bottleneck section, and generally, the larger the passenger flow congestion pressure is, the more the number of response stations needs to be. Because the passenger flow distribution characteristics of different urban rail transit are obviously different, the parameters of the urban rail transit are calibrated according to the actual conditions. The selection of the target control station is mainly judged according to the interval transport capacity passing rate, and the factors such as the external environment of the station, the current limiting implementation condition and the like are assisted to be referred. The specific implementation algorithm is as follows:
step 1: determining the number K of response control station sets according to the congestion load of the bottleneck section;
step 2: by interval operating energy occupancy
Figure BDA0001566545010000105
Based on the above, selecting K stations occupying the interval before energy transportation as the initially selected response control station set
Figure BDA0001566545010000106
And step 3: sequential determination
Figure BDA0001566545010000107
Whether the middle station has the out-station control implementation condition and whether the distance between the station and the bottleneck section meets the set range requirement, if all the conditions are met, the station is put into a response control station set N'm；
And 4, step 4: if aggregate N'mIf the number of the included stations is less than K, turning to the step 2, selecting the subsequent K stations for further judgment until the number of the stations is in the set N'mThe system comprises K response control stations.
Wherein, whether the station has the out-station control implementing condition can be limited by the area of the out-station square; and in the space distance judgment, in order to meet the response time requirement, the average travel time of the passengers in the road network or half of the average number of stations is taken as a limiting standard.
(4.1.2) station weight coefficient calculation
On the basis of determining the target control station set, how to determine the weight system of each control station is the next step. The station weight coefficient delineates the bottleneck dismissal effect of the station, which is the key to the establishment of the control scheme. The influence factors and calculation of the station weight coefficient are analyzed below.
Occupancy of interval transportation energy
Figure BDA0001566545010000111
The traffic flow passing through the section m is shown as a proportion from the station i, and a larger value indicates a stronger association between stations and sections, and the control effect of the station i becomes more remarkable when traffic congestion in the section m is relieved. Which is a key element in calculating the weight coefficients.
The response time represents the relevance of the control station and the bottleneck interval on the time and space, and the control effect of the control station is more obvious when the control station is closer to the bottleneck interval. The response time is quantitatively expressed by the train running time corresponding to the bottleneck interval between the control stations, and is defined as
Figure BDA0001566545010000112
For the station immediately in front of the bottleneck section, the response time is expressed as half of the section operation time.
The external traffic environment mainly includes the outdoor square and the public traffic operation condition, which determines whether the station has the possibility of outdoor control. Definition of
Figure BDA0001566545010000113
Is the area of the square outside the station, ViThe number of the public traffic lines for the operation outside the station. Generally, when the area of the square outside the station is small or the bus route is insufficient, the passenger flow control intensity of the station should be reduced.
The station capacity is another major factor in the establishment of the control scheme. Generally, the greater the platform carrying capacity, the greater the capacity to resist the risk of congestion. Here, the station area is used to quantify the station carrying capacity, which is defined
Figure BDA0001566545010000116
The area of the platform (up and down) in a certain direction of the station i. Because the passenger flow has obvious directionality, the utilization condition of the capacity of the platform in a certain direction is only needed to be considered when a certain bottleneck is loosened. The island type platform can realize capacity sharing, and corresponding coefficients can be set for calibration; the lateral stations are determined using the actual area.
In combination with the above analysis of the influencing factors, the station weight can be expressed as a functional form of the relevant factors, see formula:
Figure BDA0001566545010000114
and (4) taking the difference among the elements into consideration, carrying out normalization processing on the elements so as to determine the final weight coefficient. The specific calculation expression is shown as follows:
Figure BDA0001566545010000115
Figure BDA0001566545010000121
in the formula of1，μ2.., respectively representing the importance parameters corresponding to the factors such as the interval operation occupancy rate, the response time, etc., and the value is located between (0,1), which can be calibrated according to the practical experience. The interval performance occupancy, the station carrying capacity and the response time are considered main factors, and the importance is high.
Figure BDA0001566545010000122
The obtained station weight is the primary station weight.
(4.2) Multi-bottleneck fluffing strategy
Generally, a road network often has a plurality of bottleneck sections, and when a certain bottleneck section is loosened, other bottleneck sections are affected. FIG. 4 shows a schematic diagram of the multi-bottleneck interval untwining on the line when the bottleneck interval e is completed3After dismissal, the possible interval e4No longer becoming a capacity bottleneck.
In order to ensure effective untwining of the bottleneck, the principle of 'big first and small second' is followed, namely, an interval with large crowding pressure in the road network is untwined first, and then an interval with small bottleneck of capacity is untwined. FIG. 5 shows a multi-bottleneck interval fluffing algorithm under the road network condition.
(4.3) an online cooperative control strategy
Under the networked operation condition, the interaction between lines is enhanced, the congestion phenomenon is difficult to be effectively relieved by coordination control among the stations in the local line through a large amount of transfer passenger flows, at the moment, the congestion is relieved by switching the coordination control temporary lines into the local line passenger flow, and the passenger flow is cooperatively controlled from the network level, wherein the schematic process is shown in fig. 6.
The cooperative control process between adjacent lines is similar to the bottleneck untwining process on a single line, and the main difference is that the selection of the target control station is not limited to the local line during bottleneck untwining. According to the selection algorithm of the target control station, the adjacent line cooperative control does not need special treatment and is consistent with the single line condition.
(5) Passenger flow control scheme generation
(5.1) station control scheme
Defining station control flow rate betaiThe control intensity is represented quantitatively, the ratio of the passenger flow (the demand which cannot be met under the control condition) which is limited to enter the station in unit time to the actual passenger flow demand is represented, the control intensity is larger when the flow control rate is larger, and the formula is shown as follows:
Figure BDA0001566545010000123
in the formula di(t) is the actual demand (i.e. arrival) of station i in time period t; di' (t) is inbound restricted passenger flow (i.e., traffic control); beta is ai(t) is the controlled flow rate for the corresponding time period.
Effective control flow rate of target control station is
Figure BDA0001566545010000131
It should be noted that there is a certain difference between the effective flow control rate and the station flow control rate: (1) the inbound amount comprisesThe passenger flow to other stations is difficult to distinguish; (2) not all traffic will flow through the bottleneck interval. Therefore, the traffic control is corrected by using the passing rate of the station passenger flow interval, as shown in the following formula:
Figure BDA0001566545010000132
after the station flow control rate is determined, a control scheme with feasibility needs to be established, such as: fence set length and width, number of open gates, batch release rate. This is not to be considered as limiting the scope of the invention, and the inbound rate per unit time may be determined after the rate of flow control is determined, upon which corresponding control measures may be further developed.
(5.2) network control scheme Generation
The basic flow of the network passenger flow control scheme is as follows: (1) in a discretization research period (generally, a peak period), carrying out passenger flow distribution in each period, and acquiring the internal relation between the demand and the interval capacity and the area break amount; (2) identifying and untwining bottlenecks, and utilizing a bottleneck untwining strategy to untwining bottlenecks in sequence to determine a control station and control intensity; (3) and determining an integral control scheme (three elements) in the research time period according to the bottleneck defibering results in different time periods. Fig. 7 shows a specific flow of generating the road network layer passenger flow control scheme.
(5.3) control period Length analysis
The constructed algorithm has no specific requirement on the length setting of the control time interval, however, the length setting of the control time interval is not too short from the view point of current limiting implementation and input OD matrix precision. Firstly, the normal state control measures (such as fence setting) are relatively fixed and are difficult to change frequently in a short time; secondly, the premise of compiling the normal state control scheme is that the passenger flow structure is stable, the control scheme is compiled on the basis of historical passenger flow distribution characteristics, if the control time period is too short, the stability of the passenger flow distribution characteristics is inevitably reduced, and the accuracy of the constructed control scheme is also reduced.

Claims (4)

1. A road network passenger flow cooperative control optimization method based on a capacity bottleneck fluffing strategy is characterized by comprising the following steps:
(1) constructing a road network feasible path set, and establishing a passenger flow distribution model to determine a passenger flow distribution state;
(2) constructing a station-interval traffic relation through the station passenger flow interval passing rate and the interval transport energy occupancy;
(3) calculating the difference value between the interval throughput and the passenger transport capacity, and judging that a transport capacity bottleneck is formed when the interval throughput is greater than the transport capacity;
(4) if the situation that the transport capacity bottleneck is formed is judged, determining a target control station, calculating weight coefficients of all stations, performing cooperative control on a plurality of stations to obtain effective control flow required by the target control station, and formulating a capacity bottleneck relief strategy;
(5) correcting the effective flow control rate by using the passage rate of the station passenger flow interval to obtain a station flow control rate, and generating a control scheme according to the station flow control rate;
the passenger traffic flow inter-zone passing rate calculation mode of the station in the step (2) is as follows:
Figure FDA0003060604650000011
Figure FDA0003060604650000012
the passenger flow passing rate of the station i relative to the section m represents the ratio of the passenger flow passing through the section m in all the inbound passenger flows of the station i to the total inbound passenger flow,
Figure FDA0003060604650000013
the passenger flow volume of the section m from the station i to the station j, dijThe total passenger flow from station i to station j;
the calculation mode of the interval operation energy occupancy is as follows:
Figure FDA0003060604650000014
in the formula (I), the compound is shown in the specification,
Figure FDA0003060604650000015
the section capacity occupancy rate corresponding to the time period t represents the proportion of the passenger flow passing through the section m from the station i,
Figure FDA0003060604650000016
the passenger flow rate of the section m from the station i to the station j in the time period t, qm(t) is the passenger flow volume in the interval m within the time period t;
the specific mode for determining the target control station in the step (4) is as follows:
step 1) determining the number K of response control station sets according to the congestion load of the bottleneck interval;
step 2) occupying rate of interval transportation energy
Figure FDA0003060604650000017
Based on the above, selecting K stations occupying the interval before energy transportation as the initially selected response control station set
Figure FDA0003060604650000018
Step 3) determination in sequence
Figure FDA0003060604650000021
Whether the middle station has the out-station control implementation condition and whether the distance between the station and the bottleneck section meets the set range requirement, if all the conditions are met, the station is put into a response control station set N'm；
Step 4) if aggregate N'mIf the number of the included stations is less than K, turning to the step 2), selecting the subsequent K stations for further judgment until the number of the stations is set to be N'mThe system comprises K response control stations.
2. The method of claim 1,
the specific way of calculating the weight coefficient of each station in the step (4) is as follows:
Figure FDA0003060604650000022
Figure FDA0003060604650000023
in the formula
Figure FDA0003060604650000024
Is a weighting coefficient of a station, and is,
Figure FDA0003060604650000025
the occupation rate of the interval operation energy is,
Figure FDA0003060604650000026
in order to be responsive to the time of the response,
Figure FDA0003060604650000027
is the area of the square outside the station, ViThe number of the outdoor operation public traffic lines,
Figure FDA0003060604650000028
the area of a platform in a certain direction of a station i, mu1-μ5Is an importance parameter.
3. The method of claim 2,
the effective flow control calculation mode in the step (4) is as follows:
Figure FDA0003060604650000029
Δdi(t) represents an effective control flow rate of the control station i,
Figure FDA00030606046500000210
and controlling the station weight coefficient for the target.
4. The method of claim 3,
the method for correcting the effective flow control rate by the passenger flow area passing rate of the station in the step (5) comprises the following steps:
Figure FDA00030606046500000211
calculating the flow control rate of the station
Figure FDA00030606046500000212
βi(t) the station control flow rate for the corresponding time period, diAnd (t) is the actual demand of the station i in the time period t.
CN201810103968.5A 2018-02-01 2018-02-01 Road network passenger flow cooperative control optimization method based on capacity bottleneck untwining strategy Active CN108364093B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810103968.5A CN108364093B (en) 2018-02-01 2018-02-01 Road network passenger flow cooperative control optimization method based on capacity bottleneck untwining strategy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810103968.5A CN108364093B (en) 2018-02-01 2018-02-01 Road network passenger flow cooperative control optimization method based on capacity bottleneck untwining strategy

Publications (2)

Publication Number Publication Date
CN108364093A CN108364093A (en) 2018-08-03
CN108364093B true CN108364093B (en) 2021-07-27

Family

ID=63004149

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810103968.5A Active CN108364093B (en) 2018-02-01 2018-02-01 Road network passenger flow cooperative control optimization method based on capacity bottleneck untwining strategy

Country Status (1)

Country Link
CN (1) CN108364093B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109272204B (en) * 2018-08-24 2021-09-03 同济大学 Method for evaluating network reserve capacity of urban subway
CN109615180A (en) * 2018-11-16 2019-04-12 浩鲸云计算科技股份有限公司 A kind of analysis method of public bus network website demand and supply
CN111489079B (en) * 2020-04-09 2023-08-01 Oppo（重庆）智能科技有限公司 Method and device for detecting productivity bottleneck and computer readable storage medium
CN112132551B (en) * 2020-09-30 2022-05-06 西南交通大学 Urban rail transit emergency passenger flow cooperative distribution method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102169524A (en) * 2010-02-26 2011-08-31 同济大学 Staged multi-path model algorithm of urban rail transit network passenger flow distribution
CN104866931A (en) * 2015-06-12 2015-08-26 北京交通大学 Subway passenger flow organization method based on major stop parking and multi-station synergistic flow-limiting
CN105882695A (en) * 2016-03-17 2016-08-24 北京交通大学 Foresight associated control method for passenger flow congestion of urban railway traffic network
CN107248006A (en) * 2017-05-27 2017-10-13 北方工业大学 Subway line passenger flow coordination control method based on hierarchical hierarchy

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102169524A (en) * 2010-02-26 2011-08-31 同济大学 Staged multi-path model algorithm of urban rail transit network passenger flow distribution
CN104866931A (en) * 2015-06-12 2015-08-26 北京交通大学 Subway passenger flow organization method based on major stop parking and multi-station synergistic flow-limiting
CN105882695A (en) * 2016-03-17 2016-08-24 北京交通大学 Foresight associated control method for passenger flow congestion of urban railway traffic network
CN107248006A (en) * 2017-05-27 2017-10-13 北方工业大学 Subway line passenger flow coordination control method based on hierarchical hierarchy

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
城市轨道交通网络客流协同控制模型;姚向明 等;《中南大学学报（自然科学版）》;20150131;第46卷(第1期);第342页至第350页 *

Also Published As

Publication number Publication date
CN108364093A (en) 2018-08-03

Similar Documents

Publication Publication Date Title
CN108364127B (en) Road network passenger flow cooperative control optimization system
CN108364093B (en) Road network passenger flow cooperative control optimization method based on capacity bottleneck untwining strategy
Cats et al. A dynamic stochastic model for evaluating congestion and crowding effects in transit systems
CN107194497B (en) Method for planning travel path of urban rail transit passenger in emergency
CN104217129B (en) A kind of urban rail road network passenger flow evaluation method
CN107330547A (en) A kind of city bus dynamic dispatching optimization method and system
CN107273999A (en) A kind of Flow Prediction in Urban Mass Transit method under accident
CN102169524A (en) Staged multi-path model algorithm of urban rail transit network passenger flow distribution
CN104239726A (en) Passenger flow estimation system under condition of urban rail road network emergency
CN110136455A (en) A kind of traffic lights timing method
CN109300326B (en) Bus short-time dynamic scheduling method based on real-time GPS
CN103208033A (en) Access passenger flow forecasting method for urban rail transit new line under network condition
CN104966135B (en) Public bus network network optimization method based on accessibility and up to intensity
CN106530756B (en) A kind of intersection optimal period duration calculation method of consideration downstream bus station
CN104484514A (en) Flexible bus route design method capable of evading jammed road segments
CN115809729B (en) Urban rail transit junction transfer facility optimal configuration method considering newly-added line
CN114841556A (en) Rail transit cross-line operation transportation plan evaluation system and method
Yang et al. Multi-mode public transit OD prediction and scheduling model
CN117252304A (en) Rail transit congestion propagation assessment and control method considering large passenger flow early warning information
CN114841415A (en) Urban rail transit passenger flow prediction and multistage transportation organization method during large-scale activities
Li et al. Joint optimization of delay-recovery and energy-saving in a metro system: A case study from China
CN113345259A (en) Ground bus route adjusting method based on data driving
CN111882915A (en) On-demand bus route planning method adopting composite algorithm and interactive model
CN115675584A (en) Urban area line driving scheme optimization method for urban rail transit
Shen et al. How public transport subsidy policies in China affect the average passenger load factor of a bus line

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