CN106779429A - Track transfer website passenger flow congestion risk evaluating method based on AFC brushing card datas - Google Patents
Track transfer website passenger flow congestion risk evaluating method based on AFC brushing card datas Download PDFInfo
- Publication number
- CN106779429A CN106779429A CN201611211608.4A CN201611211608A CN106779429A CN 106779429 A CN106779429 A CN 106779429A CN 201611211608 A CN201611211608 A CN 201611211608A CN 106779429 A CN106779429 A CN 106779429A
- Authority
- CN
- China
- Prior art keywords
- passenger flow
- website
- transfer
- risk
- exemplary position
- 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
- 238000012546 transfer Methods 0.000 title claims abstract description 99
- 208000027744 congestion Diseases 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000001680 brushing effect Effects 0.000 title claims abstract description 28
- 241001269238 Data Species 0.000 title claims abstract description 24
- 238000011156 evaluation Methods 0.000 claims abstract description 42
- 238000012502 risk assessment Methods 0.000 claims abstract description 6
- 238000012216 screening Methods 0.000 claims description 9
- 238000013210 evaluation model Methods 0.000 claims description 8
- 238000004220 aggregation Methods 0.000 claims description 6
- 238000013459 approach Methods 0.000 claims description 5
- 230000002776 aggregation Effects 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 3
- 238000013517 stratification Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 2
- 239000011159 matrix material Substances 0.000 claims description 2
- 230000000694 effects Effects 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 4
- 238000007418 data mining Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000005303 weighing Methods 0.000 abstract 1
- 238000012544 monitoring process Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000005259 measurement Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000011144 upstream manufacturing Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
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
- G06Q10/0635—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
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Website passenger flow congestion risk evaluating method is changed to the invention discloses the track based on AFC brushing card datas, including：AFC original transaction datas are pre-processed；Track transfer website passenger flow congestion based on AFC brushing card datas is evaluated.It is to rely on database analysis and data mining technology, the original brushing card datas of AFC is extracted, rejected and screened, the pretreatment such as transfer passenger flow identification.Different periods transfer the entering the station of website, outbound and transfer passenger flow amount, the physical parameter based on each exemplary position in artificial field survey transfer stop are obtained based on AFC brushing card datas；Exemplary position passenger flow saturation degree evaluation index is built, for the comprehensive passenger flow demand and supply relation for weighing each position；Index maximum according to evaluating in the period carries out the demarcation of model parameter as the input value of model；The saturation degree index of different exemplary positions weight in a model is calculated according to entropy assessment, and risk assessment is clustered, divide track transfer website passenger flow congestion Pyatyi risk class.
Description
Technical field
Website passenger flow congestion risk evaluating method is changed to the present invention relates to a kind of track based on AFC brushing card datas, is belonged to
Field is evaluated in public transport data mining application and service.
Background technology
With the development of public transport, each metropolitan rail network is increasingly perfect, and orbit passenger transport amount is substantially improved, occupies
People's Commuting Distance and being continuously increased for track number of transfer make the passenger traffic pressure of track transfer stop progressively increase, especially in work
Morning day evening peak and great period festivals or holidays, the passenger flow monitoring inside track transfer stop how is realized, how to realize microcosmic point
Track transfer website in the quantization of passenger flow congestion risk turned into influence with classification and lifted urban railway station operation security level
Major issue.
Because China's track traffic development is swift and violent, but corresponding passenger flow monitoring means relatively lags behind, in the past for track
The monitoring of passenger flow is often not microcosmic enough, is only capable of evaluating the overall security risk of urban railway station, or data acquisition modes phase
To complicated, it is necessary to the data of comprehensive various live passenger flow collecting devices could realize final risk assessment.Application No.
201410469673.1 Chinese invention patent discloses a kind of rail traffic station dynamic security risk evaluating method.The method
A set of dynamic assessment index system is determined first, then the data according to station equipment Real-time Collection calculate every dynamic indicator
Value, the new method for being finally based on interval two types fuzzy number and TOPSIS combinations enters action to rail traffic station operation security risk
State is evaluated.The method and not yet explicitly initial data source and processing procedure, it is important that the calculating of dynamic indicator value and evaluation method.
Due to the invention concern for urban railway station security risk overall merit, therefore, it is difficult to from more microcosmic angle to urban railway station
Internal exemplary position carries out risk assessment with monitoring.Meanwhile, its method is changed for the large-scale track changed to a plurality of circuit
Multiply station specific aim weaker, it is not enough to the consideration of track interior transfer passenger flow.The Chinese invention of Application No. 201410655770.X
Patent discloses a kind of Passenger Flow Analysis of Urban Rail Transit method based on AFC passenger ticket data.The invention is with track AFC system number
Based on, with reference to urban mass transit network and train grapher, it is proposed that analysis track flow space-time distribution characteristic
Method.But the applicable object of the method is rail network, it is impossible to realize the calculating analysis to track transfer Intra-site passenger flow.
With increasingly perfect, userbase the continuous expansion and the growth of trading volume of big city rail network, track AFC
System data, being capable of round-the-clock track record website used as a kind of ageing strong, uniform format, the degree of accuracy traffic data source high
Passenger flow output, and realize the centralised storage of Star Simulator network transaction data substantially.Using track AFC data, can
With to carrying out effectively identification and statistics into and out of station and transfer passenger flow inside measurement period inner orbit transfer stop, with reference to main
Urban railway station geometric parameter, can relatively accurately calculate the passenger flow congestion risk of track transfer stop inside exemplary position, and in fact
Now further risk stratification evaluation and early warning.And track AFC data have the spies such as data volume is sufficient, scale constantly increases
Point, therefore, by the modeling to AFC system data and excavation, can effectively recognize in the urban railway station with a plurality of transfer circuit
The passenger flow congestion risk of portion's exemplary position.
The content of the invention
Present invention aim at a kind of calculating of the transfer website passenger flow congestion risk based on track AFC brushing card datas of proposition
Method, for obtaining the dynamic passenger flow saturation state of each exemplary position in track transfer stop inside, and then realizes to passenger flow congestion wind
Monitoring, evaluation and the classification of danger, for the operation security service level for improving track transfer website provides support.
To achieve these goals, the present invention uses following technical scheme.
Track transfer stop passenger flow congestion risk evaluating method based on AFC brushing card datas, it is characterised in that including following step
Suddenly：
Step 1, the pretreatment of track AFC original transaction datas；
Step 1.1, extracts primary fields content in track AFC initial data；
AFC original transaction data tables have 42 field contents, have recorded substantial amounts of Transaction Information, while also containing emphatically
The passenger flow information wanted.AFC transaction data mainly includes：The information such as user's card number, time out of the station, approach line and website.
Extracting track AFC transaction data table primary fields includes：User's card number (GRANT_CARD_CODE)；Approach line is numbered
(ENTRY_LINE_NUM)；Enter the station station code (ENTRY_STATION_NUM)；Enter the station the time (ENTRY_TIME)；Exit track
Number (EXIT_LINE_NUM) in road；Outbound station code (EXIT_STATION_NUM)；Transaction (outbound) time (DEAL_
TIME)；Stateful transaction (DEAL_STATUS).
The AFC transaction data tables of table 1
Step 1.2, towards the screening of original AFC brushing card datas and rejecting that track transfer website passenger flow is extracted；
The rule for rejecting wrong data and screening valid data is as follows：
1) time of entering the station and outbound time are rejected not in transaction data on the same day；
2) transaction data of the outbound time earlier than the time of entering the station is rejected；
3) enter the station website and outbound website identical record are rejected during brushing card data is recorded；
4) " DEAL_STATUS i.e. stateful transaction " field is filtered out for " 2 ", and 2 represent this record has completed shape in transaction
The data of state；
Step 1.3, track traffic trip website track judges and transfer passenger flow identification
Track traffic single trip only need to when out of the station the secondary card of each brush one, generally track network internal change to
Without outbound.In order to realize the monitoring for track traffic inside transfer passenger flow, it is necessary to according to AFC brushing card datas to traveler
Transfer stop point is judged, and speculates the transfer time.
(1) path and distance determine between any website of track traffic
To speculate the transfer time, it is necessary to obtain the website to the distance of transfer website that sets out.Using A* shortest path firsts, search
Shortest path and distance between any website OD of rope errant, using the path as the trip for going out pedestrian that there is track transfer
Path.Set up beeline table (RAIL_PATH) between any OD of track traffic, and the newly-built field TRACE in transaction data table
For the path between any website of storage track traffic, each website of approach between record OD.Fall in different track circuits for O, D
Situation, may be inconsistent with Actual path according to the path that shortest path first speculates, but in this case only in two kinds of paths
Exist when stroke distances are close, thus it is negligible.
The track traffic AFC brushing card data pre-processed results examples of table 2
(2) orbit traffic transfer passenger flow identification
Based on TRACE fields, track traffic inside transfer website is can recognize that, according to the station before transfer website in trip track
When counting (NUM (before transfer)) and change to the website number (NUM (after transfer)) after website and entering the station
Between (ENTRY_TIME) and transaction (outbound) time (DEAL_TIME), the transfer time (TRANSFER_TIME) can be calculated.Change
A computational methods of taking the opportunity are as follows：
Newly-built field TRANSFER_TIME is used for the transfer time inside storage track traffic in transaction data table.
Step 2, evaluation index is chosen；
When evaluating track transfer website passenger flow congestion risk, it is necessary to consider the complexity and data acquisition of model
Complexity, it is therefore desirable to evaluation index is screened.The principal dimensions that passenger flow congestion Risk Evaluation Factors consider are to hand over
Logical supply and demand, selection is entered the station, outbound, transfer passenger flow amount as transport need evaluation index, choose in track transfer website
Used as transportation supplies evaluation index, exemplary position includes stair, passage and gate three major types altogether to the traffic capacity of each exemplary position,
Passage contains transferring passage, and gate contains screening machine.Wherein enter the station, outbound, transfer passenger flow amount is brushed according to by pretreated AFC
Card data are entered based on enter the station time ENTRY_TIME, transaction/outbound time DEAL_TIME and transfer time TRANSFER_TIME
Row summation statistics, minimum statistics time dimension is set to 15 minutes.The traffic capacity of each exemplary position is based on theory in transfer website
Formula and actual measurement physical parameter are calculated.
Step 3, evaluation index nondimensionalization and aggregation；
Track transfer website passenger flow congestion risk refers to because passenger flow congestion causes certain loss occurrence in transfer website
Possibility.In passenger flow congestion risk assessment, because the physics meaning representated by each index is different, therefore exist in dimension
Difference.This different dimension is principal element of the influence to the things overall evaluation, so evaluation index is united before evaluation
One is converted into the quantized values in the range of [0,1].
To realize the nondimensionalization of evaluation index, track transfer website exemplary position passenger flow saturation degree r, i-th allusion quotation are introduced
The passenger flow saturation degree of type position is the passenger flow saturation degree r of exemplary position ii：
Correction value P of the passenger flow saturation degree according to the total volume of the flow of passengers for passing through the exemplary position in timing statisticsesiWith the typical position
Put the measuring and calculating value C of handling capacityiThe ratio between calculated, total volume of the flow of passengers is comprising entering the station, outbound and transfer passenger flow, and to riValue adds
To constrain：
Thus, it is that codomain is the dimensionless index r of [0,1] by each aggregationi, passenger flow supply and demand is considered based on this
Passenger flow congestion level of certain exemplary position in timing statisticses in the metrics evaluation track transfer stop of relation.
Step 4, sets up evaluation model
The imbalance for being in the nature supply and demand that passenger flow congestion is produced in track transfer website, i.e. confession of the demand more than traffic system
To ability, with reference to the characteristics of transfer website passenger flow congestion risk, the grade of passenger flow congestion risk is set to Pyatyi：
Prime risk：Low risk, passenger flow carrying capacity is much larger than demand；
Light breeze danger：Compared with low-risk, passenger flow carrying capacity preferably meets demand；
Tertiary risk：Moderate risk, passenger flow carrying capacity meets demand, occurs in short-term crowded；
Level Four risk：High risk, passenger flow carrying capacity substantially meets demand, and congestion situation is more obvious；
Pyatyi risk：High risk, passenger flow accommodates demand close to saturation, and ability is difficult to meet, and congestion is serious；
Using entropy assessment, the weight of each exemplary position is determined based on saturation data, stair, passage and gate are obtained respectively
The saturation degree of three major types position and the overall saturation degree of transfer stop point.It is directed to major class position and the overall saturation degree of transfer stop point
Calculate, time granularity is hour, in this, as the minimum unit for dividing peak period.The maximum for taking interior 4 15min per hour is satisfied
With angle value as the saturation degree of this hour period, then risk evaluation model is set up：
In formula, f (y) represents risk stratification function；α represents normalisation coefft, takes 10/n；wiRepresent the entropy of exemplary position i
Weight coefficient；riRepresent the passenger flow saturation degree of exemplary position i；N represents the sum of exemplary position.
Classification to passenger flow congestion risk, this method carries out the division of risk class using means clustering algorithm.
Step 5, determines each exemplary position weight；
Using entropy assessment, size according to each exemplary position saturation degree variability determines objective weight.If certain is typical
The comentropy of position saturation degree is smaller, shows that the degree of variation of the exemplary position saturation degree is bigger, there is provided information content it is more,
To be played a part of also bigger in the point overall evaluation of transfer stop, its weight is also bigger.Conversely, the letter of exemplary position saturation degree
Breath entropy it is bigger, show that the degree of variation of the exemplary position saturation degree is smaller, there is provided information content it is also fewer, the institute in overall merit
Play a part of it is also smaller, its weight also just it is smaller.Entropy assessment can more objectively react indices, it is not necessary to be related to take office
What subjective information, is the objective assignment method in a kind of complete meaning.
Be located at selected evaluation space dimension i.e. exemplary position, transfer website etc. and time dimension i.e. 1 hour, 1 day, 1
The exemplary position month evaluated is n, influences the subhead of comprehensive evaluation value to be designated as m, uses xijRepresent the saturation degree of exemplary position j
I-th data value of partial objectives for, then m subhead scale value of n exemplary position constitute matrix R=(xij)m×n
Then its entropy weight is：
In formula, gjRepresent j-th coefficient of variation of exemplary position saturation degree；ejRepresent j-th entropy of exemplary position saturation degree
Value；pijThe i-th data value proportion of partial objectives for is represented, And
The present invention compared with prior art, with following obvious advantage and beneficial effect：
(1) be to rely on database analysis and data mining technology, the original brushing card datas of AFC are extracted, reject and
The pretreatments such as screening, track interior transfer identification, improve the quality of data, reduce data acquisition cost.
(2) the passenger flow congestion risk evaluation model based on exemplary position saturation degree in track transfer website is proposed, it is comprehensive
The demand and supply capacity of passenger flow in transfer stop are considered, and can realize that the micro risks for refineing to exemplary position in transfer stop are commented
Valency, compensate for conventional correlative study can only be with rail network as object, it is difficult to evaluate the deficiency of Intra-site passenger flow congestion risk.
(3) present invention is demarcated using entropy assessment to the weight of each exemplary position saturation degree index in model, is distinguished not
The rail of the influence degree of website passenger flow congestion risk, qualitative assessment different time dimension and Spatial Dimension is changed to track with position
Website passenger flow congestion risk is changed in road, and evaluation method is more easy to operate, and evaluation result is more practical, can be in track transfer website
Passenger flow congestion Risk-warning and related management and control measures provide effective data and support.
Brief description of the drawings
Fig. 1 is the original brushing card data pretreatment process figures of AFC；
Fig. 2 is the track transfer website passenger flow congestion risk evaluating method flow chart based on AFC data；
Fig. 3 is A mouthfuls of stair saturation degree variation diagram；
Fig. 4 is AB mouthfuls of platform stair saturation degree variation diagram；
Fig. 5 is one week interior passenger flow congestion risk class variation diagram of Dongzhimen track transfer website；
Specific embodiment
The present embodiment chooses Dongzhimen track transfer website to calculate object, by AFC brushing card datas and artificial actual measurement
The physical parameter of each exemplary position calculates the transfer website in the passenger flow congestion wind of the different periods in 5 to 11 March in 2016
Dangerous grade.
The present embodiment is comprised the following steps：
Step 1, database is imported by AFC brushing card datas, and initial data is pre-processed；
Calculative related track circuit (being No. 2 lines, No. 13 lines and airport lines in case) basic data and AFC is former
Beginning brushing card data is imported into oracle database.Original AFC data are picked according to the data prediction flow shown in Fig. 1
Except and screening etc. pretreatment.
Step 2, evaluation index is chosen and is calculated；
Track transfer website passenger flow congestion risk is influenceed by passenger flow relation between supply and demand, therefore evaluation index mainly has：Enter
Stand, in outbound, transfer passenger flow amount and website each exemplary position (stair, passage (containing transferring passage) and gate (containing screening machine) are total to
Three major types) the traffic capacity.
(1) volume of the flow of passengers
Enter the station, outbound, transfer passenger flow amount can carry out summation statistics, minimum time based on pretreated AFC transaction data
Dimension is 15 minutes.Dongzhimen transfer website is the website that crosses of No. 2 lines, No. 13 lines and airport line, and each circuit is calculated respectively
Transfer passenger flow amount into and out of between standee's flow and any two circuit.
The table outbound volume of the flow of passengers result of calculation (part) of No. 2 lines of 3 2016 year March 5 day
(2) handling capacity of each exemplary position
The physical parameter of each exemplary position in the transfer stop of Dongzhimen is obtained by artificial field survey, it is public with reference to corresponding theory
Formula calculates the handling capacity of each exemplary position.
The stair parameter of table 4
| Stair | Elevator quantity, direction | Stair overall width (cm) | Guardrail width (cm) |
| AB mouthfuls of platform stair | 0 | 560 | 56 |
| CD mouthfuls of platform stair | 0 | 560 | 56 |
| A mouthfuls of stair | 1 (up) | 340 | 56 |
| B mouthfuls of stair 1 | 1 (up) | 320 | 8 |
| B mouthfuls of stair 2 | 0 | 516 | 0 |
| C mouthfuls of stair 1 | 1 (up) | 320 | 8 |
| C mouthfuls of stair 2 | 0 | 516 | 0 |
| D mouthfuls of stair | 1 (up) | 340 | 56 |
| E mouthfuls of stair | 1 (up) 1 (descending) | 190 | 40 |
| G mouthfuls of stair | 1 (descending) | 364 | 58 |
| H mouthfuls of stair | 2 (up) 2 (descending) | 780 | 44 |
| No. 13 line stair 1 | 2 (descending) | 640 | 78 |
| No. 13 line stair 2 | 2 (up) | 640 | 54 |
| Airport line stair 1 | 1 (up) 1 (descending) | 0 | 0 |
| Airport line stair 2 | 0 | 290 | 48 |
The gate parameter of table 5
The channel parameters of table 6
Step 3, evaluation index nondimensionalization and aggregation；
By by total volume of the flow of passengers of certain exemplary position divided by its handling capacity, the visitor of the exemplary position in the counting statistics time
Stream saturation degree, realizes the nondimensionalization of evaluation index.The saturation degree of each exemplary position after integration will refer to as final evaluation
Mark.By taking the A mouthfuls of passenger flow saturation computation of stair as an example：
rUp stair--- the saturation degree index at stair；
sRepair q--- it is descending to take 1 by the correction factor of the q kind passenger origin demands of stair, it is up to take 0.98；
α --- demand correction coefficient, it is considered to that Subway Facilities are used as underpass crossing facilities, by A mouthfuls of building
The passenger flow of ladder, takes 1.3.
θq--- accounting of the q kinds passenger origin in the affiliated circuit correspondence passenger flow sum, without further survey data
Support that lower each main passenger origin accounting of hypothesis is identical.
The entering the station of p --- every 15 minutes of website of transfer, outbound or transfer number；
The species number of the passenger origin of Q --- the approach stair；
bi--- i-th width (m) of elevator；
M --- upstream or downstream elevator number；
CElevator--- the theoretical maximum handling capacity (people/hm) of elevator, take 8100；
Bj--- the overall width (m) of upstream or downstream pedestrian stairway j；
bj--- the width (m) between pedestrian stairway j handrails and wall, usual value 0.24m；
N --- upstream or downstream pedestrian stairway number；
CStep ladder--- the theoretical maximum handling capacity (people/hm) of pedestrian stairway, descending stair take 4200, and up stair take
3700, two-way mixed row stair take 3200.
Substitute on March 5th, 2016 (Saturday) to and March 11 (Friday) volume of the flow of passengers data, calculating the A mouthfuls of saturation of stair
Degree is (see Fig. 3), it is evident that saturation degree change (maximum is about 0.4) of morning peak A mouthfuls of stair of working day, and its and weekend
The marked difference of (maximum is about 0.15).
Using identical algorithm calculate AB mouthfuls of saturation degree of platform stair (see Fig. 4,0.9) maximum is about, it can be found that
The significant difference of Intra-site diverse location saturation degree is changed in passenger traffic, and the index can be used as across comparison each position passenger flow congestion wind
The quantized value of dangerous size.
Step 4, determines risk assessment value；
It was Elementary Time Unit with 15 minutes based on the saturation degree of each exemplary position, calculates the full of each exemplary position
With maximum max (r of the degree in measurement periodi), measurement period can be hour, day, week, the moon, season, year etc..
Step 5, builds evaluation model；
In evaluation model, first to the weight w of each exemplary positioniDemarcated, with each exemplary position of every 15 minutes
Passenger flow saturation degree is weight evaluation of estimate.Using the weight of each exemplary position in entropy assessment computation model.
The stair quasi-representative position weight of table 7
The passage quasi-representative position weight of table 8
The gate quasi-representative position weight of table 9
Website, exemplary position sum n=28 are changed to for Dongzhimen track, therefore α takes 10/28, i.e., 5/14.Therefore track
Changing to website passenger flow congestion risk evaluation model is：
F (y) is the final comprehensive passenger flow congestion value-at-risk of Dongzhimen transfer website.
Based on hour as granularity track change to website risk value data, using k-means clustering methods, just draft by
The passenger flow congestion risk for changing to website is divided into 4 classes.Final cluster centre is as follows：
The value-at-risk cluster result of table 10
Final cluster centre
All kinds of boundary values are respectively：1.4、3.3、5.9.Sig. value is 0.000, less than 0.05, illustrates of all categories
Difference is more notable.Four class grade scales are：
The transfer website passenger flow congestion risk class of table 11 divides (level Four)
| Passenger flow congestion risk class | Value-at-risk |
| 1 | ＜ 1.4 |
| 2 | 1.4-3.3 |
| 3 | 3.3-5.9 |
| 4 | ≥5.9 |
Due to taking from 5 to 11 March in 2016 currently used for the data for being clustered, wherein being increased sharply without some passenger flows
Special circumstances (such as festivals or holidays), therefore on the basis of four class grade scales, increase strong breeze danger, for describing festivals or holidays
Etc. the situation of unconventional risk, case sample shows, one week maximum risk value of inner orbit transfer stop is during non-festivals or holidays
7.2, therefore the 5th grade of border value-at-risk takes 7.2.Obtaining five class grade scales is：
The transfer website passenger flow congestion risk class of table 12 divides (Pyatyi)
| Risk class | Value-at-risk | State description |
| 1 | ＜ 1.4 | Ability is much larger than demand, has more than needed very big |
| 2 | 1.4-3.3 | Ability preferably meets demand, has more than needed larger |
| 3 | 3.3-5.9 | Ability meets demand, occurs in short-term crowded |
| 4 | 5.9-7.2 | Ability substantially meets demand, and congestion situation is more obvious |
| 5 | 7.2-10 | Close to saturation, ability is difficult to meet demand, and congestion is serious |
According to five class grade scales, passenger flow congestion risk of the Dongzhimen transfer of 5 to 11 March in 2016 website etc. is calculated
Level, as a result as shown in Figure 5.As can be seen from the figure the regularity that transfer website passenger flow congestion risk class changes, weekend risk
Grade is relatively low (up to 2 grades), but there is also peak period.The risk class of weekday rush hours is significantly greater than weekend, most
A height of 4 grades, and double peak features are obvious.
Claims (5)
1. the track based on AFC brushing card datas changes to website passenger flow congestion risk evaluating method, it is characterised in that the method includes
Following steps：
Step 1, AFC swipe the card transaction data pretreatment；
Step 1.1, extracts primary fields content in AFC brushing card datas；
Step 1.2, towards the screening of original AFC brushing card datas and rejecting that track transfer website passenger flow is extracted；；
Step 1.3, track traffic trip website track judges and transfer passenger flow identification；
Trip website track between wild trajectory OD is speculated based on A* shortest path firsts, before website is changed in trip track
Website number NUM (before transfer) and transfer website after website number NUM (after transfer) and when entering the station
Between ENTRY_TIME and transaction/outbound time DEAL_TIME, reckoning transfer time TRANSFER_TIME；
Step 2, evaluation index is chosen；
When evaluating track transfer website passenger flow congestion risk, it is necessary to consider the complexity of model and the difficulty of data acquisition
Easy degree, therefore to needing to screen evaluation index；The principal dimensions that passenger flow congestion Risk Evaluation Factors consider are traffic
Supply and demand, selection is entered the station, outbound, transfer passenger flow amount as transport need evaluation index, choose each in track transfer website
The traffic capacity of exemplary position as transportation supplies evaluation index；Exemplary position includes stair, passage and gate three major types altogether,
Passage contains transferring passage, and gate contains screening machine；
Step 3, evaluation index nondimensionalization and aggregation；
Track transfer website passenger flow congestion risk refers to because passenger flow congestion causes the possibility of certain loss occurrence in transfer website
Property；In passenger flow congestion risk assessment, because the physics meaning representated by each index is different, therefore the difference in dimension is there is
It is different；This different dimension is principal element of the influence to the things overall evaluation, so before evaluation unifying evaluation index
It is converted into the quantized values in the range of [0,1]；
To realize the nondimensionalization of evaluation index, track transfer website exemplary position passenger flow saturation degree r, i-th typical position are introduced
The passenger flow saturation degree r of the i.e. exemplary position i of the passenger flow saturation degree puti：
Correction value P of the passenger flow saturation degree according to the total volume of the flow of passengers for passing through the exemplary position in timing statisticsesiPass through with the exemplary position
The measuring and calculating value C of abilityiThe ratio between calculated, total volume of the flow of passengers is comprising entering the station, outbound and transfer passenger flow, and to riValue uses restraint：
Thus, it is that codomain is the dimensionless index r of [0,1] by each aggregationi, passenger flow relation between supply and demand is considered based on this
Metrics evaluation track transfer stop in passenger flow congestion level of certain exemplary position in timing statisticses；
Step 4, sets up evaluation model
The imbalance for being in the nature supply and demand that passenger flow congestion is produced in track transfer website, i.e. supply energy of the demand more than traffic system
Power, with reference to the characteristics of transfer website passenger flow congestion risk, Pyatyi is set to by the grade of passenger flow congestion risk：
Prime risk：Low risk, passenger flow carrying capacity is much larger than demand；
Light breeze danger：Compared with low-risk, passenger flow carrying capacity preferably meets demand；
Tertiary risk：Moderate risk, passenger flow carrying capacity meets demand, occurs in short-term crowded；
Level Four risk：High risk, passenger flow carrying capacity substantially meets demand, and congestion situation is more obvious；
Pyatyi risk：High risk, passenger flow accommodates demand close to saturation, and ability is difficult to meet, and congestion is serious；
Using entropy assessment, the weight of each exemplary position is determined based on saturation data, stair, passage and gate three are obtained respectively big
The saturation degree of class position and the overall saturation degree of transfer stop point；It is directed to major class position and the overall saturation degree meter of transfer stop point
Calculate, time granularity is hour, in this, as the minimum unit for dividing peak period；Take the maximum saturation of interior 4 15min per hour
Angle value then sets up risk evaluation model as the saturation degree of this hour period：
In formula, f (y) represents risk stratification function；α represents normalisation coefft, takes 10/n；wiThe entropy weight for representing exemplary position i is again
Number；riRepresent the passenger flow saturation degree of exemplary position i；N represents the sum of exemplary position；
Classification to passenger flow congestion risk, this method carries out the division of risk class using means clustering algorithm；
Step 5, determines each exemplary position weight；
Using entropy assessment, size according to each exemplary position saturation degree variability determines objective weight；If certain exemplary position
The comentropy of saturation degree is smaller, shows that the degree of variation of the exemplary position saturation degree is bigger, there is provided information content it is more, transfer
To be played a part of also bigger in the website overall evaluation, its weight is also bigger；Conversely, the comentropy of exemplary position saturation degree
It is bigger, show that the degree of variation of the exemplary position saturation degree is smaller, there is provided information content it is also fewer, played in overall merit
Effect it is also smaller, its weight also just it is smaller；Entropy assessment can more objectively react indices, it is not necessary to be related to any master
Sight information, is the objective assignment method in a kind of complete meaning；
It is located at selected evaluation space dimension i.e. exemplary position, transfer website etc. and time dimension i.e. 1 hour, comments within 1 day, 1 month
The exemplary position of valency is n, influences the subhead of comprehensive evaluation value to be designated as m, uses xijRepresent the i-th of the saturation degree of exemplary position j
The data value of individual partial objectives for, then m subhead scale value of n exemplary position constitute matrix R=(xij)m×n
Then its entropy weight is：
In formula, gjRepresent j-th coefficient of variation of exemplary position saturation degree；ejRepresent j-th entropy of exemplary position saturation degree；
pijThe i-th data value proportion of partial objectives for is represented,ej∈ [0,1]；wj>=0, j=1,2,3 ...
M, and
2. the track based on AFC brushing card datas according to claim 1 changes to website passenger flow congestion risk evaluating method, its
It is characterised by, the original brushing card data fields of AFC that the step 1.1 is extracted include：User's card number, approach line numbering, enters the station
Station code, enters the station the time, outbound routes numbering, outbound station code, transaction/outbound time, stateful transaction.
3. the track based on AFC brushing card datas according to claim 1 changes to website passenger flow congestion risk evaluating method, its
It is characterised by, step 1, the 2 original AFC brushing card datas screenings extracted towards track transfer website passenger flow and rejecting rule are as follows：
1) time of entering the station and outbound time are rejected not in transaction data on the same day；
2) transaction data of the outbound time earlier than the time of entering the station is rejected；
3) enter the station website and outbound website identical record are rejected during brushing card data is recorded；
4) data that " DEAL_STATUS stateful transactions " field is " 2 " are filtered out, 2 represent this record has completed shape in transaction
State.
4. the track based on AFC brushing card datas according to claim 1 changes to website passenger flow congestion risk evaluating method, institute
The indices non-dimension method used in step 3 is stated, equivalent to being comprehensive evaluation index by iotave evaluation aggregation --- it is full
And degree, by the relation between the passenger flow demand and supply of saturation degree comprehensive consideration exemplary position.
5. the track transfer website passenger flow congestion risk evaluating method based on AFC brushing card datas according to claim 1, poor
Different coefficient gjBigger, exemplary position is more important；Normalisation coefft α takes 10 makes risk codomain in the range of 0-10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611211608.4A CN106779429B (en) | 2016-12-25 | 2016-12-25 | Track transfer station passenger flow congestion risk evaluation method based on AFC card swiping data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611211608.4A CN106779429B (en) | 2016-12-25 | 2016-12-25 | Track transfer station passenger flow congestion risk evaluation method based on AFC card swiping data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106779429A true CN106779429A (en) | 2017-05-31 |
| CN106779429B CN106779429B (en) | 2020-01-24 |
Family
ID=58920749
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611211608.4A Active CN106779429B (en) | 2016-12-25 | 2016-12-25 | Track transfer station passenger flow congestion risk evaluation method based on AFC card swiping data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106779429B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107808235A (en) * | 2017-10-10 | 2018-03-16 | 东南大学 | City rail large passenger flow model building method based on AFC big datas |
| CN110889092A (en) * | 2019-11-20 | 2020-03-17 | 北京市交通运行监测调度中心 | Short-time large-scale activity peripheral track station passenger flow volume prediction method based on track transaction data |
| CN111063190A (en) * | 2019-12-10 | 2020-04-24 | 北京工业大学 | Pre-control method and system for oversaturated passenger flow of urban rail transit station platform |
| CN111584091A (en) * | 2020-04-29 | 2020-08-25 | 北京交通大学 | Method and device for identifying cross infection risk of urban rail line level close contact person |
| CN112749862A (en) * | 2019-10-31 | 2021-05-04 | 株洲中车时代电气股份有限公司 | Subway self-adaptive scheduling method based on passenger flow classification and electronic equipment |
| CN112950943A (en) * | 2021-02-18 | 2021-06-11 | 重庆交通开投科技发展有限公司 | Transfer station calculation method based on multi-metadata |
| CN113705623A (en) * | 2021-08-06 | 2021-11-26 | 深圳集智数字科技有限公司 | Rail transit station classification method and device |
| CN115810271A (en) * | 2023-02-07 | 2023-03-17 | 安徽交欣科技股份有限公司 | Method for judging passenger flow corridor position based on card swiping data |
| CN115952173A (en) * | 2023-03-13 | 2023-04-11 | 北京全路通信信号研究设计院集团有限公司 | Passenger flow data processing method and device, big data platform and storage medium |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3131414A1 (en) * | 2021-12-24 | 2023-06-30 | Thales | SYSTEM FOR SUPERVISING THE MOVEMENT OF PASSENGERS IN A TRANSPORT NETWORK, METHOD AND PRODUCT, ASSOCIATED COMPUTER PROGRAM. |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103955744A (en) * | 2014-04-23 | 2014-07-30 | 同济大学 | Method and device for parameter automatic calibration of rail transit passenger flow distributing model |
| CN104376624A (en) * | 2014-07-22 | 2015-02-25 | 西南交通大学 | Urban rail transit passenger flow analysis method based on AFC (Automatic Fare Collection) passenger ticket data |
| CN104765974A (en) * | 2015-04-24 | 2015-07-08 | 北京城建设计发展集团股份有限公司 | Urban rail transit passenger flow density index calculating method |
| CN104809112A (en) * | 2014-01-23 | 2015-07-29 | 朱东霞 | Method for comprehensively evaluating urban public transportation development level based on multiple data |
-
2016
- 2016-12-25 CN CN201611211608.4A patent/CN106779429B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104809112A (en) * | 2014-01-23 | 2015-07-29 | 朱东霞 | Method for comprehensively evaluating urban public transportation development level based on multiple data |
| CN103955744A (en) * | 2014-04-23 | 2014-07-30 | 同济大学 | Method and device for parameter automatic calibration of rail transit passenger flow distributing model |
| CN104376624A (en) * | 2014-07-22 | 2015-02-25 | 西南交通大学 | Urban rail transit passenger flow analysis method based on AFC (Automatic Fare Collection) passenger ticket data |
| CN104765974A (en) * | 2015-04-24 | 2015-07-08 | 北京城建设计发展集团股份有限公司 | Urban rail transit passenger flow density index calculating method |
Non-Patent Citations (3)
| Title |
|---|
| KOSUKE FUJII 等: "《Experimental study on crowd flow passing through ticket gates in railway stations》", 《ELSEVIER》 * |
| 蔡昌俊等: "《基于AFC数据的城轨站间客流量分布预测》", 《中国铁道科学》 * |
| 陈艳艳等: "《综合交通枢纽客流拥挤实时评价方法》", 《公路交通科技》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107808235A (en) * | 2017-10-10 | 2018-03-16 | 东南大学 | City rail large passenger flow model building method based on AFC big datas |
| CN107808235B (en) * | 2017-10-10 | 2020-06-02 | 东南大学 | AFC big data-based urban rail large passenger flow model construction method |
| CN112749862A (en) * | 2019-10-31 | 2021-05-04 | 株洲中车时代电气股份有限公司 | Subway self-adaptive scheduling method based on passenger flow classification and electronic equipment |
| CN110889092A (en) * | 2019-11-20 | 2020-03-17 | 北京市交通运行监测调度中心 | Short-time large-scale activity peripheral track station passenger flow volume prediction method based on track transaction data |
| CN111063190A (en) * | 2019-12-10 | 2020-04-24 | 北京工业大学 | Pre-control method and system for oversaturated passenger flow of urban rail transit station platform |
| CN111584091A (en) * | 2020-04-29 | 2020-08-25 | 北京交通大学 | Method and device for identifying cross infection risk of urban rail line level close contact person |
| CN111584091B (en) * | 2020-04-29 | 2023-10-24 | 北京交通大学 | Cross infection risk identification method and device for urban rail line-level close contact person |
| CN112950943A (en) * | 2021-02-18 | 2021-06-11 | 重庆交通开投科技发展有限公司 | Transfer station calculation method based on multi-metadata |
| CN113705623A (en) * | 2021-08-06 | 2021-11-26 | 深圳集智数字科技有限公司 | Rail transit station classification method and device |
| CN115810271A (en) * | 2023-02-07 | 2023-03-17 | 安徽交欣科技股份有限公司 | Method for judging passenger flow corridor position based on card swiping data |
| CN115810271B (en) * | 2023-02-07 | 2023-04-28 | 安徽交欣科技股份有限公司 | Method for judging passenger flow corridor position based on card swiping data |
| CN115952173A (en) * | 2023-03-13 | 2023-04-11 | 北京全路通信信号研究设计院集团有限公司 | Passenger flow data processing method and device, big data platform and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106779429B (en) | 2020-01-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106779429A (en) | Track transfer website passenger flow congestion risk evaluating method based on AFC brushing card datas | |
| CN105279572B (en) | City track traffic passenger flow density index calculating and releasing system | |
| CN108346292B (en) | Urban expressway real-time traffic index calculation method based on checkpoint data | |
| CN104318324B (en) | Shuttle Bus website and route planning method based on taxi GPS records | |
| CN107895283A (en) | A kind of businessman's volume of the flow of passengers big data Forecasting Methodology based on Time Series | |
| Pljakić et al. | Macro-level accident modeling in Novi Sad: A spatial regression approach | |
| CN101964085A (en) | Method for distributing passenger flows based on Logit model and Bayesian decision | |
| Chen et al. | Data analytics approach for travel time reliability pattern analysis and prediction | |
| CN110634292B (en) | Travel time reliability estimation method based on road resistance performance function | |
| CN103456163B (en) | The city expressway interchange traffic capacity and running status method of discrimination and system | |
| Moazami et al. | The use of analytical hierarchy process in priority rating of pavement maintenance | |
| CN110889092A (en) | Short-time large-scale activity peripheral track station passenger flow volume prediction method based on track transaction data | |
| CN106651181A (en) | Bus passenger flow congestion risk evaluation method under network operation condition | |
| CN110197332A (en) | A kind of overall control of social public security evaluation method | |
| CN106898142A (en) | A kind of path forms time reliability degree calculation method of consideration section correlation | |
| CN110097264A (en) | A kind of measure of group of cities space and economic relation intensity | |
| CN113643538B (en) | Bus passenger flow measuring and calculating method integrating IC card historical data and manual investigation data | |
| CN106327867A (en) | Bus punctuality prediction method based on GPS data | |
| Sun et al. | Developing a method for estimating AADT on all Louisiana roads. | |
| Bouman et al. | Detecting activity patterns from smart card data | |
| Yoo | Transfer penalty estimation with transit trips from smartcard data in Seoul, Korea | |
| Alemazkoor et al. | Using empirical data to find the best measure of travel time reliability | |
| Song et al. | Public transportation service evaluations utilizing seoul transportation card data | |
| Guo et al. | Realistic Transport Simulation: Tackling the Small Data Challenge with Open Data | |
| CN108053646A (en) | Traffic characteristic acquisition methods, Forecasting Methodology and system based on time-sensitive feature |
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 | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20210428 Address after: 100080 319, 3rd floor, 29 Haidian West Street, Haidian District, Beijing Patentee after: Beijing Gewu botu Technology Co.,Ltd. Address before: 100124 Chaoyang District, Beijing Ping Park, No. 100 Patentee before: Beijing University of Technology |