CN107516155A - Urban Rail Transit Stations bicycle quantity of plugging into determines method - Google Patents
Urban Rail Transit Stations bicycle quantity of plugging into determines method Download PDFInfo
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Abstract
A kind of method for Urban Rail Transit Stations bicycle quantity of being plugged into the invention discloses determination, by designing Investigation of willingness questionnaire, survey is carried out to rail traffic station point outbound passenger, processing is formatted to the data of collection, establish Discrete Choice Model, the Investigation of willingness data handled well substitution Discrete Choice Model is obtained into the estimate of its parameter, calculating outbound passenger according to model result uses the probability plugged into of bicycle, so that it is determined that the bicycle quantity that goes out to plug into.The present invention is achieved a butt joint by establishing Discrete Choice Model and refutes the determination of bicycle share rate, is solved the problems, such as that actual outfit bicycle quantity of plugging into is too big with demand disruption, is improved the degree of accuracy of outfit track traffic website bicycle quantity；The model accuracy of foundation is high, and has reproducibility.
Description
Technical field
The invention belongs to urban transportation technical field, it is related to a kind of Urban Rail Transit Stations and plugs into the determination of bicycle quantity
Method.
Background technology
With the fast development of China's urban motorization, the vehicle guaranteeding organic quantity rapidly increased is brought to China's urban transportation
Immense pressure, traffic congestion phenomenon is increasingly severe, has seriously affected the development in city and the life of the people, caused by
Economic loss is difficult to estimate.According to《Beijing Communication develops annual report》, in 2011, Beijing every year will be because of traffic
Blockage problem loses nearly 105,700,000,000 yuan, and this numeral is equivalent to the 7.4% of Beijing's GDP.
Track traffic has the advantages of handling capacity of passengers is big, convenient and swift, punctual safe, green, is to solve traffic congestion
One of maximally effective means, be undisputed " green traffic ways ".But can not to solve passenger in-orbit for track traffic
The problem of road traffic end trip distance is longer.When traveler needs walking larger distance to get to nearest track traffic
During website, resident selects the probability of track traffic trip to be reduced with the increase of walking distance.It then becomes necessary to there are other
Mode of transportation be engaged with track traffic, as the connection modes of track traffic, to solve the collecting and distributing problem of passenger flow.Bicycle
The characteristics of with maneuverability, step is used to make up with the advantage of fast and flexible in short-distance and medium-distance (0.5km-2km) trip
The shortcomings that line mode is gone on a journey, so as to lift the efficiency of resident end trip.Existing research middle orbit traffic website is plugged into voluntarily
Car determination of amount, generally use Shanghai modelling, transfer amount predication method etc., these methods are generally in rail traffic station point service
Corresponding coefficient is multiplied by the basis of region construction area and size of population data, the shortcomings that subjectivity is big, theoretical property is poor be present.
On the other hand, the track traffic of plugging into of traveler selection mode of transportation is personal choice behavior, is non-collection meter.Existing model leads to
It is often collection meter, this also shows defect and deficiency in terms of theoretical and logic so that calculates inferred results and actual demand deviation
Too big, the precision of model is relatively low.
The content of the invention
A kind of to solve the above problems, side for Urban Rail Transit Stations bicycle quantity of being plugged into the invention discloses determination
Method, by designing Investigation of willingness questionnaire, survey is carried out to rail traffic station point outbound passenger, and lattice are carried out to the data of collection
Formulaization processing, establishes Discrete Choice Model, bringing the Investigation of willingness data handled well into Discrete Choice Model obtains its parameter
Estimate, outbound passenger is calculated according to model result and uses the probability plugged into of bicycle, so that it is determined that the bicycle number that goes out to plug into
Amount.
In order to achieve the above object, the present invention provides following technical scheme：
A kind of Urban Rail Transit Stations bicycle quantity of plugging into determines method, comprises the following steps：
Step 1, Investigation of willingness questionnaire is designed
Step 1.1, the existing connection modes of track record traffic website
The connection modes for being available for outbound passenger to select in record time period are Mi, wherein i be connection modes quantity, i
=1,2,3...n；
Step 1.2, with choosing survey objective
As shown in Fig. 2 centered on some track traffic website A, some survey areas are divided, from the region of each division
In respectively select a points for investigation Dj, j is the region quantity of division；
Step 1.3, questionnaire body attribute is set
The questionnaire body attribute includes：Expense, journey time, stand-by period；Wherein, journey time points out that station multiplies
Visitor is in selection connection modes MiAfterwards from point A to up to points for investigation DjThe time that need to be spent, stand-by period refer to outbound passenger and wait the side of plugging into
Formula MiThe time that need to be spent；
Step 1.3.1, determines journey time
On the basis of step 1.1 to step 1.3, determine outbound passenger with connection modes MiTo points for investigation DjIt need to spend
Journey time Tij；
Step 1.3.2, determines the stand-by period
Determine that outbound passenger waits connection modes MiThe time W that need to be spenti；
Step 1.3.3, determines expense
Define outbound passenger and select connection modes MiTo points for investigation DjRequired expense Cij；
Step 1.4, questionnaire body attribute level value is defined
Step 1.4.1, define stroke time attribute level value
The average value of various connection modes journey times is calculated according to formula (1)：
According to formula (2) and formula (3), journey time attribute level value T is setki, 1≤k≤3, i=1,2,3...n, Tki
Represent k-th of connection modes MiJourney time attribute level value
Tki=ti+x (14)
Ti1≤Tki≤Ti4 (15)
Wherein x is arbitrary value；
Step 1.4.2, define stand-by period attribute level value
Step 1.4.3, define expense attribute level value
Various connection modes M are calculated according to formula (4)iExpense average value：
According to formula (5) and formula (6) setup fee attribute level value Cki, 1≤k≤3, i=1,2,3...n, CkiRepresent
K-th of connection modes MiExpense attribute level value：
Cki=ci+y (17)
Ci1≤Cki≤Ci4 (18)
Wherein y is arbitrary value；
Step 1.5, body attribute level value Orthogonal Composite
The journey time attribute level value T that will be setki, stand-by period attribute level value WiWith expense attribute level value Cki
Orthogonal Composite is carried out, the number combined after orthogonal is the number of a set of questionnaire；
Step 1.6, questionnaire base attribute is set
Step 1.7, supplement survey explanation
After above-mentioned steps are finished, supplement and questionnaire is illustrated；
Step 1.8, complete questionnaire is formed, specific a questionnaire includes being formed in base attribute, step 1.5
Any one Orthogonal Composite；
Example see the table below 1：
The questionnaire example of table 1
Step 2, survey data is obtained
Questionnaire obtains survey data by inquiry；
Step 3, processing data
Step 3.1, data inputting
The data that typing investigation collection is returned, and CSV forms are saved as, in gauge outfit, altij represents selection branch number, i.e.,
Connection modes MiNumber, according to 1,2,3...n, 1,2,3..n typing successively；Cset represents connection modes MiSum, typing n
；Mode represents the selection result of outbound passenger, and 1 expression outbound passenger have selected the connection modes, and 0 represents that no selection should
Connection modes；Cost, intime, wtime represent expense, journey time, stand-by period respectively；Age, sex, income generation respectively
Table age, sex, income, in sex columns, 1 represents male, and 0 represents women；
Step 3.2, data import
The data of the good CSV forms of typing are imported into NLogit softwares, " number can be checked in Data Editor " windows
According to basic condition；
Step 4, model is established
Step 4.1, definition selection branch effectiveness variable
The selection branch refers to the existing connection modes M determined in step 1.1i, by body attribute, alternatively branch is special
Property variable, base attribute is as personal characteristics's variable, definition selection branch effectiveness variable-definition table, refers to table 2.
Table 2 selects branch effectiveness variable-definition table
Step 4.2, definition selection branch utility function determines item
Based on step 4.1, reference table 2 selects branch effectiveness variable-definition table, it may be determined that selection branch utility function determines
.When i ∈ [1, n-1],
Vri=ASCi+β1Xi1+β2Xi2+β3Xi3+βi+n-1Xi(i+n-1)+βi+2n-2Xi(i+2n-2)+βi+3n-3Xi(i+3n-3) (19)
As i=n,
Vrn=β1Xn1+β2Xn2+β3Xn3 (20)
In formula (7) and formula (8),
VriRepresent that i-th kind of connection modes determines item, wherein i ∈ [1, n-1] to traveler r utility function；
VrnRepresent that n connection modes determine item to traveler r utility function；
ASCiRepresent the constant term of i-th kind of connection modes, wherein i ∈ [1, n-1]；
XijEffectiveness variable is represented, wherein when i ∈ [1, n-1], j ∈ [1, n+8], works as i=n, j ∈ [1,3]；
βiRepresent the coefficient of each variable, wherein i ∈ [1, n-1]；
Step 5, calibrating parameters
Step 4 has been set up model, βi,ASCiAll it is unknown parameter, the data handled well with reference to step 3, uses
NLogit softwares demarcate unknown parameter；
Step 6：Determine that outbound passenger selects bicycle to plug into the probability of track traffic
Step 6.1：Calculating every outbound passenger selects bicycle to plug into the probability of track traffic
Every outbound passenger being investigated in formula (9) calculation procedure 2 selects bicycle to plug into the probability of track traffic.
In formula (9),
PriRepresent that outbound passenger r selects the probability of i-th kind of connection modes；
CnRepresent connection modes selection branch set；
VriRepresent that outbound passenger r selects the utility function of i-th kind of connection modes to determine item；
XriRepresent the attribute that outbound passenger r selects i-th kind of connection modes to be included；
θ represents the unknown parameter corresponding to attribute；
J' is connection modes；
Step 6.2：Calculating outbound passenger selects bicycle to plug into the average probability of track traffic
Using the probability method of average, formula (10) calculates outbound passenger and selects bicycle to plug into the average general of track traffic
Rate；
In formula (10),
PiRepresent that outbound passenger selects the average probability of i-th kind of connection modes；
PriRepresent that outbound passenger r selects the probability of i-th kind of connection modes；
Step 7, bicycle turnover rate is determined
Bicycle turnover rate refers within the unit interval, the number that unit bicycle is averagely lent；
Step 8：Determine the bicycle quantitative value B that track traffic website A needs
Formula (11), calculate the bicycle quantity that day part track traffic website A needs；
In formula (11), Bk'The bicycle quantity needed for k' period track traffic websites A, k'=1,2,3 difference tables
Show morning peak, evening peak and flat peak period；Mk'Car demand is borrowed for k' period track traffic websites A；αk'For the k' periods voluntarily
Car turnover rate；
Wherein, Mk'=k' periods outbound passenger's quantity × Pi, PiRepresent that outbound passenger selects bicycle to plug into track traffic
Probability；
After obtaining the bicycle quantity that day part track traffic website A needs, track traffic website A is obtained by formula 12
The bicycle quantitative value needed：
B=max { B1,B2,B3} (24)
Further, it is specially in the step 1.2：As shown in Fig. 2 centered on some track traffic website A, by outer
And interior equidistantly four annular region Z of setting1、Z2、Z3。
Further, when setting the attribute level value of each connection modes stand-by period to be equal to wait in the step 1.4.2
Between actual value Wi。
Further, the questionnaire designed in the step 1.6 includes following base attribute：Sex, age bracket, income
Section.
Further, bicycle turnover rate is with reference to local all types of website bicycle turnover rates in the step 7, when will be each
Section bicycle turnover rate is set to：Morning peak：6.5 times/h；Evening peak：6 times/h；Flat peak：3.5 times/h.
Compared with prior art, the invention has the advantages that and beneficial effect：
The present invention is achieved a butt joint by establishing Discrete Choice Model and refutes the determination of bicycle share rate, solves actual outfit
The problem of bicycle quantity of plugging into and too big demand disruption, improve the degree of accuracy for being equipped with track traffic website bicycle quantity；
The model accuracy of foundation is high, and has reproducibility.
Brief description of the drawings
Fig. 1 is the overall flow chart of steps of the present invention.
Fig. 2 is survey area division figure.
Fig. 3 is that NLogit data inputtings enter example.
Fig. 4 is that NLogit data import example.
Fig. 5 is points for investigation schematic diagram in embodiment.
Embodiment
Technical scheme provided by the invention is described in detail below with reference to specific embodiment, it should be understood that following specific
Embodiment is only illustrative of the invention and is not intended to limit the scope of the invention.
It is object that this example, which chooses Chengdu Xi Pu subway stations, technical solution of the present invention is made with reference to accompanying drawing and subordinate list further
Explanation.The inventive method flow is as shown in figure 1, specifically comprise the following steps：
Step 1：Design Chengdu Xi Pu subway station outbound passenger's Investigation of willingness questionnaires.
Step 1.1：Record the existing connection modes of Chengdu Xi Pu subway stations
The core content of this example research is：After setting up bicycle website at Chengdu Xi Pu stations, it is ready to select bicycle to connect
The ratio shared by passenger refuted is how many, it is assumed that setting up bicycle website needs to be equipped with how many cars.Pass through the reality stood to Xi Pu
Ground is investigated, it is found that Xi Pu stations periphery does not have suitable parking lot, the passenger to be plugged into using private car is less, it is considered herein that Xi Pu
Connection modes one share four kinds existing for standing：M1：Bus, M2：Walking, M3：Taxi, M4：Bicycle.
Step 1.2：With choosing survey objective
Centered on the subway station of rhinoceros Pu, the region away from its 0.5km is designated as region Z1, 0.5km to 1km annular region note
For region Z2, 1km to 1.5km annular region is designated as region Z3, 1.5km to 2km annular region is designated as region Z4, in Z1It is interior
Select points for investigation D1：Bolune square；In Z2In the range of select points for investigation D2：China Minsheng Banking Corporation；In Z3In the range of select points for investigation
D3：Red flag supermarket；In Z4In the range of select points for investigation D4：The high-new new science school in Chengdu, refers to accompanying drawing 5.
Step 1.3：The body attribute of questionnaire is set
The questionnaire of design includes following body attribute：Expense, journey time, stand-by period.
Step 1.3.1：Determine journey time TijSuch as table 4 below
Table 4 respectively investigates place away from rhinoceros Pu subway station difference mode of transportation journey time table
Step 1.3.2：Determine stand-by period WiSuch as table 5 below
Each connection modes stand-by period table of table 5
| Connection modes | Stand-by period (minute) |
| Bus | 5 |
| Walking | 0 |
| Taxi | 5 |
| Bicycle | 0 |
Step 1.3.3：Determine expense CijSuch as table 6 below
Table 6 respectively investigates place away from rhinoceros Pu subway station difference mode of transportation expenses statement
Step 1.4：Define questionnaire body attribute level value
Step 1.4.1：Define stroke time attribute level value
On the basis of the fixed journey times of step 1.3.1, various connection modes journey times are calculated by formula (1)
Average value.
It is calculated by above formulat1=10, t2=20, t3=7.5, t4=12.5.
According to formula (2) and formula (3), journey time attribute level value T is setki(1≤k≤3, i=1,2,3...n).
Tki=ti+x (25)
Ti1≤Tki≤Ti4 (26)
Wherein x is arbitrary value.
It is as shown in table 7 below to obtain journey time attribute level value：
Each connection modes journey time attribute level value of table 7 defines table
Step 1.4.2：Define stand-by period attribute level value
The stand-by period of various connection modes is had determined that in step 1.3.2, each connection modes stand-by period is set
Attribute level value is equal to stand-by period actual value, and therefore, each connection modes stand-by period attribute level value is as shown in table 8 below：
Each connection modes stand-by period attribute level value of table 8 defines table
Step 1.4.3：Definition expense attribute level value
Various connection modes M are calculated according to formula (4)iExpense average value.
C is calculated by above formula1=2, c2=0, c3=8, c4=0.25
According to formula (5) and formula (6) setup fee attribute level value Cki(1≤k≤3, i=1,2,3...n).
Cki=ci+y (27)
Ci1≤Cki≤Ci4 (28)
Obtain each connection modes cost level value such as table 9 below
Each connection modes cost level value of table 9 defines table
Step 1.5：Body attribute level value Orthogonal Composite
The level value of questionnaire body attribute has been defined in step 1.4.The journey time attribute level that will be set
Value, stand-by period attribute level value and expense attribute level value carry out Orthogonal Composite, and the number combined after orthogonal is exactly this
Cover the number of questionnaire.Subordinate list 10 is journey time in case, stand-by period, the result of expense attribute level value Orthogonal Composite, one
8 application forms are shared, this 8 application forms collectively constitute a set of questionnaire.
The journey time of table 10, stand-by period, expense attribute level value Orthogonal Composite result table
Step 1.6：Questionnaire base attribute is set
The questionnaire of design includes following base attribute：Sex, age bracket, income section.Age bracket is divided into 9 grades：18
Year is following, 18 years old to 24 years old, 25 years old to 30 years old, 31 years old to 35 years old, 36 years old to 40 years old, 41 years old to 45 years old, 46 years old to 50 years old, 51 years old
To 60 years old, more than 60 years old.Income section is divided into 4 grades：Less than 2000 yuan, 2000 yuan to 4000 yuan, 4000 yuan to 6000 yuan, 6000
It is more than member.
Step 1.7：Supplement survey explanation
Questionnaire is illustrated, it is necessary to supplement after above-mentioned steps are finished.The explanation of investigation includes：First to by investigation pair
As table is with gratitude, the self-introduction of investigator, this time purposes of investigation, purpose etc., this is obtained by the trust of respondent, is striven
Take a mostly important step for mutual cooperation.
Step 1.8：The questionnaire completed is formed, specific a application form example in a set of questionnaire is shown in Table 11, remaining
Application form is for details see attached table 14 to subordinate list subordinate list 20.
Application form 1 in the questionnaire of table 11
Step 2：Survey data
The investigation method that the present invention uses is field investigation method, in going out for investigation place random selection track traffic website A
The passenger that stands is investigated, and first outbound passenger is explained to this survey objective investigated by investigator, then by being investigated
Object fills in questionnaire.In 560 outbound passengers for receiving investigation, M-F is about 3:1, the age is concentrated mainly on
18-24 year and 25-30 year, income is concentrated mainly on less than 2000 yuan and more than 4000 yuan.
Step 3：Processing data
Step 3.1：Data inputting
The data that gauge outfit form typing investigation collection according to accompanying drawing 3 is returned, and save as CSV forms.In gauge outfit,
Altij represents selection branch number, that is, the number of connection modes, according to 1,2,3,4,1,2,3,4... typing successively；cset
Represent connection modes MiSum, typing 4；Mode represents the selection result of outbound passenger, and 1 expression outbound passenger have selected
The connection modes, 0 represents not selecting the connection modes；Cost, intime, wtime represent respectively expense, journey time, etc.
Treat the time；Age, sex, income represent age, sex, income respectively, and in sex columns, 1 represents male, and 0 represents women.
Step 3.2：Data import
The good data of typing are saved as into " CSV " form, " Project " file newly-built in NLogit softwares, click on dish
" import " imports data in " Project " in single column, after data import successfully, " can looked into Data Editor " windows
The basic condition of data is seen, completes to check whether data are wrong while data inputting, example is shown in accompanying drawing 4.
Step 4：Establish model
Step 4.1：Definition selection branch effectiveness variable
Branch is selected to refer to the existing connection modes M determined in step 1.1 in this patenti.Effectiveness refers in economics
The satisfaction that the happiness or demand that consumer obtains from the consumption choice of oneself obtain, the consumption in selection course is carried out
Person is to pursue " maximization of effectiveness " as target.Selection branch utility function determines item and random entry comprising utility function.Choosing
Select a utility function and determine that item mainly includes selection branch attribute and personal characteristics attribute, select branch attribute to be divided into selection branch again
Intrinsic constant item, selection branch generalized variable, the selection peculiar variable of branch, and select the peculiar variable of branch to refer in branch set is selected only
There are the attribute and feature that some selection branch just has.Generally when definition selects branch effectiveness variable, branch constant term, policymaker is selected to become
The number of parameters of amount is based on maximization of utility principle than selecting few 1 of branch number, and policymaker is when making a policy, always
It is selection maximum selection branch of effectiveness for oneself.It is specifically defined selection branch effectiveness variable for details see attached table 12.
Table 12 selects branch effectiveness variable-definition table
Step 4.2：Definition selection branch utility function determines item
Vr1=ASC1+β1X11+β2X12+β3X13+β4X14+β7X17+β10X110
Vr3=ASC3+β1X31+β2X32+β3X33+β6X36+β9X39+β12X312
Vr4=β1X41+β2X42
Vr1--- represent that the utility function of bus determines item；
Vr2--- represent that the utility function of walking determines item
Vr3--- represent that the utility function of taxi determines item；
Vr4--- represent that the utility function of bicycle determines item；
ASC1, ASC2, ASC3--- bus, walking, the constant term of taxi are represented respectively
Xni--- represent effectiveness variable (i=(1,12)), (n=(Isosorbide-5-Nitrae)))；
βi--- the coefficient (i=(1,12)) of each variable
Step 5：Calibrating parameters
Calibrating procedure is write in NLogit software windows.Specific calibration result sees attached list 13.
13 each variable parameter value of table
Vr1=1.933-0.676 × X11-0.023×X12-0.300×X13-0.522×X14+0.140×X17+0.019×
X110
Vr2=-2.161-0.023 × X22-0.242×X25+0.114×X28-0.106×X211
Vr3=3.026-0.676 × X31-0.023×X32-0.300×X33-0.753×X36-0.178×X39+0.447×
X312
Vr4=-0.676 × X41-0.023×X42
Step 6.1：Calculating every outbound passenger selects bicycle to plug into the probability of track traffic
Every outbound passenger being investigated in formula (9) calculation procedure 2 selects bicycle to plug into the probability of track traffic.
Step 6.2：Calculating outbound passenger selects bicycle to plug into the average probability of track traffic
Using the probability method of average, formula (10) calculates outbound passenger and selects bicycle to plug into the average general of track traffic
Rate.
By calculating, the probability that outbound passenger selects bicycle to plug into is 40.3%.
Step 7：Determine bicycle turnover rate
Bicycle turnover rate refers within the unit interval, the number that unit bicycle is averagely lent.This patent is joined
The local bicycle turnover rate in Chengdu is examined, for details see attached table 3, day part bicycle turnover rate is set to：Morning peak：6.5 times/h；Evening
Peak：6 times/h；Flat peak：3.5 times/h.
The all types of website bicycle turnover rate tables in the Chengdu of table 3
Step 8：Determine the bicycle quantitative value that rhinoceros Pu subway station needs
Calculate the bicycle quantity that day part rhinoceros Pu subway station needs.
In formula (11), Bk'The bicycle quantity needed for k' period track traffic websites A, k'=1,2,3 difference tables
Show morning peak, evening peak and flat peak period；Mk'Car demand is borrowed for k' period track traffic websites A；αk'For the k' periods voluntarily
Car turnover rate；Mk'=k' periods outbound passenger's quantity × Pi, wherein PiRepresent that outbound passenger selects bicycle to plug into track traffic
Probability.
B1=314, B2=403, B3=209
B=max { B1,B2,B3}=403
To sum up, if setting up bicycle website in Chengdu Xi Pu subway stations, it should be equipped with 403 bicycles.
Application form 2 in the questionnaire of table 14
Application form 3 in the questionnaire of table 15
Application form 4 in the questionnaire of table 16
Application form 5 in the questionnaire of table 17
Application form 6 in the questionnaire of table 18
Application form 7 in the questionnaire of table 19
Application form 8 in the questionnaire of table 20
Technological means disclosed in the present invention program is not limited only to the technological means disclosed in above-mentioned embodiment, in addition to
Formed technical scheme is combined by above technical characteristic.It should be pointed out that for those skilled in the art
For, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (5)
1. a kind of Urban Rail Transit Stations are plugged into, bicycle quantity determines method, it is characterised in that comprises the following steps：
Step 1, Investigation of willingness questionnaire is designed
Step 1.1, the existing connection modes of track record traffic website
The connection modes for being available for outbound passenger to select in record time period are Mi, wherein i be connection modes quantity, i=1,2,
3...n；
Step 1.2, with choosing survey objective
Centered on some track traffic website A, some survey areas are divided, one is respectively selected from the region of each division
Points for investigation Dj, j is the region quantity of division；
Step 1.3, questionnaire body attribute is set
The questionnaire body attribute includes：Expense, journey time, stand-by period；Wherein, journey time refers to outbound passenger and existed
Select connection modes MiAfterwards from point A to up to points for investigation DjThe time that need to be spent, stand-by period refer to outbound passenger and wait connection modes Mi
The time that need to be spent；
Step 1.3.1, determines journey time
On the basis of step 1.1 to step 1.3, determine outbound passenger with connection modes MiTo points for investigation DjThe stroke that need to be spent
Time Tij；
Step 1.3.2, determines the stand-by period
Determine that outbound passenger waits connection modes MiThe time W that need to be spenti；
Step 1.3.3, determines expense
Define outbound passenger and select connection modes MiTo points for investigation DjRequired expense Cij；
Step 1.4, questionnaire body attribute level value is defined
Step 1.4.1, define stroke time attribute level value
The average value of various connection modes journey times is calculated according to formula (1)：
<mrow>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mn>4</mn>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>4</mn>
</munderover>
<msub>
<mi>T</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
According to formula (2) and formula (3), journey time attribute level value T is setki, 1≤k≤3, i=1,2,3...n, TkiRepresent
K-th of connection modes MiJourney time attribute level value
Tki=ti+x (2)
Ti1≤Tki≤Ti4 (3)
Wherein x is arbitrary value；
Step 1.4.2, define stand-by period attribute level value
Step 1.4.3, define expense attribute level value
Various connection modes M are calculated according to formula (4)iExpense average value：
<mrow>
<msub>
<mi>c</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mn>4</mn>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>4</mn>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
According to formula (5) and formula (6) setup fee attribute level value Cki, 1≤k≤3, i=1,2,3...n, CkiRepresent k-th
Connection modes MiExpense attribute level value：
Cki=ci+y (5)
Ci1≤Cki≤Ci4 (6)
Wherein y is arbitrary value；
Step 1.5, body attribute level value Orthogonal Composite
The journey time attribute level value T that will be setki, stand-by period attribute level value WiWith expense attribute level value CkiCarry out
Orthogonal Composite, the number combined after orthogonal are the number of a set of questionnaire；
Step 1.6, questionnaire base attribute is set
Step 1.7, supplement survey explanation
After above-mentioned steps are finished, supplement and questionnaire is illustrated；
Step 1.8, complete questionnaire is formed, what specific a questionnaire included being formed in base attribute, step 1.5 appoints
Anticipate a kind of Orthogonal Composite；
Step 2, survey data is obtained
Questionnaire obtains survey data by inquiry；
Step 3, processing data
Step 3.1, data inputting
The data that typing investigation collection is returned, and CSV forms are saved as, including:Connection modes MiNumber；Connection modes Mi's
Sum；The selection result of outbound passenger；Expense, journey time in form, stand-by period；Base attribute；
Step 3.2, data import
The data of the good CSV forms of typing are imported into NLogit softwares, " data can be checked in Data Editor " windows
Basic condition；
Step 4, model is established
Step 4.1, definition selection branch effectiveness variable
The selection branch refers to the existing connection modes M determined in step 1.1i, by body attribute, alternatively branch characteristic becomes
Amount, base attribute is as personal characteristics's variable, definition selection branch effectiveness variable-definition table；
Step 4.2, definition selection branch utility function determines item
Based on step 4.1, reference table 2 selects branch effectiveness variable-definition table, it is determined that selection branch utility function determines item；
When i ∈ [1, n-1],
Vri=ASCi+β1Xi1+β2Xi2+β3Xi3+βi+n-1Xi(i+n-1)+βi+2n-2Xi(i+2n-2)+βi+3n-3Xi(i+3n-3) (7)
As i=n,
Vrn=β1Xn1+β2Xn2+β3Xn3 (8)
In formula (7) and formula (8),
VriRepresent that i-th kind of connection modes determines item, wherein i ∈ [1, n-1] to traveler r utility function；
VrnRepresent that n connection modes determine item to traveler r utility function；
ASCiRepresent the constant term of i-th kind of connection modes, wherein i ∈ [1, n-1]；
XijEffectiveness variable is represented, wherein when i ∈ [1, n-1], j ∈ [1, n+8], works as i=n, j ∈ [1,3]；
βiRepresent the coefficient of each variable, wherein i ∈ [1, n-1]；
Step 5, calibrating parameters
Based on the model established in step 4, the data handled well with reference to step 3, unknown parameter is demarcated with NLogit softwares；
Step 6：Determine that outbound passenger selects bicycle to plug into the probability of track traffic
Step 6.1：Calculating every outbound passenger selects bicycle to plug into the probability of track traffic
Every outbound passenger being investigated in formula (9) calculation procedure 2 selects bicycle to plug into the probability of track traffic：
<mrow>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>r</mi>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>V</mi>
<mrow>
<mi>r</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>&Sigma;</mi>
<mrow>
<msup>
<mi>j</mi>
<mo>&prime;</mo>
</msup>
<mo>&Element;</mo>
<msub>
<mi>C</mi>
<mi>n</mi>
</msub>
</mrow>
</msub>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>V</mi>
<mrow>
<msup>
<mi>rj</mi>
<mo>&prime;</mo>
</msup>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>=</mo>
<mfrac>
<mrow>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>&theta;X</mi>
<mrow>
<mi>r</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>&Sigma;</mi>
<mrow>
<msup>
<mi>j</mi>
<mo>&prime;</mo>
</msup>
<mo>&Element;</mo>
<msub>
<mi>C</mi>
<mi>n</mi>
</msub>
</mrow>
</msub>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>&theta;X</mi>
<mrow>
<msup>
<mi>rj</mi>
<mo>&prime;</mo>
</msup>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (9),
PriRepresent that outbound passenger r selects the probability of i-th kind of connection modes；
CnRepresent connection modes selection branch set；
VriRepresent that outbound passenger r selects the utility function of i-th kind of connection modes to determine item；
XriRepresent the attribute that outbound passenger r selects i-th kind of connection modes to be included；
θ represents the unknown parameter corresponding to attribute；
J' is connection modes；
Step 6.2：Calculating outbound passenger selects bicycle to plug into the average probability of track traffic
Using the probability method of average, formula (10) calculates outbound passenger and selects bicycle to plug into the average probability of track traffic：
<mrow>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (10),
PiRepresent that outbound passenger selects the average probability of i-th kind of connection modes；
PriRepresent that outbound passenger r selects the probability of i-th kind of connection modes；
Step 7, bicycle turnover rate is determined
Bicycle turnover rate refers within the unit interval, the number that unit bicycle is averagely lent；
Step 8：Determine the bicycle quantitative value B that track traffic website A needs
Formula (11), calculate the bicycle quantity that day part track traffic website A needs；
<mrow>
<msub>
<mi>B</mi>
<msup>
<mi>k</mi>
<mo>&prime;</mo>
</msup>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>M</mi>
<msup>
<mi>k</mi>
<mo>&prime;</mo>
</msup>
</msub>
<msub>
<mi>&alpha;</mi>
<msup>
<mi>k</mi>
<mo>&prime;</mo>
</msup>
</msub>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (11), Bk'The bicycle quantity needed for k' period track traffic websites A, k'=1,2,3 represent early respectively
Peak, evening peak and flat peak period；Mk'Car demand is borrowed for k' period track traffic websites A；αk'For k' periods bicycle week
Rate of rotation；
Wherein, Mk'=k' periods outbound passenger's quantity × Pi, PiRepresent that outbound passenger selects bicycle to plug into the general of track traffic
Rate；
After obtaining the bicycle quantity that day part track traffic website A needs, track traffic website A needs are obtained by formula 12
Bicycle quantitative value：
B=max { B1,B2,B3} (12)。
2. Urban Rail Transit Stations according to claim 1 are plugged into, bicycle quantity determines method, it is characterised in that institute
State in step 1.2 and be specially：As shown in Fig. 2 centered on some track traffic website A, equidistantly set from outside to inside
Four annular region Z1、Z2、Z3。
3. Urban Rail Transit Stations according to claim 1 are plugged into, bicycle quantity determines method, it is characterised in that：Institute
State and set the attribute level value of each connection modes stand-by period to be equal to stand-by period actual value W in step 1.4.2i。
4. Urban Rail Transit Stations according to claim 1 are plugged into, bicycle quantity determines method, it is characterised in that：Institute
Stating the questionnaire designed in step 1.6 includes following base attribute：Sex, age bracket, income section.
5. Urban Rail Transit Stations according to claim 1 are plugged into, bicycle quantity determines method, it is characterised in that：Institute
Bicycle turnover rate in step 7 is stated, with reference to local all types of website bicycle turnover rates, day part bicycle turnover rate to be set to：
Morning peak：6.5 times/h；Evening peak：6 times/h；Flat peak：3.5 times/h.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108229836A (en) * | 2018-01-19 | 2018-06-29 | 济南市市政工程设计研究院（集团）有限责任公司 | Public bicycles car storage amount computational methods in a kind of bicycle parking website |
| CN110633307A (en) * | 2019-08-19 | 2019-12-31 | 北京建筑大学 | Urban public bicycle connection subway space-time analysis method |
| CN112990573A (en) * | 2021-03-12 | 2021-06-18 | 东南大学 | Path selection method based on asymmetric discrete selection model |
-
2017
- 2017-05-31 CN CN201710401266.0A patent/CN107516155A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108229836A (en) * | 2018-01-19 | 2018-06-29 | 济南市市政工程设计研究院（集团）有限责任公司 | Public bicycles car storage amount computational methods in a kind of bicycle parking website |
| CN110633307A (en) * | 2019-08-19 | 2019-12-31 | 北京建筑大学 | Urban public bicycle connection subway space-time analysis method |
| CN110633307B (en) * | 2019-08-19 | 2022-05-10 | 北京建筑大学 | Urban public bicycle connection subway space-time analysis method |
| CN112990573A (en) * | 2021-03-12 | 2021-06-18 | 东南大学 | Path selection method based on asymmetric discrete selection model |
| CN112990573B (en) * | 2021-03-12 | 2021-11-09 | 东南大学 | Path selection method based on asymmetric discrete selection model |
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