CN106504535A - A kind of combination Gravity Models and the trip distribution modeling method of Fratar models - Google Patents
A kind of combination Gravity Models and the trip distribution modeling method of Fratar models Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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Abstract
The invention discloses a kind of combination Gravity Models and the trip distribution modeling method of Fratar models, comprise the steps：The generation for gathering each cell first attracts the volume of traffic and the distribution of present situation OD；Secondly demarcate the generation without constraint Gravity Models parameter and each cell of the prediction non-coming year and attract the volume of traffic；Then apply that both demarcated to calculate non-coming year OD distributions without constraint Gravity Models；Finally application Fratar model runnings once obtain new non-coming year prediction OD distributions；Convergence judgement is carried out with last time circulation result to operation result, the OD distributions for being met convergence criterion are each minizone OD forecast of distribution final results of the non-coming year.The distribution forecasting method combines the advantage of Gravity Models and Fratar models, both present situation trip distributed intelligence had been taken full advantage of, the impact that the change and Land_use change that road network can be considered simultaneously again is produced to people's trip, improves the applicability of the accuracy and forecast model for predicting the outcome.
Description
Technical field
The present invention relates to a kind of trip distribution modeling method that Gravity Models is combined with Fratar models, belongs to traffic
Demand and trip distribution modeling technical field.
Background technology
Make rational planning for too busy to get away accurate transport need analysis and the prediction of communication project.Transport need analysis and prediction are made
For the key technology of traffic programme, the degree of accuracy and reliability held by future transportation development trend is determined, so as to affect
Traffic department and the decision-making of designer.In the level of urbanization higher and higher today, emerged substantial amounts of newly-built city or
Urban planning new district (hereinafter referred to as Xincheng District), this just brings a new difficult problem and challenge to Urban Traffic Planning, particularly exists
Transport need analysis and forecast period.At present, transport need analysis is carried out in traffic programme mainly uses biography with prediction
The procedural transport need analytical model of system, i.e. four stages of Trip generation forecast, traffic distribution, traffic modal splitting and traffic assignation
Predictive mode.For the second stage trip distribution modeling of Four-stage Method, a lot of scholars propose different forecast models and
Method.
At present, in the trip distribution modeling of the Study on Highway Feasible Research using more be that present status method (also referred to as increases
Y-factor method Y), wherein Fratar methods are widely used by planning governor due to fast convergence rate.The basic assumption of Fratar methods
It is：Travel amount between traffic zone is unrelated with the change of road network structure, or prediction the time in road network without big change.Thus
Fratar methods have an open defect being not fee from, i.e.,：Future transportation amount is only realized with this sole indicator of growth rate, and
Do not account for the factors that traffic distribution is affected in network, thus in new mode of transportation, new road, new charge political affairs
Plan or new cell cannot describe the change of traffic distribution when generating.The distribution precision additionally, present status method is gone on a journey to base year
Dependence is larger, and the confidence level of trip distribution of the non-coming year can not possibly exceed base year, and any base year that occurs in is gone on a journey in distribution
Error will be exaggerated in calculating process.In contrast to this, " Gravity Models " method, or it is referred to as " collective model " method, it is believed that area
Traffic distribution between area is affected by all traffic impedances such as interzone distance, run time, expense, i.e., area and area it
Between trip distribution the attraction that goes on a journey is directly proportional with each area, and the traffic impedance between same district is inversely proportional to, for some are newly-built
The trip distribution modeling in city has higher applicability and accuracy.But gravity model method is based entirely on to trip distribution shadow
The consideration of the factor of sound, lacks the analysis of the travel behaviour to people, does not make full use of present situation trip distributed data, and predicting the outcome can
Can there is certain deviation with actual conditions.
Content of the invention
Goal of the invention：In order to overcome the deficiencies in the prior art, the present invention provide a kind of combine Gravity Models with
The trip distribution modeling method of Fratar models, the Forecasting Methodology can comprehensive gravity model method and Fratar models advantage, both
The impact of road network change and Land_use change to trip distribution is considered, and fully combines present situation trip distribution actual conditions, while
Solve the problems, such as that Fratar method growth pattern is single, gravity model method Correlative Influence Factors are difficult to obtain, can be to the non-coming year
Traffic distribution carries out reasonable prediction.
Technical scheme：For achieving the above object, the technical solution used in the present invention is：
A kind of combination Gravity Models and the trip distribution modeling method of Fratar models, comprise the following steps：
Step 10) present situation of each cell of collection occurs to attract the volume of traffic, the distribution of present situation OD and relevant rudimentary data, its
In, there is the trip generating capacity O for attracting the volume of traffic to include cell i in the present situation of each celliTrip attraction amount D with cell jj,
I=1,2 ... n, j=1,2 ... n, n represent cell number.
Step 20) demarcate without constraint Gravity Models, comprising step 201) to step 204)：
Step 201) determine without constraint Gravity Models form：qijRepresent the traffic between cell i, j
Amount, cijThe impedance between cell i, j, α is represented, beta, gamma is without constraint Gravity Models parameter to be calibrated.
Step 202) rightBoth sides are taken the logarithm, and obtain ln (qij)=ln alpha+beta ln (OiDj)-γln(cij).
Step 203) make Y=ln (qij), a0=ln α, a1=β, a2=-γ, X1=ln (OiDj), X2=ln (cij), then Y=
a0+a1X1+a2X2, wherein a0, a1, a2For coefficient to be calibrated, X1, X2, Y is the vector comprising sample data set.
Step 204) occur to attract the volume of traffic and the distribution of present situation OD to determine sample data set X by each cell present situation1, X2, Y,
Sample data is demarcated using least square method, determined without constraint Gravity Models parameter.
Step 30) predict that the generation in each cell non-coming year attracts the volume of traffic, institute based on the present situation generation attraction volume of traffic
The generation for stating each cell non-coming year attracts the volume of traffic to include the trip generating capacity P in i-th cell non-coming yeariWith i-th cell not
Trip attraction amount A in the coming yeari.
Step 40) by step 30) obtain the generation in each cell non-coming year and attract the volume of traffic to substitute into step 20) and in both demarcated
Without constraint Gravity Models, non-coming year OD distributions q is calculatedij.
Step 50)：With step 40) in application without constraint Gravity Models obtain OD distribution qijAs initial OD, operation one
Secondary Fratar models obtain new non-coming year prediction OD distribution qij 1, comprising step 501) and to step 503).
Step 501)：OD distributions q between cell i, j is obtained according to solving without constraint Gravity Modelsij, collect and obtain each cell
Generation attracts the volume of traffic：
Step 502)：Calculate Fratar model coefficient correlations Wherein, FOiWhen representing application Fratar models there is convergence coefficient, F in the trip of i-th cellDjRepresent the
The trip attraction convergence coefficient of j cell, LijRepresent that the trip of i-th cell occurs regulation coefficient, LjiRepresent j-th cell
Trip attraction regulation coefficient.
Step 503)：Solve new non-coming year OD forecast of distribution qij 1=qij*FOi*FDj*(Lii+Ljj)/2.
Step 60)：Convergence judges：According to step 50) in Fratar model one cycles obtain OD distribution qij 1, converge
The new non-coming year prediction of each cell must be arrived to occur to attract volume of traffic conduct
If there is to attract volume of traffic error within tolerance interval, i.e., in each cell：
Proceed to step 70), otherwise make Go to step 40).
Step 70) predicting the outcome meets the condition of convergence, end loop, and OD distributions now are each minizone OD of the non-coming year
Forecast of distribution result.
Preferably：The step 10) in relevant rudimentary data include cell population, area, Land_use change, locational factor number
According to.
Preferably：The step 30) in by present situation occur to attract the volume of traffic based on predict the generation in each cell non-coming year
The method of the volume of traffic is attracted to include original unit's method, growth rate method, cross classification, function method.
Beneficial effect：The present invention compared to existing technology, has the advantages that：
1) advantage of Gravity Models is inherited, many factors such as road network change, Land_use change can be considered for cell
There is the impact for attracting the volume of traffic.Traffic Demand Forecasting simultaneously for newly-built city or urban planning new district has higher standard
True property and applicability.
2) the merger advantage of Fratar model of growth, makes full use of obtainable present situation cell trip distribution letter
Breath, controls the deviation between non-coming year trip forecast of distribution result and actual trip distribution, it is ensured that prediction knot to a certain extent
The confidence level of fruit.
3) when the distribution of present situation OD is difficult to obtain or base year distributed intelligence is lacked, still can be initial first with Gravity Models
Change prediction year OD, recycle Fratar models progressively adjusted to predict the outcome.
Description of the drawings
Fig. 1 is the FB(flow block) of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings and specific embodiment, the present invention is further elucidated with, it should be understood that these examples are merely to illustrate this
Invention rather than restriction the scope of the present invention, after the present invention has been read, those skilled in the art are various to the present invention's
The modification of the equivalent form of value falls within the application claims limited range.
A kind of combination Gravity Models and the trip distribution modeling method of Fratar models, as shown in figure 1, including following step
Suddenly：
Step 10) present situation of each cell of collection occurs to attract volume of traffic Oi, Di, present situation OD distribution and relevant rudimentary data,
Wherein, the collection of relevant rudimentary data includes：Traffic zone is occurred to attract the volume of traffic to produce including for directly or indirectly impact
Cell population, area, Land_use change, the collection of position related data.
Step 20) demarcate without constraint Gravity Models, comprising step 201) to step 204)：
Step 201) determine without constraint Gravity Models form：Wherein, i=1,2 ... n, j=1,2 ... n,
N represents cell number, qijRepresent the volume of traffic between cell i, j, cijRepresent the impedance between cell i, j, OiRepresent cell i
Trip generating capacity, DjThe trip attraction amount of cell j, α is represented, beta, gamma is without constraint Gravity Models parameter to be calibrated.
Step 202) rightBoth sides are taken the logarithm, and obtain ln (qij)=ln alpha+beta ln (OiDj)-γln(cij)
Step 203) make Y=ln (qij), a0=ln α, a1=β, a2=-γ, X1=ln (OiDj), X2=ln (cij), then Y=
a0+a1X1+a2X2, wherein a0, a1, a2For coefficient to be calibrated, X1, X2, Y is the vector comprising sample data set.
Step 204) occur to attract the volume of traffic and the distribution of present situation OD to determine sample data set X by each cell present situation1, X2, Y,
Sample data is demarcated using least square method, determined without constraint Gravity Models parameter.
Step 30) by present situation occur attract the volume of traffic based on predict each cell non-coming year generation attract the volume of traffic, i-th
The generation of individual cell attracts the volume of traffic to be designated as P respectivelyi、Ai.Wherein, the generation in the cell non-coming year attracts Traffic volume forecasting method bag
Include original unit's method, growth rate method, cross classification, function method etc..
Step 40) applying step 20) in both demarcated without constraint Gravity Models, substituting into non-coming year occurs to attract the volume of traffic
Pi、Ai, it is calculated non-coming year OD distributions.
Step 50)：Using step 40) in the OD distributions that obtain without constraint Gravity Models of application as initial OD, application
Fratar models are once restrained obtains new non-coming year prediction OD distributions, comprising step 501) to step 503)：
Step 501)：OD distributions q between cell i, j is obtained according to solving without constraint Gravity Modelsij, collected respectively according to OD tables
Cell occurs to attract the volume of traffic：
Step 502)：Calculate Fratar Parameters in Mathematical Model：
Step 503)：Solve new non-coming year OD forecast of distribution
Step 60)：Convergence judges：According to step 50) in Fratar model one cycles obtain OD distribution qij 1, converge
The new non-coming year prediction of each cell must be arrived to occur to attract volume of traffic conduct
If there is to attract volume of traffic error within tolerance interval, i.e., in each cell：
Proceed to step 70), otherwise make Go to step 40).
Step 70) predicting the outcome meets the condition of convergence, end loop, and OD distributions now are each minizone OD of the non-coming year
Forecast of distribution result.
The present invention provides a kind of trip distribution modeling method of combination Gravity Models and Fratar models, and the Forecasting Methodology can
Comprehensive gravity model method and the advantage of Fratar models, had both considered the impact of road network change and Land_use change to trip distribution,
Present situation trip distribution actual conditions are fully combined again, while solving, Fratar method growth pattern is single, gravity model method phase
Close influence factor and obtain accuracy problem, reasonable prediction is carried out to the traffic distribution in the non-coming year.
The actual conditions that the present invention is likely to occur during having taken into full account trip distribution modeling, by gravity model method with
Fratar growth rate methods combine, and at utmost can reasonably predicted not on the premise of acquisition information using present situation trip distribution
Coming year trip distribution.The method of the present invention carries out initialization prediction initially with Gravity Models to the distribution of following year traffic, passes through
Fratar models are gradually restrained to initial predicted result, circulate above-mentioned two step until meeting the final condition of convergence.The present invention's
Method does not have substantial modifications to the general principle of existing forecast of distribution model, but which combines Gravity Models with Fratar models
Advantage, had both taken full advantage of present situation trip distributed intelligence, while can consider that the change of road network and Land_use change are gone on a journey to people again
The impact of generation, improves the applicability of the accuracy and forecast model for predicting the outcome.Therefore of the invention practical, can be with
Suitable for the traffic distribution of New and old community and Traffic Demand Forecasting, have important practical significance.
A specific embodiment is given below.
By taking the trip forecast of distribution of 3 traffic zones in Jiangsu Province city as an example, illustrate the practicality of the inventive method with
Advantage.
Step 10) present situation of each cell of collection occurs to attract the volume of traffic P, A, the distribution of present situation OD and relevant rudimentary data,
Count between each cell and Intra-cell present situation running time, prediction future travel time.
1 present situation OD distribution table of table
2 present situation running time of table
The 3 future travel time of table
Step 20) demarcate without constraint Gravity Models：
Step 201) determine without constraint Gravity Models form：
Step 202) rightBoth sides are taken the logarithm, and obtain ln (qij)=ln alpha+beta ln (OiDj)-γln(cij).
Step 203) make Y=ln (qij), a0=ln α, a1=β, a2=-γ, X1=ln (OiDj), X2=ln (cij), then Y=
a0+a1X1+a2X2, wherein a0, a1, a2For coefficient to be calibrated, cijTake between cell with Intra-cell running time, X1, X2, Y is bag
Vector containing sample data set.
Step 204) occur to attract the volume of traffic and the distribution of present situation OD to determine sample data set X by each cell present situation1, X2, Y,
Sample data is demarcated using least square method, determined without constraint Gravity Models parameter.
4 Gravity Models of table demarcates sample data
Solve linear equation in two unknowns parameter a0=-2.084, a1=1.173, a2=-1.455, further solve Gravity Models
Parameter alpha=0.124, β=1.173, γ=1.455, that is, the Gravity Models that demarcates is：
Step 30) by present situation occur attract the volume of traffic based on predict each cell non-coming year generation attract the volume of traffic, i-th
The generation of individual cell attracts the volume of traffic to be designated as P respectivelyi、Ai.
The 5 non-coming year of table occurs to attract the volume of traffic
Step 40) applying step 20) in both demarcated without constraint Gravity Models, substituting into non-coming year occurs to attract the volume of traffic
Pi、Ai, it is calculated non-coming year OD distributions.
6 non-coming year OD distribution tables of table
Step 50)：Using step 40) in application without the OD distribution tables that obtain of constraint Gravity Models as initial OD, application
Fratar models are once restrained obtains new non-coming year prediction OD distributions, comprising step 501) to step 502)：
Step 501)：OD distributions q between cell i, j is obtained according to solving without constraint Gravity Modelsij, collected respectively according to OD tables
Cell occurs to attract the volume of traffic：
7 Gravity Models of table occurred to attract the volume of traffic with the non-coming year under actual prediction
Step 502)：Ask
7 Fratar Parameters in Mathematical Model of table
Step 503)：The non-coming year OD forecast of distribution q for looking for noveltyij 1=qij*FOi*FDj*(Lii+Ljj)/2.
8 non-coming year OD distribution tables of table
Step 60)：Convergence judges：
According to step 50) in Fratar model one cycles obtain OD distribution qij 1, collect and obtain each cell new future
Year prediction occurs to attract volume of traffic conduct
Table 9 predicts that the non-coming year occurs to attract traffic counts
After judging this circulation, each cell occurs to attract the volume of traffic to circulate result ratio with last time Whether satisfaction can connect
Receive scope：
Cyclic error is counted table 10 twice
The condition of convergence is unsatisfactory for, and therefore makes Go to step 40).
To step 40) to step 60) carry out 8 times circulation after, obtain：
11 non-coming year OD distribution tables of table
Cyclic error is counted table 12 twice
The condition of convergence meets, i.e.,： Proceed to step 70),.
Step 70) end loop, OD distributions now are non-coming year each minizone OD forecast of distribution final results.
13 non-coming year OD distribution tables of table
The above is only the preferred embodiment of the present invention, it should be pointed out that：Ordinary skill people for the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (3)
1. a kind of trip distribution modeling method of combination Gravity Models and Fratar models, it is characterised in that comprise the following steps：
Step 10) present situation of each cell of collection occurs to attract the volume of traffic, the distribution of present situation OD and relevant rudimentary data, wherein, institute
There is the trip generating capacity O for attracting the volume of traffic to include cell i in the present situation for stating each celliTrip attraction amount D with cell jj, i=1,
2...n, j=1,2...n, n represent cell number；
Step 20) demarcate without constraint Gravity Models, comprising step 201) to step 204)：
Step 201) determine without constraint Gravity Models form：qijRepresent the volume of traffic between cell i, j, cij
The impedance between cell i, j, α is represented, beta, gamma is without constraint Gravity Models parameter to be calibrated；
Step 202) rightBoth sides are taken the logarithm, and obtain ln (qij)=ln alpha+beta ln (OiDj)-γln(cij)；
Step 203) make Y=ln (qij), a0=ln α, a1=β, a2=-γ, X1=ln (OiDj), X2=ln (cij), then Y=a0+
a1X1+a2X2, wherein a0, a1, a2For coefficient to be calibrated, X1, X2, Y is the vector comprising sample data set；
Step 204) occur to attract the volume of traffic and the distribution of present situation OD to determine sample data set X by each cell present situation1, X2, Y, employing
Least square method is demarcated to sample data, is determined without constraint Gravity Models parameter；
Step 30) predict that the generation in each cell non-coming year attracts the volume of traffic based on the present situation generation attraction volume of traffic, described each
The generation in the cell non-coming year attracts the volume of traffic to include the trip generating capacity P in i-th cell non-coming yeariWith i-th cell non-coming year
Trip attraction amount Ai；
Step 40) by step 30) obtain the generation in each cell non-coming year and attract the volume of traffic to substitute into step 20) and in both demarcated without about
Beam Gravity Models, is calculated non-coming year OD distributions qij；
Step 50)：With step 40) in application without constraint Gravity Models obtain OD distribution qijUsed as initial OD, operation is once
Fratar models obtain new non-coming year prediction OD distribution qij 1, comprising step 501) and to step 503)；
Step 501)：OD distributions q between cell i, j is obtained according to solving without constraint Gravity Modelsij, collect and obtain each cell generation
Attract the volume of traffic：
Step 502)：Calculate Fratar model coefficient correlations
Wherein, FOiWhen representing application Fratar models there is convergence coefficient, F in the trip of i-th cellDjRepresent the trip of j-th cell
Attract convergence coefficient, LijRepresent that the trip of i-th cell occurs regulation coefficient, LjiRepresent the trip attraction adjustment of j-th cell
Coefficient；
Step 503)：Solve new non-coming year OD forecast of distribution qij 1=qij*FOi*FDj*(Lii+Ljj)/2；
Step 60)：Convergence judges：According to step 50) in Fratar model one cycles obtain OD distribution qij 1, collect
Occur to attract volume of traffic conduct to the new non-coming year prediction of each cell
If there is to attract volume of traffic error within tolerance interval, i.e., in each cell：
Proceed to step 70), otherwise makeGo to step 40)；
Step 70) predicting the outcome meets the condition of convergence, end loop, and OD distributions now are each minizone OD distributions of the non-coming year
Predict the outcome.
2. the trip distribution modeling method of combination Gravity Models according to claim 1 and Fratar models, its feature exist
In：The step 10) in relevant rudimentary data include cell population, area, Land_use change, locational factor data.
3. the trip distribution modeling method of combination Gravity Models according to claim 1 and Fratar models, its feature exist
In：The step 30) in by present situation occur to attract the volume of traffic based on predict that the generation in each cell non-coming year attracts the volume of traffic
Method includes original unit's method, growth rate method, cross classification, function method.
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