CN103116702A - Bicycle-mode traveling selection forecasting method based on activity chain mode - Google Patents

Abstract

The invention discloses a bicycle-mode traveling selection forecasting method based on an activity chain mode. The forecasting method includes the steps that the data survey is carried on a situation of resident traveling, and the survey result is managed and added up; a selecting mode of the resident traveling in a day in the data survey result is extracted, and the traveling mode is carried out on a variable virtual operation and a coding operation; a correlated variable in the activity chain mode is input to multi-term logit models, and a coevolution logit model can be obtained through calculating; the calculated coevolution logit model is carried out on iterative operation, and two selecting results of traveling modes are recorded; the two selecting results of the traveling modes are carried out on statistics and analysis, the prediction accuracy is carried out on contrastive analysis. By the statistics and the analysis of the vehicle selection of residents in urban, the proportion of the bicycle-mode traveling selection can be accurate to forecast, so that the urban traffic planning and the decision of the policy can be provided with the scientific and reasonable guidance.

Description

A kind of selection bicycle mode based on activity chain pattern Forecasting Methodology of going on a journey

Technical field

The present invention relates to Traffic Demand Forecasting and traffic programme technical field, be specifically related to a kind of selection bicycle mode based on activity chain pattern Forecasting Methodology of going on a journey.

Background technology

At present, along with the quickening of Urbanization in China, city space expansion, size of urban population and urban road motorization level all change accordingly, and outstanding behaviours is aspect the urban passenger railway and highway system.The urban passenger railway and highway system exposes the series of negative problems such as traffic congestion, energy resource consumption, air pollution gradually in Fast Urbanization.

How the Optimizing City Passenger Transport System is the key of alleviating urban traffic blocking and energy-saving and emission-reduction, and bicycle has flexibly and fast in short distance trip, pollution-free, Non-energy-consumption, take the advantages such as path resource is few, therefore can with the part of bicycle as the urban passenger railway and highway system, solve the series of problems of current urban passenger railway and highway system.But over past ten years, very obvious as the share rate decline of travel modal with bicycle, the share rate rapid development of opposite individual motorization travel modal.

Therefore, by a series of researchs to the bicycle traffic trip requirements, analyze passerby and select the use purpose difference of bicycle and produce reason, be conducive to predict the bicycle traffic trip mode development trend in future, help to formulate effective and pointed Transportation Demand Management policy and rationally use cycling trip to be guided out passerby.And individual preference to selecting bicycle to use in the single trip has only been considered in existing research, does not consider that in resident one day, activity pattern is for the impact of selecting the bicycle mode to go on a journey.Thinking select to be subject to the to go on a journey impact of active characteristics of people's trip mode based on the Traffic Demand Forecasting theory of activity, for example people more are inclined in the multiple destinations activity pattern and select the higher trip mode of dirigibility, the impact of the vehicles that the trip activity pattern also is inclined to use.

So, be necessary research, analytic unit are extended to " Active-Active chain pattern " level from " single trip ", go on a journey with the reciprocation angle of activity chain pattern trip to selecting bicycle mode trip requirements to predict the city from the bicycle mode, for Urban Traffic Planning and policy making provide scientific and rational guidance.

Therefore, based on the problems referred to above, the invention provides a kind of selection bicycle mode based on activity chain pattern Forecasting Methodology of going on a journey.

Summary of the invention

Goal of the invention: the invention provides a kind of selection bicycle mode based on activity chain pattern Forecasting Methodology of going on a journey, predict selecting bicycle mode trip requirements in the city, for Urban Traffic Planning and policy making provide scientific and rational guidance.

Technical scheme: the invention provides a kind of selection bicycle mode based on activity chain pattern Forecasting Methodology of going on a journey, this Forecasting Methodology comprises the following steps:

Step (1) is carried out data survey and is arranged, counts investigation result the resident trip situation.

Step (2) is extracted the data survey result of resident's trip on the one, and the bicycle mode is gone on a journey and the activity chain pattern, and it is carried out the virtual and encoding operation of variable.

Step (3) inputs to the correlated variables in the activity chain pattern in multinomial logit model, correlated variables during the bicycle mode is gone on a journey inputs in binomial logit model, and the bicycle mode is gone on a journey and the activity chain pattern is analyzed after mutual, calculate model result, obtain coevolution logit model.

Step (4) is carried out interative computation to the coevolution logit model result that calculates, and records that the bicycle mode is gone on a journey and the selection result of activity chain pattern, when the trip mode of all travelers select complete after, the finishing iteration computing.

Step (5) respectively the bicycle mode is gone on a journey and the selection result of activity chain pattern is carried out statistics and analysis, and precision of prediction is analyzed.

In described step (1), the resident trip situation carried out data survey and arranged, count investigation result, comprising the following steps,

Step (1-1) is divided traffic zone, adopts random sampling visit to the parents of schoolchildren or young workers survey, provides questionnaire in residential quarter population ratio;

Step (1-2) is determined situational variables;

Step (1-3) gathers variable data.

Described step (1-1) is to step (1-3), at first define main activities, activity chain pattern and method for expressing in sample, then adopt the random sampling visit to the parents of schoolchildren or young workers to carry out survey, provide questionnaire in residential quarter population ratio, content comprises traveler personal feature, family's feature, the trip activity of exemplary operation day and selects which kind of mode of transportation trip, individual attribute comprises traveler sex, age and occupation etc., family's feature comprises family structure, family income etc., and the trip attribute comprises soil feature, trip distance etc.

Activity chain pattern in described step (2) is divided into by movable number, once movable simple activities chain, twice and above movable complicated activity chain; Be divided into the survival-type activity chain take trip as purpose, with the non-survival-type activity chain of amusement and recreation purpose with contain the mixed type activity chain of two kinds of trip purposes by the trip purpose; Virtual and the encoding operation of the variable of activity chain pattern, hwh is simple survival-type activity chain, without other stop, hwhwh is simple survival-type activity chain, comprises based on the round stop of the trip of family, hoh for survival the type activity chain, without other stop, hohoh is non-survival-type activity chain, repeatedly returns home, hwh+o is for simply going on a journey, comprise non-survival-type activity chain, wherein the bicycle mode is gone on a journey and is, use bicycle and do not use bicycle, cast out at last enquiry data kind sample size less than 1% activity chain pattern sample, obtain effective sample.

The hypothesis traveler has the bicycle mode to go on a journey (D1) and two kinds of activity chain patterns (D2) in described step (3), (Di ∈ D, i=1,2), respectively comprise some options and every between separate, the options of supposing the effectiveness maximum is selected, and based on the Logit model, the computing formula of the selected probability of options d is:

$P^t\left(d\right)=\frac{\exp \left[E\right\{U^{t-1}\left(d\right)\left\}\right]}{{\mathrm{\Σ}}_{d^{\′}\∈D_i}\exp \left[E\right\{U^{t-1}\left(d^{\′}\right)\left\}\right]},\∀d\∈D_i,{\∀D}_i\∈D---\left(1\right)$

Wherein, Pt (d) chooses the probability of options d, E{Ut (d) for moment t } be the expected utility of moment t options d, t is iteration cycle;

A, bicycle mode go on a journey and the activity chain pattern in have reciprocation between D1 and D2, namely a kind of selection result exerts an influence to the effectiveness of another kind of options, two kinds of utility function computing formula are:

$E\left\{U^t\left(d\right)\right\}=\underset{r}{\mathrm{\Σ}}{\mathrm{\β}}_r{X}_r+\underset{r^{\′}}{\mathrm{\Σ}}{\mathrm{\β}}_{r^{\′}}\underset{s\∈D_j}{\mathrm{\Σ}}{P}_s^t{X}_{r^{\′}}\left(d\right|s)$

$\∀d\∈D_i,{\∀D}_i\∈D,j\≠i---\left(2\right)$

Wherein, Xr is options d attribute r, and Xr ' is options d attribute r ' after given state S, and S is preference pattern Dj, State, β is the parameter of variable X;

B, Pts for moment t state S probability of happening computing formula are:

$P_S^t=\underset{d\∈S}{\mathrm{\Π}}{P}^t\left(d\right),S\∈D_j,j\≠i---\left(3\right)$

Select the reciprocal initial probability calculation formula of item number to be in c, preference pattern Di:

$P^0\left(d\right)=\frac{1}{\left|D_i\right|},\∀d\∈D_i,{\∀D}_i\∈D---\left(4\right).$

In described step (4), in each preference pattern of t, each options effectiveness is subject to the t-1 impact of preference pattern result constantly constantly, and each iteration cycle is last, and the uncertainty of each preference pattern Di depends on that the big or small computing formula of entropy is:

$H^t\left(D_i\right)=-\underset{d\∈D_i}{\mathrm{\Σ}}{P}^t\left(d\right)\×{\log }_2\left\{P^t\left(d\right)\right\},\∀D_i\∈D---\left(5\right)$

Wherein, introducing adjustment coefficient θ i in entropy function equates each decision-making branch entropy of initial time, the preference pattern item correspondence of less entropy θ iHt (Di) is less has uncertainty, so at first this preference pattern item is determined and will no longer be changed in the successive iterations process.

Compared with prior art, beneficial effect of the present invention is:

A kind of selection bicycle mode based on the activity chain pattern of the present invention Forecasting Methodology of going on a journey, this Forecasting Methodology has increased in the past to selecting bicycle mode Travel Demand Forecasting not consider the defective of activity chain pattern, by statistics, the analysis that the Urban Residential Trip vehicles under the activity chain pattern are selected, dope accurately the ratio of selecting the bicycle mode to go on a journey, and then provide scientific and rational guidance for Urban Traffic Planning and policy making.

Description of drawings

Fig. 1 is the schematic flow sheet that carries out the city bicycles Traffic Demand Forecasting of the embodiment of the present invention;

Fig. 2 a is the sequence analysis result schematic diagram that the selection bicycle mode of the embodiment of the present invention is gone on a journey;

Fig. 2 b is the activity chain pattern trip sequence analysis result schematic diagram of the embodiment of the present invention.

Embodiment

Below in conjunction with specific embodiment, a kind of selection bicycle mode based on the activity chain pattern of the present invention Forecasting Methodology of going on a journey is elaborated:

The Forecasting Methodology of going on a journey of a kind of selection bicycle mode based on the activity chain pattern as shown in Figure 1, this Forecasting Methodology comprises the following steps:

Step (1) is carried out data survey and is arranged, counts investigation result the resident trip situation.

Step (2) is extracted the data survey result of resident's trip on the one, and the bicycle mode is gone on a journey and the activity chain pattern, and it is carried out the virtual and encoding operation of variable.

Step (3) inputs to the correlated variables in the activity chain pattern in multinomial logit model, correlated variables during the bicycle mode is gone on a journey inputs in binomial logit model, and the bicycle mode is gone on a journey and the activity chain pattern is analyzed after mutual, calculate model result, obtain coevolution logit model.

Step (4) is carried out interative computation to the coevolution logit model result that calculates, and records that the bicycle mode is gone on a journey and the selection result of activity chain pattern, when the trip mode of all travelers select complete after, the finishing iteration computing.

Step (5) respectively the bicycle mode is gone on a journey and the selection result of activity chain pattern is carried out statistics and analysis, and precision of prediction is analyzed.

In step (1), the resident trip situation carried out data survey and arranged, count investigation result, comprising the following steps,

Step (1-1) is divided traffic zone, adopts random sampling visit to the parents of schoolchildren or young workers survey, provides questionnaire in residential quarter population ratio;

Step (1-2) is determined situational variables;

Step (1-3) gathers variable data.

Wherein, step (1-1) is to step (1-3), at first define main activities, activity chain pattern and method for expressing in sample, then adopt the random sampling visit to the parents of schoolchildren or young workers to carry out survey, provide questionnaire in residential quarter population ratio, content comprises traveler personal feature, family's feature, the trip activity of exemplary operation day and selects which kind of mode of transportation trip, individual attribute comprises traveler sex, age and occupation etc., family's feature comprises family structure, family income etc., and the trip attribute comprises soil feature, trip distance etc.

Activity chain pattern in step (2) is divided into by movable number, once movable simple activities chain, twice and above movable complicated activity chain; Be divided into the survival-type activity chain take trip as purpose, with the non-survival-type activity chain of amusement and recreation purpose with contain the mixed type activity chain of two kinds of trip purposes by the trip purpose; Virtual and the encoding operation of the variable of activity chain pattern, hwh is simple survival-type activity chain, without other stop, hwhwh is simple survival-type activity chain, comprises based on the round stop of the trip of family, hoh for survival the type activity chain, without other stop, hohoh is non-survival-type activity chain, repeatedly returns home, hwh+o is for simply going on a journey, comprise non-survival-type activity chain, wherein the bicycle mode is gone on a journey and is, use bicycle and do not use bicycle, cast out at last enquiry data kind sample size less than 1% activity chain pattern sample, obtain effective sample.

The hypothesis traveler has the bicycle mode to go on a journey (D1) and two kinds of activity chain patterns (D2) in step (3), (Di ∈ D, i=1,2), respectively comprise some options and every between separate, the options of supposing the effectiveness maximum is selected, and based on the Logit model, the computing formula of the selected probability of options d is:

$P^t\left(d\right)=\frac{\exp \left[E\right\{U^{t-1}\left(d\right)\left\}\right]}{{\mathrm{\Σ}}_{d^{\′}\∈D_i}\exp \left[E\right\{U^{t-1}\left(d^{\′}\right)\left\}\right]},\∀d\∈D_i,{\∀D}_i\∈D---\left(1\right)$

Wherein, Pt (d) chooses the probability of options d, E{Ut (d) for moment t } be the expected utility of moment t options d, t is iteration cycle;

A, bicycle mode go on a journey and the activity chain pattern in have reciprocation between D1 and D2, namely a kind of selection result exerts an influence to the effectiveness of another kind of options, two kinds of utility function computing formula are:

$E\left\{U^t\left(d\right)\right\}=\underset{r}{\mathrm{\Σ}}{\mathrm{\β}}_r{X}_r+\underset{r^{\′}}{\mathrm{\Σ}}{\mathrm{\β}}_{r^{\′}}\underset{s\∈D_j}{\mathrm{\Σ}}{P}_s^t{X}_{r^{\′}}\left(d\right|s)$

$\∀d\∈D_i,{\∀D}_i\∈D,j\≠i---\left(2\right)$

Wherein, Xr is options d attribute r, and Xr ' is options d attribute r ' after given state S, and S is preference pattern Dj,

State, β is the parameter of variable X;

B, Pts for moment t state S probability of happening computing formula are:

$P_S^t=\underset{d\∈S}{\mathrm{\Π}}{P}^t\left(d\right),S\∈D_j,j\≠i---\left(3\right)$

Select the reciprocal initial probability calculation formula of item number to be in c, preference pattern Di:

$P^0\left(d\right)=\frac{1}{\left|D_i\right|},\∀d\∈D_i,{\∀D}_i\∈D---\left(4\right).$

In step (4), in each preference pattern of t, each options effectiveness is subject to the t-1 impact of preference pattern result constantly constantly, and each iteration cycle is last, and the uncertainty of each preference pattern Di depends on that the big or small computing formula of entropy is:

$H^t\left(D_i\right)=-\underset{d\∈D_i}{\mathrm{\Σ}}{P}^t\left(d\right)\×{\log }_2\left\{P^t\left(d\right)\right\},\∀D_i\∈D---\left(5\right)$

Wherein, introducing adjustment coefficient θ i in entropy function equates each decision-making branch entropy of initial time, the preference pattern item correspondence of less entropy θ iHt (Di) is less has uncertainty, so at first this preference pattern item is determined and will no longer be changed in the successive iterations process.

Embodiment 1

adopt step (1) to select bicycle mode trip requirements to predict to the Bengbu city dweller, the Bengbu is positioned at Northern Anhui, urban area is 601.5km2, 2006 the year end urban population reach 91.43 ten thousand, the Bengbu is the type city of typically forming a team, comprise that the center forms a team, forming a team in the north and forms a team in the east, be divided into altogether 98 traffic little, adopt random sampling visit to the parents of schoolchildren or young workers survey, provide questionnaire in residential quarter population ratio, content comprises the traveler personal feature, family's feature, the trip activity of exemplary operation day and mode are selected, individual attribute mainly comprises the traveler sex, age, occupation, schooling etc., family's feature mainly comprises family structure and scale, the vehicles, family income etc., the trip attribute comprises the soil feature, trip distance etc., the variable description statistics that relates in the survey data analysis process is as shown in table 1,

carry out step (2), Bengbu resident trip activity chain pattern is extracted, cast out sample size in Bengbu trip survey data less than 1% activity chain sample, obtain 5632 effective samples, go out row mode to two kinds and carry out the virtual and encoding operation of variable, hwh is simple survival-type activity chain pattern, without other stop, hwhwh is simple survival-type activity chain pattern, the trip that comprises based on family comes and goes stop, hoh is type activity chain pattern for survival, without other stop, hohoh is non-survival-type activity chain pattern, repeatedly return home, hwh+o is for simply going out row mode, comprise non-survival-type activity chain pattern, use bicycle and do not use bicycle, the present embodiment is only considered above 5 class activity chain patterns and 2 class bicycle operating positions, as shown in table 2,

By table 2 as can be known, use bicycle in the activity on the one of 37.2% traveler, hwh type activity ratio is up to 44.0%, the activity of hohoh type and the activity of hwh+h type are less, account for respectively 3.7% and 1.6%, survival-type activity chain pattern (hwh and hwhwh) accounts for sum 80%, and complicated activity chain pattern (hwhwh, hohoh, hwh+o) accounts for sum 40.8%.

Carry out step (3), according to the logit model, the bicycle of each sample is used and the activity chain pattern between selecting sequence calculate, wherein 54 samples need 3 iteration to complete all selections, 4100 samples need 4 iteration, and 1478 samples need 5 iteration, on average need 4.25 iteration cycles to complete whole selections, show between bicycle use and activity pattern and have reciprocation, decision process is more complicated, and in different activity chain patterns, individual traveler decision-making order is as shown in table 3

By table 3 as can be known, then the at first definite activity chain pattern of average 65% traveler is carried out trip mode and is selected, only have 35% traveler at first whether to use the decision-making of bicycle then to determine the activity chain pattern, in non-survival-type activity chain pattern (hoh, hohoh), whether at first make near the traveler of half (45.7%) and use the bicycle mode to go on a journey, this ratio is apparently higher than survival-type activity chain pattern.

Shown in Fig. 2 a as can be known, at first 76.33% bicycle user has determined the activity chain pattern, and only at first 23.67% bicycle user has determined trip mode, and in non-bicycle user colony, the ratio of at first carrying out the trip mode selection is 39.91%, far above bicycle user colony.

Shown in Fig. 2 b as can be known, trip mode is selected in preferential colony, 25.69% individual choice cycling trip mode only, 74.31% individuality is not selected bicycle, but in activity chain model selection preferential colony, using the ratio of cycling trip mode is 42.55%, selects preferential colony apparently higher than trip mode.

Both mutual rear binary Logit model uses the precision of prediction of bicycle as shown in table 4 for individual traveler,

Bring up to 81.8% from 73.8%, do not use the precision of prediction of bicycle to bring up to 86.0% from 78.5%.Binary Logit model macro-forecast precision brings up to 84.4% from 76.7%.

The above is only the preferred embodiment of the present invention, should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the principle of the invention, can also make some improvement, and these improvement also should be considered as protection scope of the present invention.

Claims (6)

1. the selection bicycle mode based on activity chain pattern Forecasting Methodology of going on a journey, it is characterized in that: the method comprises the following steps,

Step (1) is carried out data survey and is arranged, counts investigation result the resident trip situation;

Step (2) is extracted the data survey result of resident's trip on the one, and the bicycle mode is gone on a journey and the activity chain pattern, and it is carried out the virtual and encoding operation of variable;

Step (3) inputs to the correlated variables in the activity chain pattern in multinomial logit model, correlated variables during the bicycle mode is gone on a journey inputs in binomial logit model, and the bicycle mode is gone on a journey and the activity chain pattern is analyzed after mutual, calculate model result, obtain coevolution logit model;

Step (4) is carried out interative computation to the coevolution logit model result that calculates, and records that the bicycle mode is gone on a journey and the selection result of activity chain pattern, when the trip mode of all travelers select complete after, the finishing iteration computing;

Step (5) respectively the bicycle mode is gone on a journey and the selection result of activity chain pattern is carried out statistics and analysis, and precision of prediction is analyzed.

2. a kind of selection bicycle mode based on the activity chain pattern according to claim 1 Forecasting Methodology of going on a journey is characterized in that:

In described step (1), the resident trip situation carried out data survey and arranged, count investigation result, comprising the following steps,

Step (1-1) is divided traffic zone, adopts random sampling visit to the parents of schoolchildren or young workers survey, provides questionnaire in residential quarter population ratio;

Step (1-2) is determined situational variables;

Step (1-3) gathers variable data.

3. a kind of selection bicycle mode based on the activity chain pattern according to claim 2 Forecasting Methodology of going on a journey is characterized in that:

Described step (1-1) is to step (1-3), at first define main activities, activity chain pattern and method for expressing in sample, then adopt the random sampling visit to the parents of schoolchildren or young workers to carry out survey, provide questionnaire in residential quarter population ratio, content comprises traveler personal feature, family's feature, the trip activity of exemplary operation day and selects which kind of mode of transportation trip, individual attribute comprises traveler sex, age and occupation, family's feature comprises family structure, family income, and the trip attribute comprises soil feature, trip distance.

4. a kind of selection bicycle mode based on the activity chain pattern according to claim 1 Forecasting Methodology of going on a journey is characterized in that:

Activity chain pattern in described step (2) is divided into by movable number, once movable simple activities chain, twice and above movable complicated activity chain; Be divided into the survival-type activity chain take trip as purpose, with the non-survival-type activity chain of amusement and recreation purpose with contain the mixed type activity chain of two kinds of trip purposes by the trip purpose; Virtual and the encoding operation of the variable of activity chain pattern, hwh is simple survival-type activity chain, without other stop, hwhwh is simple survival-type activity chain, comprises based on the round stop of the trip of family, hoh for survival the type activity chain, without other stop, hohoh is non-survival-type activity chain, repeatedly returns home, hwh+o is for simply going on a journey, comprise non-survival-type activity chain, wherein the bicycle mode is gone on a journey and is, use bicycle and do not use bicycle, cast out at last enquiry data kind sample size less than 1% activity chain sample, obtain effective sample.

5. a kind of selection bicycle mode based on the activity chain pattern according to claim 1 Forecasting Methodology of going on a journey is characterized in that:

The hypothesis traveler has the bicycle mode to go on a journey (D1) and two kinds of activity chain patterns (D2) in described step (3), (Di ∈ D, i=1,2), respectively comprise some options and every between separate, the options of supposing the effectiveness maximum is selected, and based on the Logit model, the computing formula of the selected probability of options d is:

$P^t\left(d\right)=\frac{\exp \left[E\right\{U^{t-1}\left(d\right)\left\}\right]}{{\mathrm{\Σ}}_{d^{\′}\∈D_i}\exp \left[E\right\{U^{t-1}\left(d^{\′}\right)\left\}\right]},\∀d\∈D_i,{\∀D}_i\∈D---\left(1\right)$

Wherein, Pt (d) chooses the probability of options d, E{Ut (d) for moment t } be the expected utility of moment t options d, t is iteration cycle;

A, bicycle mode go on a journey and the activity chain pattern in have reciprocation between D1 and D2, namely a kind of selection result exerts an influence to the effectiveness of another kind of options, two kinds of utility function computing formula are:

$E\left\{U^t\left(d\right)\right\}=\underset{r}{\mathrm{\Σ}}{\mathrm{\β}}_r{X}_r+\underset{r^{\′}}{\mathrm{\Σ}}{\mathrm{\β}}_{r^{\′}}\underset{s\∈D_j}{\mathrm{\Σ}}{P}_s^t{X}_{r^{\′}}\left(d\right|s)$

$\∀d\∈D_i,{\∀D}_i\∈D,j\≠i---\left(2\right)$

Wherein, Xr is options d attribute r, and Xr ' is options d attribute r ' after given state S, and S is preference pattern Dj,

State, β is the parameter of variable X;

B, Pts for moment t state S probability of happening computing formula are:

$P_S^t=\underset{d\∈S}{\mathrm{\Π}}{P}^t\left(d\right),S\∈D_j,j\≠i---\left(3\right)$

Select the reciprocal initial probability calculation formula of item number to be in c, preference pattern Di:

$P^0\left(d\right)=\frac{1}{\left|D_i\right|},\∀d\∈D_i,{\∀D}_i\∈D---\left(4\right).$

6. a kind of selection bicycle mode based on the activity chain pattern according to claim 1 Forecasting Methodology of going on a journey is characterized in that:

In described step (4), in each preference pattern of t, each options effectiveness is subject to the t-1 impact of preference pattern result constantly constantly, and each iteration cycle is last, and the uncertainty of each preference pattern Di depends on that the big or small computing formula of entropy is:

$H^t\left(D_i\right)=-\underset{d\∈D_i}{\mathrm{\Σ}}{P}^t\left(d\right)\×{\log }_2\left\{P^t\left(d\right)\right\},\∀D_i\∈D---\left(5\right)$

Wherein, introduce to adjust coefficient θ i in entropy function each decision-making branch entropy of initial time is equated, the preference pattern item correspondence of less entropy θ iHt (Di) is less has uncertainty, and at first this preference pattern item is determined and no longer changed in the successive iterations process.

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