CN102708283A - Modeling and simulating method for travelling times of residents based on travelling chain - Google Patents

Abstract

The invention relates to a modeling and simulating method for travelling times of residents based on a travelling chain. The method comprises the following steps: firstly, establishing basis databases including ages, incomes and the like of travelers; secondly, carrying out daily travelling activity simulation on the current travelers; carrying out binomial logistics regression modeling on students, carrying out Poisson distribution modeling on old people, and carrying out tree-based decilog modeling; and finally, generating a daily travelling record of all the travelers and counting the travelling times. Compared with the prior art, the method applies city macroscopic society economic data to microcosmic individual travelling activities; the data is easy to obtain, and the reliability of a result is high.

Description

Resident trip number of times modeling and simulation method based on Trip chain

Technical field

The present invention relates to modeling and simulation, especially based on the thought of Trip chain to the resident trip number of times.Belong to the demand forecast part in the traffic programme field.

Background technology

The number of times of going on a journey per capita mainly reflects resident trip demand and trip ability, is the trip situation and the important indicator of estimating the Urban Residential Trip desirability of weighing the city dweller.In traditional traffic forecast stage, the trip number of times also is usually used in the generation traffic attraction of estimation range trip.Experts and scholars are main to the research of trip number of times or with qualitative analysis both at home and abroad at present, and quantitative examination mainly is to adopt mathematical statistic method.External researcher has also done a large amount of research to the trip number of times.It is main different stressing in the recurrence of macroscopic effects factor, cluster analysis with domestic research, and external research emphasis is aspect resident trip chain characteristic and the trip of simulation go off daily chain.Yet the equation difference opposite sex that obtains through methods such as recurrence, clusters in the different documents is bigger.On the other hand, the required personal feature data of non-collection meter model are difficult to obtain.

Summary of the invention

In order to overcome the deficiency of existing method, the present invention applies to bulk parameters such as population structure, revenue and expenditure in the non-collection meter model of microcosmic, and the resident trip chain is carried out emulation, thereby is on average gone on a journey number of times.

City dweller's sunrise places per capita number is relevant with the urban society level of economic development, population structure, city size, trip purpose, mode and time and traffic environment.Different times go on a journey the per capita change of number of times in same city enlarges owing to economic development, population growth and city size on the one hand; The influence that changed by resident's trip mode, purpose: urban population is more, scale more greatly, the average trip distance of resident is just far away more, the number of times of going on a journey accordingly is just low more; And along with expanding economy, the income of residents and urban mobile level improve thereupon, cause the trip proportion of travelling frequently to descend, and the motor vehicle trip proportion rises, and this has improved the number of times of going on a journey per capita to a certain extent again.

Change influence through can react these preferably to the research of Trip chain to the trip number of times.Trip chain is from family, the chain of the sealing that the caused a series of trip of a series of activities of finally going back home is again constituted, and it has portrayed individual travel behaviour visually.This Trip chain possibility that is starting point with the family, finally goes back home again is more than one in one day, therefore is divided into main Trip chain and less important Trip chain.Generally speaking, according to " time is distributed maximum principle " consuming time maximum activity of participating in one day is defined as main activities, and Trip chain that should the activity place is defined as main Trip chain, other Trip chain are less important Trip chain.Travel frequently for the trip activity for the city dweller, working is generally the main activities in a day, therefore. and the chain of travelling frequently at working place just be main chain. and other Trip chain then are less important chain.

Consider China the retired and driver's age limit; The present invention is divided into four types with the traveler among the city dweller: 7 years old to 18 years old student; 19 to 60 years old the male sex, 19 to 55 years old women, remaining male sex and women more than 55 years old more than 60 years old is referred to as the old man.Consider that non-collection meter model is difficult to obtain for traveler individual difference data; The individual parameter of choosing in the Trip chain preference pattern of the present invention all comes from the city macro-data, and wherein fixed data comprises: sex, age, income, whether have private car and choice for traveling time first; Variable data comprises: can arrange expense, time and trip number of times.

For the student, suppose all to go to school, except that going to school, there are not other purpose trips; Consider whether go home noon; Its probability is relevant to the travel time of school with family, adopts binomial logistics to return (Rogers's enlightening gram returns), and student's sunrise places number is 2 times or 4 times.

For the old man, their trip receives the restriction of physical efficiency maximum, only considers the time that trip is spent, and the number of times of going on a journey the same day is many more, selects the probability of trip low more next time, and therefore can old man's trip probability be regarded as and meet Poisson distribution,

For the employed, trip purpose is divided into working, does shopping and goes home, and wherein shopping can produce consumption, and consumption figure is relevant with personal income, when surpassing the regular hour or lacking the expense that is used for next time going on a journey, and no longer trip.Employed's income is high more, and the feasibility that has private car is big more.

Based on accompanying drawing 1, whether the employed, selects to go out for going out beginning-of-line with family, does not then face selection once more through certain hour if select; If select to be, then face rigidity trip (working) and elasticity trip (shopping), wherein this selection limb of working trip has the regular hour window.Can whether go home based on the selection of time from the family to the job site at noon in addition, if go home then to increase by 2 trips.Come off duty from the work place and can select to go home or do shopping, equally from family also can directness to the shop, after the shop has consumed regular hour and cost; Select whether to stop, stop and then continue consumption, leave and then transfer to other shops or go home; Finally go back home, finish one day trip.

Whenever traveler through a displacement, increase the number of times of once going on a journey, wherein whether go on a journey and selection probability and traveler sex, the age of the terminal point of going on a journey, whether have private car, time, income, the current costs associated of arranging.Consider the level of decision-making, the present invention adopts the maximization of utility theoretical description employed's choice for traveling behavior, and when certain is big more to the effectiveness difference of selecting limb, the selection limb selected probability that then effectiveness is bigger is also high more.

Using multinomial Logit (decilog) model is to separate to collect the simple effective method of meter problem by no means; Yet when selecting between limb similarity to be arranged; Similar famous " red-blue bus antinomy " such IIA (Independence form Irrelevant Alternatives, the separate characteristic of irrelevant selection scheme) problem will occur, therefore can cause overrate to have the selection limb crowd of similarity; And peg model parameter by error causes the problem of prediction deviation.Based on this, the present invention adopts NL (Nested-Logit, tree-shaped decilog) model.

The technical scheme that the present invention adopts is specific as follows:

Resident trip number of times modeling and simulation method based on Trip chain is characterized in that this method may further comprise the steps:

1) at first sets up city dweller's social-economic base database;

2) carry out the trip activity emulation of traveler day;

3) generate the trip report, calculate average trip number of times.

Preferably, wherein:

Database creation process in the said step 1) is following:

11) at first, confirm research range,, add up local correlation parameter, comprise owning rate speed, expense, the comfort level of sex, age, structure of earnings and each trip mode according to social economy's present situation or prediction index;

12) produce n traveler at random according to statistical distribution, n increases with the raising that model accuracy requires;

Said step 2) the traveler day trip activity simulation process in is following:

21) confirm people's heap sort that this traveler is affiliated, i.e. student, old man or the employed;

22) adopt binomial logistics regression modeling for the student, adopt the Poisson distribution modeling, directly obtain the number of times of going on a journey for the old man;

23), need whether go on a journey according to the NL Model Selection and trip purpose ground according to current time, place and disposable income, till not satisfying the condition of trip once more for the employed;

Generation trip report in the said step 3), it is following to calculate average trip number process:

31) with the total trip number of times summation of n traveler and ask the arithmetic mean value, the number of times of on average being gone on a journey;

32) for the employed, can obtain other reference datas, comprise that the time of its trip distributes, expense.

External researcher has also done a large amount of research to the trip number of times.Domestic existing research to the trip number of times stresses to return in the macroscopic effects factor, cluster analysis is that main, different regression equation otherness is bigger, and the result who obtains is also different, does not have versatility.External research emphasis but is that the required personal feature data of non-collection meter model are difficult to obtain aspect resident trip chain characteristic and the trip of simulation go off daily chain.The present invention considers these two limitations and not enough, is intended to bulk parameters such as population structure, revenue and expenditure are applied in the non-collection meter model of microcosmic, the resident trip chain is carried out emulation, thereby on average gone on a journey number of times.Compared with prior art, through city macro society economic data being applied in the individual trip activity of microcosmic, analog simulation then, data are obtained easily, reliable results degree height.

Description of drawings

Fig. 1 is the Trip chain structural representation;

Fig. 2 is based on the resident trip number of times modeling and simulation method flow diagram of Trip chain.

Embodiment

As shown in Figure 2, the resident trip number of times modeling and simulation method based on Trip chain comprises the steps:

Step1: the non-variable element initialization of traveler, generate the unit traveler sex, age, income, whether have private car and choice for traveling time first;

Step2:, then draw according to student or old man's trip number of times model respectively and go on a journey number of times and finish an emulation if traveler be student or old man; If traveler is the adult, put the current time and be choice for traveling time first, get into next step;

If student: consider whether go home noon, its probability is relevant to the travel time t of school with family, adopts binomial logistics to return, and student's sunrise places number is 2 times or 4 times.

T=2×(1+x)，x={0,1} (1)

s.t. $\ln \frac{P(x=0)}{1-P(x=0)}=b_0+b_{1}t---\left(2\right)$

(the corresponding illustrated in table 1 of variable, down together)

If old man: their trip receives the restriction of physical efficiency maximum, only considers the time that trip is spent, and the number of times of going on a journey the same day is many more, selects the probability of trip low more next time, therefore can old man's trip probability be regarded as and meet Poisson distribution, promptly

$P(T=k)=\frac{{\mathrm{\λ}}^k}{k!}{e}^{-\mathrm{\λ}},$ k=1,2,… (3)

Step3: carry out the employed's odd-numbered day trip emulation.If the current time is not satisfied travel requirement with the expense of can arranging, get into step5; Otherwise; According to the traveler position, bring the non-variable element of adult traveler that generates among the step1 into respective formula, obtain the effectiveness of its each item choice for traveling limb of traveler that is in the different location; Utilize the NL model respectively to be selected the limb probability again, and carry out choice for traveling;

Not student or old man, promptly get into step3, be the employed from step2:

◆ be in:

Work as T _Begin≤t≤t _Wdl, promptly the current time surpasses working selection limb time window lower limit, and when possessing the possibility of working trip, one-level choice for traveling limb comprises go out (move) and stay in (stay), and secondary choice for traveling limb comprises work (work) and shopping (shop).

$V_{\mathrm{move}}-V_{\mathrm{stay}}={\mathrm{\α}}_1{e}^{{\∂}_{2}g}(a^2+{\mathrm{\α}}_{3}a+{\mathrm{\α}}_4)---\left(4\right)$

$V_{\mathrm{work}}-V_{\mathrm{shop}}={\mathrm{\β}}_1{e}^{{\mathrm{\β}}_{2}g}(a^2+{\mathrm{\β}}_{3}a+{\mathrm{\β}}_4)---\left(5\right)$

Work as t _Wdl≤t≤T _End, promptly the current time has surpassed working and has selected limb time window lower limit, and trip purpose can only be shopping, and the choice for traveling limb of this time comprises and goes out shopping (shop) and stay in.

Whether go out shopping except receiving sex, age effects, economic conditions and time more can determine its effectiveness.Have private car, the time, the more current expense of arranging was higher than more with income, selects the probability of trip big more, and can see the exponential function of time, expense income ratio as, made up formula (6) in view of the above.

$V_{\mathrm{shop}}-V_{\mathrm{stay}}={\mathrm{\γ}}_1{e}^{{\mathrm{\γ}}_{2}g+{\mathrm{\γ}}_{3}c}({\mathrm{\γ}}_4{a}^2+{\mathrm{\γ}}_{5}a+{\mathrm{\γ}}_6){\left(\frac{m}{i}\right)}^{{\mathrm{\γ}}_7}{(t_{\mathrm{end}}-t)}^{{\mathrm{\γ}}_8}---\left(6\right)$

◆ in the work place:

Choice for traveling limb after the work comprises shopping (shop) and go home (home), and its influence factor mainly contains sex (need buy vegetables as the women is more), age (whether need meet child), whether private car and economic conditions are arranged.Here each variable adopts and the identical distributional pattern of formula (6), makes up formula (7) in view of the above.

$V_{\mathrm{shop}}-V_{\mathrm{home}}={\mathrm{\μ}}_1{e}^{{\mathrm{\μ}}_{2}g+{\mathrm{\μ}}_{3}c}({\mathrm{\μ}}_4{a}^2+{\mathrm{\μ}}_{5}a+{\mathrm{\μ}}_6){\left(\frac{m}{i}\right)}^{{\mathrm{\μ}}_7}---\left(7\right)$

◆ in the shop:

After once shopping finishes; Whether traveler is selected to go home; If select not; Select whether to be transferred to other shops or stop once more; Be that one-level choice for traveling limb comprises shopping (shop) and go home (home), secondary choice for traveling limb comprises and goes to other shops (moveshop) and stop (stay).

Whether go home with sex, age, current time, whether have private car, economic conditions relevant with the accumulative total shopping-time; General; Time is more late, and the age is big more, does not have private car, currently arranges that expense is low than more with income, totally the long more women of shopping-time is more prone to go home.Under the prerequisite that continues shopping, whether change the effectiveness difference in shop and should obey the Poisson distribution of the number of having gone window-shopping.Make up formula (8) in view of the above to formula (9).

$V_{\mathrm{shop}}-V_{\mathrm{home}}=v_1{e}^{v_{2}g+v_{3}c}({\mathrm{\γ}}_{4}a+{\mathrm{\γ}}_5){\left(\frac{m}{i}\right)}^{{\mathrm{\γ}}_6}{(t_{\mathrm{end}}-t)}^{v_6}{t}_{\mathrm{shop}}^{v_7}---\left(8\right)$

$V_{\mathrm{moveshop}}-V_{\mathrm{stay}}={\mathrm{\τ}}_1\frac{{\mathrm{\τ}}_2^n}{n!}{e}^{-{\mathrm{\τ}}_2},n=\mathrm{1,2},...---\left(9\right)$

Step4: with the travel time and each the trip terminal point stop period sum be simulation step length, upgrades the current expense of arranging, the propelling simulation time, return step3;

Step5: get back to step1, circulation k time, k is the emulation number;

Step6: generate the trip report, analyze simulation result, with total trip number of times summation of k traveler and ask the arithmetic mean value, calculate the number of times of on average going on a journey.For the employed, can also obtain other reference datas through analysis, like the time distribution of its trip, expense etc.

The explanation of table 1 model variable

Variable	Explanation
		T	The trip number of times
t	Current time
		x	The 0-1 variable
t wdl	Limb time window lower bound is selected in working
		t begin、t end	The upper and lower boundary of choice for traveling limb time window
t shop、n	Accumulative total shopping-time and shop number
		V	Select limb effectiveness
g	Sex, man=1, woman=0
		a	Age
m	The current expense of arranging
		c	Whether have private car, be=1, not=0
i	Income
		b，λ，α,β,γ,μ,v,τ	Undetermined parameter

Claims (4)

1. based on the resident trip number of times modeling and simulation method of Trip chain, it is characterized in that this method may further comprise the steps:

1) at first sets up city dweller's social-economic base database;

2) carry out the trip activity emulation of traveler day;

3) generate the trip report, calculate average trip number of times.

2. the resident trip number of times modeling and simulation method based on Trip chain according to claim 1 is characterized in that the database creation process in the described step 1) is following:

11) at first, confirm research range,, add up local correlation parameter, comprise owning rate speed, expense, the comfort level of sex, age, structure of earnings and each trip mode according to social economy's present situation or prediction index;

12) produce n traveler at random according to statistical distribution, n increases with the raising that model accuracy requires.

3. the resident trip number of times modeling and simulation method based on Trip chain according to claim 1 is characterized in that described step 2) in traveler day trip activity simulation process following:

21) confirm people's heap sort that this traveler is affiliated, i.e. student, old man or the employed;

22) adopt binomial logistics regression modeling for the student, adopt the Poisson distribution modeling, directly obtain the number of times of going on a journey for the old man;

23), need whether go on a journey according to the NL Model Selection and trip purpose ground according to current time, place and disposable income, till not satisfying the condition of trip once more for the employed.

4. the resident trip number of times modeling and simulation method based on Trip chain according to claim 1 is characterized in that, the generation trip report in the described step 3), and it is following to calculate average trip number process:

31) with the total trip number of times summation of n traveler and ask the arithmetic mean value, the number of times of on average being gone on a journey;

32) for the employed, obtain other reference datas, comprise that the time of its trip distributes, expense.

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