CN103761138A - Parameter correction method for traffic simulation software - Google Patents

Parameter correction method for traffic simulation software Download PDF

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CN103761138A
CN103761138A CN201410019829.6A CN201410019829A CN103761138A CN 103761138 A CN103761138 A CN 103761138A CN 201410019829 A CN201410019829 A CN 201410019829A CN 103761138 A CN103761138 A CN 103761138A
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parameter
value
simulation
chromosome
formula
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CN103761138B (en
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成卫
肖海承
陈昱光
金成英
刘峰
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Kunming University of Science and Technology
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Kunming University of Science and Technology
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Abstract

The invention discloses a parameter correction method for traffic simulation software and belongs to the technical field of parameter correction. According to the method, parameter correction is performed on simulation software; a simulation network is built with Paramics according to actual road conditions; travel time and traffic actually measured and respective ranges are entered as correction basis; the simulation software automatically calls a program to calculate the number of times of initial evaluation simulation, and simulation is performed to determine whether the simulation software requires parameter correction or not; if correction is required, the program analyzes sensitivity of simulation model parameters to correct the parameters that have significant influence; an optimal parameter combination is retrieved by means of a genetic simulated annealing algorithm; a Modeller module is automatically called for simulation; after simulation, data is statistically counted and compared to actually measured data; if errors are unsatisfactory, simulation is performed again after automatic parameter adjustment until the errors are satisfactory, and optimized parameters are output. The method has the advantages that simulation effect is more practical and obtained results are more significant to application and practicality.

Description

A kind of parameter correcting method of traffic simulation software
Technical field
The present invention relates to a kind of parameter correcting method of traffic simulation software, belong to parameter correction technical field.
Background technology
Along with socioeconomic development, the car transportation amount especially private car volume of traffic increases sharply, transport need is along with automobile traffic amount increases sharply and increases, and the construction of urban road and means of transportation can not unconfinedly meet the demand that car increases substantially, no matter be big city or small and medium-sized cities, traffic imbalance between supply and demand sharpening, traffic congestion is day by day serious.The problem that a series of traffic problems such as traffic circulation efficiency poor efficiency, traffic accidents take place frequently, automobile exhaust pollution have become citizen's concern even becomes the key factor that restricts urban development.For extenuating urban traffic blocking and improving traffic system service efficiency, corresponding traffic improvement and management and control scheme are proposed, and traffic system is a complicated system, influence factor is many, reach is wide, very difficult validity, reliability and feasibility by knowwhy proof scheme, the enforcement of the rebuilding and expansion of the of the existing enterprises, traffic administration and the control program of traffic environment, its reach is wide, the implementation cycle long, the quality of the large and difficult more selected scheme of investment.Traffic simulation can real simulation with reproduce traffic, analyze and evaluate all kinds of traffic systems, by the data result of simulation data, carried out the ratio choosing of scheme.
The parameter calibration of Traffic Flow Simulation Models is the basis of any one traffic simulating system success, Traffic Flow Simulation Models is only after through effective demarcation, just can be for the test to various actual traffic operating schemes, its predict the outcome be only believable, the software that current widely used traffic simulation software is Introduced From Abroad mostly carries out the evaluation study of domestic communication stream operation conditions, its realistic model is the traffic simulation software of developing for external traffic stream characteristics, and the traffic characteristics of China is compared with abroad, no matter at road environment, vehicle performance or driving behavior aspect all exists certain difference, therefore before carrying out traffic simulation, need to be for the actual traffic investigation result of road network, the correlation parameter of the realistic model using is proofreaied and correct, and searches out one group of more excellent parameter combinations.
Its objective is the threshold value (being generally minimum value or zero) that makes its simulation data result and the difference of the actual observation result of check index reach setting, thereby improve the precision of realistic model, make simulation result more truly, more accurate, make micro traffic model better reflect the traffic characteristics of China's road, and the traffic circulation situation that can farthest reflect reality, for Traffic Organization design provides quantized data, it is the decision-making foundation that provides of traffic management measure.
Summary of the invention
In order overcoming, to adopt default parameter value to carry out traffic simulation and cannot simulate accurately the deficiency of traffic flow situation, to the invention provides the parameter correcting method of traffic simulation software: specifically comprise following concrete steps:
(1) initial evaluation
1. the journey time of selection actual measurement and speed are as evaluation index;
2. road network modeling: road network structure, Road width, crossing and section canalization to survey region are investigated and built emulation road network, investigation crossing and the section flow direction, section road speed, mode of transportation composition, traffic administration and control mode are inputted data as traffic simulation;
3. determining of objective function target threshold values, default value condition emulation 10 times, carries out initial simulation evaluation, by objective function (1), judges whether simulation software model needs the demarcation research of parameter;
Figure 997685DEST_PATH_IMAGE001
（1）
In formula:
Figure 260618DEST_PATH_IMAGE003
for target threshold values, value is 0.2 here,
Figure 527651DEST_PATH_IMAGE004
for evaluation index,
Figure 523289DEST_PATH_IMAGE005
for the mean value of 10 output evaluation indexes of analogue system emulation;
If target threshold values F≤0.2, says that simulation evaluation result and measured value error are little, do not need Simulation Software System parameter again to demarcate;
If 0.2 needs of target threshold values F > are demarcated again to this Simulation Software System parameter;
(2) the parameter calibration stage
1. parameter to be calibrated chooses
Adopt the method for parameters sensitivity analysis to choose required parameter of again demarcating, wherein said parameter and span thereof are: average headway is 0.3-2.6, and its default value is 1.0s; Average reaction time is 0.3-2.4, and its default value is 1.0s; Speed record is 3-8, and its default value is 2; Simulation step length is 2-5, and its default value is 3; The rate curve factor is 1.0-5.0, and its default value is 1.0; The adventurous 1-4 that is distributed as; Vigilance is distributed as 1-4; Comprise the following steps:
A, choose different emulation seeds at random and carry out 10 emulation, record the changing value of the maximum volume of traffic exported in these 10 simulation processes and the minimum volume of traffic , maximum average velocity and minimum average velocity changing value
Figure 575745DEST_PATH_IMAGE007
, wherein emulation seed random value;
B, choose a fixing emulation seed, get any number, guarantee in the situation of other parameter constants, change the value of a parameter, in span, get respectively maximal value and minimum value and carry out emulation, the volume of traffic of exporting when recording while getting maximal value and getting minimum value changes
Figure 639995DEST_PATH_IMAGE008
variation with average velocity
Figure 704903DEST_PATH_IMAGE009
, based on to the choosing of above eight parameters, same process is got different model parameter values and is carried out simulation analysis result
Figure 290605DEST_PATH_IMAGE010
......
Figure 517187DEST_PATH_IMAGE011
,
Figure 322595DEST_PATH_IMAGE012
......
Figure 863297DEST_PATH_IMAGE013
;
C,
Figure 303506DEST_PATH_IMAGE006
with
Figure 966569DEST_PATH_IMAGE014
......
Figure 429911DEST_PATH_IMAGE015
compare,
Figure 836622DEST_PATH_IMAGE007
with
Figure 898381DEST_PATH_IMAGE016
...... compare, if having ......
Figure 627805DEST_PATH_IMAGE015
in have ratio
Figure 42606DEST_PATH_IMAGE006
large or
Figure 283357DEST_PATH_IMAGE016
......
Figure 721291DEST_PATH_IMAGE017
in have ratio
Figure 469805DEST_PATH_IMAGE007
large, illustrate that this parameter needs to proofread and correct;
2. Global Genetic Simulated Annealing Algorithm model is proofreaied and correct parameter, and concrete steps are as follows:
A, coding: adopt real coding to express chromosome x, can be expressed as
Figure 739112DEST_PATH_IMAGE018
, average headway ∈ (0.3,0.4 ... 2.5,2.6); Average reflecting time
Figure 840109DEST_PATH_IMAGE020
∈ (0.3,0.4 ... 2.3,2.4); The rate curve factor
Figure 628199DEST_PATH_IMAGE020
∈ (3,4 ... 7,8), simulation step length
Figure 17592DEST_PATH_IMAGE020
∈ (2,3,4,5), the rate curve factor
Figure 364259DEST_PATH_IMAGE020
∈ (1.0,1.1 ... 4.9,5.0)
B, initialization: given initial value and chromosome is carried out to real coding, described initial value is: maximum genetic algebra
Figure 511207DEST_PATH_IMAGE021
, value 30; Annealing speed
Figure 601523DEST_PATH_IMAGE022
, value 0.9; Initial temperature
Figure 346887DEST_PATH_IMAGE023
; Temperature damping's function
Figure 802139DEST_PATH_IMAGE024
; Population size is , value 20; Space Solutions span
Figure 392706DEST_PATH_IMAGE026
;
Wherein, Space Solutions span
Figure 491112DEST_PATH_IMAGE027
as follows: average headway span (0.3,0.4 ... 2.5,2.6), average reflecting time span (0.3,0.4 ... 2.3,2.4), speed record span (3,4 ... 7,8), simulation step length span (2,3,4,5), rate curve factor span (1.0,1.1 ... 4.9,5.0);
Figure 117266DEST_PATH_IMAGE028
,
Figure 802587DEST_PATH_IMAGE029
for the average adaptive value of initial population target function value;
Figure 234706DEST_PATH_IMAGE030
, comprise 20 individualities, i.e. 20 chromosomes,
Figure 390881DEST_PATH_IMAGE031
for parameter to be calibrated;
The foundation of C, end condition
End condition: simultaneously meet
Figure 250252DEST_PATH_IMAGE032
, reach maximum genetic algebra
Figure 921405DEST_PATH_IMAGE021
two conditions;
Wherein
Figure 829318DEST_PATH_IMAGE021
be 30,
Figure 403781DEST_PATH_IMAGE033
represent infinitesimal, be the mean value of the L time each chromosome adaptation value of iteration,
Figure 592503DEST_PATH_IMAGE035
be the mean value of the L-1 time each chromosome adaptation value of iteration;
3. set up objective function and fitness function
Choose different emulation seeds and carry out emulation 5 times, the result of output is evaluated, if met, stop bar correction end, if do not meet end condition, by selection, intersection, mutation operation, calculate fitness function value;
The expression formula of objective function is as follows:
Figure 366424DEST_PATH_IMAGE036
（2）
In formula:
Figure 497191DEST_PATH_IMAGE037
be
Figure 188111DEST_PATH_IMAGE021
chromosome during inferior iteration
Figure 568277DEST_PATH_IMAGE038
the average relative error of simulation data, be
Figure 131162DEST_PATH_IMAGE021
chromosome during inferior iteration
Figure 503238DEST_PATH_IMAGE038
parameter combinations,
Figure 872165DEST_PATH_IMAGE021
for genetic iteration algebraically,
Figure 987888DEST_PATH_IMAGE040
for chromosome numbering,
Figure 827668DEST_PATH_IMAGE041
for the section number of choosing,
Figure 370645DEST_PATH_IMAGE042
for
Figure 990982DEST_PATH_IMAGE043
the section actual measurement travel time,
Figure 146282DEST_PATH_IMAGE044
for
Figure 106148DEST_PATH_IMAGE043
link traffic simulation is simulated the travel time drawing;
Fitness function expression formula is as shown in Equation (3):
Figure 820026DEST_PATH_IMAGE045
（3）
In formula:
Figure 662080DEST_PATH_IMAGE046
for fitness function value, ,
Figure 497760DEST_PATH_IMAGE048
for the parameter of fitness function, value be on the occasion of, choose at random,
Figure 320223DEST_PATH_IMAGE049
be chromosome during inferior iteration
Figure 910790DEST_PATH_IMAGE051
simulation data average error;
By above formula, calculate fitness function and calculate adaptation value, whether meet end condition, if meet end condition, proofread and correct end, if do not meet end condition, proceed to simulated annealing selection behaviour step;
4. simulated annealing is selected operation
Select operation to use simulated annealing pulling method to calculate each chromosome and be copied to follow-on probability
Figure 641986DEST_PATH_IMAGE052
, by formula (6), calculate each chromosome accumulated probability
Figure 635349DEST_PATH_IMAGE053
:
Figure 18368DEST_PATH_IMAGE055
（4）
In formula:
Figure 604071DEST_PATH_IMAGE056
be that i article of chromosome is chosen to follow-on probability,
Figure 768336DEST_PATH_IMAGE057
be i article of chromosome appropriateness functional value,
Figure 72278DEST_PATH_IMAGE058
,
Figure 176763DEST_PATH_IMAGE059
for the parameter of fitness function, get on the occasion of,
Figure 616971DEST_PATH_IMAGE060
for population chromosome number;
In formula (4) expression formula is as shown in Equation (5):
Figure 743376DEST_PATH_IMAGE062
（5）
In formula:
Figure 150087DEST_PATH_IMAGE061
for contemporary temperature,
Figure 946267DEST_PATH_IMAGE063
for initial temperature, for genetic iteration sequence number;
Chromosome accumulated probability
Figure 730869DEST_PATH_IMAGE054
expression formula is as shown in Equation (6):
Figure 613374DEST_PATH_IMAGE065
（6）
By roulette method, select new population, in interval [0,1] the inside, select an Arbitrary Digit
Figure 28175DEST_PATH_IMAGE066
if,
Figure 33040DEST_PATH_IMAGE066
≤
Figure 470975DEST_PATH_IMAGE067
, copy first individuality, if
Figure 720953DEST_PATH_IMAGE068
, copy
Figure 990260DEST_PATH_IMAGE066
individual chromosome is as colony of future generation, and this step that circulates N time, produces new colony;
5. intersect
Newly mining massively that step is obtained in 4. carried out interlace operation with arithmetic hybrid method, establishes parent chromosome and is
Figure 166026DEST_PATH_IMAGE069
,
Figure 91257DEST_PATH_IMAGE070
, the child chromosome after hybridization is that crossover probability gets 0.9 herein:
Figure 377882DEST_PATH_IMAGE071
（7）
In formula:
Figure 280459DEST_PATH_IMAGE072
it is the random number between 0 to 1;
6. variation
Adopt the chromosome after non-uniform mutation method is intersected in 5. to step to carry out mutation operation, the probability that chromosomal variation occurs
Figure 361547DEST_PATH_IMAGE073
be 0.05, the chromosome of the parent morphing is
Figure 774074DEST_PATH_IMAGE074
, the gene of the parent morphing is
Figure 864390DEST_PATH_IMAGE075
, after obtaining after variation, be
Figure 108289DEST_PATH_IMAGE076
, ,
Figure 761567DEST_PATH_IMAGE078
......
Figure 389994DEST_PATH_IMAGE079
, expression formula is:
Figure 753979DEST_PATH_IMAGE080
=[
Figure 442450DEST_PATH_IMAGE081
、
Figure 563989DEST_PATH_IMAGE082
、
Figure 497573DEST_PATH_IMAGE083
……
Figure 716064DEST_PATH_IMAGE084
] （8）
In above formula,
Figure 575436DEST_PATH_IMAGE085
change randomly following two kinds of possibilities:
Figure 184272DEST_PATH_IMAGE086
In formula: for
Figure 728965DEST_PATH_IMAGE088
the upper bound, the maximal value of parameter k span to be calibrated in step (2), for
Figure 855370DEST_PATH_IMAGE090
lower bound, i.e. the minimum value of parameter k span to be calibrated in step (2),
Figure 629291DEST_PATH_IMAGE091
be a random number that meets non-uniform Distribution in 0 ~ y, when random number is 0, wherein y is
Figure 556796DEST_PATH_IMAGE092
, when random number is 1, y is
Figure 695653DEST_PATH_IMAGE093
;
7. finish
The parameter obtaining after selecting, intersect, making a variation is input to simulation software and carries out analog simulation, if meet simultaneously
Figure 842863DEST_PATH_IMAGE094
, reach maximum genetic algebra
Figure 420475DEST_PATH_IMAGE021
two conditions, finish to proofread and correct, if do not meet, to the computing of lowering the temperature of new colony
Figure 140169DEST_PATH_IMAGE095
after, get back to step and 2. with Global Genetic Simulated Annealing Algorithm model, parameter is proofreaied and correct again, until meet end condition;
(3) parameter calibration results model output evaluation analysis
By the parameter calibration stage of step (2), select optimum parameter combinations, set it as the parameter input of realistic model, road network is simulated, if the computing formula (9) that meets evaluation index finishes the demarcation of parameter, if do not meet the parameter calibration stage that turns back to Global Genetic Simulated Annealing Algorithm, re-start the demarcation of parameter, until meet formula (9), shown in being expressed as follows of formula (9):
Figure 512244DEST_PATH_IMAGE096
≤0.2 （9）
In formula:
Figure 582969DEST_PATH_IMAGE097
for target threshold values,
Figure 698692DEST_PATH_IMAGE098
for checking index measured data,
Figure 102254DEST_PATH_IMAGE099
for proofreading and correct result output, check index mean value.
Beneficial effect of the present invention is:
(1) the invention provides a kind of simulation system parameters bearing calibration, realize that traffic simulating system parameter parameter correction is integrated, robotization, alleviated staff's workload;
(2) by after correlation parameter correction, simulated effect is closing to reality more, and the result drawing has more application and realistic meaning.
Accompanying drawing explanation
Fig. 1 is paramics Simulation Software System parameter correction flow scheme design schematic diagram;
Fig. 2 is Global Genetic Simulated Annealing Algorithm parameter optimal combinatorial search design diagram;
Fig. 3 is parameter correction surface chart schematic flow sheet.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail, but protection domain of the present invention is not limited to described content:
Embodiment 1
The parameter correcting method of traffic simulation software described in the present embodiment, choose Kunming 1, Wei Zhu arterial street, Universities Road is that survey region is carried out parameter calibration analysis and research, rationality and practicality to parameter correction flow process proposed by the invention and parameter optimal combinatorial search algorithm are carried out exemplary application, and carry out check analysis to proofreading and correct result, concrete steps are as follows:
(1) the initial evaluation stage
1. the journey time of selection actual measurement and speed are as evaluation index;
2. road network modeling: road network structure, Road width, crossing and section canalization to survey region are investigated, use paramics simulation software to build emulation road network, investigation crossing and the section flow direction, section road speed, mode of transportation composition, traffic administration and control mode are inputted data as traffic simulation;
3. determining of objective function target threshold values, default value condition emulation 10 times, carries out initial simulation evaluation, by objective function (1), judges whether simulation software model needs the demarcation research of parameter;
Figure 582914DEST_PATH_IMAGE001
Figure 203251DEST_PATH_IMAGE100
（1）
In formula: for target threshold values, value is 0.2 here,
Figure 551373DEST_PATH_IMAGE004
for evaluation index,
Figure 530830DEST_PATH_IMAGE005
for the mean value of 10 output evaluation indexes of analogue system emulation, result is as shown in following table 1 and table 2:
Table 1 Yi Er mono-street simulation result statistical form
Figure DEST_PATH_IMAGE101
Table 2 Universities Road simulation result statistical form
Select relative error absolute value to analyze simulation data result, target threshold values F > 0.2, therefore needs model to carry out parameter correction research;
2, the parameter correction stage
(1) required demarcation number chooses
After the driver behavior model in paramics is analyzed, selection is on the model parameter that affects driving behavior as initial calibration parameter, and wherein said parameter and span thereof are: average headway is 0.3-2.6, and its default value is 1.0s; Average reaction time is 0.3-2.4, and its default value is 1.0s; Speed record is 3-8, and its default value is 2; Simulation step length is 2-5, and its default value is 3; The rate curve factor is 1.0-5.0, and its default value is 1.0; The adventurous 1-4 that is distributed as; Vigilance is distributed as 1-4; Comprise the following steps:
A, choose different emulation seeds at random and carry out 10 emulation, record the changing value of the maximum volume of traffic exported in these 10 simulation processes and the minimum volume of traffic
Figure 331875DEST_PATH_IMAGE006
, maximum average velocity and minimum average velocity changing value
Figure 146247DEST_PATH_IMAGE007
, wherein emulation seed random value, simulation result is as follows:
The initial emulation road network running state data analytical table of table 3
Figure 31027DEST_PATH_IMAGE103
B, choose a fixing emulation seed, get any number, guarantee in the situation of other parameter constants, change the value of a parameter, in span, get respectively maximal value and minimum value and carry out emulation, the volume of traffic of exporting when recording while getting maximal value and getting minimum value changes
Figure 360377DEST_PATH_IMAGE008
variation with average velocity
Figure 559277DEST_PATH_IMAGE009
, based on to the choosing of above eight parameters, same process is got different model parameter values and is carried out simulation analysis result
Figure 791938DEST_PATH_IMAGE010
......
Figure 847618DEST_PATH_IMAGE011
,
Figure 601948DEST_PATH_IMAGE012
......
Figure 401276DEST_PATH_IMAGE013
;
C,
Figure 986978DEST_PATH_IMAGE006
with
Figure 416823DEST_PATH_IMAGE014
......
Figure 210511DEST_PATH_IMAGE015
compare,
Figure 547952DEST_PATH_IMAGE007
with
Figure 253740DEST_PATH_IMAGE016
......
Figure 651223DEST_PATH_IMAGE017
compare, if having
Figure 943926DEST_PATH_IMAGE014
......
Figure 85057DEST_PATH_IMAGE015
in have ratio
Figure 583035DEST_PATH_IMAGE006
large or
Figure 416999DEST_PATH_IMAGE016
......
Figure 429954DEST_PATH_IMAGE017
in have ratio
Figure 876241DEST_PATH_IMAGE007
large, illustrate that this parameter needs to proofread and correct, in simulation process, the parameter that simulation result is exerted an influence and mobility scale as shown in the following chart:
The factor analysis of table 4 driver driving behavior parameter influence
Figure 228725DEST_PATH_IMAGE104
Default value in analysis result after each parameter change and upper table 4 is analyzed, as shown in following table:
Table 5 parameter mobility scale comparative analysis table
Figure 233590DEST_PATH_IMAGE105
Based on the above sensitivity analysis to model parameter value, with Global Genetic Simulated Annealing Algorithm model, average headway, average reflecting time, the rate curve factor, simulation step length, 5 parameters of speed record to be proofreaied and correct, concrete steps are as follows:
A, coding: adopt real coding to express chromosome x, can be expressed as
Figure 733842DEST_PATH_IMAGE106
, average headway
Figure 216776DEST_PATH_IMAGE019
∈ (0.3,0.4 ... 2.5,2.6); Average reflecting time
Figure 253127DEST_PATH_IMAGE020
∈ (0.3,0.4 ... 2.3,2.4); The rate curve factor ∈ (3,4 ... 7,8), simulation step length
Figure 354124DEST_PATH_IMAGE020
∈ (2,3,4,5), the rate curve factor
Figure 640749DEST_PATH_IMAGE020
∈ (1.0,1.1 ... 4.9,5.0)
B, initialization: given initial value and chromosome is carried out to real coding, described initial value is: maximum genetic algebra , value 30; Annealing speed
Figure 612695DEST_PATH_IMAGE022
, value 0.9; Initial temperature
Figure 25222DEST_PATH_IMAGE023
; Temperature damping's function ; Population size is
Figure 177855DEST_PATH_IMAGE025
, value 20; Space Solutions span
Figure 421754DEST_PATH_IMAGE026
;
Wherein, Space Solutions span
Figure 611427DEST_PATH_IMAGE027
as follows: average headway span (0.3,0.4 ... 2.5,2.6), average reflecting time span (0.3,0.4 ... 2.3,2.4), speed record span (3,4 ... 7,8), simulation step length span (2,3,4,5), rate curve factor span (1.0,1.1 ... 4.9,5.0);
Figure 75032DEST_PATH_IMAGE108
,
Figure 703459DEST_PATH_IMAGE029
for the average adaptive value of initial population target function value;
Figure 5128DEST_PATH_IMAGE109
, comprise 20 individualities, i.e. 20 chromosomes,
Figure 428019DEST_PATH_IMAGE110
for parameter to be calibrated;
The foundation of C, end condition
End condition: simultaneously meet
Figure 877455DEST_PATH_IMAGE111
, reach maximum genetic algebra
Figure 811038DEST_PATH_IMAGE021
two conditions;
Wherein
Figure 701633DEST_PATH_IMAGE021
be 30,
Figure 561005DEST_PATH_IMAGE033
represent infinitesimal,
Figure 232158DEST_PATH_IMAGE112
be the mean value of the L time each chromosome adaptation value of iteration,
Figure 405650DEST_PATH_IMAGE113
be the mean value of the L-1 time each chromosome adaptation value of iteration;
3. set up objective function and fitness function
Choose different emulation seeds and carry out emulation 5 times, the result of output is evaluated, if met, stop bar correction end, if do not meet end condition, by selection, intersection, mutation operation, calculate fitness function value;
The expression formula of objective function is as follows:
Figure 478648DEST_PATH_IMAGE114
（2）
In formula:
Figure 446604DEST_PATH_IMAGE115
be
Figure 106518DEST_PATH_IMAGE021
chromosome during inferior iteration the average relative error of simulation data,
Figure 745627DEST_PATH_IMAGE116
be chromosome during inferior iteration
Figure 592546DEST_PATH_IMAGE038
parameter combinations,
Figure 417762DEST_PATH_IMAGE021
for genetic iteration algebraically,
Figure 403036DEST_PATH_IMAGE040
for chromosome numbering,
Figure 775111DEST_PATH_IMAGE041
for the section number of choosing, for
Figure 961559DEST_PATH_IMAGE043
the section actual measurement travel time,
Figure 863656DEST_PATH_IMAGE118
for
Figure 908098DEST_PATH_IMAGE043
link traffic simulation is simulated the travel time drawing;
Fitness function expression formula is as shown in Equation (3):
Figure 262856DEST_PATH_IMAGE119
（3）
In formula:
Figure 119953DEST_PATH_IMAGE120
for fitness function value, , for the parameter of fitness function, value be on the occasion of, choose at random,
Figure 199533DEST_PATH_IMAGE121
be
Figure 594742DEST_PATH_IMAGE122
chromosome during inferior iteration
Figure 205852DEST_PATH_IMAGE051
simulation data average error;
By above formula, calculate fitness function and calculate adaptation value, whether meet end condition, if meet end condition, proofread and correct end, if do not meet end condition, proceed to simulated annealing selection behaviour step;
4. simulated annealing is selected operation
Select operation to use simulated annealing pulling method to calculate each chromosome and be copied to follow-on probability
Figure 356211DEST_PATH_IMAGE123
, by formula (6), calculate each chromosome accumulated probability
Figure 685561DEST_PATH_IMAGE053
Figure 884461DEST_PATH_IMAGE124
:
Figure 851542DEST_PATH_IMAGE055
（4）
In formula:
Figure 172802DEST_PATH_IMAGE125
be that i article of chromosome is chosen to follow-on probability,
Figure 989448DEST_PATH_IMAGE057
be i article of chromosome appropriateness functional value, ,
Figure 312162DEST_PATH_IMAGE059
for the parameter of fitness function, get on the occasion of,
Figure 305788DEST_PATH_IMAGE060
for population chromosome number;
In formula (4)
Figure 344152DEST_PATH_IMAGE061
expression formula is as shown in Equation (5):
Figure 884854DEST_PATH_IMAGE126
（5）
In formula:
Figure 590642DEST_PATH_IMAGE061
for contemporary temperature,
Figure 988125DEST_PATH_IMAGE063
for initial temperature,
Figure 717047DEST_PATH_IMAGE064
for genetic iteration sequence number;
Chromosome accumulated probability
Figure 359643DEST_PATH_IMAGE124
expression formula is as shown in Equation (6):
Figure 919938DEST_PATH_IMAGE127
（6）
By roulette method, select new population, in interval [0,1] the inside, select an Arbitrary Digit
Figure 488322DEST_PATH_IMAGE066
if,
Figure 704540DEST_PATH_IMAGE066
≤
Figure 649362DEST_PATH_IMAGE067
, copy first individuality, if
Figure 565628DEST_PATH_IMAGE128
, copy
Figure 508176DEST_PATH_IMAGE066
individual chromosome is as colony of future generation, and this step that circulates N time, produces new colony;
5. intersect
Newly mining massively that step is obtained in 4. carried out interlace operation with arithmetic hybrid method, establishes parent chromosome and is
Figure 8427DEST_PATH_IMAGE069
,
Figure 429044DEST_PATH_IMAGE070
, the child chromosome after hybridization is that crossover probability gets 0.9 herein:
（7）
In formula:
Figure 874118DEST_PATH_IMAGE072
it is the random number between 0 to 1;
6. variation
Adopt the chromosome after non-uniform mutation method is intersected in 5. to step to carry out mutation operation, the probability that chromosomal variation occurs
Figure 363131DEST_PATH_IMAGE130
be 0.05, the chromosome of the parent morphing is
Figure 587439DEST_PATH_IMAGE074
, the gene of the parent morphing is , after obtaining after variation, be
Figure 196DEST_PATH_IMAGE076
,
Figure 475040DEST_PATH_IMAGE077
,
Figure 299776DEST_PATH_IMAGE078
......
Figure 481359DEST_PATH_IMAGE079
, expression formula is:
Figure 562710DEST_PATH_IMAGE080
=[
Figure 462533DEST_PATH_IMAGE081
、
Figure 90960DEST_PATH_IMAGE082
、
Figure 189366DEST_PATH_IMAGE131
……
Figure 379301DEST_PATH_IMAGE084
] （8）
In above formula, change randomly following two kinds of possibilities:
Figure 932959DEST_PATH_IMAGE133
In formula:
Figure 151451DEST_PATH_IMAGE134
for
Figure 10823DEST_PATH_IMAGE088
the upper bound, the maximal value of parameter k span to be calibrated in step (2),
Figure 183440DEST_PATH_IMAGE135
for
Figure 419250DEST_PATH_IMAGE090
lower bound, i.e. the minimum value of parameter k span to be calibrated in step (2),
Figure 429931DEST_PATH_IMAGE136
be a random number that meets non-uniform Distribution in 0 ~ y, when random number is 0, wherein y is
Figure 460204DEST_PATH_IMAGE137
, when random number is 1, y is ;
7. finish
The parameter obtaining after selecting, intersect, making a variation is input to simulation software and carries out analog simulation, if meet simultaneously
Figure 628459DEST_PATH_IMAGE139
, reach maximum genetic algebra
Figure 759226DEST_PATH_IMAGE021
two conditions, finish to proofread and correct, if do not meet, to the computing of lowering the temperature of new colony
Figure 960400DEST_PATH_IMAGE140
after, get back to step and 2. with Global Genetic Simulated Annealing Algorithm model, parameter is proofreaied and correct again, until meet end condition;
(3) parameter calibration results model output evaluation analysis
By the parameter calibration stage of step (2), select optimum parameter combinations, set it as the parameter input of realistic model, road network is simulated, if the computing formula (9) that meets evaluation index finishes the demarcation of parameter, if do not meet the parameter calibration stage that turns back to Global Genetic Simulated Annealing Algorithm, re-start the demarcation of parameter, until meet formula (9), shown in being expressed as follows of formula (9):
≤0.2 （9）
In formula:
Figure 855861DEST_PATH_IMAGE097
for target threshold values,
Figure 903452DEST_PATH_IMAGE142
for checking index measured data,
Figure 776992DEST_PATH_IMAGE143
for proofreading and correct result output, check index mean value.
With C language, carry out the programming of algorithm, implant in paramics analogue system it is carried out to secondary development, be combined with analogue system, complete parameter calibration, be combined with the algorithm of parameter optimum combination, the emulation that iterates, finally need find out the simulation parameters combination being consistent with real road traffic;
Finally trying to achieve best parameter group is: average criterion time headway is that average criterion time headway is 0.52s for proofreading and correct result, and average reaction time is 0.62s, and simulation step length is 4, the rate curve factor: 0.95, and speed record is 5;
Using in final argument combinatorial input analogue system as the model parameter of emulation again, emulation is carried out to Universities Road and relevant road traffic in Yi Er mono-street, evaluation result is as shown in the table:
Result contrast is proofreaied and correct in each section, table 6 Yi Er mono-street (small garden grade separation-western station grade separation)
Figure 847716DEST_PATH_IMAGE144
Result contrast is proofreaied and correct in each section, table 7 Universities Road (elder brother's Sha Lu-small garden grade separation)
Figure 963440DEST_PATH_IMAGE145
By Global Genetic Simulated Annealing Algorithm, carry out best parameter group search, proofread and correct all basic accuracy requirements meeting driving behavior parameter correction in 20% of relative error absolute value of result, validity and the feasibility of the flow configuraion and parameter correcting algorithm of this parameter correction design is described.

Claims (1)

1. a parameter correcting method for traffic simulation software, is characterized in that, comprises the steps:
(1) initial evaluation
1. the journey time of selection actual measurement and speed are as evaluation index;
2. road network modeling: road network structure, Road width, crossing and section canalization to survey region are investigated and built emulation road network, investigation crossing and the section flow direction, section road speed, mode of transportation composition, traffic administration and control mode are inputted data as traffic simulation;
3. determining of objective function target threshold values, default value condition emulation 10 times, carries out initial simulation evaluation, by objective function (1), judges whether simulation software model needs the demarcation research of parameter;
Figure 2014100198296100001DEST_PATH_IMAGE001
Figure 59905DEST_PATH_IMAGE002
（1）
In formula: for target threshold values, value is 0.2 here, for evaluation index,
Figure 2014100198296100001DEST_PATH_IMAGE005
for the mean value of 10 output evaluation indexes of analogue system emulation;
If target threshold values F≤0.2, says that simulation evaluation result and measured value error are little, do not need Simulation Software System parameter again to demarcate;
If 0.2 needs of target threshold values F > are demarcated again to this Simulation Software System parameter;
(2) the parameter calibration stage
1. parameter to be calibrated chooses
Adopt the method for parameters sensitivity analysis to choose required parameter of again demarcating, wherein said parameter and span thereof are: average headway is 0.3-2.6, and its default value is 1.0s; Average reaction time is 0.3-2.4, and its default value is 1.0s; Speed record is 3-8, and its default value is 2; Simulation step length is 2-5, and its default value is 3; The rate curve factor is 1.0-5.0, and its default value is 1.0; The adventurous 1-4 that is distributed as; Vigilance is distributed as 1-4; Comprise the following steps:
A, choose different emulation seeds at random and carry out 10 emulation, record the changing value of the maximum volume of traffic exported in these 10 simulation processes and the minimum volume of traffic
Figure 535677DEST_PATH_IMAGE006
, maximum average velocity and minimum average velocity changing value
Figure 2014100198296100001DEST_PATH_IMAGE007
, wherein emulation seed random value;
B, choose a fixing emulation seed, get any number, guarantee in the situation of other parameter constants, change the value of a parameter, in span, get respectively maximal value and minimum value and carry out emulation, the volume of traffic of exporting when recording while getting maximal value and getting minimum value changes
Figure 472540DEST_PATH_IMAGE008
variation with average velocity
Figure 2014100198296100001DEST_PATH_IMAGE009
, based on to the choosing of above eight parameters, same process is got different model parameter values and is carried out simulation analysis result
Figure 193109DEST_PATH_IMAGE010
......
Figure DEST_PATH_IMAGE011
,
Figure 481002DEST_PATH_IMAGE012
......
Figure DEST_PATH_IMAGE013
;
C,
Figure 446684DEST_PATH_IMAGE006
with
Figure 67415DEST_PATH_IMAGE014
......
Figure DEST_PATH_IMAGE015
compare,
Figure 132454DEST_PATH_IMAGE007
with
Figure 32277DEST_PATH_IMAGE016
......
Figure DEST_PATH_IMAGE017
compare, if having
Figure 909972DEST_PATH_IMAGE014
......
Figure 211640DEST_PATH_IMAGE015
in have ratio
Figure 509897DEST_PATH_IMAGE006
large or
Figure 834699DEST_PATH_IMAGE016
......
Figure 643649DEST_PATH_IMAGE017
in have ratio
Figure 471928DEST_PATH_IMAGE007
large, illustrate that this parameter needs to proofread and correct;
2. Global Genetic Simulated Annealing Algorithm model is proofreaied and correct parameter, and concrete steps are as follows:
A, coding: adopt real coding to express chromosome x, can be expressed as
Figure 206665DEST_PATH_IMAGE018
, average headway
Figure DEST_PATH_IMAGE019
∈ (0.3,0.4 ... 2.5,2.6); Average reflecting time ∈ (0.3,0.4 ... 2.3,2.4); The rate curve factor
Figure 300578DEST_PATH_IMAGE020
∈ (3,4 ... 7,8), simulation step length ∈ (2,3,4,5), the rate curve factor
Figure 154582DEST_PATH_IMAGE020
∈ (1.0,1.1 ... 4.9,5.0)
B, initialization: given initial value and chromosome is carried out to real coding, described initial value is: maximum genetic algebra
Figure DEST_PATH_IMAGE021
, value 30; Annealing speed
Figure 627545DEST_PATH_IMAGE022
, value 0.9; Initial temperature
Figure DEST_PATH_IMAGE023
; Temperature damping's function
Figure 276832DEST_PATH_IMAGE024
; Population size is , value 20; Space Solutions span
Figure 79703DEST_PATH_IMAGE026
;
Wherein, Space Solutions span
Figure DEST_PATH_IMAGE027
as follows: average headway span (0.3,0.4 ... 2.5,2.6), average reflecting time span (0.3,0.4 ... 2.3,2.4), speed record span (3,4 ... 7,8), simulation step length span (2,3,4,5), rate curve factor span (1.0,1.1 ... 4.9,5.0);
Figure 592461DEST_PATH_IMAGE028
,
Figure DEST_PATH_IMAGE029
for the average adaptive value of initial population target function value;
Figure 113573DEST_PATH_IMAGE030
, comprise 20 individualities, i.e. 20 chromosomes,
Figure DEST_PATH_IMAGE031
for parameter to be calibrated;
The foundation of C, end condition
End condition: simultaneously meet
Figure 728401DEST_PATH_IMAGE032
, reach maximum genetic algebra
Figure 713674DEST_PATH_IMAGE021
two conditions;
Wherein
Figure 961116DEST_PATH_IMAGE021
be 30, represent infinitesimal,
Figure 907206DEST_PATH_IMAGE034
be the mean value of the L time each chromosome adaptation value of iteration,
Figure DEST_PATH_IMAGE035
be the mean value of the L-1 time each chromosome adaptation value of iteration;
3. set up objective function and fitness function
Choose different emulation seeds and carry out emulation 5 times, the result of output is evaluated, if met, stop bar correction end, if do not meet end condition, by selection, intersection, mutation operation, calculate fitness function value;
The expression formula of objective function is as follows:
Figure 334514DEST_PATH_IMAGE036
（2）
In formula:
Figure DEST_PATH_IMAGE037
be
Figure 111978DEST_PATH_IMAGE021
chromosome during inferior iteration
Figure 530321DEST_PATH_IMAGE038
the average relative error of simulation data,
Figure DEST_PATH_IMAGE039
be chromosome during inferior iteration parameter combinations,
Figure 688660DEST_PATH_IMAGE021
for genetic iteration algebraically,
Figure 277904DEST_PATH_IMAGE040
for chromosome numbering,
Figure DEST_PATH_IMAGE041
for the section number of choosing,
Figure 697122DEST_PATH_IMAGE042
for
Figure DEST_PATH_IMAGE043
the section actual measurement travel time, for
Figure 516490DEST_PATH_IMAGE043
link traffic simulation is simulated the travel time drawing;
Fitness function expression formula is as shown in Equation (3):
Figure DEST_PATH_IMAGE045
（3）
In formula: for fitness function value,
Figure DEST_PATH_IMAGE047
,
Figure 186080DEST_PATH_IMAGE048
for the parameter of fitness function, value be on the occasion of, choose at random,
Figure DEST_PATH_IMAGE049
be
Figure 322663DEST_PATH_IMAGE050
chromosome during inferior iteration simulation data average error;
By above formula, calculate fitness function and calculate adaptation value, whether meet end condition, if meet end condition, proofread and correct end, if do not meet end condition, proceed to simulated annealing selection behaviour step;
4. simulated annealing is selected operation
Select operation to use simulated annealing pulling method to calculate each chromosome and be copied to follow-on probability , by formula (6), calculate each chromosome accumulated probability
Figure DEST_PATH_IMAGE053
:
（4）
In formula:
Figure 988502DEST_PATH_IMAGE056
be that i article of chromosome is chosen to follow-on probability,
Figure DEST_PATH_IMAGE057
be i article of chromosome appropriateness functional value,
Figure 102345DEST_PATH_IMAGE058
,
Figure DEST_PATH_IMAGE059
for the parameter of fitness function, get on the occasion of, for population chromosome number;
In formula (4)
Figure DEST_PATH_IMAGE061
expression formula is as shown in Equation (5):
Figure 367159DEST_PATH_IMAGE062
（5）
In formula:
Figure 343206DEST_PATH_IMAGE061
for contemporary temperature,
Figure DEST_PATH_IMAGE063
for initial temperature,
Figure 759274DEST_PATH_IMAGE064
for genetic iteration sequence number;
Chromosome accumulated probability
Figure 340429DEST_PATH_IMAGE054
expression formula is as shown in Equation (6):
Figure DEST_PATH_IMAGE065
（6）
By roulette method, select new population, in interval [0,1] the inside, select an Arbitrary Digit
Figure 126462DEST_PATH_IMAGE066
if,
Figure 793066DEST_PATH_IMAGE066
≤ , copy first individuality, if
Figure 809564DEST_PATH_IMAGE068
, copy
Figure 245224DEST_PATH_IMAGE066
individual chromosome is as colony of future generation, and this step that circulates N time, produces new colony;
5. intersect
Newly mining massively that step is obtained in 4. carried out interlace operation with arithmetic hybrid method, establishes parent chromosome and is
Figure DEST_PATH_IMAGE069
,
Figure 125193DEST_PATH_IMAGE070
, the child chromosome after hybridization is that crossover probability gets 0.9 herein:
（7）
In formula:
Figure 279094DEST_PATH_IMAGE072
it is the random number between 0 to 1;
6. variation
Adopt the chromosome after non-uniform mutation method is intersected in 5. to step to carry out mutation operation, the probability that chromosomal variation occurs
Figure DEST_PATH_IMAGE073
be 0.05, the chromosome of the parent morphing is
Figure 538431DEST_PATH_IMAGE074
, the gene of the parent morphing is
Figure DEST_PATH_IMAGE075
, after obtaining after variation, be
Figure 828598DEST_PATH_IMAGE076
,
Figure DEST_PATH_IMAGE077
, ......
Figure DEST_PATH_IMAGE079
, expression formula is:
Figure 520665DEST_PATH_IMAGE080
=[ 、
Figure 816648DEST_PATH_IMAGE082
、
Figure DEST_PATH_IMAGE083
……
Figure 666049DEST_PATH_IMAGE084
] （8）
In above formula,
Figure DEST_PATH_IMAGE085
change randomly following two kinds of possibilities:
Figure 326969DEST_PATH_IMAGE086
In formula:
Figure DEST_PATH_IMAGE087
for
Figure 688417DEST_PATH_IMAGE088
the upper bound, the maximal value of parameter k span to be calibrated in step (2),
Figure 2014100198296100001DEST_PATH_IMAGE089
for
Figure 788092DEST_PATH_IMAGE090
lower bound, i.e. the minimum value of parameter k span to be calibrated in step (2),
Figure DEST_PATH_IMAGE091
be a random number that meets non-uniform Distribution in 0 ~ y, when random number is 0, wherein y is
Figure 52851DEST_PATH_IMAGE092
, when random number is 1, y is
Figure DEST_PATH_IMAGE093
;
7. finish
The parameter obtaining after selecting, intersect, making a variation is input to simulation software and carries out analog simulation, if meet simultaneously
Figure 714033DEST_PATH_IMAGE094
, reach maximum genetic algebra
Figure 1926DEST_PATH_IMAGE021
two conditions, finish to proofread and correct, if do not meet, to the computing of lowering the temperature of new colony
Figure DEST_PATH_IMAGE095
after, get back to step and 2. with Global Genetic Simulated Annealing Algorithm model, parameter is proofreaied and correct again, until meet end condition;
(3) parameter calibration results model output evaluation analysis
By the parameter calibration stage of step (2), select optimum parameter combinations, set it as the parameter input of realistic model, road network is simulated, if the computing formula (9) that meets evaluation index finishes the demarcation of parameter, if do not meet the parameter calibration stage that turns back to Global Genetic Simulated Annealing Algorithm, re-start the demarcation of parameter, until meet formula (9), shown in being expressed as follows of formula (9):
Figure 200564DEST_PATH_IMAGE096
≤0.2 （9）
In formula:
Figure DEST_PATH_IMAGE097
for target threshold values,
Figure 319829DEST_PATH_IMAGE098
for checking index measured data,
Figure DEST_PATH_IMAGE099
for proofreading and correct result output, check index mean value.
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