CN113592246A - PCA-TOPSIS model-based public traffic line network evaluation method in road construction period - Google Patents

PCA-TOPSIS model-based public traffic line network evaluation method in road construction period Download PDF

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CN113592246A
CN113592246A CN202110756109.8A CN202110756109A CN113592246A CN 113592246 A CN113592246 A CN 113592246A CN 202110756109 A CN202110756109 A CN 202110756109A CN 113592246 A CN113592246 A CN 113592246A
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赵瑞松
邵俊豪
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Abstract

The invention discloses a PCA-TOPSIS model-based public traffic network evaluation method in a road construction period, which comprises the following steps: step 1: constructing a comprehensive evaluation system of a public traffic network; step 2: acquiring index parameters of each scheme evaluation system; and step 3: establishing an initial decision matrix, and carrying out standardization processing to obtain a standardized decision matrix; and 4, step 4: considering subjective and objective factors, establishing a comprehensive weight model; and 5: synthesizing and simplifying data information by a PCA method depending on objective data characteristics, and establishing a PCA-TOPSIS analysis model; step 6: and processing the standardized decision matrix according to the comprehensive weighted PCA-TOPSIS analysis model, and calculating the proximity of the bus network scheme in each construction period to obtain an optimal decision scheme. The invention provides scientific basis for solving the evaluation decision of the bus route scheme during road construction, and provides reference and use for urban traffic managers.

Description

PCA-TOPSIS model-based public traffic line network evaluation method in road construction period
Technical Field
The invention relates to the field of traffic, in particular to a PCA-TOPSIS model-based public traffic network evaluation method in a road construction period.
Background
With the development of urbanization, the upgrading and reconstruction projects of the original urban roads are more and more. In the transformation and promotion process of roads, the enclosing construction is an indispensable ring, and the traffic space of buses is often extruded, so that the passing efficiency of public transportation is greatly reduced.
In the process of road construction reconstruction and extension, most cities are mainly heavily treated on traffic jam of private cars, but operation of buses is neglected. At the present stage, the research on the bus lines during the urban road construction is less, the actual management process is mostly based on experience, and the problem of evaluation and analysis of the bus network scheme during the road construction cannot be comprehensively, objectively and scientifically solved. Meanwhile, most of the existing related benefits are bus network evaluation research based on conventional roads, and few bus network evaluation decisions are made during road construction.
Aiming at the problems, the invention provides a PCA-TOPSIS model-based evaluation and analysis method for a public traffic line network in a road construction period, provides scientific basis for solving the evaluation decision of a public traffic line scheme in the road construction period, and is used for reference and use by an urban traffic manager.
Disclosure of Invention
The invention aims to solve the technical problem of providing a PCA-TOPSIS model-based public traffic network evaluation method in the road construction period aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a PCA-TOPSIS model-based public traffic line network evaluation method in a road construction period is characterized by comprising the following steps:
step 1: aiming at the characteristics of the bus lines in the road construction period, a bus network comprehensive evaluation system with five system layers and fifteen index layers is constructed from the aspects of traffic benefit and social benefit;
step 2: according to the meaning of each index in a public transport network comprehensive evaluation system in the road construction period, a calculation method and a basis of each index parameter of the public transport network in the road construction period are given; the index parameters are quantitative index parameters and qualitative index parameters respectively, wherein each quantitative index parameter is calculated by using TRANSCAD and each index formula, and each qualitative index parameter is determined by using a fuzzy index grading mode;
and step 3: establishing an initial decision matrix according to the index parameters obtained in the step 2, and performing standardization processing to obtain a standardized decision matrix;
and 4, step 4: considering subjective and objective factors, establishing a G1 comprehensive weight model;
and 5: the PCA method depending on objective data characteristics integrates and simplifies data information, reduces uncertain factors in an evaluation process, reduces correlation among index factors, improves the processing efficiency of a TOPSIS evaluation function, and accordingly establishes a PCA-TOPSIS analysis model;
step 6: and processing the standardized decision matrix according to the comprehensive weighted PCA-TOPSIS analysis model, and calculating the proximity of the bus network scheme in each construction period to obtain an optimal decision scheme.
Further, the step 1 of the present invention includes:
wherein, the following indexes mean all indexes in a road construction traffic influence area; the road construction traffic affected zone range is an area surrounded by traffic main roads adjacent to the periphery of a construction road;
step 1.1: the bus network comprehensive evaluation system A is divided into five system layers, namely a network structure B1, a passenger transport effect B2, an implementability B3, social benefits B4 and a development effect B5;
step 1.2: the wire mesh structure B1 is divided into 5 index layers, namely, the wire mesh length C11, the wire mesh density C12, the number of transfer nodes C13, the coverage area ratio C14 and the number of connection with large-scale passenger flow distribution points C15;
step 1.3: the passenger transport effect B2 is divided into 4 index layers, namely a passenger flow section non-uniform coefficient C21, a transfer coefficient C22, a daily passenger transport total amount C23 and a line network passenger flow density C24;
step 1.4: the feasibility B3 is divided into 2 index layers, namely the engineering difficulty degree C31 and the operability of bus network adjustment C32;
step 1.5: the social benefit B4 is divided into 2 index layers, the average bus travel time is saved by C41, and the average bus travel time accounts for C42;
step 1.6: the social benefit B4 is divided into 2 index layers, traffic connection C51 and the land utilization effect around the construction road along the line C52.
Further, the step 2 of the present invention includes:
step 2.1: the wire mesh structure B1 is divided into 5 index layers, and the parameter acquisition method is as follows:
(1) c11 wire mesh length: l ═ L1+l2+…+ln；
Wherein: l is the total length of the line network in the construction affected area, m; ln is the nth line net length, m; n is the number of the line network lines;
(2) c12 construction affected zone wire mesh density:
Figure BDA0003147552530000031
wherein: d is the density of the public traffic line network in the construction affected area, km/km2(ii) a ln is the length of the nth public traffic network in the construction affected area, m; s is the area of the construction affected area, km2(ii) a n is the number of the line network lines;
(3) c13 number of transfer nodes: n ═ N1+n2+…+nn；
Wherein: n is the total number of transfer nodes of the bus net in the construction affected area; n isnThe number of points of intersection of the public traffic line network; n is the number of the line network lines;
(4)C14construction affected area wire mesh coverage area rate:
Figure BDA0003147552530000032
wherein: z is the wire mesh coverage area rate of the construction affected area; an is the coverage area of 500 meters on each side of the nth bus net in the construction affected area, km2(ii) a S is the area of the construction affected area, km2(ii) a n is the number of lines of the netAn amount;
(5) c15 number of large-scale passenger flow distribution points: m ═ M1+m2+…+mn；
Wherein: m is the number of the connection of the bus net and the large-scale passenger flow distribution points in the construction affected area; m isnThe number of the n-th public traffic network and the large-scale passenger flow distribution points is as follows: n is the number of the line network lines;
step 2.2: the passenger transport effect B2 is divided into 4 index layers, and the parameter acquisition method comprises the following steps:
(1) c21 coefficient of unevenness of passenger flow profile:
Figure BDA0003147552530000033
wherein: p is the uneven coefficient of the cross section of the passenger flow of the wire network; rn is the maximum passenger flow section value of the nth line, and is ten thousands of people; kn is the average passenger flow of the nth line, and is ten thousand people; km is the cross-section passenger flow of the mth line, and ten thousands of people; n is the number of the line network lines; m is the number of passenger flow sections of a certain line, and ten thousands of people;
(2) c22 passenger transfer coefficient:
Figure BDA0003147552530000041
wherein: e is the transfer coefficient of the bus network passengers in the construction affected area; a is the number of people going out of the public transport network, and ten thousand people; b is the number of people transferred by the public traffic line network, and ten thousand people;
(3) total passenger traffic daily C23: q ═ Q1+q2+…+qn；
Wherein: q is the total daily passenger traffic of the line network scheme, ten thousand people; q. q.snThe passenger capacity of the nth line network is ten thousands of people; n is the number of the line network lines;
(4) c24 wire mesh passenger flow density:
Figure BDA0003147552530000042
wherein: rho is the passenger flow density of the public transport network in the construction affected area, ten thousand persons/km.d; f is daily passenger capacity of a public transport network, and ten thousand people; l is the total length of the public traffic network, m;
step 2.3: the implementability B3 is divided into 2 index layers, and the obtaining method is as follows:
(1) c31 engineering difficulty degree, which is divided into: easiest, easy, easier, general, harder, difficult, hardest;
(2) c32 operability of bus net adjustment is divided into: easiest, easy, easier, general, harder, difficult, hardest;
step 2.4: the social benefit B4 is divided into 2 index layers, and the acquisition method is as follows:
(1) when the average public transport travel of the room C41 is saved, the index is obtained through a traffic prediction model, and T is the time saved by the average public transport travel of residents in min/times;
(2) c42 bus travel ratio
Figure BDA0003147552530000043
Wherein: h is the proportion of the construction road bus trip to the total bus trip in the construction road influence area; cn is the line running amount of the nth line; d is the amount of the traveler taking the bus; n is the number of the line network lines;
step 2.5: the development effect B5 is divided into 2 index layers, traffic connection C51 and the land utilization effect C52 around the construction road along the line;
(1) c51 traffic connection: the external traffic connection degree is divided into: strongest, strong, stronger, general, weaker, weak, weakest;
(2) c52 construction road is land utilization effect around along the line, divide into according to the effect: best, good, better, normal, worse, worst;
step 2.6: through TRANSCAD software, acquiring the analysis and prediction data of the public traffic network in the construction period, wherein the analysis and prediction data comprises the passenger flow density, the uneven coefficient of the passenger flow section, the average travel time saving time of the public traffic, the daily passenger capacity of the public traffic and the passenger transfer coefficient of the public traffic network in a road construction affected area, and calculating according to the meaning and formula of each index to obtain the specific numerical value of each index;
step 2.7: the fuzzy attribute index of the parameter can not be directly obtained by formula calculation, and qualitative evaluation can be converted into quantitative evaluation by utilizing the membership degree theory, so that a relatively objective, correct and actual evaluation is performed on an object which is restricted by various factors, and the actual problem of fuzziness is solved. The fuzzy index classification and magnitude determination results are shown in table 1 below.
TABLE 1 benefit fuzzy and cost fuzzy index grading List
Figure BDA0003147552530000051
Further, the step 3 of the present invention includes:
step 3.1: constructing a sample matrix according to the comprehensive evaluation system parameters of the net, and assuming that n net planning schemes x are equal to (x)1,x2,…,xn)TEach scheme has p evaluation indexes xi＝(xi1,xi2,…,xip)TWith the matrix X ═ Xij)n×p，xijIf the index value is the jth index value of the ith scheme, then:
Figure BDA0003147552530000052
step 3.2: carrying out standardization treatment according to each index attribute of the comprehensive evaluation system;
benefit type, the larger the more preferable:
Figure BDA0003147552530000053
i＝1,2,…,n，j＝1,2,…,p
cost type, the smaller the more preferable:
Figure BDA0003147552530000054
i＝1,2,…,n，j＝1,2,…,p
step 3.3: obtaining a standardized decision matrix;
Figure BDA0003147552530000061
further, the step 4 of the present invention includes:
step 4.1: subjective weighting;
(1) in the index set { x1,x2,…,xpIn the above, sorting is performed according to the importance degree of the index, and is recorded as
Figure BDA0003147552530000062
(2) Giving a comparative judgment of the relative importance degree between two adjacent indexes, i.e. rk＝αk-1/αk(ii) a The importance value is assigned with reference to table 2 below.
Table 2 assignment reference table
Figure BDA0003147552530000063
(3) And determining the calculation of the weight coefficient, namely:
Figure BDA0003147552530000064
step 4.2: objective weighting;
entropy of matrix Z is
Figure BDA0003147552530000065
θipWhen 0, then θip lnθipDegree of deviation g from information of 0i＝1-epAnd obtaining the weight:
Figure BDA0003147552530000066
step 4.3: comprehensive weight:
Figure BDA0003147552530000067
further, the step 5 of the present invention includes:
step 5.1: constructing a comprehensive weighting decision matrix;
Figure BDA0003147552530000071
step 5.2: PCA treatment;
let the nth column vector of the weighted decision matrix F be FiP vectors of the matrix F, i.e. p index vectors X1，X2，…，XpThe linear combination is:
Fi＝a1iX1+a2iX2+…+api X p 1＝1，2，…，p
note ai＝(a1i1,a2i,…,api) Satisfy ai·ai T1, then the coefficient aiIs determined by the following principle:
(1)Fi,Fj(i ≠ j, i ≠ 1,2, …, p) is irrelevant;
(2)F1is X1，X2，…，XpThe one with the greatest degree in all linear combinations of (1), then F1Is a first main component; f2Is a reaction of with F1Independent X1，X2，…，XpThe maximum equation in all linear combinations is F2The second principal component, and so on, can define the remaining principal components;
let m be the number of main components, uijFor m principal component components, considering the contribution rate of the principal components, and having a PCA decision matrix U;
U＝[uij]n×m
step 5.3: constructing a TOPSIS comprehensive evaluation function;
(1) constructing a non-negative PCA decision matrix H;
Figure BDA0003147552530000072
H＝[hij]n×p，i＝1,2,…,n，j＝1,2,…,m
(2) constructing TOPSIS function and defining positive ideal scheme h+Negative ideal scheme h-；
Figure BDA0003147552530000073
(3) The distance from each scheme to the positive and negative ideal schemes is d+、d-；
Figure BDA0003147552530000074
Step 5.4: calculating relative proximity;
Figure BDA0003147552530000075
further, the step 6 of the present invention includes:
according to the relative proximity value in step 5, the closer the value is to 1, the closer the solution n is to the ideal solution, and the optimal net solution is selected accordingly.
The PCA-TOPSIS model-based public traffic line network evaluation method in the road construction period has the following beneficial effects:
1. the invention provides a public transport network evaluation method based on a road construction period, which is different from a conventional public transport network evaluation method of a common city and is an evaluation method based on comprehensive consideration of internal and external influence factors of the network layout in a road construction influence area.
2. The invention comprehensively considers the influence of the public traffic network in the construction period on the aspects of urban traffic benefit, social benefit and the like, and constructs a multi-level and multi-target comprehensive evaluation system of five system layers and fifteen index layers for evaluating the public traffic network traffic benefit and the social benefit in the road construction period, and the evaluation system is complete, reasonable and effective.
3. The comprehensive weighting method combines experience and theory, avoids the defect that the prior weighting method can not reflect expert opinions or objective condition changes, and ensures the scientificity of index value analysis.
4. The analysis and processing of the PCA method ensure the objectivity of the data characteristics of the weighted decision matrix, reduce the correlation among index data, keep the integrity of information which can be covered by the data, reduce the data processing capacity of the TOPSIS evaluation function, and show that the model is simple, efficient, reasonable and scientific.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic flow chart of a PCA-TOPSIS model-based public transportation network evaluation method in a road construction period according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a comprehensive evaluation system of a public traffic network in a road construction period according to an embodiment of the invention;
FIG. 3 is a schematic flow chart of an integrated entitlement model according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a weighted PCA-TOPSIS evaluation analysis model according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a public transportation network according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a public transportation network in a road construction affected zone according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1 to 4, the invention provides a PCA-TOPSIS model-based public traffic network evaluation method in a road construction period, which comprises the following specific evaluation method processes:
1) aiming at different schemes provided by the public traffic network in the road construction period, the comprehensive evaluation system (shown in figure 2) of the public traffic network in the road construction period is utilized to compare index parameters of each scheme.
2) And calculating each index parameter in a public traffic network comprehensive evaluation system in the road construction period, wherein the quantitative index parameters are obtained by carrying out data prediction according to TRANSCAD software and combining formula calculation, and the qualitative index parameters are obtained by grading according to fuzzy indexes. And constructing an initial decision matrix according to the obtained quantitative and qualitative index parameters.
3) And (4) carrying out standardization processing on all index parameters according to two different attribute types of benefit type and cost type of each index, and constructing a standard decision matrix.
4) The comprehensive weighting model is constructed through subjective weighting and objective weighting, and is shown in figure 3.
5) And obtaining a comprehensive weighting decision matrix according to the comprehensive weighting model.
6) And analyzing and processing the weighted decision matrix by utilizing a PCA-TOPSIS model to obtain the relative closeness of the public traffic network scheme in each road construction period.
7) The closer the relative proximity is to 1, the closer the scheme is to the ideal scheme, and the optimal bus network scheme in the road construction period can be obtained through evaluation and decision.
In one embodiment of the invention, taking a certain urban construction road as an example, during the construction of the road, the urban public transport network is schematically shown in fig. 5, the construction area public transport network is shown in fig. 6, and four preselection schemes are provided for optimizing the construction area public transport network. Wherein, the total length of the public traffic line network in the construction area of the first scheme is 87.90km, and 3 seats of the transfer station are arranged; the total length of a public transport network in a construction area is 113.80km, and 7 seats of a transfer station are arranged; the total length of a wire net in a construction area of the third scheme is 110.3km, and 7 transfer stations are arranged; the fourth scheme is that the total length of a wire net in a construction area is as follows: 110.0km, transfer station 7 seats. In order to decide the optimal scheme, the method provided by the invention is adopted to calculate and select the optimal scheme. Fig. 1 is a PCA-TOPSIS model-based method for evaluating a public traffic network during a road construction period according to an embodiment of the present invention, wherein scheme evaluation is performed according to the flow shown in fig. 1, and an optimal scheme is selected by the present invention.
Step one, constructing a comprehensive evaluation system of a public traffic network in a road construction period.
The invention provides a comprehensive evaluation system of a public traffic network in a road construction period, which is shown in figure 2.
And step two, acquiring the index parameters of the evaluation system of each scheme.
Through TRANSCAD software, acquiring analysis and prediction data of a public traffic network in a road construction affected area, wherein the analysis and prediction data comprises data such as public traffic network passenger flow density, passenger flow section uneven coefficients, public traffic average travel time saving, public traffic daily passenger capacity, passenger transfer coefficients and the like in the road construction affected area, and acquiring fifteen index parameters of 4 schemes according to the calculation method of each index parameter of the public traffic network comprehensive evaluation system in the road construction period. The parameter values are shown in Table 3 below.
Figure BDA0003147552530000101
Step three, standardization treatment.
And performing benefit type and cost type standardization processing according to the index attributes.
Wherein the benefit type indexes are as follows: the method comprises the following steps of line network length C11, line network density C12, transfer node number C13, coverage area ratio C14, number of connections with large passenger flow collecting and distributing points C15, transfer coefficient C22, total daily passenger traffic amount C23, line network passenger flow density C24, operability of bus line network adjustment C32, bus average travel saving time C41, traffic connection C51 and land utilization effect around the construction road along the line C52.
The cost index is as follows: the passenger flow section non-uniform coefficient C21, the engineering difficulty degree C31 and the bus trip proportion C42.
A normalized decision Z is obtained:
Figure BDA0003147552530000102
and step four, acquiring a comprehensive weighting decision matrix F according to the comprehensive weighting model.
Obtaining comprehensive weight Wp and a comprehensive weighting decision matrix F according to a subjective weighting G1 method and an objective weighting entropy value method
G1 weight:
α＝(0.037 0.067 0.037 0.067 0.051 0.038 0.065 0.078 0.078 0.071 0.071 0.079 0.119 0.071 0.071)
entropy weight:
β＝(0.041 0.041 0.041 0.042 0.054 0.136 0.045 0.041 0.061 0.107 0.198 0.044 0.042 0.053 0.53)
comprehensive weight:
w＝(0.025 0.045 0.025 0.046 0.045 0.085 0.048 0.053 0.078 0.125 0.231 0.057 0.082 0.062 0.062)
Figure BDA0003147552530000111
and step five, calculating by utilizing a PCA-TOPSIS evaluation analysis model.
Firstly, PCA calculation is carried out on the comprehensive weighting decision matrix F, a decision matrix U is obtained through nonnegativity treatment,
Figure BDA0003147552530000112
performing TOPSIS analysis on the PCA decision matrix U, determining the distance between the alternative scheme and the positive and negative ideal schemes,
Figure BDA0003147552530000113
Figure BDA0003147552530000114
calculating relative proximity values of the four schemes;
d1＝0.158，d2＝0.91，d3＝0765，d4＝0.991
step seven, selecting a wire net scheme with the proximity value closest to 1;
from step six, scheme d4If the value is 0.991 and is closest to 1, the bus network scheme in the road construction period is the best, and the evaluation result is also the same as the actual resultAnd (4) sign.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.

Claims (7)

1. A PCA-TOPSIS model-based public traffic line network evaluation method in a road construction period is characterized by comprising the following steps:
step 1: aiming at the characteristics of the bus lines in the road construction period, a bus network comprehensive evaluation system with five system layers and fifteen index layers is constructed from the aspects of traffic benefit and social benefit;
step 2: according to the meaning of each index in a public transport network comprehensive evaluation system in the road construction period, a calculation method and a basis of each index parameter of the public transport network in the road construction period are given; the index parameters are quantitative index parameters and qualitative index parameters respectively, wherein each quantitative index parameter is calculated by using TRANSCAD and each index formula, and each qualitative index parameter is determined by using a fuzzy index grading mode;
and step 3: establishing an initial decision matrix according to the index parameters obtained in the step 2, and performing standardization processing to obtain a standardized decision matrix;
and 4, step 4: considering subjective and objective factors, establishing a G1 comprehensive weight model;
and 5: the PCA method depending on objective data characteristics integrates and simplifies data information, reduces uncertain factors in an evaluation process, reduces correlation among index factors, improves the processing efficiency of a TOPSIS evaluation function, and accordingly establishes a PCA-TOPSIS analysis model;
step 6: and processing the standardized decision matrix according to the comprehensive weighted PCA-TOPSIS analysis model, and calculating the proximity of the bus network scheme in each construction period to obtain an optimal decision scheme.
2. The PCA-TOPSIS model-based evaluation method for public traffic network in road construction period according to claim 1, wherein the step 1 comprises the following steps:
wherein, the following indexes mean all indexes in a road construction traffic influence area; the road construction traffic affected zone range is an area surrounded by traffic main roads adjacent to the periphery of a construction road;
step 1.1: the bus network comprehensive evaluation system A is divided into five system layers, namely a network structure B1, a passenger transport effect B2, an implementability B3, social benefits B4 and a development effect B5;
step 1.2: the wire mesh structure B1 is divided into 5 index layers, namely, the wire mesh length C11, the wire mesh density C12, the number of transfer nodes C13, the coverage area ratio C14 and the number of connection with large-scale passenger flow distribution points C15;
step 1.3: the passenger transport effect B2 is divided into 4 index layers, namely a passenger flow section non-uniform coefficient C21, a transfer coefficient C22, a daily passenger transport total amount C23 and a line network passenger flow density C24;
step 1.4: the feasibility B3 is divided into 2 index layers, namely the engineering difficulty degree C31 and the operability of bus network adjustment C32;
step 1.5: the social benefit B4 is divided into 2 index layers, the average bus travel time is saved by C41, and the average bus travel time accounts for C42;
step 1.6: the social benefit B4 is divided into 2 index layers, traffic connection C51 and the land utilization effect around the construction road along the line C52.
3. The PCA-TOPSIS model-based evaluation method for public traffic network in road construction period according to claim 2, wherein the step 2 comprises the following steps:
step 2.1: the wire mesh structure B1 is divided into 5 index layers, and the parameter acquisition method is as follows:
(1) c11 wire mesh length: l ═ L1+l2+…+ln；
Wherein: l is the total length of the line network in the construction affected area, m; ln is the nth line net length, m; n is the number of the line network lines;
(2) c12 construction affected zone wire mesh density:
Figure FDA0003147552520000021
wherein: d is the density of the public traffic line network in the construction affected area, km/km2(ii) a ln is the length of the nth public traffic network in the construction affected area, m; s is the area of the construction affected area, km2(ii) a n is the number of the line network lines;
(3) c13 number of transfer nodes: n ═ N1+n2+…+nn；
Wherein: n is the total number of transfer nodes of the bus net in the construction affected area; n isnThe number of points of intersection of the public traffic line network; n is the number of the line network lines;
(4)C14construction affected area wire mesh coverage area rate:
Figure FDA0003147552520000022
wherein: z is the wire mesh coverage area rate of the construction affected area; an is the coverage area of 500 meters on each side of the nth bus net in the construction affected area, km2(ii) a S is the area of the construction affected area, km2(ii) a n is the number of the line network lines;
(5) c15 number of large-scale passenger flow distribution points: m ═ M1+m2+…+mn；
Wherein: m is the number of the connection of the bus net and the large-scale passenger flow distribution points in the construction affected area; m isnThe number of the n-th public traffic network and the large-scale passenger flow distribution points is as follows: n is the number of the line network lines;
step 2.2: the passenger transport effect B2 is divided into 4 index layers, and the parameter acquisition method comprises the following steps:
(1) c21 coefficient of unevenness of passenger flow profile:
Figure FDA0003147552520000031
wherein: p is the uneven coefficient of the cross section of the passenger flow of the wire network; rn is the maximum passenger flow section value of the nth line, and is ten thousands of people; kn is the average passenger flow of the nth line, and is ten thousand people; km is the cross-section passenger flow of the mth line, and ten thousands of people; n is the number of the line network lines; m is the number of passenger flow sections of a certain line, and ten thousands of people;
(2) c22 passenger transfer coefficient:
Figure FDA0003147552520000032
wherein: e is the transfer coefficient of the bus network passengers in the construction affected area; a is the number of people going out of the public transport network, and ten thousand people; b is the number of people transferred by the public traffic line network, and ten thousand people;
(3) total passenger traffic daily C23: q ═ Q1+q2+…+qn；
Wherein: q is the total daily passenger traffic of the line network scheme, ten thousand people; q. q.snThe passenger capacity of the nth line network is ten thousands of people; n is the number of the line network lines;
(4) c24 wire mesh passenger flow density:
Figure FDA0003147552520000033
wherein: rho is the passenger flow density of the public transport network in the construction affected area, ten thousand persons/km.d; f is daily passenger capacity of a public transport network, and ten thousand people; l is the total length of the public traffic network, m;
step 2.3: the implementability B3 is divided into 2 index layers, and the obtaining method is as follows:
(1) c31 engineering difficulty degree, which is divided into: easiest, easy, easier, general, harder, difficult, hardest;
(2) c32 operability of bus net adjustment is divided into: easiest, easy, easier, general, harder, difficult, hardest;
step 2.4: the social benefit B4 is divided into 2 index layers, and the acquisition method is as follows:
(1) when the average public transport travel of the room C41 is saved, the index is obtained through a traffic prediction model, and T is the time saved by the average public transport travel of residents in min/times;
(2) c42 bus travel ratio
Figure FDA0003147552520000034
Wherein: h is the proportion of the construction road bus trip to the total bus trip in the construction road influence area; cn is the line running amount of the nth line; d is the amount of the traveler taking the bus; n is the number of the line network lines;
step 2.5: the development effect B5 is divided into 2 index layers, traffic connection C51 and the land utilization effect C52 around the construction road along the line;
(1) c51 traffic connection: the external traffic connection degree is divided into: strongest, strong, stronger, general, weaker, weak, weakest;
(2) c52 construction road is land utilization effect around along the line, divide into according to the effect: best, good, better, normal, worse, worst;
step 2.6: through TRANSCAD software, acquiring the analysis and prediction data of the public traffic network in the construction period, wherein the analysis and prediction data comprises the passenger flow density, the uneven coefficient of the passenger flow section, the average travel time saving time of the public traffic, the daily passenger capacity of the public traffic and the passenger transfer coefficient of the public traffic network in a road construction affected area, and calculating according to the meaning and formula of each index to obtain the specific numerical value of each index;
step 2.7: the fuzzy attribute index of the parameter can not be directly obtained by formula calculation, and qualitative evaluation can be converted into quantitative evaluation by utilizing the membership degree theory.
4. The PCA-TOPSIS model-based evaluation method for public traffic network in road construction period according to claim 1, wherein the step 3 comprises the following steps:
step 3.1: constructing a sample matrix according to the comprehensive evaluation system parameters of the net, and assuming that n net planning schemes x are equal to (x)1,x2,…,xn)TEach scheme has p evaluation indexes xi＝(xi1,xi2,…,xip)TWith the matrix X ═ Xij)n×p，xijIf the index value is the jth index value of the ith scheme, then:
Figure FDA0003147552520000041
step 3.2: carrying out standardization treatment according to each index attribute of the comprehensive evaluation system;
benefit type, the larger the more preferable:
Figure FDA0003147552520000042
cost type, the smaller the more preferable:
Figure FDA0003147552520000043
step 3.3: obtaining a standardized decision matrix;
Figure FDA0003147552520000044
5. the PCA-TOPSIS model-based evaluation method for public traffic network in road construction period according to claim 4, wherein the step 4 comprises the following steps:
step 4.1: subjective weighting;
(1) in the index set { x1,x2,…,xpIn the above, sorting is performed according to the importance degree of the index, and is recorded as
Figure FDA0003147552520000051
(2) Giving a comparative judgment of the relative importance degree between two adjacent indexes, i.e. rk＝αk-1/αk；
(3) And determining the calculation of the weight coefficient, namely:
Figure FDA0003147552520000052
step 4.2: objective weighting;
entropy of matrix Z is
Figure FDA0003147552520000053
θipWhen 0, then θiplnθip0, is biased by informationDifference gi＝1-epAnd obtaining the weight:
Figure FDA0003147552520000054
step 4.3: comprehensive weight:
Figure FDA0003147552520000055
6. the PCA-TOPSIS model-based evaluation method for public traffic network in road construction period according to claim 5, wherein the step 5 comprises the following steps:
step 5.1: constructing a comprehensive weighting decision matrix;
Figure FDA0003147552520000056
step 5.2: PCA treatment;
let the nth column vector of the weighted decision matrix F be FiP vectors of the matrix F, i.e. p index vectors X1，X2，…，XpThe linear combination is:
Fi＝a1iX1+a2iX2+…+apiXp i＝1，2，…，p
note ai＝(a1i1,a2i,…,api) Satisfy ai·ai T1, then the coefficient aiIs determined by the following principle:
(1)Fi,Fj(i ≠ j, i ≠ 1,2, …, p) is irrelevant;
(2)F1is X1，X2，…，XpThe one with the greatest degree in all linear combinations of (1), then F1Is a first main component; f2Is a reaction of with F1Independent X1，X2，…，XpThe maximum equation in all linear combinations is F2The second principal component, and so on, can define the remaining principal components;
let m be the number of main components, uijFor m principal component components, considering the contribution rate of the principal components, and having a PCA decision matrix U;
U＝[uij]n×m
step 5.3: constructing a TOPSIS comprehensive evaluation function;
(1) constructing a non-negative PCA decision matrix H;
Figure FDA0003147552520000061
H＝[hij]n×p，i＝1,2,…,n，j＝1,2,…,m
(2) constructing TOPSIS function and defining positive ideal scheme h+Negative ideal scheme h-；
Figure FDA0003147552520000062
(3) The distance from each scheme to the positive and negative ideal schemes is d+、d_；
Figure FDA0003147552520000063
Step 5.4: calculating relative proximity;
Figure FDA0003147552520000064
7. the PCA-TOPSIS model-based evaluation method for public traffic network in road construction period according to claim 6, wherein the step 6 comprises the following steps:
according to the relative proximity value in step 5, the closer the value is to 1, the closer the solution n is to the ideal solution, and the optimal net solution is selected accordingly.
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