CN110119890B - Railway riding scheme sorting method based on AHP-grey correlation analysis - Google Patents
Railway riding scheme sorting method based on AHP-grey correlation analysis Download PDFInfo
- Publication number
- CN110119890B CN110119890B CN201910346862.2A CN201910346862A CN110119890B CN 110119890 B CN110119890 B CN 110119890B CN 201910346862 A CN201910346862 A CN 201910346862A CN 110119890 B CN110119890 B CN 110119890B
- Authority
- CN
- China
- Prior art keywords
- riding
- station
- index
- train
- scheme
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000010219 correlation analysis Methods 0.000 title claims abstract description 23
- 230000008569 process Effects 0.000 claims abstract description 28
- 238000011156 evaluation Methods 0.000 claims abstract description 21
- 238000012163 sequencing technique Methods 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims description 65
- 238000012546 transfer Methods 0.000 claims description 42
- 239000010410 layer Substances 0.000 claims description 34
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 10
- 238000011084 recovery Methods 0.000 claims description 9
- 239000000126 substance Substances 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 claims description 2
- 150000001875 compounds Chemical class 0.000 claims description 2
- 239000002356 single layer Substances 0.000 claims description 2
- 238000010606 normalization Methods 0.000 claims 1
- 238000013139 quantization Methods 0.000 claims 1
- 230000007547 defect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a railway riding scheme sequencing method based on AHP-grey correlation analysis, which combines the indexes of travel expense, travel time, travel convenience, travel fatigue, travel time satisfaction and the like in the riding process of railway passengers, determines the preference of different types of railways for each riding index by utilizing an analytic hierarchy process, and performs the priority sequencing on the riding scheme by utilizing the analytic hierarchy process based on the grey correlation analysis; finally, sorting alternative riding schemes of the passengers according to riding preferences of the passengers of different types by adopting a grey correlation analysis method; the method realizes the evaluation of the quality of alternative riding schemes of different types of railway passengers going to different classes, and provides reference basis for the preferred riding and traveling schemes.
Description
Technical Field
The invention relates to the technical field of railway transportation, in particular to a railway riding scheme sequencing method based on AHP-grey correlation analysis.
Background
Several speed increases of the existing railway line and the gradual improvement of the high-speed railway network greatly increase the alternative riding schemes for selecting passengers for railway travel, and for a certain passenger going to, various riding schemes such as high-speed trains, express trains, different types of trains and different train numbers are often available. The passenger riding behavior mechanism is analyzed, different riding schemes are provided according to different types of passenger riding selection preferences, and the method has important significance for improving railway passenger transportation service level and market competitiveness.
The selection process of the riding scheme of passengers going out of a railway relates to various factors, the preferences of different types of passengers on different factors are different, and the sequencing of the riding scheme is a complex system problem. Although the existing 12306 ticket system can provide passengers between different stations with direct riding schemes and partial transfer schemes, these transfer schemes often have long waiting time of transfer stations, which is difficult for passengers to accept, and because the system lacks a sequencing mechanism for riding schemes, passengers need to spend long time to select a riding scheme suitable for themselves from these alternatives.
For the problem of ordering railway riding schemes, relevant researches at home and abroad are rare. The problem belongs to the category of travel behavior problems of passengers, and related achievements such as travel scheme evaluation and the like exist in the fields of aviation and road traffic. Railway travel has particularity, and because direct trains do not exist between certain destination point pairs, travel can be carried out only in a transfer riding mode, and meanwhile, because the grades of trains running on a railway network are different, different riding schemes also have different transportation attributes.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a railway riding scheme sequencing method based on AHP-grey correlation analysis, and the method is used for sequencing the advantages and the disadvantages of feasible riding schemes of passengers of different classes going to each side according to a high-speed railway network, the existing railway network and transportation organization conditions and the preference of traveling passengers to riding scheme indexes, so that the adaptability of a train driving scheme and passenger flow requirements is improved.
The technical scheme of the invention is as follows: a railway riding scheme sequencing method based on AHP-grey correlation analysis comprises the following steps:
s1) alternative selection set of railway riding method
S101), for any drive direction trip demand od, namely setting the starting station as o station and the final station as d station, obtaining the travel demand od by using the train schedule of the railway passenger service systemThe alternative train riding schemes for the destination respectively make the train sets passing through the start station o station and the end station d station L o 、L d ；
S102), direct riding scheme, and searching train set L o And L d Of (2) intersection L i ＝L o ∩L d If the following conditions are satisfied:
train l i ∈L i At the departure time of the origin station o station
Earlier than arrival time at terminal d
Namely that
Then it represents the train l i A direct riding scheme from an origin station o to a destination station d is adopted;
s103), a primary transfer riding scheme is adopted, and a large passenger station v on the shortest path of the railway between the o station of the starting station and the d station of the destination station is selected h As transfer stations, respectively searching for stations v passing through the origin station o and the large passenger station h Train set of stations L m ＝L o -L d And only pass through the large passenger station v h Train set L of station and terminal d station k ＝L d -L o I.e. v h ∈V lm ∩V lk If the train l m To the train l k The following three conditions are simultaneously satisfied:
(1) train l m Departure time from o station is earlier than arrival at transfer station v h Time, i.e.
(2) Train l k From transfer stations v h Departure time is earlier than arrival time of d stations, i.e.
(3) Train l m To transfer station v h Time and train k By transfer stations v h The interval between departure times is larger than the minimum transfer time of passengers
Namely, it is
Then it means that the train is taken from the origin station o station m At transfer station v h Changeable train k D, arriving at a terminal station d, so as to obtain a one-time transfer riding scheme;
s2), calculating evaluation indexes of the railway riding scheme;
s201), trip expense U 1
Setting the fare of the train from the station i to the station j of the train when the passengers take k times of trains as c ij (k) Trip expense U 1 Is the train fare c ij (k)；
S202), travel time U 2 Time of trip U 2 The method comprises the steps of running time of a train and waiting time during transfer; wherein the content of the first and second substances,
(1) the train operation time influencing factors comprise the operation mileage of the train and the type of the selected train, and the taking time of passengers from i station to j station by taking k trains
Is composed of
In the formula (I), the compound is shown in the specification,
for the time k trains arrive at the j station,
the time when k times of trains start from the station i;
(2) the time of transfer waiting is that the passengers are driven by k at the h station 1 Transfer of sub-train to k 2 Waiting time of the secondary train, i.e. k 2 Departure time and k of secondary train at h station 1 The time difference of the arrival time of the train at the h station is smaller than 1 immediate continuation standard t if the difference between the arrival time of the train at the h station and the time difference of the arrival time of the train at the h station 0 =20min, the time difference should be increased by one day, i.e. 1440min, which is calculated as:
in summary, the travel time consumption is
S203), convenience for trip U 3 Because the transfer is time-consuming and labor-consuming, the fatigue degree of the transfer passenger is high and the travel process is complicated, therefore, the convenience U of the travel 3 Mainly embodied as the maximum number of transfers
S204), travel fatigue degree U 4 Fatigue U for passenger going out 4 The fatigue recovery time is defined as the fatigue recovery time after passengers take the train, the time is related to the train operation time and the train type, and the calculation formula of the fatigue recovery time from the i station to the j station when passengers take the k trains is as follows:
wherein, T max Represents the upper limit of fatigue recovery time; parameter alpha k And beta k Time intensity coefficient for fatigue recovery of riding;
s205), satisfaction degree of departure time U 5 The departure time satisfaction refers to the convenience degree of train departure time for passengers in a railway trip passenger riding scheme, and the preference degree of the passengers for each departure time period is set as s k ；
S3), passenger category classification and passenger index preference survey,
dividing railway passengers into time type, economical type and comfortable type according to personal attributes;
wherein the content of the first and second substances,
the time type, the traveltime can be taken as the factor to be considered firstly in the whole process of railway travel by the passenger of the type;
the passenger can take the travel expense with the least expenditure as the primary consideration of travel;
the passenger in the comfortable type, which is the most important, is the comfort level in the whole riding and traveling process;
determining the relative importance judgment of different types of passengers on riding indexes of travel cost, travel time, travel convenience, travel fatigue and departure time satisfaction by combining a questionnaire mode;
s4), determining the preference of various passenger riding scheme indexes, determining various passenger quantitative weight values aiming at each index by adopting an analytic hierarchy process based on questionnaire survey of relative importance of various railway passenger riding indexes, wherein the calculation steps are as follows:
s401), establishing a hierarchical structure model
Dividing the decision-making target and the considered factors into a high layer and a low layer according to the mutual relation between the decision-making target and the considered factors, and drawing a hierarchical structure chart;
the high layer is a target layer, and means the purpose of decision making and the problem to be solved, namely, the bus taking scheme sorting;
the lower layer is a criterion layer and refers to a reference index in decision making, namely a riding scheme evaluation index;
s402), constructing a judgment matrix
When determining the weight among the factors of the criterion layer, if the result is only qualitative, comparing every two factors by adopting a consistent matrix method to reduce the difficulty of comparing the factors with different properties as much as possible so as to improve the accuracy, namely for a certain target, comparing every two factors aiming at the importance of each index in the criterion layer, taking a matrix formed by the results of the comparison as a judgment matrix, and grading according to the importance degree of the judgment matrix;
s403), hierarchical single sorting and consistency checking
Maximum characteristic root lambda corresponding to the decision matrix max The normalized index weight of the feature vector is recorded as W, the element of W is the sorting weight of the relative importance of the same layer factor to a certain factor of the previous layer factor, and the process is called single-layer sorting; carrying out consistency check on the sequence of the layer list;
s5), carrying out grey correlation sequencing on the riding scheme;
s501), determining an original sequence
Aiming at a certain destination passenger flow demand, a riding scheme alternative set F = { X) is constructed i I =1,2,. And m }, and the evaluation index system C = { C } according to the railway riding scheme constructed above j L j =1,2, a, n }, and respectively collecting numerical values X corresponding to evaluation indexes of each riding scheme i ＝[x i1 ,x i2 ,…,x in ]And according to the above-mentioned data sequence X i (i =1, 2.... M), constituting the following raw data matrix D 0 ：
Wherein m is the number of riding plans in the riding plan alternative set, n is the number of indexes in the riding plan evaluation system, and x ij A value corresponding to the index j is set for the riding scheme i;
s502), determining a reference sequence
Reference sequence X as an ideal reference standard for a ride plan 0 Is formed by the optimal values of each riding scheme under each index in the original data matrix D, namely
In the formula, x 0j The optimal value of the jth riding scheme index is obtained;
wherein, for the cost-type index, the reference sequence index takes the value of x 0j ＝min{x ij }；i＝1,2,...,m；
For benefit-type indexes, the reference sequence index takes the value of x 0j ＝max{x ij }；i＝1,2,...,m；
Merging the reference sequence and the original sequence to obtain a comprehensive data sequence:
s503) comprehensive data sequence dimensionless processing
The comprehensive data sequence has different dimensions or orders of magnitude, in order to ensure the reliability of the analysis result, all values in the sequence need to be subjected to non-dimensionalization processing, and a normalized formula based on an averaging method is adopted as follows:
in the formula, x ij The riding plan i corresponds to the value, x, of the index j i ' j Is x after dimensionless processing ij The number m is the number of riding schemes in the riding scheme alternative set, and the number n is the number of indexes in the riding scheme evaluation system;
after all numerical values in the original comprehensive data sequence are subjected to dimensionless processing, a matrix is formed as follows:
s504), calculating absolute difference value sequence
Calculating each riding scheme index sequence corresponding to ideal riding scheme index sequence one by oneAbsolute difference of each index element, i.e. | x i ' j -x' 0j L, wherein i =1,2, ·, m; j =1,2, n, forming an absolute difference matrix as follows:
defining maximum values in the absolute difference matrix Delta
And minimum value
Maximum difference and minimum difference respectively;
s505), calculating the correlation coefficient
Respectively settling the association coefficient xi of each riding scheme index sequence and the corresponding element of the ideal riding scheme index sequence ij The calculation formula is as follows:
wherein i =1,2, ·, m; j =1,2,. Ang, n; rho epsilon (0, 1) is a resolution coefficient, the smaller the value of rho epsilon is, the more the difference between the correlation coefficients can be improved, and the value of rho epsilon is generally 0.5; correlation coefficient xi ij E (0, 1) reflects riding scheme index sequence X i Ideal riding scheme index sequence X 0 The quantitative association degree on the jth index;
s506), calculating the comprehensive relevance
Calculating the comprehensive association degree r of each index of each riding scheme to be evaluated and each index element corresponding to each ideal riding scheme i So as to reflect the correlation between each riding scheme and the ideal riding scheme, the calculation formula is as follows
In the formula, w j Weight coefficient, ξ, of jth ride plan index ij Is a correlation coefficient;
and sequencing the comprehensive relevance of each riding scheme and the ideal riding scheme from large to small, wherein the greater the relevance is, the higher the similarity of the scheme and the ideal scheme is, namely the riding scheme is more suitable for the type of passengers.
Further, in step S402), the determination matrix is recorded as:
wherein, a ij The importance comparison result of the index i and the index j is obtained;
and the decision matrix has the property that a ij ＝1/a ji ；
Determining matrix elements a ij On a scale of:
further, in step S403), the consistency check means determining an allowable inconsistent range for a, where a unique non-zero characteristic root of the n-order consistent array is n; the maximum characteristic root lambda of the n-order positive reciprocal array A is more than or equal to n, and A is a consistent matrix if and only if lambda = n;
due to the continuous dependence of lambda on a ij If the lambda is larger than n, the inconsistency of A is more serious, the consistency index is calculated by using CI, and if the CI is smaller, the consistency is higher;
the feature vector corresponding to the maximum feature value is used as a weight vector of the influence degree of the compared factor on a certain factor of an upper layer, and the larger the inconsistency degree is, the larger the caused judgment error is; therefore, the degree of inconsistency of A can be measured by the size of the value of lambda-n, and the consistency index is defined as:
the value of CI is 0, and the consistency is complete; the value of CI is close to 0, and the consistency is satisfied; the larger the CI, the more severe the inconsistency;
in order to measure the CI size, a random consistency index RI is introduced, the index is related to the order of a judgment matrix, and the probability of occurrence of consistency random deviation is higher when the order of the matrix is higher, namely
| Order of matrix | 1 | 2 | 3 | 4 | 5 |
| |
0 | 0 | 0.58 | 0.90 | 1.12 |
| Order of |
6 | 7 | 8 | 9 | 10 |
| RI | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |
Considering that the deviation of consistency may be caused by random reasons, when checking and judging whether the matrix has satisfactory consistency, the consistency index CI and the random consistency index RI are compared to obtain a check coefficient CR, and the calculation formula is as follows:
CR＝CI/RI (7)
if CR <0.1, the decision matrix is deemed to pass the consistency check, otherwise satisfactory consistency is not achieved.
The invention has the beneficial effects that:
1. the method selects different railway riding schemes which go to different types of railways for travel and can be selected under the conditions of current transportation equipment and transportation organization as research objects, systematically analyzes influence factors of the railway passenger riding schemes, screens evaluation indexes of the railway passenger riding schemes, determines preference weights of the passengers of different types for each riding selection evaluation index by using an analytic hierarchy process, defines an optimal index set, a grey correlation coefficient, a riding scheme comprehensive correlation degree and the like on the basis, and ranks the advantages and the disadvantages of the passengers by comparing the comprehensive correlation degree method of each alternative riding scheme with an ideal riding scheme;
2. according to the method, railway riding scheme evaluation indexes are screened according to a riding selection behavior mechanism of railway traveling passengers, preference weights of the railway traveling passengers on the indexes in a riding selection process are determined based on questionnaire survey and an analytic hierarchy process aiming at different types of railway traveling passengers, and then alternative riding schemes are sequenced according to preferences of the passengers of different types by adopting a grey correlation analysis method, so that decision support for railway traveling passenger riding scheme selection is realized.
3. The taking scheme rapid generation algorithm based on network path heuristic search and the taking scheme sorting method combined with AHP-grey correlation analysis can rapidly and objectively screen adaptive taking schemes for various passengers, overcomes the defects of low efficiency and low precision of a manual searching and comparing method of taking schemes, and provides decision support for railway passengers to take a bus.
4, the invention can be used as an extension part of a functional module of a railway 12306 passenger ticket system, and can quickly generate a riding scheme meeting the preference of different types of passengers according to the traveling demands of the passengers, thereby improving the competitiveness of railway passenger transportation market.
Drawings
FIG. 1 is a flow chart of a railway ride scheme sequencing based on AHP-grey correlation analysis according to the present invention;
FIG. 2 is a schematic diagram of convenience levels for passengers during different departure periods in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a riding scheme index sorting hierarchical structure according to an embodiment of the present invention;
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings:
as shown in fig. 1, the invention provides a railway riding scheme ordering method based on AHP-gray correlation analysis, because different riding schemes are different in indexes such as travel cost, travel time, travel comfort and the like, and different indexes have different preferences for different types of railway traveling passengers when riding, preferences of different types of passengers when riding are selected are determined by combining questionnaire survey and an analytic hierarchy process, a riding scheme alternative set of passenger flow going rooms is obtained by adopting a path search algorithm on the basis, and a gray correlation analysis method is used for evaluating and respectively determining comprehensive ordering of the riding schemes of different types of passengers. The present invention will be further described in connection with the Guangzhou-building railway ride scheme.
S1) for the travel demand od of any Guangzhou building door, the starting station is Guangzhou, the ending station is a building door, and a plurality of alternative train riding schemes for going can be obtained in a short time for selection by utilizing a train schedule of a 12306 railway passenger service system;
(1) Direct riding scheme
The Guangzhou building door direct riding scheme comprises D2381 trains, G1607 trains, K229 trains and K297 trains, and the related information is shown in Table 3:
TABLE 3 Guangzhou-mansion railway direct riding scheme
(2) One-time transfer riding scheme
Selecting Shenzhen north station on the shortest path of the railways between Guangzhou and mansion as a transfer station, and respectively searching a train set L only passing through the Shenzhen north station and the Guangzhou north station m And a train set L only passing through Shenzhen north station and mansion door k Setting passenger minimum transfer time
The available one-transfer ride plans and their associated information are shown in table 4:
TABLE 4 Guangzhou-mansion railway one-time transfer riding scheme
Step2, calculating railway riding scheme evaluation index value
Table 5 shows the obtained index values corresponding to the direct riding plan between Guangzhou and building and the one-time transfer riding plan according to the riding plan evaluation index definition and calculation method.
Table 5 index value corresponding to each riding plan
The departure time satisfaction degree refers to the convenience degree of train departure time for passengers in a railway trip passenger riding scheme. In order to determine the convenience of the passengers at different departure times, 0 to 00 are divided into 24 departure time periods at intervals of 1h, and the passengers select the most satisfactory departure time period according to the preference of the passengers based on the questionnaire, and the data statistics result shows that the passengers prefer two peaks for the departure time periods, wherein the first peak is between 9 and 11.
Step3 survey various passenger riding index preferences
Adopting a passenger behavior survey method (RP), conducting questionnaire survey on various passenger riding index preferences at Guangzhou south station and Shenzhen north station at 18-24 days 3 and 18 in 2019, and issuing 1000 questionnaires in total to recover 967 effective questionnaires, wherein 308 parts of time-type passenger questionnaires, 422 parts of economic passengers and 237 parts of comfortable passenger questionnaires are recovered.
Step4, determining various passenger riding scheme index weights
And analyzing and calculating the riding scheme index preference of three passengers of time type, economy type, comfort type and the like by adopting an analytic hierarchy process.
(1) Building a hierarchical model
Dividing the decision target, the considered factors (decision criteria) and the decision object into a highest layer, a middle layer and a lowest layer according to the mutual relation among the decision target, the considered factors (decision criteria) and the decision object, and drawing a hierarchical structure diagram. The highest level refers to the purpose of the decision, the problem to be solved. The lowest layer refers to the alternative at decision time. The middle layer refers to the factor to be considered and the decision criterion. For two adjacent layers, namely a high layer is a target layer, a low layer is a factor layer, and the riding scheme index hierarchical structure is shown in fig. 3.
(2) Construction judgment (pairwise comparison) matrix
The statistical time type passenger questionnaire, the relative importance matrix of this type passenger to each riding index is as follows
The statistical economic passenger questionnaire is characterized in that the relative importance matrix of the type of passengers to each riding index is as follows
The statistical comfortable passenger questionnaire is characterized in that the relative importance matrix of the passenger type to each riding index is as follows
(3) Hierarchical single ordering and consistency check thereof
According to the riding index relative importance matrix of the time type passengers, the maximum characteristic root lambda of the matrix max =5.16, the consistency index CI =0.0410 is calculated by equation (2), the random consistency index RI =1.12 with the matrix order n =5 according to table 2, the check coefficient CR =0.0366 <0.1 is calculated by equation (3), and the judgment matrix passes the consistency check, that is, the weight of the comfortable passenger taking index is W 3 ＝[0.063,0.366,0.114,0.205,0.252]。
According to the relative importance matrix of riding indexes of economic passengers, the maximum characteristic root lambda of the matrix max =5.06, consistency index CI =0.0139 calculated by formula (2), random consistency index RI =1.12 of matrix order n =5 according to table 2, check coefficient CR =0.0124 <0.1 by formula (3), and consistency check is passed by the judgment matrix, that is, the weight of the comfortable passenger taking index is W 3 ＝[0.370,0.110,0.209,0.128,0.183]；
According to the riding index relative importance matrix of comfortable passengers, the maximum characteristic root lambda of the matrix max =5.05, consistency index CI =0.0136 calculated by formula (2), random consistency index RI =1.12 of matrix order n =5 according to table 2, check coefficient CR =0.0121 <0.1 by formula (3), and consistency check is passed by the judgment matrix, i.e. the weight of the comfortable passenger taking index is W 3 ＝[0.058,0.167,0.232,0.277,0.266]。
Step5 riding scheme gray correlation sorting
(1) Determining an original sequence
Aiming at the passenger flow demand between Guangzhou and building, the index values corresponding to the direct riding scheme and the one-time transfer riding scheme are shown in the table 5, and the index travel time U is 2 The units are converted into minutes, constituting the following raw data matrix D 0 ：
(2) Determining a reference sequence
Reference sequence X as an ideal reference standard for a ride plan 0 Is formed by the optimal values of each riding scheme under each index in the original data matrix D, namely
Combining the reference sequence with the original sequence to obtain a comprehensive data sequence of the riding scheme
(3) Comprehensive sequence dimensionless processing
After carrying out dimensionless processing on all numerical values in the riding scheme comprehensive data sequence, a matrix is formed as follows:
(4) Calculating a sequence of absolute differences
Calculating the absolute difference value of each index element corresponding to each riding scheme index sequence and the ideal riding scheme index sequence one by one to form an absolute difference value matrix as follows:
wherein the maximum value in the absolute difference matrix Delta
And minimum value
Maximum and minimum differences, respectively.
(5) Calculating the correlation coefficient
Calculating a correlation coefficient matrix of corresponding elements of each riding scheme index sequence and the ideal riding scheme index sequence as follows:
(6) Calculating the comprehensive degree of association
And calculating the comprehensive association degree of each riding scheme and an ideal riding scheme according to the riding scheme index preference of three passengers of time type, economy type, comfort type and the like.
For the time-type passengers, the comprehensive relevance degree of each riding scheme and the ideal riding scheme is as follows:
r 1 ＝[0.761 0.813 … 0.742]
sequencing the comprehensive association degrees of each riding scheme and the ideal riding scheme from large to small, wherein the maximum value of the riding scheme G6015 corresponding to 0.894 is converted into D3112, namely the riding scheme is more suitable for the type of passengers, then the riding scheme G1607 corresponding to 0.813 is adopted, and the rest is analogized;
for the economical passengers, the comprehensive relevance degree of each riding scheme and the ideal riding scheme is as follows:
r 2 ＝[0.696 0.714 … 0.587]
sorting the comprehensive association degrees of each riding scheme and an ideal riding scheme from large to small, wherein the maximum value is 0.762 corresponding riding scheme K229, namely the riding scheme is more suitable for the type of passengers, and then is 0.716 corresponding riding scheme K297, and the rest is analogized;
for comfortable passengers, the comprehensive relevance degree of each riding scheme and the ideal riding scheme is as follows:
r 3 ＝[0.779 0.806 … 0.668]
and sequencing the comprehensive association degree of each riding scheme and the ideal riding scheme from large to small, wherein the riding scheme G6015 corresponding to the maximum value of 0.822 is converted into D3112, namely the riding scheme is more suitable for the type of passengers, the riding scheme G1607 corresponding to 0.806 is adopted, and the rest is analogized.
In this embodiment, by defining reference indexes and a calculation method thereof when a riding scheme of a railway trip passenger is selected, and combining with a 12306 railway ticket query system, a network path search heuristic algorithm is adopted to quickly generate riding schemes of different going passengers and various index values corresponding to the riding schemes, and a rank of merits of different types of riding schemes of passengers is determined according to AHP-gray correlation analysis, and a main conclusion is as follows:
(1) The taking scheme rapid generation algorithm based on network path heuristic search and the taking scheme sorting method combined with AHP-grey correlation analysis can rapidly and objectively screen adaptive taking schemes for various passengers, overcomes the defects of low efficiency and low precision of a manual searching and comparing method of taking schemes, and provides decision support for railway passengers to take a bus.
(2) Example studies of passenger riding schemes ranked by Guangzhou-Xiamen railway show that the railway riding schemes meeting the preferences of different types of passengers are different.
(3) The passenger ticket system can be used as an extension part of a functional module of a railway 12306 passenger ticket system, a riding scheme meeting the preference of passengers of different types can be quickly generated according to the traveling demands of the passengers of different types, and the competitiveness of a railway passenger transportation market is improved.
The questionnaire content adopted in this example is as follows:
1. selecting which type of passenger you belong to when traveling on railway ()
A. Time type B, economic type C, comfortable type
2. During riding, think that the travel cost is relative to the travel time ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. Between equal and slightly important G, between slightly and more important
H. Between more important and very important I, between very important and extremely important
3. In the riding process, consider the travel cost relative to the travel convenience ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. G between equal and slightly important, G between slightly and more important
H. Between more and very important, I, between very and extremely important
4. During the riding process, consider the travel cost relative to the travel fatigue ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. Between equal and slightly important G, between slightly and more important
H. Between more important and very important I, between very important and extremely important
5. During the riding process, think that the travel cost is satisfied with the departure time ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. G between equal and slightly important, G between slightly and more important
H. Between more and very important, I, between very and extremely important
6. In the riding process, consider the travel time relative to the travel convenience ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. Between equal and slightly important G, between slightly and more important
H. Between more important and very important I, between very important and extremely important
7. In the riding process, consider the travel time relative to the travel fatigue ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. G between equal and slightly important, G between slightly and more important
H. Between more important and very important I, between very important and extremely important
8. During the riding process, consider the satisfaction degree of the travel time relative to the departure time ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. G between equal and slightly important, G between slightly and more important
H. Between more important and very important I, between very important and extremely important
9. In the riding process, consider the travel convenience relative to the travel fatigue ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. Between equal and slightly important G, between slightly and more important
H. Between more important and very important I, between very important and extremely important
10. In the riding process, consider the travel convenience degree to the satisfaction degree of departure time ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. Between equal and slightly important G, between slightly and more important
H. Between more important and very important I, between very important and extremely important
11. During the riding process, think that the travel fatigue degree is satisfied with respect to the departure time ()
A. Equally important B, slightly important C, more important D, very important E, extremely important
F. G between equal and slightly important, G between slightly and more important
H. Between more and very important, I, between very and extremely important
The foregoing embodiments and description have been presented only to illustrate the principles and preferred embodiments of the invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention as hereinafter claimed.
Claims (8)
1. A railway riding scheme sequencing method based on AHP-grey correlation analysis comprises the following steps:
s1), obtaining a spare selection set of a railway riding method, wherein the spare selection set comprises a direct riding scheme and a primary transfer riding scheme;
s2) calculating evaluation indexes of the railway riding scheme, wherein the evaluation indexes comprise 1) trip cost U 1 And 2) travel time U 2 And 3) convenience of trip U 3 And 4) exhaustion degree U of travel 4 And 5) satisfaction degree U of departure time 5 ；
S3), passenger classification and passenger index preference survey,
dividing railway passengers into time type, economical type and comfortable type according to personal attributes, and determining that passengers of different types carry out relative importance judgment on riding indexes of travel cost, travel time, travel convenience, travel fatigue and departure time satisfaction in a mode of combining questionnaires;
s4), determining the preference of various passenger riding scheme indexes, and determining various passenger quantitative weight values aiming at each index by adopting an analytic hierarchy process based on questionnaire survey of relative importance of various railway passenger riding indexes;
s5), carrying out grey correlation sequencing on the riding scheme;
s501), determining an original sequence
Aiming at a certain destination passenger flow demand, a riding scheme alternative set F = { X) is constructed i I =1,2,. And m }, and the evaluation index system C = { C } according to the railway riding scheme constructed above j L j =1,2,. And n }, and respectively collecting numerical values X corresponding to the evaluation indexes of the riding schemes i ＝[x i1 ,x i2 ,…,x in ]And according to the data sequence X i (i =1, 2.... M), constituting the following raw data matrix D 0 ：
Wherein m is the number of riding plans in the riding plan alternative set, n is the number of indexes in the riding plan evaluation system, and x ij A value corresponding to the index j is set for the riding scheme i;
s502), determining a reference sequence
Reference sequence X as an ideal reference standard for a ride plan 0 Is formed by the optimal values of each riding scheme under each index in the original data matrix D, namely
In the formula, x 0j The optimal value of the jth riding scheme index is obtained;
wherein, for the cost-type index, the reference sequence index takes the value of x 0j ＝min{x ij }；i＝1,2,...,m；
For benefit-type indexes, the reference sequence index takes the value of x 0j ＝max{x ij }；i＝1,2,...,m；
Merging the reference sequence and the original sequence to obtain a comprehensive data sequence:
s503) comprehensive data sequence dimensionless processing
The comprehensive data sequence has different dimensions or orders of magnitude, in order to ensure the reliability of the analysis result, all values in the sequence need to be subjected to non-dimensionalization processing, and a normalization formula based on an averaging method is adopted as follows:
in the formula, x ij Is that the ride plan i corresponds to a numerical value, x 'of the index j' ij Is x after dimensionless processing ij The number m is the number of the bus schemes in the bus scheme alternative set, and the number n is the number of indexes in the bus scheme evaluation system;
after all numerical values in the original comprehensive data sequence are subjected to dimensionless processing, a matrix is formed as follows:
s504), calculating absolute difference value sequence
Calculating absolute difference values | x 'of each index element corresponding to each riding scheme index sequence and ideal riding scheme index sequence one by one' ij -x′ 0j L, wherein i =1,2, ·, m; j =1, 2.. N, forming an absolute difference matrix as follows:
defining the maximum value in the absolute difference matrix Delta
And minimum value
Maximum difference and minimum difference respectively;
s505), calculating the correlation coefficient
Respectively settling the association coefficient xi of each riding scheme index sequence and the corresponding element of the ideal riding scheme index sequence ij The calculation formula is as follows:
wherein i =1,2, ·, m; j =1,2,. N; rho epsilon (0, 1) is a resolution coefficient, the smaller the value of rho epsilon is, the more the difference between the correlation coefficients can be improved, and the value of rho epsilon is generally 0.5; correlation coefficient xi ij E (0, 1) reflects riding scheme index sequence X i Ideal riding scheme index sequence X 0 The quantitative association degree on the jth index;
s506), calculating the comprehensive relevance
Calculating the comprehensive association degree r of each index of each riding scheme to be evaluated and each index element corresponding to each ideal riding scheme i And reflecting the incidence relation between each riding scheme and the ideal riding scheme, wherein the calculation formula is as follows:
in the formula, w j Weight coefficient, ξ, of jth ride plan index ij Is a correlation coefficient;
and sequencing the comprehensive relevance of each riding scheme and the ideal riding scheme from large to small, wherein the greater the relevance is, the higher the similarity of the scheme and the ideal scheme is, namely the riding scheme is more suitable for the type of passengers.
2. The AHP-grey correlation analysis based railway ride scheme sequencing method of claim 1, wherein: the step S1) of establishing the alternative set of railway riding methods specifically comprises the following steps:
s101) for any drive direction trip demand od, namely setting a starting station as an o station and a final station as a d station, acquiring a plurality of alternative train riding schemes for the drive direction by utilizing a train schedule of a railway passenger service system, and respectively setting a train set passing through the o station of the starting station and the d station of the final station as L o 、L d ；
S102), direct riding scheme, and searching train set L o And L d Of (2) intersection L i ＝L o ∩L d If the following conditions are satisfied:
train l i ∈L i At the departure time of the o station of the origin station
Earlier than arrival time at terminal d
Namely, it is
Then it represents the train l i A direct riding scheme from an origin station o to a destination station d is adopted;
s103), a primary transfer riding scheme, and a large passenger station v on the shortest path of the railway between the o station of the starting station and the d station of the destination station is selected h As transfer stations, search for stations passing only the origin station o and the large passenger station v h Train set of stations L m ＝L o -L d And only pass through the large passenger station v h Train set L of station and terminal d station k ＝L d -L o I.e. by
If the train l m To the train l k The following three conditions are simultaneously satisfied:
(1) train l m Departure from o station earlier than arrival at transfer station v h Time, i.e.
(2) Train l k By transfer stations v h Departure time is earlier than arrival time of d stations, i.e.
(3) Train l m To transfer station v h Time and train k From transfer stations v h The interval between departure times is larger than the minimum transfer time of passengers
Namely that
Then it means that the train is taken from the origin station o station m At transfer station v h Changeable train k And d, arriving at the destination station, so as to obtain a one-time transfer riding scheme.
3. The railway ride scheme ordering method based on AHP-grey correlation analysis as claimed in claim 1, wherein: in the step S2), calculating the evaluation index of the railway riding scheme, which is concretely as follows:
s201), trip expense U 1
Setting the fare of the train from the station i to the station j of the train when the passengers take k times of trains as c ij (k) The trip cost U 1 Namely the train fare c ij (k)；
S202), travel time U 2 Time of trip U 2 The method comprises the steps of running time of a train and waiting time during transfer; wherein the content of the first and second substances,
(1) the train running time influence factors comprise the running mileage of the train and the type of the selected train, and the riding time of passengers from i station to j station in k times of train
Is composed of
In the formula (I), the compound is shown in the specification,
for the time k trains arrive at the j station,
the time when k times of trains start from the station i;
(2) the time of transfer waiting is that the passengers are driven by k at the h station 1 Transfer of sub-train to k 2 Waiting time of the secondary train, i.e. k 2 Departure time and k of secondary train at h station 1 The time difference of the arrival time of the train at the h station is smaller than 1 immediate continuation standard t if the difference between the arrival time of the train at the h station and the time difference of the arrival time of the train at the h station 0 =20min, the time difference should be increased by one day, i.e. 1440min, which is calculated as:
in summary, the travel time consumption is
S203), convenience for trip U 3 Because the transfer is time-consuming and labor-consuming, the fatigue degree of the transfer passenger is high in the way and the trip process is complicated, therefore, the trip convenience is U 3 Mainly embodied as the maximum number of transfers
S204), travel fatigue degree U 4 Fatigue U for passenger going out 4 Is defined asThe fatigue recovery time after passengers take the train is related to the train running time and the train type, and the calculation formula of the fatigue recovery time from the station i to the station j after passengers take the train for k times is as follows:
wherein, T max Represents the upper limit of fatigue recovery time; parameter alpha k And beta k Time strength coefficient for fatigue recovery in riding
S205), satisfaction degree of departure time U 5 The departure time satisfaction refers to the convenience degree of train departure time for passengers in a railway trip passenger riding scheme, and the preference degree of the passengers for each departure time period is set as s k 。
4. The railway ride scheme ordering method based on AHP-grey correlation analysis as claimed in claim 1, wherein: in the step S3), the time type refers to that the travel time of the passenger is taken as a factor to be considered firstly in the whole process of railway travel; the economy is that the passenger can take the travel fee with the least expenditure as the primary consideration of travel; the comfort type refers to the comfort degree of the passengers who pay the most attention to the whole riding and traveling process.
5. The AHP-grey correlation analysis based railway ride scheme sequencing method of claim 1, wherein: in step S4), the index quantization weight value is calculated as follows:
s401), establishing a hierarchical structure model
Dividing the decision-making target and the considered factors into a high layer and a low layer according to the mutual relation between the decision-making target and the considered factors, and drawing a hierarchical structure chart;
the high layer is a target layer, and means the purpose of decision making and the problem to be solved, namely, the bus taking scheme sorting;
the lower layer is a criterion layer and refers to a reference index in decision making, namely a riding scheme evaluation index;
s402), constructing a judgment matrix
When determining the weight among the factors of the criterion layer, if only the qualitative result is obtained, comparing every two factors by adopting a consistent matrix method so as to reduce the difficulty of the mutual comparison of the factors with different properties as much as possible and improve the accuracy, namely for a certain target, comparing every two factors aiming at the importance of each index in the criterion layer, taking a matrix formed by the results of the two comparison as a judgment matrix and evaluating the grade according to the importance degree of the matrix;
s403), hierarchical single sorting and consistency checking
Maximum characteristic root lambda corresponding to the decision matrix max The normalized index weight of the feature vector is recorded as W, the element of W is the sorting weight of the relative importance of the same layer factor to a certain factor of the previous layer factor, and the process is called single-layer sorting; and (5) carrying out consistency check on the ranking of the layer order.
6. The railway ride scheme ordering method based on AHP-grey correlation analysis of claim 5, wherein: in step S402), the determination matrix is recorded as:
wherein, a ij The importance comparison result of the index i and the index j is obtained;
and the decision matrix has the property, a ij ＝1/a ji 。
7. The railway ride scheme ordering method based on AHP-grey correlation analysis of claim 6, wherein: determining matrix elements a ij On a scale of:
8. the AHP-grey correlation analysis based railway ride scheme sequencing method of claim 5, wherein: in step S403), the consistency check refers to determining an inconsistent allowable range for a, where the only non-zero characteristic root of the n-order consistent array is n; the maximum characteristic root lambda of the n-order positive reciprocal array A is more than or equal to n, and A is a consistent matrix if and only if lambda = n;
due to the continuous dependence of lambda on a ij If the lambda is larger than n, the inconsistency of A is more serious, the consistency index is calculated by using CI, and if the CI is smaller, the consistency is higher;
the feature vector corresponding to the maximum feature value is used as a weight vector of the influence degree of the compared factor on a certain factor of an upper layer, and the larger the inconsistency degree is, the larger the caused judgment error is; therefore, the degree of inconsistency of A can be measured by the size of the value of lambda-n, and the consistency index is defined as:
CI is 0, and the consistency is complete; the value of CI is close to 0, and the consistency is satisfied; the larger the CI, the more severe the inconsistency;
in order to measure the size of CI, introducing a random consistency index RI, wherein the index is related to the order of a judgment matrix, and the larger the order of the matrix is, the higher the probability of occurrence of consistency random deviation is; the random consistency index RI corresponding to the matrix order is specifically as follows:
When the matrix is checked and judged to have satisfactory consistency, the consistency index CI is compared with the random consistency index RI to obtain a check coefficient CR, and the calculation formula is as follows:
CR＝CI/RI (7)
if CR <0.1, the decision matrix is deemed to pass the consistency check, otherwise satisfactory consistency is not achieved.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910346862.2A CN110119890B (en) | 2019-04-27 | 2019-04-27 | Railway riding scheme sorting method based on AHP-grey correlation analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910346862.2A CN110119890B (en) | 2019-04-27 | 2019-04-27 | Railway riding scheme sorting method based on AHP-grey correlation analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110119890A CN110119890A (en) | 2019-08-13 |
| CN110119890B true CN110119890B (en) | 2022-10-14 |
Family
ID=67521475
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910346862.2A Active CN110119890B (en) | 2019-04-27 | 2019-04-27 | Railway riding scheme sorting method based on AHP-grey correlation analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110119890B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110879866B (en) * | 2019-11-05 | 2020-12-29 | 东南大学 | Air-rail joint transit location determination method based on passenger travel multivariate data analysis |
| CN111256724A (en) * | 2020-03-25 | 2020-06-09 | 交通运输部科学研究院 | Bus and subway transfer path planning method according to subway congestion degree |
| CN111882211B (en) * | 2020-07-27 | 2023-09-29 | 陕西延长石油（集团）有限责任公司 | Gas collecting technology evaluation method based on AHP-gray correlation analysis |
| CN113536112A (en) * | 2021-06-11 | 2021-10-22 | 五邑大学 | Passenger connection prediction method, device and storage medium |
| CN113762896B (en) * | 2021-09-14 | 2024-06-11 | 福建三钢闽光股份有限公司 | Logistics garden vehicle dynamic queuing method based on analytic hierarchy process |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102354337A (en) * | 2011-10-14 | 2012-02-15 | 河海大学常州校区 | Reconfigurable assembly line multi-target scheduling decision method |
| CN102624805A (en) * | 2011-10-31 | 2012-08-01 | 李宗诚 | ICT technical support design for providing engineering for internet user terminal market configuration |
| US20150017985A1 (en) * | 2013-07-10 | 2015-01-15 | Samsung Electronics Co., Ltd. | Systems and methods for handoff in heterogeneous networks |
-
2019
- 2019-04-27 CN CN201910346862.2A patent/CN110119890B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 模糊层次分析法和灰色关联度在铁路方案决策中的应用;周小川;《中国硕士学位论文全文数据库 工程科技II辑》;20120415;第1-75页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110119890A (en) | 2019-08-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110119890B (en) | Railway riding scheme sorting method based on AHP-grey correlation analysis | |
| Redding et al. | Transportation costs and the spatial organization of economic activity | |
| CN109753694A (en) | Small and medium-sized cities Transit Network Design method based on overall process trip detecting period | |
| CN109344991B (en) | Method for predicting passenger flow of highest section of bus line | |
| CN107609677A (en) | A kind of customization public bus network planing method based on taxi GPS big datas | |
| CN110020745B (en) | Real-time large-scale aviation hub parking lot scale prediction method based on flight schedule | |
| CN115392949A (en) | Rail transit early-peak time-sharing pricing method based on passenger departure time selection | |
| CN108681741B (en) | Subway commuting crowd information fusion method based on IC card and resident survey data | |
| CN111640294B (en) | Method for predicting passenger flow change of urban bus line under influence of newly-built subway line | |
| CN110879866B (en) | Air-rail joint transit location determination method based on passenger travel multivariate data analysis | |
| CN110119884B (en) | High-speed railway passenger flow time interval division method based on neighbor propagation clustering | |
| CN113592246B (en) | PCA-TOPSIS model-based road construction period bus network evaluation method | |
| CN110533215A (en) | A kind of taxi of cruising based on GPS data seeks objective behavior prediction method | |
| CN115713206A (en) | Bus individual trip decision model | |
| CN115293743A (en) | Rail transit operation time interval dividing method, device and storage medium | |
| CN109633716A (en) | City distribution vehicle driving chain and its characteristic recognition method and equipment based on GPS | |
| CN115186905A (en) | Multi-task optimization method and system for inter-city network taxi appointment path planning | |
| CN114037160A (en) | Method for constructing passenger flow prediction model of SEM-Logit tourism railway | |
| CN111079008B (en) | Scheme recommendation method and system for taxi driver to leave in storage pool | |
| CN112950265A (en) | Regional multi-parking-lot joint pricing method | |
| CN113642841A (en) | Taxi service quality evaluation method | |
| CN114881423B (en) | Method for determining in-process transit city aiming at hub transfer scene | |
| CN110728461A (en) | Stroke replacement method, device and system | |
| Bhui et al. | Investigating Factors Responsible for Reduction in Bus Ridership Due to Fixed Route Paratransit Modes | |
| CN115018454B (en) | Passenger travel time value calculation method based on travel mode identification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |