CN109064044B - Public transport comprehensive evaluation and problem positioning method and system - Google Patents
Public transport comprehensive evaluation and problem positioning method and system Download PDFInfo
- Publication number
- CN109064044B CN109064044B CN201810926048.3A CN201810926048A CN109064044B CN 109064044 B CN109064044 B CN 109064044B CN 201810926048 A CN201810926048 A CN 201810926048A CN 109064044 B CN109064044 B CN 109064044B
- Authority
- CN
- China
- Prior art keywords
- evaluation
- matrix
- index
- indexes
- item
- 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
- 238000011156 evaluation Methods 0.000 title claims abstract description 100
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 51
- 238000011161 development Methods 0.000 claims abstract description 16
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 230000006872 improvement Effects 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 64
- 230000009467 reduction Effects 0.000 claims description 16
- 238000013480 data collection Methods 0.000 claims description 8
- 230000003068 static effect Effects 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 4
- 230000007246 mechanism Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 238000005457 optimization Methods 0.000 abstract description 4
- 238000011867 re-evaluation Methods 0.000 abstract description 3
- 230000007547 defect Effects 0.000 description 11
- 238000010276 construction Methods 0.000 description 6
- 238000007726 management method Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003032 molecular docking Methods 0.000 description 1
- 238000013215 result calculation Methods 0.000 description 1
- 230000000007 visual effect Effects 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
- G06Q10/06—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
-
- 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
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a system for comprehensive evaluation and problem positioning of public transportation, wherein the method eliminates the influence of index correlation on a comprehensive evaluation result through data processing on the basis of an initial itemized evaluation index, backtracks the problems of an original index according to the performance of an independent parameter and identifies an index item to be improved; according to the method and the system for the comprehensive evaluation and problem positioning of the public transport, after index evaluation is completed, autonomous problem analysis is carried out, so that index items needing to be improved are identified and distributed to relevant departments and units for processing, the dynamic state of the item indexes is continuously tracked before problems are eliminated, and continuous optimization and improvement are realized. The intelligent decision effect of the public transport development level evaluation system is improved through a closed-loop evaluation mode of 'evaluation-problem finding-problem distribution-problem solving-re-evaluation'.
Description
Technical Field
The invention relates to a method and a system for comprehensive evaluation and problem positioning of public transportation.
Background
Since the department of transportation started the public transportation city creation project in 2012, project construction tasks of 15 first national public transportation city construction demonstration project creation cities are about to enter the acceptance phase. 20 public transportation city constraint indexes and 10 important reference indexes are clarified in the public transportation city assessment index system. Quantitative assessment standards provide a clear target for public transportation urban construction, and unscheduled self-examination and assessment in a 5-year construction period are one of necessary means for checking construction effects. However, 30 indexes set in the assessment index system and more than 50 parameters and data related to index calculation bring certain difficulty to the examination and assessment work of each place. Similarly, various evaluations and evaluations of other projects of the urban public transport development level all relate to a complex index system, and are expanded from multiple dimensions of different levels such as overall scale, operation condition and service condition.
At present, the main research and application in this field are also focused on the research of evaluation systems for rationality and feasibility, such as patent nos. CN201410032296.5 and CN 201710543797.3. However, in practice, the purpose of the evaluation is to improve, and how to find short plates and defects from the evaluation should be also taken into consideration. However, a closed-loop public transportation evaluation system is not available at present, and can autonomously perform problem positioning and backtracking after index evaluation and directly link to a responsible person, so as to achieve a coherent overall dynamic optimization effect.
In a public transport development level evaluation system, a common comprehensive evaluation result calculation method is to give different weights to indexes for summarizing, correlation exists among a considerable number of indexes, and if the indexes are qualified or not is evaluated one by using a conventional threshold value method, influence of the correlation on a final summarizing result is ignored, and defect index identification is possibly missed. This makes it difficult to accurately and completely locate the item that has a defect or is to be improved from the final evaluation result.
Disclosure of Invention
The invention aims to provide a method and a system for comprehensive evaluation and problem positioning of public transport, which solve the problems of defect identification and problem positioning on the basis of traditional index analysis and comprehensive scoring, eliminate the influence of correlation among original indexes on a comprehensive evaluation result through data processing, accurately position defective items and responsible persons in a large number of basic indexes, distribute problem processing tasks to the responsible persons through a cooperative disposal process, and supervise and urge improvement and resolution of defects.
The technical solution of the invention is as follows:
a public transport comprehensive evaluation and problem positioning method is characterized in that on the basis of an initial subentry evaluation index, the influence of index correlation on a comprehensive evaluation result is eliminated through data processing, problems existing in an original index are traced back according to the performance of an independent parameter, and an index item to be improved is identified; comprises the following steps of (a) carrying out,
s1, determining a data acquisition time interval and a data collection stage start-stop time; the data acquisition is to acquire various static and dynamic data required by the public traffic development level evaluation and calculate evaluation indexes; for time of data acquisitionTime tiThe evaluation indexes of (a) form an n x 1 dimensional matrix IiWherein n is the number of evaluation indexes;
s2, establishing an association table of the evaluation indexes and the users; the user refers to a group and an individual related to the responsibility and the evaluation index;
s3, combining the evaluation index matrixes obtained in the data collection stage in the step S1 into n X t dimensional training matrix X ═ I1,…,Ii,…,It]T is the total time interval number of data acquisition; the matrix X is subjected to centralized processing and converted into X*I.e. for the element X in the matrix X located in row a and column babConvert it to xab *＝xab-μaIn which μaThe element mean value of the row a, a and b respectively satisfy the conditions: a is in [1, n ]]、b∈[1,t]Any integer of (a);
s4, matrix X＊Performing dimensionality reduction processing to obtain a dimensionality reduction processing matrix P;
s5, performing dimension reduction processing on the analysis object matrix I, namely I1PI, the dimension of the matrix after processing is k x 1 dimension; wherein the analysis object matrix is an evaluation index in a certain time interval;
s6 matrix I after marking1Negative value element of (1), marked matrix is I2；
S7 matrix I with labels2Performing an inverse transformation, i.e. I2 *＝I2P-1，P-1Is the inverse matrix of P; matrix I2After the elements marked in the middle are inversely transformed, the corresponding indexes can be positioned, and the positioning indexes are problem indexes to be improved;
and S8, associating the indexes to be promoted with the corresponding users through the evaluation index and user association table generated in the step S2, and sending question index details to the associated users.
Further, in step S4, specifically,
s41, calculating matrix X＊Covariance matrix C of (a):
s42, calculating the eigenvalue lambda of the covariance matrix C1,…,λnAnd a feature vector e1,…,en；
S43, arranging the eigenvectors according to the order of the eigenvalues from big to small to form a matrix P0(ii) a Determining the dimensionality of the dimensionality reduction processing matrix P according to the variance interpretation rate V; wherein
And determining the value of the row number k of the matrix P according to the value V.
A public transport comprehensive evaluation and problem positioning system realizes the comprehensive evaluation, problem backtracking and distribution processing of the public transport development level by a data closed loop processing mechanism, and comprises an evaluation module, a problem identification module and a problem distribution processing module;
an evaluation module: an index calculation model is built in, dynamic data and static data are extracted from a database, then the itemized indexes are calculated, and further, comprehensive indexes are output according to a comprehensive grading model on the basis of the itemized indexes; outputting the item indexes and the comprehensive indexes;
the problem identification module is used for constructing a data processing model according to the public traffic comprehensive evaluation and problem positioning method, processing the indexes output by the evaluation module and outputting a problem index item to be promoted and associated users;
a problem distribution processing module: the index project problem conclusion output by the problem identification module is implemented to a specific responsible person, namely, according to the index project to be promoted output by the problem identification module, the problems existing in the project to be promoted are distributed to corresponding associated users, and a tracking disposal process is started; in the tracking handling process, through the cooperation of the evaluation module and the problem identification module, the corresponding index item dynamic numerical value output by the evaluation module is periodically sent to the associated user in a tracking period, and the item improvement condition is analyzed; the tracking period is until the problem is resolved, i.e., the problem identification module no longer outputs the item.
The invention has the beneficial effects that:
after index evaluation is completed, autonomous problem analysis is carried out, so that index items needing to be improved are identified and distributed to relevant departments and units for processing, the dynamic state of the item indexes is continuously tracked before problems are eliminated, and continuous optimization and improvement are realized. The intelligent decision effect of the public transport development level evaluation system is improved through a closed-loop evaluation mode of 'evaluation-problem finding-problem distribution-problem solving-re-evaluation'.
Secondly, on the basis of performing item evaluation on an index system, the invention performs dimension reduction treatment on the index system to eliminate correlation, analyzes defect items from independent indexes, performs defect item positioning in the original indexes through inverse treatment, and further corresponds the problem to a specific responsible person. Through the processing flow, the problem of defect index identification omission caused by index correlation can be solved, and the closed-loop public transport development level evaluation is effectively realized.
Drawings
Fig. 1 is a flow chart illustrating a method for comprehensively evaluating the development level of public transportation and identifying problem defects according to an embodiment of the present invention.
Fig. 2 is an explanatory block diagram of a system for comprehensive evaluation of the development level of public transportation and problem defect identification according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Examples
A public transport comprehensive evaluation and problem positioning method is characterized in that on the basis of an initial subentry evaluation index, the influence of index correlation on a comprehensive evaluation result is eliminated through data processing, problems existing in an original index are traced back according to the performance of an independent parameter, and an index item to be improved is identified; as shown in fig. 1, the method specifically comprises the following steps:
s1: determining a data acquisition time interval and a data collection stage start-stop time; the data acquisition is to acquire various static and dynamic data required by the public traffic development level evaluation and calculate evaluation indexes; for data acquisition time tiThe evaluation indexes of (a) form an n x 1 dimensional matrix IiWherein n is the number of evaluation indexes.
S2: establishing an association table of the evaluation indexes and the users; the user refers to a group and an individual related to the responsibility and the evaluation index; such as a department of transportation, a department associated with a public transportation company, or a person in charge of a related administrative affair, etc.
S3: the evaluation index matrix obtained in the data collection stage of S1 is combined into n X t dimensional training matrix X ═ I1,…,Ii,…,It]T is the total time interval number of data acquisition; the matrix X is subjected to centralized processing and converted into X＊I.e. for the element X in the matrix X located in row a and column babConvert it to xab *＝xab-μaIn which μaThe element mean value of the row a, a and b respectively satisfy the conditions: a is in [1, n ]]、b∈[1,t]Any integer of (1).
S4: for matrix X＊Performing dimensionality reduction processing to obtain a dimensionality reduction processing matrix P; the method comprises the following specific steps:
s41: calculating the matrix X＊Is given by the formula
S42: calculating an eigenvalue λ of the covariance matrix C1,…,λnAnd a feature vector e1,…,en；
S43: arranging the eigenvectors according to the order of the eigenvalues from big to small to form a matrix P0(ii) a Determining the dimensionality of the dimensionality reduction processing matrix P according to the variance interpretation rate V; wherein
And determining the value of the row number k of the matrix P according to the value V.
S5: the matrix of objects under analysis I is subjected to dimension reduction, i.e. I1PI, the dimension of the matrix after processing is k x 1 dimension; wherein the analysis object matrix is an evaluation index within a certain time interval.
S6: marking of the processed matrix I1Negative value element of (1), marked matrix is I2。
S7: matrix I with labels2Performing an inverse transformation, i.e. I2 *＝I2P-1，P-1Is the inverse matrix of P; matrix I2After the elements marked in the middle are inversely transformed, the corresponding indexes can be positioned, and the positioning indexes are problem indexes to be improved.
S8: the evaluation index and user association table generated in step S2 associates the index to be promoted with the corresponding user, and sends the question index details to the associated user.
The embodiment also provides a public transport comprehensive evaluation and problem positioning system, which realizes the comprehensive evaluation, problem backtracking and distribution processing of the public transport development level by a data closed-loop processing mechanism, and comprises an evaluation module, a problem identification module and a problem distribution processing module as shown in figure 2;
the evaluation module is internally provided with an index calculation model, calculates the itemized indexes after extracting dynamic data and static data from the database, and further outputs the comprehensive indexes according to the comprehensive grading model on the basis of the itemized indexes; outputting the item indexes and the comprehensive indexes;
the problem identification module is used for constructing a data processing model according to the method 1, processing the indexes output by the evaluation module and outputting the problem index items to be promoted and associated users;
a problem distribution processing module: the module realizes the index item problem conclusion output by the problem identification module to a specific responsible person, namely, according to the index item to be promoted output by the problem identification module, the problem existing in the item to be promoted is distributed to the corresponding associated user, and a tracking disposal process is started; in the tracking handling process, through the cooperation of the evaluation module and the problem identification module, the corresponding index item dynamic numerical value output by the evaluation module is periodically sent to the associated user in a tracking period, and the item improvement condition is analyzed; the tracking period is until the problem is resolved, i.e., the problem identification module no longer outputs the item.
The embodiment provides a public transport comprehensive evaluation and problem positioning method, which aims to realize defect identification, problem backtracking and responsible person positioning by eliminating correlation among evaluation indexes; on the basis, the public transport comprehensive evaluation system aims to realize automatic comprehensive evaluation, problem positioning and problem distribution, realize full-flow closed-loop public transport development evaluation and intelligent decision, is oriented to departments such as planning, management and operation, various users such as transportation enterprises and the like, and transmits related single evaluation problems to the users, supervises and urges the correction or improvement of the problems so as to implement a continuous optimization mode of 'evaluation-problem finding-problem distribution-problem solving-re-evaluation', and meets the practical requirement of various levels of cities for improving the local public transport development level.
One specific example of an embodiment is as follows:
the method and the system of the embodiment are applied to the comprehensive evaluation application of the public transportation city. Determining data acquisition contents including public transportation travel volume, motorized travel total volume, public transportation and electric vehicle line network length, urban road network length, 500-meter radius coverage area of a public transportation station, built-up area of a central urban area, public transportation vehicle station total number, urban area population, initial punctual shift, final station-to-station punctual shift, planned departure shift/actual departure shift and other dynamic and static data according to 'a public transportation urban assessment evaluation index system' and the current public transportation urban creation and acceptance period, and calculating 30 evaluation indexes of public transportation motorized travel sharing rate, road transportation and electric vehicle line network ratio, public transportation station 500-meter coverage rate, ten-thousand public transportation vehicle holding capacity, public transportation punctual rate and the like according to an index calculation method explained by the 'index system'; and setting a data collection stage from the application year to the acceptance year of the urban public transport, wherein the data collection time interval is 1 month.
The public transport development level comprehensive evaluation system acquires dynamic data in a data docking mode, and the static data is mainly acquired in a background recording mode and the like. Taking 2016-2017 public transport city construction evaluation of a certain city as an example, the system evaluation module calculates the 30 evaluation indexes every month; problem identification module for 2016 month 1 to month 12 month each 30 x 1 dimensional initial index matrix IiProcessing is carried out to generate a 30 x 12 dimensional training matrix, dimension reduction processing is carried out to obtain a dimension reduction matrix, and then an index matrix I of 1 month in 2017 is subjected to dimension reduction processing13Go on and fallDimension processing and inverse transformation processing are carried out, and problem indexes to be improved are identified and positioned.
The problem distribution processing module sends problem indexes and current values thereof to users with corresponding authorities in a user management table according to the association table of each index, a transportation enterprise and a management department, and pushes the current values of the problem indexes every month after 2017 and 1 month until the problem is solved; the module counts the monthly index improvement condition through data statistics and other processing means, and provides visual trend tracking assistance for users.
Claims (2)
1. A public transport comprehensive evaluation and problem positioning method is characterized in that: on the basis of the initial item evaluation index, eliminating the influence of index correlation on the comprehensive evaluation result through data processing, backtracking the problems of the original index according to the performance of independent parameters, and identifying the index item to be improved; comprises the following steps of (a) carrying out,
s1, determining a data acquisition time interval and a data collection stage start-stop time; the data acquisition is to acquire various static and dynamic data required by the public traffic development level evaluation and calculate evaluation indexes; for data acquisition time tiThe evaluation indexes of (a) form an n x 1 dimensional matrix IiWherein n is the number of evaluation indexes;
s2, establishing an association table of the evaluation indexes and the users; the user refers to a group and an individual related to the responsibility and the evaluation index;
s3, combining the evaluation index matrixes obtained in the data collection stage in the step S1 into n X t dimensional training matrix X ═ I1,…,Ii,…,It]T is the total time interval number of data acquisition; the matrix X is subjected to centralized processing and converted into X*I.e. for the element X in the matrix X located in row a and column babConvert it to xab*＝xab-μaIn which μaThe element mean value of the row a, a and b respectively satisfy the conditions: a is in [1, n ]]、b∈[1,t]Any integer of (a);
s4, matrix X*Performing dimensionality reduction processing to obtain a dimensionality reduction processing matrix P; in step S4, specifically, the step,
s41, calculating matrix X*Covariance matrix C of (a):
s42, calculating the eigenvalue lambda of the covariance matrix C1,…,λnAnd a feature vector e1,…,en；
S43, arranging the eigenvectors according to the order of the eigenvalues from big to small to form a matrix P0(ii) a Determining the dimensionality of the dimensionality reduction processing matrix P according to the variance interpretation rate V; wherein
Determining the value of the row number k of the matrix P according to the value V;
s5, performing dimension reduction processing on the analysis object matrix I, namely I1PI, the dimension of the matrix after processing is k x 1 dimension; wherein the analysis object matrix is an evaluation index in a certain time interval;
s6 matrix I after marking1Negative value element of (1), marked matrix is I2；
S7 matrix I with labels2Performing an inverse transformation, i.e. I2 *＝I2P-1，P-1Is the inverse matrix of P; matrix I2After the elements marked in the middle are inversely transformed, the corresponding indexes can be positioned, and the positioning indexes are problem indexes to be improved;
and S8, associating the indexes to be promoted with the corresponding users through the evaluation index and user association table generated in the step S2, and sending question index details to the associated users.
2. A public transport comprehensive evaluation and problem positioning system is characterized in that: the system realizes comprehensive evaluation, problem backtracking and distribution processing of the public traffic development level by a data closed-loop processing mechanism, and comprises an evaluation module, a problem identification module and a problem distribution processing module;
an evaluation module: an index calculation model is built in, dynamic data and static data are extracted from a database, then the itemized indexes are calculated, and further, comprehensive indexes are output according to a comprehensive grading model on the basis of the itemized indexes; outputting the item indexes and the comprehensive indexes;
the problem identification module is used for constructing a data processing model according to the public transport comprehensive evaluation and problem positioning method of claim 1, processing the indexes output by the evaluation module and outputting a problem index item to be promoted and associated users;
a problem distribution processing module: the problem index item conclusion output by the problem identification module is implemented to a specific responsible person, namely, according to the problem index item to be promoted output by the problem identification module, the problem existing in the item to be promoted is distributed to the corresponding associated user, and a tracking disposal process is started; in the tracking handling process, through the cooperation of the evaluation module and the problem identification module, the corresponding index item dynamic numerical value output by the evaluation module is periodically sent to the associated user in a tracking period, and the item improvement condition is analyzed; the tracking period is until the problem is resolved, i.e., the problem identification module no longer outputs the item.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810926048.3A CN109064044B (en) | 2018-08-14 | 2018-08-14 | Public transport comprehensive evaluation and problem positioning method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810926048.3A CN109064044B (en) | 2018-08-14 | 2018-08-14 | Public transport comprehensive evaluation and problem positioning method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109064044A CN109064044A (en) | 2018-12-21 |
| CN109064044B true CN109064044B (en) | 2021-08-10 |
Family
ID=64678362
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810926048.3A Active CN109064044B (en) | 2018-08-14 | 2018-08-14 | Public transport comprehensive evaluation and problem positioning method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109064044B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100106545A1 (en) * | 2008-10-27 | 2010-04-29 | Noblis, Inc. | Systems and methods for facilitating evaluation in an acquisition process |
| CN104200284A (en) * | 2014-09-01 | 2014-12-10 | 山东科技大学 | Mine fault structure prediction method |
| CN104809112A (en) * | 2014-01-23 | 2015-07-29 | 朱东霞 | Method for comprehensively evaluating urban public transportation development level based on multiple data |
| CN106815333A (en) * | 2016-12-30 | 2017-06-09 | 江苏物联网研究发展中心 | A kind of wisdom gridding Regional Management System |
| CN106846805A (en) * | 2017-03-06 | 2017-06-13 | 南京多伦科技股份有限公司 | A kind of dynamic road grid traffic needing forecasting method and its system |
| CN107729938A (en) * | 2017-12-11 | 2018-02-23 | 北方工业大学 | Rail station classification method based on bus connection radiation zone characteristics |
-
2018
- 2018-08-14 CN CN201810926048.3A patent/CN109064044B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100106545A1 (en) * | 2008-10-27 | 2010-04-29 | Noblis, Inc. | Systems and methods for facilitating evaluation in an acquisition process |
| CN104809112A (en) * | 2014-01-23 | 2015-07-29 | 朱东霞 | Method for comprehensively evaluating urban public transportation development level based on multiple data |
| CN104200284A (en) * | 2014-09-01 | 2014-12-10 | 山东科技大学 | Mine fault structure prediction method |
| CN106815333A (en) * | 2016-12-30 | 2017-06-09 | 江苏物联网研究发展中心 | A kind of wisdom gridding Regional Management System |
| CN106846805A (en) * | 2017-03-06 | 2017-06-13 | 南京多伦科技股份有限公司 | A kind of dynamic road grid traffic needing forecasting method and its system |
| CN107729938A (en) * | 2017-12-11 | 2018-02-23 | 北方工业大学 | Rail station classification method based on bus connection radiation zone characteristics |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109064044A (en) | 2018-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Takeuchi et al. | Autoregressive tensor factorization for spatio-temporal predictions | |
| CN107862450A (en) | Performance appraisal system and method | |
| CN107609835A (en) | A kind of power network manapower allocation application system and method | |
| CN103336894B (en) | A kind of city plot ratio automatic Mesh Partition Method | |
| CN102591929B (en) | Library data processing system and data processing method thereof | |
| CN110889092A (en) | Short-time large-scale activity peripheral track station passenger flow volume prediction method based on track transaction data | |
| CN113344467A (en) | Method and device for predicting capital of electric power engineering | |
| CN111539764A (en) | Big data multiple access selection method based on submodular function | |
| CN109409563B (en) | Method, system and storage medium for analyzing real-time number of people in public transport operation vehicle | |
| Arabghalizi et al. | Data-driven bus crowding prediction models using context-specific features | |
| CN101226621A (en) | Client information system and application method as well as corresponding market researching machine thereof | |
| CN109064044B (en) | Public transport comprehensive evaluation and problem positioning method and system | |
| CN113076855A (en) | Distributed photovoltaic resource integration method, system, device and storage medium | |
| Chou | Applying AHP-based CBR to estimate pavement maintenance cost | |
| CN117114169A (en) | Emergency material warehouse optimal location method and system based on comprehensive evaluation | |
| Tu et al. | Analysis and forecast of passenger flow based on public transportation IC card and GPS data | |
| CN110807012A (en) | Big data supporting platform of all-round power supply station | |
| CN114881309A (en) | Method for measuring characteristic correlation between urban vitality and carbon emptying | |
| Musinguzi et al. | Opportunities and challenges for SDI development in developing countries-A case study of Uganda | |
| Huang et al. | Towards automated model calibration and validation in rail transit simulation | |
| Young et al. | An automated online tool to forecast demand for new railway stations and analyse potential abstraction effects | |
| RU113386U1 (en) | DECISION MAKING SUPPORT SYSTEM FOR REMOTE TRAINING SYSTEM OF SPECIALISTS IN THE FIELD OF GLONASS USER NAVIGATION EQUIPMENT | |
| CN103295093A (en) | Urban center automatic site-selection method based on integration level | |
| Li et al. | The application of fuzzy neural network in distribution center location | |
| Feng et al. | Study on grey relation analysis based on entropy method in evaluation of logistics center location |
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 | ||
| CB02 | Change of applicant information |
Address after: 211100 No. 19 Suyuan Avenue, Jiangning Economic and Technological Development Zone, Nanjing City, Jiangsu Province Applicant after: JIANGSU ZHITONG TRAFFIC TECHNOLOGY Co.,Ltd. Address before: 210006, Qinhuai District, Jiangsu, Nanjing should be 388 days street, Chenguang 1865 Technology Creative Industry Park E10 building on the third floor Applicant before: JIANGSU ZHITONG TRAFFIC TECHNOLOGY Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |