CN116402390A - Road construction area traffic influence evaluation method and system based on primitive model - Google Patents
Road construction area traffic influence evaluation method and system based on primitive model Download PDFInfo
- Publication number
- CN116402390A CN116402390A CN202310356238.7A CN202310356238A CN116402390A CN 116402390 A CN116402390 A CN 116402390A CN 202310356238 A CN202310356238 A CN 202310356238A CN 116402390 A CN116402390 A CN 116402390A
- Authority
- CN
- China
- Prior art keywords
- traffic
- index
- impact evaluation
- traffic impact
- road construction
- 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.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 167
- 238000010276 construction Methods 0.000 title claims abstract description 74
- 238000012423 maintenance Methods 0.000 claims abstract description 34
- 238000012544 monitoring process Methods 0.000 claims abstract description 21
- 239000000463 material Substances 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims description 41
- 238000000034 method Methods 0.000 claims description 19
- 230000008859 change Effects 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000007781 pre-processing Methods 0.000 claims description 7
- 230000007704 transition Effects 0.000 claims description 6
- 238000011144 upstream manufacturing Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012821 model calculation Methods 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 abstract description 7
- 238000007726 management method Methods 0.000 abstract description 5
- 238000013468 resource allocation Methods 0.000 abstract description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000011160 research 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
- G06Q10/06—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
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Computer Security & Cryptography (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a road construction area traffic influence evaluation method and system based on a material element model, comprising the following steps: setting up a traffic impact evaluation grade and a traffic impact evaluation index, calculating classical domain, section domain and index weight of the traffic impact evaluation index, and constructing a traffic impact evaluation object model; and acquiring real-time monitoring data of the road construction area based on the real-time traffic flow state of the road construction area, calculating the association degree of the traffic impact evaluation level and the traffic impact evaluation index based on the real-time monitoring data and the traffic impact evaluation object model, further calculating the association degree of the real-time traffic flow state and each traffic impact evaluation level, determining the traffic impact real-time evaluation level, and finishing the traffic impact evaluation of the road construction area. According to the invention, the real-time evaluation of the influence of the road maintenance construction on the traffic operation is realized by constructing the comprehensive evaluation model of the building elements, so that the resource allocation of traffic management facilities in the road construction area is improved, and the transparency of the surrounding information of the road construction area is realized.
Description
Technical Field
The invention relates to the technical field of road construction area safety management and evaluation, in particular to a road construction area traffic influence evaluation method and system based on a primitive model.
Background
Road construction is a common mode of operation on roads. With the continuous increase of the mileage and the automobile conservation amount of the roads in China, more road maintenance work will be carried out in China. During road maintenance, because the road maintenance needs to occupy the road, traffic environment mutation brought by a road construction area has great influence on traffic flow operation and traffic safety, and even traffic accidents occur. The traffic influence of maintenance construction on the road sections around the construction can be mastered and known in real time, so that decision-makers can be helped to better select road traffic management methods, and related safety facilities are arranged; meanwhile, even if the real-time road information is released to a road user, the road traffic flow can be regulated, the resource optimization configuration is realized, and the method has important significance for reducing traffic accidents.
Disclosure of Invention
The invention aims to provide a road construction area traffic influence evaluation method and system based on a primitive model, which evaluate the influence caused by maintenance construction from the angle of traffic flow running state and realize the information transparency and resource optimization configuration of the periphery of a road maintenance area.
In order to achieve the above object, the present invention provides the following solutions:
a road construction area traffic influence evaluation method based on a primitive model comprises the following steps:
setting up a traffic impact evaluation grade and a traffic impact evaluation index, calculating classical domain, section domain and index weight of the traffic impact evaluation index, and constructing a traffic impact evaluation object model;
acquiring real-time monitoring data of the road construction area based on the real-time traffic flow state of the road construction area, and calculating the association degree of the traffic impact evaluation grade and the traffic impact evaluation index based on the real-time monitoring data and the traffic impact evaluation object model;
calculating the association degree of the real-time traffic flow state and each traffic impact evaluation level according to the association degree of the traffic impact evaluation level and the traffic impact evaluation index;
and determining the real-time traffic impact evaluation level according to the real-time traffic flow state and the association degree of each traffic impact evaluation level, and completing the traffic impact evaluation of the road construction area.
Further, setting up the traffic impact assessment level includes:
and determining the traffic influence evaluation grade according to the traffic influence evaluation range and the influence size of the road construction area, wherein the traffic influence evaluation range of the road construction area comprises a warning area, an upstream transition area, a buffer area, a working area, a downstream transition area, a termination area and an upstream and downstream extension road section.
Further, setting up the traffic impact evaluation index includes:
and selecting a parameter index capable of representing the running state of the traffic flow as the traffic influence evaluation index, wherein the traffic influence evaluation index comprises the running speed of the traffic flow, the average acceleration of the traffic flow, the traffic flow in time periods, the standard deviation of the speed, the accumulated lane change times in time periods and the accumulated overtaking times in time periods.
Further, calculating the classical domain, the festival domain and the index weight of the traffic impact evaluation index comprises:
and acquiring training data, and calculating based on the training data to acquire classical domains, festival domains and index weights of each traffic impact evaluation index.
Further, acquiring the training data includes:
and acquiring traffic flow state data of different periods in the early maintenance period of the road construction area, comparing the traffic flow state data with the traffic flow state data of the same period in the non-construction period of the road, acquiring the change percentage of the traffic influence evaluation index of the different periods in the early maintenance period of the road construction area, and taking the change percentage of the traffic influence evaluation index of the different periods in the early maintenance period of the road construction area as the training data.
Further, the step of obtaining the classical domain and the festival domain of each traffic impact evaluation index comprises the following steps:
and classifying the training data according to the traffic impact evaluation grades by using a mean value clustering method to obtain classical domains and festival domains of each traffic impact evaluation index, wherein the data range in each class is the classical domain of the corresponding grade, and the maximum value and the minimum value of all the training data of one traffic impact evaluation index are the festival domains of the traffic impact evaluation index.
Further, the obtaining the index weight of each traffic impact evaluation index includes:
and calculating the index weight of each traffic impact evaluation index by adopting an entropy weight method, and determining the index weight of each traffic impact evaluation index according to the disorder degree of the index data of each traffic impact evaluation index in the training data.
Further, calculating the association degree of the traffic impact evaluation level and the traffic impact evaluation index includes:
inputting the real-time monitoring data into the traffic impact evaluation object model, and calculating the association degree of the traffic impact evaluation grade and the traffic impact evaluation index, wherein a calculation formula is as follows:
wherein,,
|V ij |＝|b ij -a ij |
wherein a is ij And b ij Upper and lower limits of classical domain of traffic impact evaluation index, a ip And b ip V is the upper and lower limit of the node area i Calculating the change percentage value of the traffic impact evaluation index parameter for real-time monitoring data, V ij The range of the j-th class classical domain as the i-th index, V ip For the range of the ith index section domain, K ij For the degree of association of the ith index and the jth level, ρ is a degree of association calculation function.
Further, calculating the association degree of the real-time traffic flow state and each traffic impact evaluation level includes:
multiplying the association degree of each traffic impact evaluation index with the same traffic impact evaluation level by a weight matrix to obtain the association degree of the real-time monitoring data and each traffic impact evaluation level, wherein a calculation formula is as follows:
wherein K is j (P) is the degree of association of the j-level, K j0 (P) is the final rating, w i Weight value K of the ith index j (V i ) Is the degree of association with the jth level of the ith index.
In order to further optimize the technical scheme, the invention also provides a road construction area traffic influence evaluation system based on the object model, which comprises the following steps:
the material element model construction module is used for selecting traffic influence evaluation grades and traffic influence evaluation indexes to construct a material element model;
the training data acquisition module is used for acquiring traffic flow state data in the earlier maintenance period of the road construction area and preprocessing the traffic flow state data to serve as training data;
the index classical domain and section domain selection module is used for clustering based on the training data to obtain classical domains and section domains of each traffic impact evaluation index;
the index weight operation module is used for calculating the information entropy of each traffic impact evaluation index training data based on an entropy weight method and determining the weight of each traffic impact evaluation index;
the real-time monitoring data acquisition module is used for acquiring traffic flow state data during maintenance of a road construction area and preprocessing the traffic flow state data to serve as real-time monitoring data;
the material element model calculation module is used for inputting the real-time monitoring data into the material element model to calculate the association degree of the real-time traffic flow state and each traffic influence evaluation level;
and the result analysis module is used for carrying out the result analysis of the real-time traffic influence based on the association degree of the real-time traffic flow state and each traffic influence evaluation grade.
The beneficial effects of the invention are as follows:
according to the invention, from the aspect of traffic flow operation characteristics, the comprehensive evaluation model of the physical elements is constructed based on the influence of road maintenance construction on traffic operation, and real-time evaluation is carried out based on the comprehensive evaluation model of the physical elements, so that the resource allocation of traffic management facilities in a road construction area is improved, and the transparency of surrounding information of the road construction area is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a road construction area traffic impact evaluation method based on a primitive model according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The embodiment provides a road construction area traffic influence evaluation method based on a primitive model, as shown in fig. 1, including:
s1, determining the traffic influence evaluation range and the number of the object element evaluation grades of the influence size of the road construction area.
The evaluation range comprises 6 components of a maintenance area in road traffic sign and marking (GB 5768.4-2017): the warning area, the upstream transition area, the buffer area, the working area, the downstream transition area, the termination area and the upstream and downstream extension road sections are determined according to actual needs, and the eye length is in positive correlation with the total length of the area 6.
The number of traffic influence evaluation grades can be determined to be 3-5 evaluation grades according to the evaluation requirement, and when 5 evaluation grades are selected, the evaluation grades can be as follows: "greatly affected", "greater influence", "generally affected", "less affected", "hardly affected".
S2, selecting a plurality of parameter indexes capable of representing the running state of the traffic flow as evaluation indexes of the physical element comprehensive evaluation model according to research requirements.
Selecting a plurality of parameter indexes capable of representing the running state of the traffic flow as evaluation indexes for the comprehensive evaluation of the material elements, wherein the evaluation indexes comprise: traffic flow running speed, traffic flow uniform acceleration, time period traffic flow, speed standard deviation, time period accumulated lane change times and time period accumulated overtaking times.
And S3, selecting, acquiring and preprocessing index data, and taking the index data as training data.
The method comprises the steps of acquiring related data of traffic flow in different periods and evaluation road section ranges in the early maintenance period of a road construction area through GPS or radar equipment, comparing the data with data of the same period of the non-construction period of the road to obtain the change percentage of evaluation index parameters of different periods in the early maintenance period of the road construction area, and using the data as training data of classical domains, node domains and weight values of various indexes.
The change percentage of traffic flow operation data in the maintenance process of other maintenance road sections in the same area compared with the traffic flow operation data in the non-maintenance process can also be considered as training data. The data needs to be collected and divided according to different time periods, and the longer the divided time period is, the longer the time for serving as training data is, the more accurate the influence on traffic flow is detected in real time. The traffic flow parameter data in the S2 is collected in the divided time period and then needs to be preprocessed, the traffic flow data in the construction is compared with the traffic flow data in the same time period before the construction, the absolute change percentage value of the parameter index after the construction compared with the parameter index before the construction is calculated, and the absolute change percentage value is taken as training data for determining the weight of the classical domain, the node domain and the index.
S4, determining classical domains and section domains of each index in the object model through K-means mean value clustering by using training data.
And determining classical domains and section domains of different evaluation indexes by using a K-means mean clustering method. Determining classical domains and section domains of each index by adopting a K-means mean value clustering method for training data in the step S3; the training data are classified according to the number of the comprehensive evaluation grades selected in the step S1 (from good to poor in 3-5 types), the data range in each type is the classical domain of the corresponding grade, and the minimum value and the maximum value of all the training data of one parameter index are the node domains of the parameter index.
S5, determining information entropy of each index training data by utilizing the training data, and determining the weight of each index according to the size of the index information entropy.
And calculating the weight of each evaluation index by adopting an entropy weight method. Determining the weight of each index according to the disorder degree of the data under each index parameter in the training data; the higher the disorder of the index data, the greater the weight corresponding to the index.
S6, acquiring traffic flow operation data in real time, and calculating the association degree of each index and each evaluation grade through the material element model after preprocessing.
S6.1, acquiring data such as traffic flow running speed, traffic flow uniform acceleration, time period traffic flow, speed standard deviation, time period accumulated lane change times, time period accumulated overtaking times and the like in the influence range of a construction area in real time through tools such as a radar, a GPS and the like; and calculating the change percentage value of the data obtained in real time compared with the data value of the same period before maintenance.
S6.2, except training data collected in the early maintenance period, data in different periods of the rest maintenance period can be used for evaluating the traffic influence generated by road maintenance in real time, and the data collected in real time also need to be preprocessed before being evaluated by the object model, wherein the preprocessing method is consistent with that of the S3 training data.
Combining the classical domain, the node domain and the index weight coefficients in the S4 and the S5, applying the data obtained in the S6.1 to a material element evaluation model, and calculating the association degree of each index and each evaluation grade. The calculation formula is as follows:
wherein,,
|V ij |＝|b ij -a ij |
wherein a is ij And b ij Upper and lower limits of classical domain of traffic impact evaluation index, a ip And b ip V is the upper and lower limit of the node area i Calculating the change percentage value of the traffic impact evaluation index parameter for real-time monitoring data, V ij The range of the j-th class classical domain as the i-th index, V ip For the range of the ith index section domain, K ij For the degree of association of the ith index and the jth level, ρ is a degree of association calculation function.
And S7, calculating the association degree of the real-time running state of the traffic flow and each evaluation level through the object element model, and determining the final real-time evaluation level of the traffic influence, thereby evaluating the traffic influence of the road construction area.
And multiplying the association degree of the same evaluation level of each index by a weight matrix to obtain the association degree of the measured data and each evaluation level, and taking the evaluation level corresponding to the highest association degree as the final real-time traffic influence evaluation level. The calculation formula is as follows:
wherein K is j (P) is the degree of association of the j-level, K j0 (P) is the final rating, w i Weight value K of the ith index j (V i ) Is the degree of association with the jth level of the ith index.
In order to further optimize the technical scheme, the embodiment also provides a road construction area traffic influence evaluation system based on the object model, which comprises:
building module of primitive model
And selecting the traffic influence evaluation grade and the traffic influence evaluation index, and constructing a building element model.
Training data acquisition module
The source of the training data is traffic flow operation data in the early stage of road maintenance; the data can be evaluated by applying the meta-model through pretreatment, and the pretreatment method comprises the following steps: after the traffic flow operation index selected in the previous module is obtained by using tools such as radar, GPS and the like, the percentage value of the index data in the curing construction period compared with the data change before curing construction is calculated, and the percentage value is the data processing result.
Index classical domain and node domain selection module
Clustering training data of each index by using a K-means mean value clustering method, wherein the clustering number is equal to the evaluation level number, the minimum value and the maximum value of data in each class after clustering are classical domains of the corresponding evaluation level of the index, and the minimum value and the maximum value of all training data of one index are node domains of the index;
index weight operation module
Determining the weight level of different indexes by calculating the information entropy of training data of different indexes; the larger the information entropy is, the larger the weight of the corresponding index is.
Real-time monitoring data acquisition module
And acquiring traffic flow operation data in the influence range of the maintenance area in real time, comparing the traffic flow operation data with data in the same time period before road maintenance, calculating the percentage value of the change of the data value after maintenance compared with the data value before maintenance, and taking the percentage value as actual calculation data of the object element model.
Object model calculation module
According to the calculated classical domain, the node domain and the weight level of each index, the real-time monitoring and processing data are applied to calculate the association degree of each index and each grade, and then calculate the association degree of the real-time traffic flow state and each evaluation grade.
Result analysis module
And according to the calculated association degree of the traffic flow and each evaluation grade, taking the evaluation grade corresponding to the maximum association degree value as the evaluation grade of real-time traffic influence.
According to the invention, from the aspect of traffic flow operation characteristics, the comprehensive evaluation model of the physical elements is constructed based on the influence of road maintenance construction on traffic operation, and real-time evaluation is carried out based on the comprehensive evaluation model of the physical elements, so that the resource allocation of traffic management facilities in a road construction area is improved, and the transparency of surrounding information of the road construction area is realized.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (10)
1. The method for evaluating the traffic influence of the road construction area based on the primitive model is characterized by comprising the following steps of:
setting up a traffic impact evaluation grade and a traffic impact evaluation index, calculating classical domain, section domain and index weight of the traffic impact evaluation index, and constructing a traffic impact evaluation object model;
acquiring real-time monitoring data of the road construction area based on the real-time traffic flow state of the road construction area, and calculating the association degree of the traffic impact evaluation grade and the traffic impact evaluation index based on the real-time monitoring data and the traffic impact evaluation object model;
calculating the association degree of the real-time traffic flow state and each traffic impact evaluation level according to the association degree of the traffic impact evaluation level and the traffic impact evaluation index;
and determining the real-time traffic impact evaluation level according to the real-time traffic flow state and the association degree of each traffic impact evaluation level, and completing the traffic impact evaluation of the road construction area.
2. The road construction area traffic impact evaluation method based on the primitive model according to claim 1, wherein setting up the traffic impact evaluation level comprises:
and determining the traffic influence evaluation grade according to the traffic influence evaluation range and the influence size of the road construction area, wherein the traffic influence evaluation range of the road construction area comprises a warning area, an upstream transition area, a buffer area, a working area, a downstream transition area, a termination area and an upstream and downstream extension road section.
3. The road construction area traffic impact evaluation method based on the primitive model according to claim 1, wherein setting up the traffic impact evaluation index comprises:
and selecting a parameter index capable of representing the running state of the traffic flow as the traffic influence evaluation index, wherein the traffic influence evaluation index comprises the running speed of the traffic flow, the average acceleration of the traffic flow, the traffic flow in time periods, the standard deviation of the speed, the accumulated lane change times in time periods and the accumulated overtaking times in time periods.
4. The road construction area traffic impact evaluation method based on the primitive model according to claim 1, wherein calculating classical domain, pitch domain and index weight of the traffic impact evaluation index comprises:
and acquiring training data, and calculating based on the training data to acquire classical domains, festival domains and index weights of each traffic impact evaluation index.
5. The method for evaluating traffic impact in a road construction area based on a primitive model as claimed in claim 4, wherein obtaining the training data comprises:
and acquiring traffic flow state data of different periods in the early maintenance period of the road construction area, comparing the traffic flow state data with the traffic flow state data of the same period in the non-construction period of the road, acquiring the change percentage of the traffic influence evaluation index of the different periods in the early maintenance period of the road construction area, and taking the change percentage of the traffic influence evaluation index of the different periods in the early maintenance period of the road construction area as the training data.
6. The method for evaluating traffic impact on a road construction area based on a primitive model according to claim 4, wherein obtaining the classical domain and the joint domain of each traffic impact evaluation index comprises:
and classifying the training data according to the traffic impact evaluation grades by combining a mean value clustering method to obtain classical domains and festival domains of each traffic impact evaluation index, wherein the data range in each class is the classical domain of the corresponding grade, and the maximum value and the minimum value of all the training data of one traffic impact evaluation index are the festival domains of the traffic impact evaluation index.
7. The method for evaluating traffic impact on a road construction area based on a primitive model as claimed in claim 4, wherein obtaining the index weight of each traffic impact evaluation index comprises:
and calculating the index weight of each traffic impact evaluation index by adopting an entropy weight method, and determining the index weight of each traffic impact evaluation index according to the disorder degree of the index data of each traffic impact evaluation index in the training data.
8. The road construction area traffic impact evaluation method based on the primitive model according to claim 1, wherein calculating the association degree of the traffic impact evaluation level and the traffic impact evaluation index comprises:
inputting the real-time monitoring data into the traffic impact evaluation object model, and calculating the association degree of the traffic impact evaluation grade and the traffic impact evaluation index, wherein a calculation formula is as follows:
wherein,,
|V ij |＝|b ij -a ij |
wherein a is ij And b ij Upper and lower limits of classical domain of traffic impact evaluation index, a ip And b ip V is the upper and lower limit of the node area i Calculating the change percentage value of the traffic impact evaluation index parameter for real-time monitoring data, V ij The range of the j-th class classical domain as the i-th index, V ip For the range of the ith index section domain, K ij For the degree of association of the ith index and the jth level, ρ is a degree of association calculation function.
9. The road construction area traffic impact evaluation method based on the primitive model according to claim 8, wherein calculating the degree of association of the real-time traffic flow state and each traffic impact evaluation level comprises:
multiplying the association degree of each traffic impact evaluation index with the same traffic impact evaluation level by a weight matrix to obtain the association degree of the real-time monitoring data and each traffic impact evaluation level, wherein a calculation formula is as follows:
wherein K is j (P) is the degree of association of the j-level, K j0 (P) is the final rating, w i Weight value K of the ith index j (V i ) Is the degree of association with the jth level of the ith index.
10. The evaluation system of the road construction area traffic influence evaluation method based on the meta model according to any one of claims 1 to 9, comprising:
the material element model construction module is used for selecting traffic influence evaluation grades and traffic influence evaluation indexes to construct a material element model;
the training data acquisition module is used for acquiring traffic flow state data in the earlier maintenance period of the road construction area and preprocessing the traffic flow state data to serve as training data;
the index classical domain and section domain selection module is used for clustering based on the training data to obtain classical domains and section domains of each traffic impact evaluation index;
the index weight operation module is used for calculating the information entropy of each traffic impact evaluation index training data based on an entropy weight method and determining the weight of each traffic impact evaluation index;
the real-time monitoring data acquisition module is used for acquiring traffic flow state data during maintenance of a road construction area and preprocessing the traffic flow state data to serve as real-time monitoring data;
the material element model calculation module is used for inputting the real-time monitoring data into the material element model to calculate the association degree of the real-time traffic flow state and each traffic influence evaluation level;
and the result analysis module is used for carrying out the result analysis of the real-time traffic influence based on the association degree of the real-time traffic flow state and each traffic influence evaluation grade.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310356238.7A CN116402390A (en) | 2023-04-06 | 2023-04-06 | Road construction area traffic influence evaluation method and system based on primitive model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310356238.7A CN116402390A (en) | 2023-04-06 | 2023-04-06 | Road construction area traffic influence evaluation method and system based on primitive model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116402390A true CN116402390A (en) | 2023-07-07 |
Family
ID=87006979
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310356238.7A Pending CN116402390A (en) | 2023-04-06 | 2023-04-06 | Road construction area traffic influence evaluation method and system based on primitive model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116402390A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117010932A (en) * | 2023-08-01 | 2023-11-07 | 达州领投信息技术有限公司 | Information intelligent processing system and method based on big data |
| CN117057682A (en) * | 2023-10-12 | 2023-11-14 | 深圳市睿拓新科技有限公司 | Traffic safety evaluation method and system during road construction |
-
2023
- 2023-04-06 CN CN202310356238.7A patent/CN116402390A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117010932A (en) * | 2023-08-01 | 2023-11-07 | 达州领投信息技术有限公司 | Information intelligent processing system and method based on big data |
| CN117057682A (en) * | 2023-10-12 | 2023-11-14 | 深圳市睿拓新科技有限公司 | Traffic safety evaluation method and system during road construction |
| CN117057682B (en) * | 2023-10-12 | 2024-01-23 | 深圳市睿拓新科技有限公司 | Traffic safety evaluation method and system during road construction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116402390A (en) | Road construction area traffic influence evaluation method and system based on primitive model | |
| CN113380033B (en) | Urban traffic safety early warning method and system based on man-machine hybrid enhanced intelligence | |
| CN109993223A (en) | Pavement Condition prediction technique, storage medium, electronic equipment | |
| CN108921372A (en) | Based on step analysis and the freeway tunnel operation security risk evaluating method that matter-element can be opened up | |
| CN102074124A (en) | Dynamic bus arrival time prediction method based on support vector machine (SVM) and H-infinity filtering | |
| CN105574593A (en) | Track state static-state detection and control system and method based on cloud computing and big data | |
| CN105550515A (en) | Multi-level comprehensive judgment method for air quality data | |
| CN108415884A (en) | A kind of modal parameters real-time tracing method | |
| Fuentes et al. | Pavement serviceability evaluation using whole body vibration techniques: A case study for urban roads | |
| Fakhri et al. | Modelling of 3D response pulse at the bottom of asphalt layer using a novel function and artificial neural network | |
| CN109636184B (en) | Method and system for evaluating account assets of brands | |
| CN103218664A (en) | Warning weight determination method based on wavelet neural network | |
| CN110867075A (en) | Method for evaluating influence of road speed meter on reaction behavior of driver under rainy condition | |
| CN104123447A (en) | Manufacturing system comprehensive evaluation method based on improved data envelopment analysis | |
| CN107679630A (en) | Maintenance operation time estimation method based on proportional maintenance model | |
| CN113450565B (en) | Method and system for reducing noise of asphalt pavement | |
| CN115907719A (en) | Hierarchical operation and maintenance management method and device for charging station | |
| CN111667568B (en) | Variable information board information release effect evaluation method based on driving simulation technology | |
| CN110942260B (en) | College traffic safety evaluation method based on Bayesian maximum entropy | |
| Xiao et al. | Study on comprehensive evaluation of pavement condition based on comprehensive integration weighting method and cloud model | |
| Li et al. | Research on comprehensive decision model based on analytic hierarchy process and entropy method | |
| CN109671273A (en) | A kind of traffic overflow severity evaluation method based on cloud model | |
| Citir et al. | Predicting Iowa concrete overlay performance and remaining service life using statistics and deep learning techniques | |
| Li et al. | Research on Network Level Pavement Maintenance Decision-Making Based on Matter-Element Model | |
| Li et al. | Optimization of Optimal Pre Maintenance Timing Decision for Asphalt Pavement Based on Matter Element Analysis and Combination Weighting |
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 |