CN113920727A - Method and system for predicting road congestion caused by construction - Google Patents
Method and system for predicting road congestion caused by construction Download PDFInfo
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Abstract
The invention provides a method for predicting road congestion caused by construction, which comprises the steps of obtaining the traffic flow, the traffic following percentage and the average traffic distance in a period of time on a construction road to solve the average traffic group length and the average traffic group interval, and further analyzing the relation between the average traffic group length and the average traffic group interval and the standard deviation of the corresponding intervals of the average traffic group interval and the standard deviation of the traffic group interval by combining the actual running condition of vehicles on the construction road; the average vehicle group length and the standard deviation of the vehicle group length are combined, the average vehicle group interval and the standard deviation of the vehicle group interval are combined, two groups of sample data in normal distribution are generated randomly, the running condition of the road vehicle is simulated, the probability of congestion is solved by simulation analysis, and the congestion or no congestion of the construction road is predicted according to the solved probability of congestion. The method and the device realize the prediction of the road congestion caused by the construction road occupation and have the characteristics of rapidness and accuracy.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method and a system for predicting road congestion caused by construction.
Background
According to the statistics of the highway group company limited in China, the total mileage of the highway in China exceeds 14 kilometers by 12 months in 2019, and the mileage is the first in the global position. Because the asphalt concrete pavement has the characteristics of low noise, high stability, comfortable running and the like, more than 90 percent of the asphalt concrete pavement is the asphalt concrete pavement. However, it has been found that many road surfaces have not reached their designed service life due to long-term overload, and various troubles such as cracks, irregularities, and the like are generated. Therefore, construction and maintenance of roads are necessary means for improving the vehicle running experience and prolonging the service life of roads.
At present, in the process of road construction and maintenance, the inconvenience of normal traffic operation caused by occupying one side of a road is an important reason of urban traffic jam. Traffic congestion increases travel time of people, reduces labor efficiency, increases fuel consumption and increases difficulty of traffic management, city development is restricted, inconvenience is brought to work and life of people, congestion is caused to a great extent by the proficiency of drivers, quantification is difficult to be carried out on extra human reasons, and great difficulty is caused to prediction of congestion.
However, in the prior art, research on traffic congestion models based on driver behaviors is few, so that an effective method for predicting road congestion caused by construction is not available at present.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a method and a system for predicting road congestion caused by construction, so that the prediction of road congestion caused by construction lane occupation is realized, the method and the system have the characteristics of quickness and accuracy, have guiding significance for road construction and driver driving route planning, reduce traffic pressure and bring convenience to the work and life of people.
In order to solve the above technical problem, an embodiment of the present invention provides a method for predicting road congestion caused by construction, including the following steps:
obtaining the traffic flow, the following percentage and the average inter-vehicle distance in a period of time on a construction road to solve the average inter-vehicle distance and the average inter-vehicle distance, and further analyzing the relationship between the average inter-vehicle distance and the standard deviation of the inter-vehicle distance and the relationship between the average inter-vehicle distance and the standard deviation of the inter-vehicle distance to obtain the standard deviation of the inter-vehicle distance and the standard deviation of the inter-vehicle distance by combining the actual running condition of the vehicles on the construction road;
combining the average vehicle group length with the standard deviation of the vehicle group length, combining the average vehicle group interval with the standard deviation of the vehicle group interval, randomly generating two groups of sample data in normal distribution, simulating the running condition of the road vehicle, solving the probability of congestion by using simulation analysis, and further predicting the congestion or non-congestion of the construction road according to the solved probability of congestion.
Wherein, by the formula
Solving the average train group length La(ii) a Wherein the content of the first and second substances,
Lfrepresents the total length of the following vehicle, and Lf＝N×LminN represents the number of following vehicles, N is P × Q, LminRepresents a minimum following distance; p represents the percentage of car following; q represents the traffic flow.
Wherein, by the formula
Solving the average vehicle group interval Ld(ii) a Wherein L represents the average distance between vehicles without following the vehicle.
Wherein the probability of congestion generation is expressed by a formula
Wherein σdA standard deviation representing the fleet spacing; sigmaaRepresents the standard deviation of the length of the vehicle group.
Wherein, in the calculation of the car following percentage, the car following is regarded as the car following when the distance between two cars is less than 50 m; the average inter-vehicle distance is the average inter-vehicle distance of vehicles passing through the construction road within 5 minutes.
The embodiment of the invention also provides a prediction system for road congestion caused by construction, which comprises a road parameter solving unit and a road congestion prediction unit; wherein the content of the first and second substances,
the road parameter solving unit is used for obtaining the traffic flow, the following percentage and the average inter-vehicle distance in a period of time on the construction road to solve the average inter-vehicle distance and the average inter-vehicle distance, and further analyzing the relation between the average inter-vehicle distance and the inter-vehicle distance standard deviation and the relation between the average inter-vehicle distance and the inter-vehicle distance standard deviation according to the actual running condition of the vehicles on the construction road to obtain the inter-vehicle distance standard deviation and the inter-vehicle distance standard deviation;
the road congestion prediction unit is used for combining the average vehicle group length with the standard deviation of the vehicle group length, combining the average vehicle group interval with the standard deviation of the vehicle group interval, randomly generating two groups of sample data in normal distribution, simulating the running condition of the road vehicle, solving the probability of congestion by using simulation analysis, and further predicting the congestion or no congestion of the construction road according to the solved probability of congestion.
Wherein, by the formula
Solving the average train group length La(ii) a Wherein the content of the first and second substances,
Lfrepresents the total length of the following vehicle, and Lf＝N×LminN represents the number of following vehicles, N is P × Q, LminRepresents a minimum following distance; p represents the percentage of car following; q represents the traffic flow.
Wherein, by the formula
Solving the average vehicle group interval Ld(ii) a Wherein L represents the average distance between vehicles without following the vehicle.
Wherein the probability of congestion generation is expressed by a formula
Wherein σdRepresents a standard deviation of the inter-vehicle group spacing,σaand the standard deviation representing the length of the vehicle group has correlation with the distance between the vehicle group and the length of the vehicle group according to the actual running condition of the vehicle on the road, and the vehicle group on the road and the distance condition of the vehicle group on the road are collected to analyze and obtain the standard deviation.
Wherein, in the calculation of the car following percentage, the car following is regarded as the car following when the distance between two cars is less than 50 m; the average inter-vehicle distance is the average inter-vehicle distance of vehicles passing through the construction road within 5 minutes.
The embodiment of the invention has the following beneficial effects:
the method collects data such as traffic flow, following percentage, average inter-vehicle distance and the like as dependent variables to obtain average inter-vehicle group distance and average inter-vehicle group length, combines standard deviation of inter-vehicle group distance and standard deviation of inter-vehicle group length, carries out simulation analysis according to normal distribution of inter-vehicle group distance and inter-vehicle group length, obtains probability of congestion generation, and carries out threshold value comparison, thereby realizing prediction of road congestion caused by construction lane occupation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for predicting road congestion caused by construction according to an embodiment of the present invention;
fig. 2 is a schematic diagram of two vehicle driving states on a construction road in an application scenario of a prediction method for road congestion caused by construction according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a prediction system for road congestion caused by construction according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, a method for predicting road congestion caused by construction in an embodiment of the present invention includes the following steps:
step S1, obtaining the traffic flow, the traffic following percentage and the average inter-vehicle distance in a period of time on the construction road to solve the average inter-vehicle distance and the average inter-vehicle distance, and further combining the actual running condition of the vehicles on the construction road to analyze the relation between the average inter-vehicle distance and the inter-vehicle distance standard deviation and the relation between the average inter-vehicle distance and the inter-vehicle distance standard deviation to obtain the inter-vehicle distance standard deviation and the inter-vehicle distance standard deviation;
and S2, combining the average vehicle group length with the standard deviation of the vehicle group length, combining the average vehicle group interval with the standard deviation of the vehicle group interval, randomly generating two groups of sample data in normal distribution, simulating the running condition of the road vehicle, solving the probability of congestion by using simulation analysis, and further predicting the congestion or no congestion of the construction road according to the solved probability of congestion.
In step S1, data such as traffic flow Q, percentage P of following vehicles, and average inter-vehicle distance L on the construction road are collected as dependent variables. In one example, the calculation of the following percentage P considers the distance between two vehicles less than 50m as the following vehicle; the average inter-vehicle distance L is an average inter-vehicle distance of vehicles passing through the construction area within 5 minutes.
By the formula
The average train group length L is solveda(ii) a Wherein L isfRepresents the total length of the following vehicle, and Lf＝N×LminN represents the number of following vehicles, N is P × Q, LminRepresents a minimum following distance; p represents the percentage of car following; q represents the traffic flow.
Through a maleFormula (II)
Find out the average train group interval Ld(ii) a Wherein L represents the average distance between vehicles without following the vehicle. It should be noted that the formula for solving the inter-vehicle group spacing is based on
And L isn＝(1-P)×Q×LdAnd (4) deducing.
Considering the average vehicle group length L obtained in step S1aAnd average vehicle group interval LdThe average value alone does not actually represent the magnitude of the true value, and therefore, the magnitude of the congestion probability cannot be judged by directly comparing the magnitude of the average value with the magnitude of the true value.
Based on the above consideration, the length L of the vehicle group is adoptedaAnd the interval L of the train groupdAnd comparing the standard probability distribution of the average value, and judging whether congestion occurs or not by introducing a preset threshold value according to the probability of congestion or not. Therefore, the standard deviation of the inter-vehicle group interval and the standard deviation of the inter-vehicle group length are introduced to perform the combination analysis.
In step S2, the probability of congestion being generated may be expressed by a formula
Wherein σdStandard deviation of vehicle group interval, σaThe standard deviation of the length of the vehicle group has correlation with the distance between the vehicle group and the length of the vehicle group according to the actual running condition of the vehicle on the road, and the standard deviation is analyzed and obtained by collecting the vehicle group on the road and the distance condition of the vehicle group on the road.
However, it is difficult to calculate an analytical solution using the above formula, and therefore the probability is calculated using a software simulation method.
Therefore, the length L of the fleet is determined using python softwareaStandard deviation of vehicle group length, vehicle group interval LdStandard deviation σ of vehicle group spacingdAnd respectively randomly generating a plurality of (such as 10000) sample data which accord with normal distribution, and combining and comparing every two sample data to determine whether the probability of congestion is generated.
On the basis, a probability upper limit (such as 70%) and a probability lower limit (such as 40%) are set, and if the obtained probability is higher than the set probability upper limit, the congestion is considered to be generated; if the obtained probability is lower than the lower probability limit, it is considered that the congestion does not occur.
It should be noted that, in the above process, the average value and the standard deviation of the inter-vehicle group distance and the inter-vehicle group length are obtained through experiments for different roads, and the upper limit and the lower limit of the probability are also set in combination with the actual road conditions, and the optimal accuracy is used as an index for solving.
As shown in fig. 2, an application scenario of the prediction method for road congestion caused by construction according to the embodiment of the present invention is further described:
taking the high-speed section of road in Jiangsu province in fig. 2 as an example, the road is relatively simple, only has an entrance and an exit, has no bifurcation, and has the same number of vehicles entering and exiting.
And collecting data such as traffic flow, percentage of following vehicles, average inter-vehicle distance and the like as dependent variables. Wherein the following percentage is calculated by considering the following vehicle when the distance between two vehicles is less than 50 m; the average inter-vehicle distance is an average inter-vehicle distance of vehicles passing through the construction area within 5 minutes (or 10 minutes, 15 minutes, or the like). In total, 400 data of the road segment are collected, wherein 312 data are used as training sets, and 88 data are used as prediction sets.
Firstly, the congestion caused by construction is predicted by adopting a decision tree method, and the result statistics are shown in table 1. For the training set, 109 data are actually congested, 203 data are not congested, the algorithm is finally adopted for prediction, and 150 data are finally congested, wherein 65 data are predicted correctly, and the congestion prediction accuracy is 43.33%; for the prediction set, 30 data are actually congested, 38 data are not congested, and 48 data are finally predicted to be congested, wherein 16 data are predicted correctly, and the congestion prediction accuracy is 33.33%. It can be seen that the prediction accuracy using this algorithm is not high.
TABLE 1
Then, the method provided by the invention is adopted to predict the congestion caused by construction, according to the actual running condition of the road vehicles, the standard deviation of the distance between the vehicle groups and the length of the vehicle groups is defined as half of the average value, the data with the congestion probability higher than 20% is considered to be congested, the data with the congestion probability lower than 10% is considered to be not congested, if the congestion probability is between 10% and 20%, the judgment is considered to be difficult, experts judge according to the actual condition, and the result statistics are shown in table 2. For the training set, 109 data are actually congested, 203 data are not congested, the algorithm is finally adopted for prediction, 97 data are congested, 74 data are correct in prediction, the congestion prediction accuracy is 76.29%, 194 data are not congested, 170 data are correct in prediction, and the congestion prediction accuracy is 87.63%; for the prediction set, 30 data are actually congested, 58 data are not congested, and 29 data are finally predicted to be congested, wherein 21 data are correctly predicted, the congestion prediction accuracy is 72.41%, and 50 data are predicted to be uncongested, wherein 44 data are correctly predicted, and the congestion prediction accuracy is 88%. It can thus be seen that better results are achieved using this algorithm.
TABLE 2
As shown in fig. 3, a prediction system for road congestion caused by construction according to an embodiment of the present invention includes a road parameter solving unit 110 and a road congestion prediction unit 120; wherein the content of the first and second substances,
the road parameter solving unit 110 is configured to obtain a traffic flow, a traffic following percentage, and an average inter-vehicle distance in a period of time on the construction road, to solve for an average inter-vehicle distance and an average inter-vehicle distance, and further analyze a relationship between the average inter-vehicle distance and a standard deviation of the inter-vehicle distance and a relationship between the average inter-vehicle distance and a standard deviation of the inter-vehicle distance in combination with an actual operation condition of a vehicle on the construction road, to obtain an inter-vehicle distance standard deviation and a standard deviation of the inter-vehicle distance;
the road congestion prediction unit 120 is configured to combine the average vehicle group length with the standard deviation of the vehicle group length, combine the average vehicle group interval with the standard deviation of the vehicle group interval, randomly generate two groups of sample data in normal distribution, simulate a road vehicle operation condition, solve a probability of congestion generation by using simulation analysis, and further predict congestion or non-congestion of a construction road according to the solved probability of congestion generation.
Wherein, by the formula
Solving the average train group length La(ii) a Wherein the content of the first and second substances,
Lfrepresents the total length of the following vehicle, and Lf＝N×LminN represents the number of following vehicles, N is P × Q, LminRepresents a minimum following distance; p represents the percentage of car following; q represents the traffic flow.
Wherein, by the formula
Solving the average vehicle group interval Ld(ii) a Wherein L represents the average distance between vehicles without following the vehicle.
Wherein the probability of congestion generation is expressed by a formula
Wherein σdRepresents the standard deviation, σ, of the vehicle group intervalaAnd the standard deviation representing the length of the vehicle group has correlation with the distance between the vehicle group and the length of the vehicle group according to the actual running condition of the vehicle on the road, and the vehicle group on the road and the distance condition of the vehicle group on the road are collected to analyze and obtain the standard deviation.
Wherein, in the calculation of the car following percentage, the car following is regarded as the car following when the distance between two cars is less than 50 m; the average inter-vehicle distance is the average inter-vehicle distance of vehicles passing through the construction road within 5 minutes.
The embodiment of the invention has the following beneficial effects:
the method collects data such as traffic flow, following percentage, average inter-vehicle distance and the like as dependent variables to obtain inter-vehicle group distance and inter-vehicle group length, carries out simulation analysis on the inter-vehicle group distance and inter-vehicle group length in normal distribution, obtains the probability of congestion generation and compares the threshold value, thereby realizing prediction of road congestion caused by road occupation during construction.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A method for predicting road congestion caused by construction is characterized by comprising the following steps:
obtaining the traffic flow, the following percentage and the average inter-vehicle distance in a period of time on a construction road to solve the average inter-vehicle distance and the average inter-vehicle distance, and further analyzing the relationship between the average inter-vehicle distance and the standard deviation of the inter-vehicle distance and the relationship between the average inter-vehicle distance and the standard deviation of the inter-vehicle distance to obtain the standard deviation of the inter-vehicle distance and the standard deviation of the inter-vehicle distance by combining the actual running condition of the vehicles on the construction road;
combining the average vehicle group length with the standard deviation of the vehicle group length, combining the average vehicle group interval with the standard deviation of the vehicle group interval, randomly generating two groups of sample data in normal distribution, simulating the running condition of the road vehicle, solving the probability of congestion by using simulation analysis, and further predicting the congestion or non-congestion of the construction road according to the solved probability of congestion.
2. The method for predicting road congestion caused by construction according to claim 1, wherein the formula is
Solving the average train group length La(ii) a Wherein the content of the first and second substances,
Lfrepresents the total length of the following vehicle, and Lf＝N×LminN represents the number of following vehicles, N is P × Q, LminRepresents a minimum following distance; p represents the percentage of car following; q represents the traffic flow.
3. The method for predicting road congestion caused by construction according to claim 2, wherein the formula is
Solving the average vehicle group interval Ld(ii) a Wherein L represents the average distance between vehicles without following the vehicle.
4. The method as claimed in claim 3, wherein the probability of congestion generation is expressed by a formula
Wherein σdA standard deviation representing the fleet spacing; sigmaaRepresents the standard deviation of the length of the vehicle group.
5. The method for predicting road congestion caused by construction according to claim 4, wherein the calculation of the following percentage considers that the distance between two vehicles is less than 50m as the following vehicle; the average inter-vehicle distance is the average inter-vehicle distance of vehicles passing through the construction road within 5 minutes.
6. A prediction system for road congestion caused by construction is characterized by comprising a road parameter solving unit and a road congestion prediction unit; wherein the content of the first and second substances,
the road parameter solving unit is used for obtaining the traffic flow, the following percentage and the average inter-vehicle distance in a period of time on the construction road to solve the average inter-vehicle distance and the average inter-vehicle distance, and further analyzing the relation between the average inter-vehicle distance and the inter-vehicle distance standard deviation and the relation between the average inter-vehicle distance and the inter-vehicle distance standard deviation according to the actual running condition of the vehicles on the construction road to obtain the inter-vehicle distance standard deviation and the inter-vehicle distance standard deviation;
the road congestion prediction unit is used for combining the average vehicle group length with the standard deviation of the vehicle group length, combining the average vehicle group interval with the standard deviation of the vehicle group interval, randomly generating two groups of sample data in normal distribution, simulating the running condition of the road vehicle, solving the probability of congestion by using simulation analysis, and further predicting the congestion or no congestion of the construction road according to the solved probability of congestion.
7. The system for predicting road congestion caused by construction according to claim 6, wherein the formula is
Solving the average train group length La(ii) a Wherein the content of the first and second substances,
Lfrepresents the total length of the following vehicle, and Lf＝N×LminN represents the number of following vehicles, N is P × Q, LminRepresents a minimum following distance; p represents the percentage of car following; q represents the traffic flow.
8. The system for predicting road congestion caused by construction according to claim 7, wherein the formula is
Solving the average vehicle group interval Ld(ii) a Wherein L represents the average distance between vehicles without following the vehicle.
9. The system for predicting congestion in roads caused by construction according to claim 8, wherein the probability of congestion generation is expressed by a formula
Wherein σdA standard deviation representing the fleet spacing; sigmaaRepresents the standard deviation of the length of the vehicle group.
10. The system for predicting construction-induced road congestion as claimed in claim 9, wherein the calculation of the percentage of following vehicles considers a distance between two vehicles less than 50m as a following vehicle; the average inter-vehicle distance is the average inter-vehicle distance of vehicles passing through the construction road within 5 minutes.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115457772A (en) * | 2022-09-13 | 2022-12-09 | 合肥革绿信息科技有限公司 | Intelligent traffic digital twin system, storage medium and equipment |
| CN117252449A (en) * | 2023-11-20 | 2023-12-19 | 水润天府新材料有限公司 | Full-penetration drainage low-noise pavement construction process and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020077742A1 (en) * | 1999-03-08 | 2002-06-20 | Josef Mintz | Method and system for mapping traffic congestion |
| CN103038117A (en) * | 2010-06-29 | 2013-04-10 | 本田技研工业株式会社 | Method of traffic congestion estimation |
| CN103249627A (en) * | 2010-12-15 | 2013-08-14 | 本田技研工业株式会社 | Vehicular driving assist device |
| CN103262135A (en) * | 2010-12-15 | 2013-08-21 | 本田技研工业株式会社 | Traffic congestion prediction method |
| CN104268415A (en) * | 2014-09-30 | 2015-01-07 | 北京交通发展研究中心 | Travel congestion probability estimation method |
| CN104919509A (en) * | 2013-03-11 | 2015-09-16 | 本田技研工业株式会社 | Server device, congestion prediction information display system, congestion prediction information distribution method, congestion prediction information display method, and program |
| CN112466119A (en) * | 2020-11-26 | 2021-03-09 | 清华大学 | Method and system for predicting vehicle following speed of vehicle by using vehicle-road cooperative data |
-
2021
- 2021-10-08 CN CN202111173074.1A patent/CN113920727B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020077742A1 (en) * | 1999-03-08 | 2002-06-20 | Josef Mintz | Method and system for mapping traffic congestion |
| CN103038117A (en) * | 2010-06-29 | 2013-04-10 | 本田技研工业株式会社 | Method of traffic congestion estimation |
| CN103249627A (en) * | 2010-12-15 | 2013-08-14 | 本田技研工业株式会社 | Vehicular driving assist device |
| CN103262135A (en) * | 2010-12-15 | 2013-08-21 | 本田技研工业株式会社 | Traffic congestion prediction method |
| CN104919509A (en) * | 2013-03-11 | 2015-09-16 | 本田技研工业株式会社 | Server device, congestion prediction information display system, congestion prediction information distribution method, congestion prediction information display method, and program |
| CN104268415A (en) * | 2014-09-30 | 2015-01-07 | 北京交通发展研究中心 | Travel congestion probability estimation method |
| CN112466119A (en) * | 2020-11-26 | 2021-03-09 | 清华大学 | Method and system for predicting vehicle following speed of vehicle by using vehicle-road cooperative data |
Non-Patent Citations (2)
| Title |
|---|
| 王殿海;李凤;宋现敏;: "一种新的车队离散模型及其应用" * |
| 纪廷婷;陈少军;陈雨人;: "在虚拟环境下分析车群行为安全的方法" * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115457772A (en) * | 2022-09-13 | 2022-12-09 | 合肥革绿信息科技有限公司 | Intelligent traffic digital twin system, storage medium and equipment |
| CN115457772B (en) * | 2022-09-13 | 2023-11-07 | 合肥革绿信息科技有限公司 | Intelligent traffic digital twin system, storage medium and equipment |
| CN117252449A (en) * | 2023-11-20 | 2023-12-19 | 水润天府新材料有限公司 | Full-penetration drainage low-noise pavement construction process and system |
| CN117252449B (en) * | 2023-11-20 | 2024-01-30 | 水润天府新材料有限公司 | Full-penetration drainage low-noise pavement construction process and system |
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