CN114005275A - Highway vehicle congestion judging method based on multi-data source fusion - Google Patents
Highway vehicle congestion judging method based on multi-data source fusion Download PDFInfo
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Abstract
The invention provides a highway vehicle congestion judging method based on multi-data-source fusion, which realizes six-dimensional real-time data analysis from congestion index, vehicle speed, congestion mileage, congestion tendency, congestion reason and section flow by fusing effective data such as road conditions, high-speed geographic information, free flow speed, free flow rate, high-speed events and the like and utilizing a clustering algorithm and a big data real-time flow analysis technology, and has the advantages of wider data source and higher accuracy.
Description
Technical Field
The invention belongs to the technical field of traffic information, and particularly relates to a method for judging vehicle congestion on a highway based on multi-data-source fusion.
Background
With the continuous development of economy, the quantity of motor vehicles kept continuously increases, the traffic volume of a road network rapidly and stably rises, and the traffic volume of a main busy high-speed road section tends to be saturated. The real-time detection for judging the congestion of the expressway is one of the core problems with great difficulty in the field of intelligent transportation at present, but the conventional detection methods are insufficient in precision and instantaneity. Map operators in China, such as high-grade, hundredth, Tencent and the like, can only know rough congestion positions and have great defects for highway management. On one hand, the accuracy of the jam position is not enough, and the jam position can not be connected with professional high-speed services. On the other hand, the operator cannot know the original reason of congestion without events and accident data, and cannot perform vehicle management and control and congestion management. The method for judging the vehicle congestion on the expressway in real time is provided for effectively relieving the congestion pressure of the expressway and supporting road smoothness, and has important significance.
Disclosure of Invention
The invention aims to solve the technical problems and provides a method for judging vehicle congestion on a highway based on multi-data source fusion.
In order to achieve the purpose, the invention adopts the following technical scheme:
a highway vehicle congestion judging method based on multi-data source fusion comprises the following steps:
a1, collecting traffic vehicle information data through a high-speed portal system, arranging two to three portal devices on a high-speed road section, and obtaining a section hour flow threshold value, wherein the section hour flow threshold value is between two adjacent portals: determining the maximum traffic volume of the section according to the service life of each section, the number of lanes, the road construction condition, the weather condition of the day and the current time period;
a2, calculating the cross section real-time flow and the cross section average speed in real time by using free flow transaction data and adopting a big data real-time flow analysis technology;
a3, acquiring congestion data in real time, wherein the data comprise congestion mileage, longitude and latitude of a congestion starting point, longitude and latitude of a congestion ending point and congestion reliability, and converting the congestion longitude and latitude information data into pile number data of a section facility by means of a high-speed geographic information platform;
a4, acquiring high-speed event data in real time, wherein the high-speed event data comprises event occurrence time, event types, pile number information of event occurrence, road section IDs and road section affiliated units, determining congestion occurrence reasons according to the event information, and pushing the congestion causes to a management unit so as to make corresponding control measures;
and A5, defining the congestion level as three-level congestion, wherein the first-level congestion is light congestion, the second-level congestion is severe congestion, and the third-level congestion is abnormal congestion, collecting historical road congestion data, and judging the congestion level according to the cross-section flow N, the cross-section flow threshold T, the cross-section speed V and the congestion mileage S.
As a preferred technical solution, in step a1, for the determination of the threshold, an appropriate equivalent threshold T1 is selected according to a priori experience of historical data, and the threshold dynamically changes according to the environment w1, the site construction condition w2, and the current time period condition w 3:
the equivalent weight threshold value is calculated by the formula T of w1 xw 2 xw 3 xT 1;
wherein w1 represents the climate change condition, the climate is divided into three types of excellent, general and severe, and the corresponding values are 1, 0.8 and 0.6;
w2 represents the road construction condition, and the road construction seals partial lanes, and can cause great influence to road congestion, and the calculation formula is as follows:
wherein N is the total number of section lanes, and N is the number of closed lanes;
wherein w3 is the current time period condition, the visibility has great influence on the road carrying traffic flow, the higher the visibility road carrying capacity is, and vice versa, the value of w3 is 1 in the time period of 5:00-19:00, and the value of w3 is 0.9 in the time period of 00:00-4:59 or 19:00-23: 59.
As a preferred technical scheme, in the step a2, the vehicles are classified into six types, i.e., a first type, a second type, a third type, a fourth type, a fifth type and a sixth type, i.e., a first type, a second type, a third type, a fourth type and a fifth type, of passenger cars and a first type, a second type, a third type, a fourth type and a fifth type, of trucks according to vehicle types, and the conversion rule is as follows: the equivalent transformation coefficient of a class II passenger car is 1, the equivalent transformation coefficient of a class III and class IV passenger car is 1.5, the transformation coefficient of a class I truck is 1, the transformation coefficient of a class II and class III truck is 1.5, the transformation coefficient of a class IV truck is 2.5, and the transformation coefficient of a class V and class VI truck is 4;
the cross section real-time flow is as follows:
N-S1 +1 × S2+1.5 × S3+1.5 × S4+ S11+1.5 × S12+1.5 × S13+2.5 × S14+4 × S15+4 × S16, wherein S1-S4 is a type 1-4 passenger flow rate and S11-S16 is a type 1-6 cargo flow rate;
the average cross-sectional velocity is:
wherein l is section mileage with unit of km; t is the section passing time of each vehicle, and the unit is h; v is the section speed of each vehicle, and the unit is km/h; v1-v4 is the average speed of 1-4 passenger cars, and the unit is km/h; s1-s4 is the equivalent of 1-4 passenger cars, and the unit is a vehicle; v11-v16 is the average speed of the 1-6 trucks, and the unit is km/h; s11-s16 is the equivalent weight of a class 1-6 wagon, and the unit is a vehicle; n is the equivalent of the section, and the unit is a vehicle; v is the average speed of the section, and the unit is km/h.
As a preferable technical scheme, in step a3, original congestion data is screened, link information with congestion reliability greater than 80% is extracted, start-stop longitude and latitude information of a congested link is converted into high-speed stake number information through a high-speed geographic information platform, so that related business information of a link to which a congestion point belongs, a jurisdiction and the like is associated, and a control policy is made for accurate congestion information to be subsequently issued.
As a preferable technical solution, in step a5, the method for calculating the congestion level includes: carrying out congestion judgment by matching the real-time flow and the average speed of the section with congestion data provided by a map operator; when the section real-time flow is between the threshold and 1.1 times of the threshold and the section average speed is between 30km/h and 60km/h, judging the section congestion to be primary congestion, or judging the section congestion to be primary congestion when the congestion mileage provided by a map operator is less than or equal to 2 km; when the section real-time flow is between 1.1 time of threshold value and 1.2 times of threshold value and the section average speed is between 10km/h and 30km/h, judging the second-level congestion, or judging the second-level congestion when the congestion mileage provided by a map operator is more than 2 km and less than or equal to 5 km; and when the section real-time flow is greater than 1.2 times of the threshold value and the section average speed is less than or equal to 10km/h, judging the three-level congestion, or judging the three-level congestion when the congestion mileage provided by a map operator is greater than 5 km.
As a preferable technical solution, in step a5, modeling is performed by a K-Means clustering algorithm, K is determined to be 3, after the number of K is determined, 3 centroids are randomly selected, and a sample value is D { N, T, V, S }, where N is a cross-sectional flow rate, T is a cross-sectional flow rate threshold, V is a cross-sectional average speed, and S is a congestion mileage.
After the technical scheme is adopted, the invention has the following advantages:
after the provincial toll station cancels national networking operation, a large amount of free flow transaction data and portal snapshot data are accumulated on the highway, and the invention reasonably utilizes the data and extracts valuable information through the large amount of data to make a decision on highway operation management. The data of the high-speed event is related to the road condition degree of the highway, and the road construction, the road congestion, the road control and the like have great influence on the road condition of the highway. The high-speed geographic information data provides accurate positioning information for the accurate positioning of the highway, the position of an accident is known for the first time, and accurate position information is provided for high-speed recourse.
The invention uses map operator data for reference, and reasonably applies and integrates the data through an algorithm. The invention discloses a highway vehicle congestion judging method based on multi-data-source fusion, which realizes six-dimensional real-time data analysis from congestion index, vehicle speed, congestion mileage, congestion tendency, congestion reason and section flow by fusing effective data such as road conditions, high-speed geographic information, free flow speed, free flow rate, high-speed events and the like and utilizing a clustering algorithm and a big data real-time flow analysis technology, and has the advantages of wider data source and higher accuracy. In addition, in order to enable high-speed monitoring personnel to know the running condition of the highway in real time and push the congestion information in real time, the operation informatization level of the highway is improved.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
A highway vehicle congestion judging method based on multi-data source fusion comprises the following steps:
a1, collecting traffic vehicle information data through a high-speed portal system, arranging two to three portal devices on a high-speed road section, and obtaining a section hour flow threshold value, wherein the section hour flow threshold value is between two adjacent portals: and determining the maximum traffic volume of the section according to the service life of each section, the number of lanes, the road construction condition, the weather condition of the day and the current time period.
For the determination of the threshold, firstly, a proper equivalent weight threshold T1 is selected according to a priori experience of historical data, and the threshold dynamically changes according to the environment w1, the field construction condition w2 and the current time period condition w 3:
the equivalent weight threshold value is calculated by the formula T of w1 xw 2 xw 3 xT 1;
wherein w1 represents the climate change condition, the climate is divided into three types of excellent, general and severe, and the corresponding values are 1, 0.8 and 0.6;
w2 represents the road construction condition, and the road construction seals partial lanes, and can cause great influence to road congestion, and the calculation formula is as follows:
wherein N is the total number of section lanes, and N is the number of closed lanes;
wherein w3 is the current time period condition, the visibility has great influence on the road carrying traffic flow, the higher the visibility road carrying capacity is, and vice versa, the value of w3 is 1 in the time period of 5:00-19:00, and the value of w3 is 0.9 in the time period of 00:00-4:59 or 19:00-23: 59.
A2, calculating the cross section real-time flow and the cross section average speed in real time by using free flow transaction data and adopting a big data real-time flow analysis technology.
The vehicles are classified into a first class, a second class, a third class and a fourth class of a passenger car and a first class, a second class, a third class, a fourth class, a fifth class and a sixth class of a freight car according to vehicle types, and the conversion rule is as follows: the equivalent transformation coefficient of a class II passenger car is 1, the equivalent transformation coefficient of a class III and class IV passenger car is 1.5, the transformation coefficient of a class I truck is 1, the transformation coefficient of a class II and class III truck is 1.5, the transformation coefficient of a class IV truck is 2.5, and the transformation coefficient of a class V and class VI truck is 4; the vehicle type conversion factor is shown in table 1:
TABLE 1 vehicle type conversion factor
| Type of vehicle | Conversion coefficient |
| 1-2 passenger cars | 1 |
| Class 3-4 passenger cars | 1.5 |
| Truck class 1 | 1 |
| Truck class 2 | 1.5 |
| Truck class 3 | 1.5 |
| Truck class 4 | 2.5 |
| Truck class 5 | 4 |
| Truck class 6 | 4 |
The cross section real-time flow is as follows:
N-S1 +1 × S2+1.5 × S3+1.5 × S4+ S11+1.5 × S12+1.5 × S13+2.5 × S14+4 × S15+4 × S16, wherein S1-S4 is a type 1-4 passenger flow rate and S11-S16 is a type 1-6 cargo flow rate;
the average cross-sectional velocity is:
wherein l is section mileage with unit of km; t is the section passing time of each vehicle, and the unit is h; v is the section speed of each vehicle, and the unit is km/h; v1-v4 is the average speed of 1-4 passenger cars, and the unit is km/h; s1-s4 is the equivalent of 1-4 passenger cars, and the unit is a vehicle; v11-v16 is the average speed of the 1-6 trucks, and the unit is km/h; s11-s16 is the equivalent weight of a class 1-6 wagon, and the unit is a vehicle; n is the equivalent of the section, and the unit is a vehicle; v is the average speed of the section, and the unit is km/h.
And A3, acquiring congestion data in real time, wherein the data comprises congestion mileage, the longitude and latitude of a congestion starting point, the longitude and latitude of a congestion ending point and congestion reliability, and converting the congestion longitude and latitude information data into pile number data of a section facility by means of a high-speed geographic information platform. The congestion data format is shown in table 2:
TABLE 2 Congestion data Format
| HEADINDEX | Road name |
| DISTANCE | Congestion mileage |
| FIRSTPOINT | Congestion origin latitude and longitude |
| ENDPOINT | Congestion destination latitude and longitude |
| RELIABILIT | Degree of confidence |
Screening original congestion data, taking out road section information with congestion credibility larger than 80%, converting start-stop longitude and latitude information of a congested road section into high-speed pile number information through a high-speed geographic information platform, associating relevant business information of a road section to which a congestion point belongs, a jurisdiction unit and the like, and making a control policy for accurately issuing congestion information subsequently.
A4, acquiring high-speed event data in real time, wherein the high-speed event data comprises event occurrence time, event types, pile number information of event occurrence, road section IDs and road section affiliated units, determining congestion occurrence reasons according to the event information, and pushing the congestion causes to a management unit so as to make corresponding control measures; the high speed event data format is shown in table 3:
TABLE 3 high speed event data Format
And A5, defining the congestion level as three-level congestion, wherein the first-level congestion is light congestion, the second-level congestion is severe congestion, and the third-level congestion is abnormal congestion, collecting historical road congestion data, and judging the congestion level according to the cross-section flow N, the cross-section flow threshold T, the cross-section speed V and the congestion mileage S. Modeling is carried out through a K-Means clustering algorithm, firstly, attention is paid to the selection of a K value, and the congestion level is divided into three classes according to empirical judgment. And determining K to be 3, after the number of K is determined, randomly selecting 3 centroids, wherein the sample value is D to be { N, T, V, S }, wherein N is the section flow, T is the section flow threshold, V is the section average speed, and S is the congestion mileage.
Training the historical samples through a K-Means algorithm to obtain conditions corresponding to various congestion conditions, wherein the conditions are shown in a table 4:
TABLE 4 Condition for various types of congestion
The calculation method of the congestion level comprises the following steps: carrying out congestion judgment by matching the real-time flow and the average speed of the section with congestion data provided by a map operator; when the section real-time flow is between the threshold and 1.1 times of the threshold and the section average speed is between 30km/h and 60km/h, judging the section congestion to be primary congestion, or judging the section congestion to be primary congestion when the congestion mileage provided by a map operator is less than or equal to 2 km; when the section real-time flow is between 1.1 time of threshold value and 1.2 times of threshold value and the section average speed is between 10km/h and 30km/h, judging the second-level congestion, or judging the second-level congestion when the congestion mileage provided by a map operator is more than 2 km and less than or equal to 5 km; and when the section real-time flow is greater than 1.2 times of the threshold value and the section average speed is less than or equal to 10km/h, judging the three-level congestion, or judging the three-level congestion when the congestion mileage provided by a map operator is greater than 5 km.
Other embodiments of the present invention than the preferred embodiments described above will be apparent to those skilled in the art from the present invention, and various changes and modifications can be made therein without departing from the spirit of the present invention as defined in the appended claims.
Claims (6)
1. A highway vehicle congestion judging method based on multi-data source fusion is characterized by comprising the following steps:
a1, collecting traffic vehicle information data through a high-speed portal system, arranging two to three portal devices on a high-speed road section, and obtaining a section hour flow threshold value, wherein the section hour flow threshold value is between two adjacent portals: determining the maximum traffic volume of the section according to the service life of each section, the number of lanes, the road construction condition, the weather condition of the day and the current time period;
a2, calculating the cross section real-time flow and the cross section average speed in real time by using free flow transaction data and adopting a big data real-time flow analysis technology;
a3, acquiring congestion data in real time, wherein the data comprise congestion mileage, longitude and latitude of a congestion starting point, longitude and latitude of a congestion ending point and congestion reliability, and converting the congestion longitude and latitude information data into pile number data of a section facility by means of a high-speed geographic information platform;
a4, acquiring high-speed event data in real time, wherein the high-speed event data comprises event occurrence time, event types, pile number information of event occurrence, road section IDs and road section affiliated units, determining congestion occurrence reasons according to the event information, and pushing the congestion causes to a management unit so as to make corresponding control measures;
and A5, defining the congestion level as three-level congestion, wherein the first-level congestion is light congestion, the second-level congestion is severe congestion, and the third-level congestion is abnormal congestion, collecting historical road congestion data, and judging the congestion level according to the cross-section flow N, the cross-section flow threshold T, the cross-section speed V and the congestion mileage S.
2. The method as claimed in claim 1, wherein in step a1, for determining the threshold, an appropriate equivalent threshold T1 is selected according to a priori experience of historical data, and the threshold dynamically changes according to environment w1, field construction condition w2, and current time period condition w 3:
the equivalent weight threshold value is calculated by the formula T of w1 xw 2 xw 3 xT 1;
wherein w1 represents the climate change condition, the climate is divided into three types of excellent, general and severe, and the corresponding values are 1, 0.8 and 0.6;
w2 represents the road construction condition, and the road construction seals partial lanes, and can cause great influence to road congestion, and the calculation formula is as follows:
wherein N is the total number of section lanes, and N is the number of closed lanes;
wherein w3 is the current time period condition, the visibility has great influence on the road carrying traffic flow, the higher the visibility road carrying capacity is, and vice versa, the value of w3 is 1 in the time period of 5:00-19:00, and the value of w3 is 0.9 in the time period of 00:00-4:59 or 19:00-23: 59.
3. The method for judging the congestion of the vehicles on the highway based on the fusion of multiple data sources as claimed in claim 1, wherein in the step A2, the vehicles are classified into a first class, a second class, a third class and a fourth class for passenger cars, a first class, a second class, a third class, a fourth class, a fifth class and a sixth class for trucks according to vehicle types, and the conversion rule is as follows: the equivalent transformation coefficient of a class II passenger car is 1, the equivalent transformation coefficient of a class III and class IV passenger car is 1.5, the transformation coefficient of a class I truck is 1, the transformation coefficient of a class II and class III truck is 1.5, the transformation coefficient of a class IV truck is 2.5, and the transformation coefficient of a class V and class VI truck is 4;
the cross section real-time flow is as follows:
N-S1 +1 × S2+1.5 × S3+1.5 × S4+ S11+1.5 × S12+1.5 × S13+2.5 × S14+4 × S15+4 × S16, wherein S1-S4 is a type 1-4 passenger flow rate and S11-S16 is a type 1-6 cargo flow rate;
the average cross-sectional velocity is:
wherein l is section mileage with unit of km; t is the section passing time of each vehicle, and the unit is h; v is the section speed of each vehicle, and the unit is km/h; v1-v4 is the average speed of 1-4 passenger cars, and the unit is km/h; s1-s4 is the equivalent of 1-4 passenger cars, and the unit is a vehicle; v11-v16 is the average speed of the 1-6 trucks, and the unit is km/h; s11-s16 is the equivalent weight of a class 1-6 wagon, and the unit is a vehicle; n is the equivalent of the section, and the unit is a vehicle; v is the average speed of the section, and the unit is km/h.
4. The method for judging the vehicle congestion on the expressway based on the fusion of multiple data sources as claimed in claim 1, wherein in the step A3, the original congestion data is screened, the information of the road section with the congestion reliability higher than 80% is extracted, and the start and stop longitude and latitude information of the congested road section is converted into the information of the high-speed stake number through the high-speed geographic information platform, so as to associate the relevant service information of the road section to which the congestion point belongs, the administration unit and the like, and the control policy is made for the accurate congestion information which is subsequently issued.
5. The method for judging congestion of vehicles on a highway based on fusion of multiple data sources as claimed in claim 1, wherein in step a5, the calculation method of the congestion level comprises the following steps: carrying out congestion judgment by matching the real-time flow and the average speed of the section with congestion data provided by a map operator; when the section real-time flow is between the threshold and 1.1 times of the threshold and the section average speed is between 30km/h and 60km/h, judging the section congestion to be primary congestion, or judging the section congestion to be primary congestion when the congestion mileage provided by a map operator is less than or equal to 2 km; when the section real-time flow is between 1.1 time of threshold value and 1.2 times of threshold value and the section average speed is between 10km/h and 30km/h, judging the second-level congestion, or judging the second-level congestion when the congestion mileage provided by a map operator is more than 2 km and less than or equal to 5 km; and when the section real-time flow is greater than 1.2 times of the threshold value and the section average speed is less than or equal to 10km/h, judging the three-level congestion, or judging the three-level congestion when the congestion mileage provided by a map operator is greater than 5 km.
6. The method for judging the vehicle congestion on the expressway based on the fusion of multiple data sources as claimed in claim 1 or 5, wherein in the step A5, modeling is performed through a K-Means clustering algorithm, K is determined to be 3, after the number of K is determined, 3 centroids are randomly selected, and the sample value is D { N, T, V, S }, wherein N is the section flow, T is the section flow threshold, V is the section average speed, and S is the congestion mileage.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115079238A (en) * | 2022-08-23 | 2022-09-20 | 安徽交欣科技股份有限公司 | RTK-based intelligent and accurate positioning system and method for road traffic |
| CN115565365A (en) * | 2022-08-31 | 2023-01-03 | 阿里巴巴（中国）有限公司 | Expressway data processing method, device, equipment and computer program product |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101038700A (en) * | 2007-04-20 | 2007-09-19 | 东南大学 | Mixed controlling method of single dot signal controlling crossing |
| CN103021176A (en) * | 2012-11-29 | 2013-04-03 | 浙江大学 | Discriminating method based on section detector for urban traffic state |
| CN103280098A (en) * | 2013-05-23 | 2013-09-04 | 北京交通发展研究中心 | Traffic congestion index calculation method |
| CN104794919A (en) * | 2015-04-28 | 2015-07-22 | 辽宁省交通规划设计院 | Real-time optimal configuration method for lane resources of multi-lane expressway |
| KR101688218B1 (en) * | 2016-08-25 | 2016-12-20 | 델리아이 주식회사 | System for managing traffic flow and incidents using image detection in real time based on object recognition and method for processing thereof |
| CN106710216A (en) * | 2017-02-28 | 2017-05-24 | 广东省交通运输档案信息管理中心 | Expressway real-time traffic jam road condition detection method and system |
| CN107749165A (en) * | 2017-12-06 | 2018-03-02 | 四川九洲视讯科技有限责任公司 | Computational methods based on urban road congestion index |
| CN108492557A (en) * | 2018-03-23 | 2018-09-04 | 四川高路交通信息工程有限公司 | Highway jam level judgment method based on multi-model fusion |
| CN109326121A (en) * | 2017-07-31 | 2019-02-12 | 交通运输部公路科学研究所 | Highway construction area upstream vehicle cocurrent control method |
| CN110517481A (en) * | 2019-07-26 | 2019-11-29 | 厦门卫星定位应用股份有限公司 | Vehicle flowrate prediction technique, medium, equipment and device |
| KR20200053316A (en) * | 2018-11-08 | 2020-05-18 | 주식회사 데이터위즈 | Method for estimation of link travel time and signal delay |
| KR102124955B1 (en) * | 2019-11-29 | 2020-06-19 | 세종대학교산학협력단 | Method and server for identifying the cause of traffic congestion using visual analytics |
| CN112382095A (en) * | 2020-11-26 | 2021-02-19 | 长沙理工大学 | Urban expressway traffic state estimation method based on multi-source data fusion |
| CN112396834A (en) * | 2020-11-05 | 2021-02-23 | 河北上元智能科技股份有限公司 | Foggy-day expressway coordination control method based on cellular automaton model |
| CN113178083A (en) * | 2021-03-04 | 2021-07-27 | 山东科技大学 | Congestion control method and system for multi-stage dynamic speed limit of expressway |
| CN113538898A (en) * | 2021-06-04 | 2021-10-22 | 南京美慧软件有限公司 | Multisource data-based highway congestion management and control system |
-
2021
- 2021-10-25 CN CN202111239557.7A patent/CN114005275B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101038700A (en) * | 2007-04-20 | 2007-09-19 | 东南大学 | Mixed controlling method of single dot signal controlling crossing |
| CN103021176A (en) * | 2012-11-29 | 2013-04-03 | 浙江大学 | Discriminating method based on section detector for urban traffic state |
| CN103280098A (en) * | 2013-05-23 | 2013-09-04 | 北京交通发展研究中心 | Traffic congestion index calculation method |
| CN104794919A (en) * | 2015-04-28 | 2015-07-22 | 辽宁省交通规划设计院 | Real-time optimal configuration method for lane resources of multi-lane expressway |
| KR101688218B1 (en) * | 2016-08-25 | 2016-12-20 | 델리아이 주식회사 | System for managing traffic flow and incidents using image detection in real time based on object recognition and method for processing thereof |
| CN106710216A (en) * | 2017-02-28 | 2017-05-24 | 广东省交通运输档案信息管理中心 | Expressway real-time traffic jam road condition detection method and system |
| CN109326121A (en) * | 2017-07-31 | 2019-02-12 | 交通运输部公路科学研究所 | Highway construction area upstream vehicle cocurrent control method |
| CN107749165A (en) * | 2017-12-06 | 2018-03-02 | 四川九洲视讯科技有限责任公司 | Computational methods based on urban road congestion index |
| CN108492557A (en) * | 2018-03-23 | 2018-09-04 | 四川高路交通信息工程有限公司 | Highway jam level judgment method based on multi-model fusion |
| KR20200053316A (en) * | 2018-11-08 | 2020-05-18 | 주식회사 데이터위즈 | Method for estimation of link travel time and signal delay |
| CN110517481A (en) * | 2019-07-26 | 2019-11-29 | 厦门卫星定位应用股份有限公司 | Vehicle flowrate prediction technique, medium, equipment and device |
| KR102124955B1 (en) * | 2019-11-29 | 2020-06-19 | 세종대학교산학협력단 | Method and server for identifying the cause of traffic congestion using visual analytics |
| CN112396834A (en) * | 2020-11-05 | 2021-02-23 | 河北上元智能科技股份有限公司 | Foggy-day expressway coordination control method based on cellular automaton model |
| CN112382095A (en) * | 2020-11-26 | 2021-02-19 | 长沙理工大学 | Urban expressway traffic state estimation method based on multi-source data fusion |
| CN113178083A (en) * | 2021-03-04 | 2021-07-27 | 山东科技大学 | Congestion control method and system for multi-stage dynamic speed limit of expressway |
| CN113538898A (en) * | 2021-06-04 | 2021-10-22 | 南京美慧软件有限公司 | Multisource data-based highway congestion management and control system |
Non-Patent Citations (1)
| Title |
|---|
| 万林云: "基于可靠度的高墩大跨连续刚构桥的行车舒适性研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115079238A (en) * | 2022-08-23 | 2022-09-20 | 安徽交欣科技股份有限公司 | RTK-based intelligent and accurate positioning system and method for road traffic |
| CN115079238B (en) * | 2022-08-23 | 2023-10-03 | 安徽交欣科技股份有限公司 | Highway traffic intelligent accurate positioning system and method based on RTK |
| CN115565365A (en) * | 2022-08-31 | 2023-01-03 | 阿里巴巴（中国）有限公司 | Expressway data processing method, device, equipment and computer program product |
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