CN118506583B - Vehicle-road collaborative management method and system based on intelligent transportation - Google Patents
Vehicle-road collaborative management method and system based on intelligent transportation Download PDFInfo
- Publication number
- CN118506583B CN118506583B CN202410971916.5A CN202410971916A CN118506583B CN 118506583 B CN118506583 B CN 118506583B CN 202410971916 A CN202410971916 A CN 202410971916A CN 118506583 B CN118506583 B CN 118506583B
- Authority
- CN
- China
- Prior art keywords
- road
- early warning
- data
- traffic
- traffic flow
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007726 management method Methods 0.000 title claims abstract description 59
- 238000012544 monitoring process Methods 0.000 claims abstract description 15
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 230000002159 abnormal effect Effects 0.000 claims abstract description 7
- 238000011156 evaluation Methods 0.000 claims description 67
- 238000012502 risk assessment Methods 0.000 claims description 28
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000000034 method Methods 0.000 claims description 13
- 206010039203 Road traffic accident Diseases 0.000 claims description 10
- 238000012549 training Methods 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 8
- 238000007405 data analysis Methods 0.000 claims description 6
- 238000011161 development Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 5
- 230000003068 static effect Effects 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 5
- 230000001934 delay Effects 0.000 abstract 1
- 230000006399 behavior Effects 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000000428 dust Substances 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/095—Traffic lights
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Traffic Control Systems (AREA)
Abstract
本发明公开了基于智慧交通的车路协同管理方法及系统，涉及智慧交通技术领域，包括以下步骤：收集道路环境数据，并将数据传输至交通管理中心，其中，收集的道路环境数据包括历史数据和实时数据；对收集的历史数据和实时数据进行预处理，提取道路安全干扰因子。本发明通过收集和处理道路环境数据，实时监测道路状况，并对潜在的风险进行预警，不仅提高了交通管理的效率，也大大增强了道路使用的安全性，当交通拥堵、事故或其他异常情况发生时，系统能够迅速作出反应，为驾驶员提供准确的导航和避让建议，减少交通延误和事故发生率，同时，系统还能为交通管理部门提供决策支持，使其能够更精准地制定交通管理策略，优化交通流量。
The present invention discloses a vehicle-road collaborative management method and system based on smart transportation, which relates to the field of smart transportation technology and includes the following steps: collecting road environment data and transmitting the data to a traffic management center, wherein the collected road environment data includes historical data and real-time data; pre-processing the collected historical data and real-time data to extract road safety interference factors. The present invention not only improves the efficiency of traffic management, but also greatly enhances the safety of road use by collecting and processing road environment data, monitoring road conditions in real time, and issuing early warnings for potential risks. When traffic congestion, accidents or other abnormal situations occur, the system can respond quickly and provide drivers with accurate navigation and avoidance suggestions to reduce traffic delays and accident rates. At the same time, the system can also provide decision support for traffic management departments, enabling them to formulate traffic management strategies more accurately and optimize traffic flow.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a vehicle-road collaborative management method and system based on intelligent traffic.
Background
With the rapid development of urban, the number of motor vehicles is rapidly increased, the traffic pressure is increasingly increased, the problems of traffic jam, accident frequency and the like are increasingly serious, the traditional traffic management mode is difficult to meet the requirements of modern urban traffic, with the rapid development of new generation information technologies such as big data, cloud computing, internet of things, artificial intelligence and the like, a powerful technical support is provided for the development of intelligent traffic, and data and technical support are provided for the cooperation of roads and roads.
In the prior art, the road environment is complex and changeable, the vehicle-road cooperative system needs to collect multi-source data to comprehensively judge potential road dangerous conditions, the direction of dangerous sources is difficult to accurately judge due to the complexity of the data, early warning information cannot give clear indication, the condition of inaccurate information or false alarm occurs, and how to analyze the road data in intelligent traffic and determine the early warning level is a problem to be solved.
Disclosure of Invention
The invention aims to provide a vehicle-road collaborative management method and system based on intelligent traffic, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme:
In a first aspect, a vehicle-road collaborative management method based on intelligent traffic includes the following steps:
Step 1, collecting road environment data and transmitting the data to a traffic management center, wherein the collected road environment data comprises historical data and real-time data;
step 2, preprocessing the collected historical data and real-time data, and extracting road safety interference factors, wherein the road safety interference factors comprise traffic flow interference factors and weather interference factors;
Step 3, analyzing the preprocessed historical data and combining the extracted road safety interference factors to construct a road risk assessment model, and acquiring a traffic flow interference index and a weather interference index so as to predict and analyze the vehicle driving trend and the traffic flow trend;
Step 4, obtaining a road early warning evaluation coefficient based on the traffic flow interference index and the weather interference index, determining different early warning grades of road dangerous conditions by combining the preprocessed historical data, respectively setting early warning evaluation thresholds for the different early warning grades, and obtaining an early warning evaluation sequence table;
and 5, inputting the preprocessed real-time data into a road risk assessment model for analysis to obtain a numerical value of a road early warning assessment coefficient, determining an early warning level of the current road risk condition, generating corresponding early warning information according to the corresponding early warning level, and carrying out collaborative management on road traffic.
The technical scheme of the invention is further improved as follows: in the step 1, the collecting and transmitting process of the road environment data is as follows:
step 101, installing sensor equipment in a road network, monitoring and collecting real-time data of a road environment in real time, and acquiring historical data of the road environment from a public database of traffic management, wherein the historical data comprises historical traffic flow and historical traffic accident record data, the sensor equipment is used for acquiring real-time traffic flow, vehicle speed, weather conditions, road conditions and vehicle position information, the acquisition of the historical data of the road environment has a sufficient length to analyze long-term trend of road dangerous conditions, and the historical data of the road environment is identical to the time and season span of the real-time data of the road environment so as to reduce errors of the historical data of the road environment for trend analysis; the traffic flow, the vehicle speed and the weather condition data are respectively acquired through a traffic flow sensor, a speed sensor and a weather sensor, the road condition is acquired by utilizing a high-definition camera and an image processing technology, the vehicle position information is acquired through radar equipment detection, and the acquisition of historical data and real-time data needs to be authorized;
102, transmitting the acquired real-time data and history data to a traffic management center through a network, and acquiring static information of a road network structure and traffic facility layout by combining a Geographic Information System (GIS) to comprehensively obtain a vehicle-road collaborative management sequence, wherein the static information generally comprises road geometric features, traffic signal control equipment, traffic monitoring equipment and position and attribute information of bus stops, and the road geometric features comprise road length, road width and road curvature;
And 103, constructing a database in the traffic management center, storing the road environment data and the vehicle-road collaborative management sequence, and backing up the road environment data and the vehicle-road collaborative management sequence.
The technical scheme of the invention is further improved as follows: in the step 2, the extraction process of the road safety interference factor is as follows:
step 201, performing data cleaning and preprocessing on collected historical data and real-time data to remove noise, abnormal values and redundant information, wherein the specific process is that firstly, cleaning the collected road environment data to remove repeated, wrong, incomplete or abnormal data, including checking rationality, range and missing values of the data, integrating the data from different sensor devices, including data format conversion and time stamp alignment, and performing normalization processing on the data;
step 202, extracting features from the preprocessed historical data, and respectively extracting features aiming at traffic flow and weather conditions to obtain road safety interference factors, in particular traffic flow interference factors and weather interference factors;
step 203, acquiring associated data of traffic flow interference factors, namely vehicle flow data, vehicle speed data and vehicle density data, according to the preprocessed historical data; the method comprises the steps of acquiring associated data of weather interference factors, namely humidity data, wind speed data, rainfall data and visibility data, wherein the fact that the associated data of the weather interference factors have the snowfall data is needed to be described, but the snowfall is not considered because the period of the snowfall is smaller in conventional traffic behaviors.
The technical scheme of the invention is further improved as follows: in the step 3, the construction of the road risk assessment model and the acquisition process of the traffic flow interference index and the weather interference index are as follows:
Step 301, integrating the traffic flow interference factors and the associated data of the weather interference factors in the preprocessed historical data to form a unified data set;
Step 302, feature coding is performed on the traffic flow interference factors and the associated data of the weather interference factors in the data set, and a multi-element nonlinear regression model is defined as a road risk assessment model;
Step 303, dividing the integrated data set into a training set, a verification set and a test set, training the multiple nonlinear regression model by using the training set data, constructing a road risk assessment model, verifying the trained road risk assessment model by using the data of the verification set and the test set, and evaluating the performance of the road risk assessment model;
And 304, obtaining a traffic flow interference index and a weather interference index by combining the preprocessed historical data by using the trained model, and predicting and analyzing the vehicle driving trend and the traffic flow trend.
The technical scheme of the invention is further improved as follows: the calculation formula of the traffic flow interference index is as follows:
；
；
Wherein, As an index of the disturbance of the traffic flow,For the current traffic volume of the vehicle,As a minimum for traffic flow in the historical data,For the maximum value of the vehicle flow in the history data,Is the standard deviation of the vehicle speed,As an average value of the vehicle speed in the history data,The calculation formula of (2) is as follows:， Is the first The vehicle speed of the data points,The number of data points is a function of the number of data points,For the current vehicle density,As an average of the vehicle density in the history data,As a minimum for vehicle density in the historical data,For the maximum value of the vehicle density in the history data,；
The calculation formula of the weather interference index is as follows:
；
Wherein, As an index of the weather disturbance (weather disturbance),For the current humidity level to be the current humidity level,For the maximum value of humidity in the history data,For the current wind speed,As an average value of wind speed in the history data,For the current amount of rainfall,For the maximum value of the rainfall in the history data,For the current level of visibility the display is,For a minimum of visibility in the historical data,。
The technical scheme of the invention is further improved as follows: in the step 4, the acquisition process of the road early warning evaluation coefficient and the early warning evaluation sequence table is as follows:
Step 401, traversing the preprocessed historical data, combining the traffic flow interference index and the weather interference index with the historical traffic accident record data, and analyzing the association degree of the road dangerous condition, the traffic flow interference index and the weather interference index;
Step 402, different weights are distributed to the traffic flow interference index and the weather interference index, and the two indexes are subjected to weighted calculation to obtain a road early warning evaluation coefficient;
Step 403, according to the preprocessed historical data, matching the road dangerous situation in the historical traffic accident record data, determining different early warning grades, namely a first early warning grade, a second early warning grade, a third early warning grade and a fourth early warning grade, wherein the road dangerous situation of the early warning grade is gradually increased from the first early warning grade to the fourth early warning grade;
Step 404, matching the determined different early warning grades with the result of the road early warning evaluation coefficient, and setting corresponding early warning evaluation thresholds for the different early warning grades;
And 405, arranging all the road early warning evaluation coefficient values in a sequence from high to low, distributing corresponding early warning grades for each value, making an early warning evaluation sequence table, and listing the corresponding relation between the road early warning evaluation coefficient and the early warning grade.
The technical scheme of the invention is further improved as follows: the calculation formula of the road early warning evaluation coefficient is as follows:
；
Wherein, The early warning evaluation coefficient is used for the road,As an index of the disturbance of the traffic flow,As the weight of the traffic flow disturbance index,As an index of the weather disturbance (weather disturbance),As a weight for the weather interference index,，The range of the values is as follows。
The technical scheme of the invention is further improved as follows: the early warning levels correspond to the early warning evaluation thresholds, wherein the early warning evaluation thresholds comprise an upper limit threshold and a lower limit threshold;
the early warning levels and the early warning evaluation thresholds satisfy the following relation:
First-level early warning grade The early warning measures of the first-level early warning level are to keep regular monitoring of traffic flow and weather conditions, ensure that the road is smooth, continuously conduct traffic safety propaganda and education, enhance public traffic safety consciousness, set up green warning signs on a road large screen to remind drivers, ensure that the drivers and pedestrians know traffic rules, and conduct regular traffic safety propaganda and prompt in vehicle-mounted broadcasting;
Secondary early warning level The early warning measures of the secondary early warning level are to increase the monitoring force on traffic flow and weather conditions, grasp the latest situation in time, strengthen traffic safety propaganda and prompt through media such as broadcasting, television, network and the like, remind a driver to pay attention to road conditions and safe driving in a mode of vehicle-mounted broadcasting and mobile phone APP, set a yellow warning mark on a road large screen to remind the driver of traffic safety, increase broadcasting of traffic safety propaganda and yellow early warning information in vehicle-mounted broadcasting, and push the yellow early warning information and road condition prompt to users through mobile APP of traffic management departments;
Three-level early warning level The early warning measures of the three-level early warning level are to increase the number of traffic police and patrol personnel, strengthen the monitoring of roads and traffic conditions, implement speed limit and traffic limit measures according to weather or traffic conditions, reduce accident risk, release orange early warning information through multiple channels, remind drivers and pedestrians of paying attention to safety, set up orange warning marks on a road large screen, remind drivers of paying attention to road conditions and speed limit, add traffic safety propaganda in vehicle-mounted broadcasting, release orange early warning information and road condition information in real time, cooperate with a vehicle-mounted navigation system, and push the orange early warning information to a running vehicle;
Four-level early warning level The early warning measures of the four-level early warning level are that for the affected area, the current limiting blocking of the road or the area is carried out to prevent more vehicles and pedestrians from entering, red early warning information and road detour instructions are displayed on a road large screen, red early warning notification is broadcast in real time through vehicle-mounted broadcasting and mobile phone APP, and a short message of traffic control notification is sent to drivers in the affected area;
Wherein, The early warning evaluation coefficient is used for the road,The upper threshold value corresponding to the first-level early warning level and the lower threshold value corresponding to the second-level early warning level,An upper threshold corresponding to the second-level early warning level and a lower threshold corresponding to the third-level early warning level,The upper threshold value corresponding to the three-level early warning level and the lower threshold value corresponding to the four-level early warning level,，，。
The technical scheme of the invention is further improved as follows: in the step 5, the process of the collaborative management of the road traffic is as follows:
Step 501, inputting characteristic data of traffic flow and weather conditions in the preprocessed real-time data into a road risk assessment model, and respectively calculating a traffic flow interference index and a weather interference index;
Step 502, combining the calculated traffic flow interference index and the weather interference index to obtain a road early warning evaluation coefficient, and determining an early warning level of the current road dangerous condition according to a preset early warning evaluation threshold;
Step 503, generating corresponding early warning information according to the early warning level of the current road dangerous situation, and transmitting the early warning information to related personnel and departments for the cooperative management of road traffic, wherein the related personnel and departments comprise a road management department, a traffic supervision center and a driver;
Step 504, monitoring the development execution condition of the early warning information and recording feedback information.
The intelligent traffic-based vehicle-road cooperative management system is used for realizing an intelligent traffic-based vehicle-road cooperative management method and comprises a traffic management center, wherein the traffic management center is in communication connection with a data acquisition module, a data analysis module, a model construction module, a road early warning evaluation module, an early warning module and a traffic scheduling module, and the modules are in electrical signal connection;
The data acquisition module is used for acquiring real-time data of the road environment and acquiring historical data of the road environment from a public database of traffic management, so that the system can acquire comprehensive and accurate road environment data, and data support is provided for subsequent analysis and decision making;
The data analysis module is used for preprocessing the road environment data acquired by the data acquisition module and extracting road safety interference factors;
The model construction module is used for constructing a road risk assessment model by combining the preprocessed road environment historical data and the road safety interference factors, acquiring a traffic flow interference index and a weather interference index, and analyzing the running speed and the traffic flow trend of the vehicle;
The road early warning evaluation module is used for combining the historical data of the road environment with the road risk evaluation model to obtain the road early warning evaluation coefficient to evaluate the safety and risk of the road, and the real-time road risk condition is predicted by analyzing the road environment and combining the real-time data of the road environment so as to provide decision support for vehicle driving and traffic management;
The early warning module is used for determining the early warning grade of the current road dangerous condition according to the combination of the road early warning evaluation coefficient and the real-time data of the road environment, and generating corresponding early warning information based on the road early warning evaluation result;
and the traffic scheduling module is used for managing and scheduling road traffic according to the early warning information and the real-time traffic condition.
By adopting the technical scheme, compared with the prior art, the invention has the following technical progress:
1. The invention provides a vehicle-road collaborative management method and a system based on intelligent traffic, which are used for collecting and processing road environment data in real time, monitoring road conditions in real time and carrying out early warning on potential risks, so that the traffic management efficiency is improved, the safety of road use is greatly enhanced, when traffic jams, accidents or other abnormal conditions occur, the system can rapidly respond, accurate navigation and avoidance suggestions are provided for drivers, traffic delay and accident occurrence rate are reduced, and meanwhile, the system can also provide decision support for traffic management departments, so that traffic management strategies can be formulated more accurately, and traffic flow is optimized.
2. The invention provides a vehicle-road collaborative management method and a vehicle-road collaborative management system based on intelligent traffic, which are used for carrying out deep mining on road environment data, providing scientific basis for traffic decision, and predicting future traffic conditions according to historical data and real-time data, so as to formulate traffic optimization strategies in advance, provide rich driving auxiliary information for drivers, help the drivers to plan driving routes better, provide timely safety reminding, reduce traffic accidents, effectively reduce driving pressure of the drivers and improve driving safety under severe weather or complex road conditions.
3. The invention provides a vehicle-road collaborative management method and a vehicle-road collaborative management system based on intelligent traffic, which are based on real-time road data and early warning information, wherein an intelligent traffic system can realize dynamic traffic scheduling and navigation optimization, intelligently adjust signal lamps and speed limiting measures according to traffic conditions, provide real-time traffic navigation advice for drivers, and help the drivers to select optimal driving routes and time, thereby reducing traffic jams and improving road traffic efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of the collection and transmission of road environment data according to the present invention;
FIG. 3 is a flowchart for constructing a road risk assessment model and obtaining a traffic flow disturbance index and a weather disturbance index according to the present invention;
FIG. 4 is a flowchart for constructing a road risk assessment model and obtaining a traffic flow disturbance index and a weather disturbance index according to the present invention;
fig. 5 is a block diagram of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. 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.
Embodiment 1, as shown in fig. 1-4, the present invention provides a vehicle-road collaborative management method based on intelligent traffic, comprising the following steps:
Step 1, collecting road environment data and transmitting the data to a traffic management center, wherein the collected road environment data comprises historical data and real-time data, a sensor device is installed in a road network, the real-time data of the road environment is monitored and collected in real time, the historical data of the road environment is obtained from a public database of traffic management, the historical data comprises historical traffic flow and historical traffic accident record data, the sensor device is used for obtaining the real-time traffic flow, vehicle speed, weather conditions, road conditions and vehicle position information, the obtaining of the historical data of the road environment has enough length to analyze long-term trend of dangerous conditions of the road, and the historical data of the road environment is identical with the time and season span of the real-time data of the road environment so as to reduce errors of the historical data of the road environment for trend analysis; the traffic flow, the vehicle speed and the weather condition data are respectively acquired through a traffic flow sensor, a speed sensor and a weather sensor, the road condition is acquired by utilizing a high-definition camera and an image processing technology, the vehicle position information is acquired through radar equipment detection, the acquisition of historical data and real-time data is required to be authorized, the acquired real-time data and the historical data are transmitted to a traffic management center through a network and are combined with a Geographic Information System (GIS) to acquire static information of a road network structure and a traffic facility layout, a vehicle-road collaborative management sequence is comprehensively obtained, wherein the static information generally comprises road geometric features, traffic signal control equipment, traffic monitoring equipment and position and attribute information of bus stations, the road geometric features comprise road length, road width and road curvature, integrating GIS data into a database of a traffic management center, correlating and matching the GIS data with real-time and historical data, fusing and analyzing the real-time data, the historical data and the GIS data on the traffic management center, extracting key information of traffic flow modes, accident high-rise road sections and traffic jam areas, analyzing the running condition of traffic facilities and the structural characteristics of a road network by combining the GIS data, identifying potential road dangerous conditions and traffic bottlenecks, generating a vehicle-road collaborative management sequence according to the result of data fusion and analysis, constructing the database at the traffic management center, storing the road environment data and the vehicle-road collaborative management sequence, backing up the road environment data and the vehicle-road collaborative management sequence, the reliability and the safety of the data are ensured;
Step 2, preprocessing collected historical data and real-time data, extracting road safety interference factors, wherein the road safety interference factors comprise traffic flow interference factors and weather interference factors, carrying out data cleaning and preprocessing on the collected historical data and the real-time data, removing noise, abnormal values and redundant information, specifically, firstly, cleaning the collected road environment data, removing repeated, wrong, incomplete or abnormal data, including checking rationality, range and missing values of the data, ensuring the accuracy of subsequent analysis, integrating the data from different sensor devices, ensuring the consistency and integrity of the data, including conversion of data formats and alignment of time stamps, carrying out normalization processing on the data so as to better reveal the relation and mode among the data, extracting features from the preprocessed historical data, respectively carrying out feature extraction on traffic flow and weather conditions, acquiring the road safety interference factors, specifically the traffic flow interference factors and the weather interference factors, and acquiring the related data of the traffic flow interference factors, namely traffic flow, vehicle density data and vehicle density data according to the preprocessed data; acquiring associated data of weather interference factors, namely humidity data, wind speed data, rainfall data and visibility data, wherein the associated data of the weather interference factors need to be described, and the snowfall data exist in the associated data of the weather interference factors, but the snowfall is not considered because the period of the snowfall is smaller in conventional traffic behaviors;
Step 3, analyzing the preprocessed historical data and combining the extracted road safety interference factors to construct a road risk assessment model, acquiring a traffic flow interference index and a weather interference index to predict and analyze the running trend and the traffic flow trend of the vehicle, integrating the traffic flow interference factors and the associated data of the weather interference factors in the preprocessed historical data to form a unified data set, carrying out feature coding on the traffic flow interference factors and the associated data of the weather interference factors in the data set, defining a multi-element nonlinear regression model as a road risk assessment model, dividing the integrated data set into a training set, a verification set and a test set, training the multi-element nonlinear regression model by using the training set data to construct a road risk assessment model, verifying the trained road risk assessment model by using the data of the verification set and the test set, evaluating the performance of the road risk assessment model, obtaining the traffic flow interference index and the weather interference index by using the trained model, and predicting and analyzing the running trend and the traffic flow trend by combining the preprocessed historical data;
Further, the calculation formula of the traffic flow disturbance index is:
；
；
；
Wherein, As an index of the disturbance of the traffic flow,For the current traffic volume of the vehicle,As a minimum for traffic flow in the historical data,For the maximum value of the vehicle flow in the history data,Is the standard deviation of the vehicle speed,As an average value of the vehicle speed in the history data,Is the firstThe vehicle speed of the data points,The number of data points is a function of the number of data points,For the current vehicle density,As an average of the vehicle density in the history data,As a minimum for vehicle density in the historical data,For the maximum value of the vehicle density in the history data,When the following is performedNear maximumWhen the first itemThe traffic flow is increased, and the traffic jam can be caused; standard deviation of vehicle speedWhen the vehicle speed is increased, the vehicle speed fluctuation is large, possibly due to poor road conditions or unstable running of the vehicle, and the traffic flow interference is increased;
The calculation formula of the weather interference index is as follows:
；
Wherein, As an index of the weather disturbance (weather disturbance),For the current humidity level to be the current humidity level,For the maximum value of humidity in the history data,For the current wind speed,As an average value of wind speed in the history data,For the current amount of rainfall,For the maximum value of the rainfall in the history data,For the current level of visibility the display is,For a minimum of visibility in the historical data,It should be noted that, whenWhen the road surface is increased, the road surface is possibly wet and slippery, and the driving safety is affected; when (when)When the dust is increased, adverse factors such as side wind, dust and the like can be accompanied; when (when)When the road conditions are increased, the road conditions become worse, such as ponding, road surface wet and slippery, etc.; when (when)When the driving condition is lowered (i.e., the value is reduced), the driving condition is deteriorated, and the driver's visual field is limited;
Step 4, obtaining a road early warning evaluation coefficient based on the traffic flow interference index and the weather interference index, determining different early warning grades of road dangerous situations by combining the preprocessed historical data, respectively setting early warning evaluation thresholds for the different early warning grades, acquiring an early warning evaluation sequence table, traversing the preprocessed historical data, combining the traffic flow interference index and the weather interference index with the historical traffic accident record data, analyzing the association degree of the road dangerous situations with the traffic flow interference index and the weather interference index, distributing different weights for the traffic flow interference index and the weather interference index, carrying out weighted calculation on the two indexes to obtain the road early warning evaluation coefficient, matching the road dangerous situations in the historical traffic accident record data according to the preprocessed historical data, determining different early warning grades, respectively increasing the road dangerous situations of the first grade to the fourth grade, matching the determined different early warning grades with the result of the road evaluation coefficient, setting corresponding early warning thresholds for the different early warning grades, and making all the early warning coefficients into corresponding road evaluation relation with the road evaluation sequence table according to the high and low road evaluation coefficient values;
Further, the calculation formula of the road early warning evaluation coefficient is as follows:
；
Wherein, The early warning evaluation coefficient is used for the road,As an index of the disturbance of the traffic flow,As the weight of the traffic flow disturbance index,As an index of the weather disturbance (weather disturbance),As a weight for the weather interference index,，The range of the values is as followsIt should be noted that, whenWhen the road condition is close to 0, the road condition is indicated to be safer; when (when)When approaching 1, it indicates that the road condition is very dangerous; as an example of the presence of a metal such as,The larger the value of (c) is, the larger the traffic flow is or the more unstable the vehicle is running; the larger the value of (c) is, the worse the weather conditions are indicated, The method is used for evaluating dangerous conditions of roads for integrating the values of traffic flow and weather interference factors; indicating the degree of disturbance of the traffic flow, Indicating the extent to which weather conditions affect the passage of the road,AndRepresenting the importance of traffic flow disturbance index and weather disturbance index in assessing road dangerous conditions;
further, the early warning levels correspond to early warning evaluation thresholds, wherein the early warning evaluation thresholds comprise an upper limit threshold and a lower limit threshold;
The plurality of early warning levels and the plurality of early warning evaluation thresholds satisfy the following relationship:
First-level early warning grade The early warning measures of the first-level early warning level are to keep regular monitoring of traffic flow and weather conditions, ensure that the road is smooth, continuously conduct traffic safety propaganda and education, enhance public traffic safety consciousness, set up green warning signs on a road large screen to remind drivers, ensure that the drivers and pedestrians know traffic rules, and conduct regular traffic safety propaganda and prompt in vehicle-mounted broadcasting;
Secondary early warning level The early warning measures of the secondary early warning level are to increase the monitoring force on traffic flow and weather conditions, grasp the latest situation in time, strengthen traffic safety propaganda and prompt through media such as broadcasting, television, network and the like, remind a driver to pay attention to road conditions and safe driving in a mode of vehicle-mounted broadcasting and mobile phone APP, set a yellow warning mark on a road large screen to remind the driver of traffic safety, increase broadcasting of traffic safety propaganda and yellow early warning information in vehicle-mounted broadcasting, and push the yellow early warning information and road condition prompt to users through mobile APP of traffic management departments;
Three-level early warning level The early warning measures of the three-level early warning level are to increase the number of traffic police and patrol personnel, strengthen the monitoring of roads and traffic conditions, implement speed limit and traffic limit measures according to weather or traffic conditions, reduce accident risk, release orange early warning information through multiple channels, remind drivers and pedestrians of paying attention to safety, set up orange warning marks on a road large screen, remind drivers of paying attention to road conditions and speed limit, add traffic safety propaganda in vehicle-mounted broadcasting, release orange early warning information and road condition information in real time, cooperate with a vehicle-mounted navigation system, and push the orange early warning information to a running vehicle;
Four-level early warning level The early warning measures of the four-level early warning level are that for the affected area, the current limiting blocking of the road or the area is carried out to prevent more vehicles and pedestrians from entering, red early warning information and road detour instructions are displayed on a road large screen, red early warning notification is broadcast in real time through vehicle-mounted broadcasting and mobile phone APP, and a short message of traffic control notification is sent to drivers in the affected area;
Wherein, The early warning evaluation coefficient is used for the road,The upper threshold value corresponding to the first-level early warning level and the lower threshold value corresponding to the second-level early warning level,An upper threshold corresponding to the second-level early warning level and a lower threshold corresponding to the third-level early warning level,The upper threshold value corresponding to the three-level early warning level and the lower threshold value corresponding to the four-level early warning level,，，；
And 5, inputting the preprocessed real-time data into a road risk assessment model to analyze to obtain a value of a road early warning assessment coefficient, determining an early warning level of the current road dangerous condition, generating corresponding early warning information according to the corresponding early warning level, carrying out collaborative management of road traffic, inputting characteristic data of traffic flow and weather conditions in the preprocessed real-time data into the road risk assessment model, respectively calculating traffic flow interference indexes and weather interference indexes, combining the calculated traffic flow interference indexes and weather interference indexes to obtain the road early warning assessment coefficient, determining the early warning level of the current road dangerous condition according to a preset early warning assessment threshold, generating corresponding early warning information according to the early warning level of the current road dangerous condition, transmitting the early warning information to related personnel and departments for collaborative management of road traffic, wherein the related personnel and departments comprise a road management department, a traffic supervision center and drivers, monitoring the development execution condition of the early warning information, and recording feedback information.
The embodiment 2, as shown in fig. 5, further provides a vehicle-road collaborative management system based on intelligent traffic on the basis of the embodiment 1, which is used for realizing a vehicle-road collaborative management method based on intelligent traffic, and comprises a traffic management center, wherein the traffic management center is in communication connection with a data acquisition module, a data analysis module, a model construction module, a road early warning evaluation module, an early warning module and a traffic scheduling module, and the modules are in electrical signal connection;
The data acquisition module is used for acquiring real-time data of the road environment and acquiring historical data of the road environment from a public database of traffic management, so that the system can acquire comprehensive and accurate road environment data, and data support is provided for subsequent analysis and decision making;
The data analysis module is used for preprocessing the road environment data acquired by the data acquisition module and extracting road safety interference factors;
The model construction module is used for combining the preprocessed road environment historical data and the road safety interference factors to construct a road risk assessment model, acquiring a traffic flow interference index and a weather interference index, and analyzing the vehicle running speed and traffic flow trend;
The road early warning evaluation module is used for combining the historical data of the road environment with the road risk evaluation model to obtain the road early warning evaluation coefficient to evaluate the safety and risk of the road, and the real-time road risk condition is predicted by analyzing the road environment and combining the real-time data of the road environment so as to provide decision support for vehicle driving and traffic management;
The early warning module is used for determining the early warning grade of the current road danger condition according to the combination of the road early warning evaluation coefficient and the real-time data of the road environment, and generating corresponding early warning information based on the road early warning evaluation result;
And the traffic scheduling module is used for managing and scheduling road traffic according to the early warning information and the real-time traffic condition.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. The intelligent traffic-based vehicle-road collaborative management method is characterized by comprising the following steps of: the method comprises the following steps:
Step 1, collecting road environment data and transmitting the data to a traffic management center, wherein the collected road environment data comprises historical data and real-time data, and in the step 1, the collecting and transmitting process of the road environment data is as follows:
Step 101, installing sensor equipment in a road network, monitoring and collecting real-time data of a road environment in real time, and acquiring historical data of the road environment from a public database of traffic management, wherein the historical data comprise historical traffic flow and historical traffic accident record data, and the sensor equipment is used for acquiring real-time traffic flow, vehicle speed, weather conditions, road conditions and vehicle position information;
102, transmitting the acquired real-time data and historical data to a traffic management center through a network, acquiring static information of a road network structure and traffic facility layout by combining a geographic information system, and comprehensively obtaining a vehicle-road collaborative management sequence;
Step 103, constructing a database in a traffic management center, storing road environment data and a vehicle-road collaborative management sequence, and backing up the road environment data and the vehicle-road collaborative management sequence;
Step 2, preprocessing the collected historical data and real-time data, and extracting road safety interference factors, wherein the road safety interference factors comprise traffic flow interference factors and weather interference factors, and in the step 2, the extraction process of the road safety interference factors is as follows:
step 201, performing data cleaning and preprocessing on the collected historical data and real-time data to remove noise, abnormal values and redundant information;
step 202, extracting features from the preprocessed historical data, and respectively extracting features aiming at traffic flow and weather conditions to obtain road safety interference factors, in particular traffic flow interference factors and weather interference factors;
Step 203, acquiring associated data of traffic flow interference factors, namely vehicle flow data, vehicle speed data and vehicle density data, according to the preprocessed historical data; acquiring associated data of weather interference factors, namely humidity data, wind speed data, rainfall data and visibility data;
step 3, analyzing the preprocessed historical data and combining the extracted road safety interference factors to construct a road risk assessment model, and acquiring a traffic flow interference index and a weather interference index to predict and analyze a vehicle driving trend and a traffic flow trend, wherein in the step 3, the construction of the road risk assessment model and the acquisition processes of the traffic flow interference index and the weather interference index are as follows:
Step 301, integrating the traffic flow interference factors and the associated data of the weather interference factors in the preprocessed historical data to form a unified data set;
Step 302, feature coding is performed on the traffic flow interference factors and the associated data of the weather interference factors in the data set, and a multi-element nonlinear regression model is defined as a road risk assessment model;
Step 303, dividing the integrated data set into a training set, a verification set and a test set, training the multiple nonlinear regression model by using the training set data, constructing a road risk assessment model, verifying the trained road risk assessment model by using the data of the verification set and the test set, and evaluating the performance of the road risk assessment model;
Step 304, a trained model is used, and a traffic flow interference index and a weather interference index are obtained by combining the preprocessed historical data, so that a vehicle driving trend and a traffic flow trend are predicted and analyzed, and a calculation formula of the traffic flow interference index is as follows:
Wherein JG is the traffic flow disturbance index, Q is the current traffic flow, Q min is the minimum value of the traffic flow in the history data, Q max is the maximum value of the traffic flow in the history data, SV is the standard deviation of the vehicle speed, V i is the vehicle speed of the ith data point, n is the number of data points, D is the current vehicle density,For the average value of the vehicle density in the history data, D min is the minimum value of the vehicle density in the history data, D max is the maximum value of the vehicle density in the history data, JG >0;
the calculation formula of the weather interference index is as follows:
Wherein TG is weather interference index, H is current humidity, H max is maximum value of humidity in history data, W is current wind speed, The average value of wind speed in the historical data is that Y is the current rainfall, Y max is the maximum value of the rainfall in the historical data, J is the current visibility, J min is the minimum value of the visibility in the historical data, and TG >0;
Step 4, obtaining a road early warning evaluation coefficient based on the traffic flow interference index and the weather interference index, determining different early warning grades of road dangerous conditions by combining the preprocessed historical data, respectively setting early warning evaluation thresholds for the different early warning grades, and obtaining an early warning evaluation sequence table, wherein in the step 4, the road early warning evaluation coefficient and the acquisition process of the early warning evaluation sequence table are as follows:
Step 401, traversing the preprocessed historical data, combining the traffic flow interference index and the weather interference index with the historical traffic accident record data, and analyzing the association degree of the road dangerous condition, the traffic flow interference index and the weather interference index;
Step 402, different weights are distributed to the traffic flow interference index and the weather interference index, and the two indexes are subjected to weighted calculation to obtain a road early warning evaluation coefficient;
Step 403, according to the preprocessed historical data, matching the road dangerous situation in the historical traffic accident record data, determining different early warning grades, namely a first early warning grade, a second early warning grade, a third early warning grade and a fourth early warning grade, wherein the road dangerous situation of the early warning grade is gradually increased from the first early warning grade to the fourth early warning grade;
Step 404, matching the determined different early warning grades with the result of the road early warning evaluation coefficient, and setting corresponding early warning evaluation thresholds for the different early warning grades;
Step 405, arranging all the road early warning evaluation coefficient values in order from high to low, and allocating corresponding early warning grades to each value to prepare an early warning evaluation sequence table, and listing the corresponding relation between the road early warning evaluation coefficient and the early warning grade, wherein the calculation formula of the road early warning evaluation coefficient is as follows:
wherein DP is a road early warning evaluation coefficient, JG is a traffic flow interference index, w 1 is a weight of the traffic flow interference index, TG is a weather interference index, w 2 is a weight of the weather interference index, w 1+w2 =1, and the DP has a value range of 0,1;
and 5, inputting the preprocessed real-time data into a road risk assessment model for analysis to obtain a numerical value of a road early warning assessment coefficient, determining an early warning level of the current road risk condition, generating corresponding early warning information according to the corresponding early warning level, and carrying out collaborative management on road traffic.
2. The intelligent traffic-based vehicle-road collaborative management method and system according to claim 1, wherein: the early warning levels correspond to the early warning evaluation thresholds, wherein the early warning evaluation thresholds comprise an upper limit threshold and a lower limit threshold;
the early warning levels and the early warning evaluation thresholds satisfy the following relation:
first-level early warning level 0< DP A;
Second-level pre-warning level DP A≤DP<DPW;
Three-level early warning grade DP W≤DP<DPZ;
the DP Z of the four-stage early warning level is less than or equal to DP <1;
The DP is a road early warning evaluation coefficient, DP A is an upper threshold corresponding to a first-level early warning level and a lower threshold corresponding to a second-level early warning level, DP W is an upper threshold corresponding to a second-level early warning level and a lower threshold corresponding to a third-level early warning level, DP Z is an upper threshold corresponding to a third-level early warning level and a lower threshold corresponding to a fourth-level early warning level, and DP A＝0.2,DPW＝0.5,DPZ =0.8.
3. The intelligent traffic-based vehicle-road cooperative management method according to claim 2, wherein: in the step 5, the process of the collaborative management of the road traffic is as follows:
Step 501, inputting characteristic data of traffic flow and weather conditions in the preprocessed real-time data into a road risk assessment model, and respectively calculating a traffic flow interference index and a weather interference index;
Step 502, combining the calculated traffic flow interference index and the weather interference index to obtain a road early warning evaluation coefficient, and determining an early warning level of the current road dangerous condition according to a preset early warning evaluation threshold;
Step 503, generating corresponding early warning information according to the early warning level of the current road dangerous situation, and transmitting the early warning information to related personnel and departments for the cooperative management of road traffic, wherein the related personnel and departments comprise a road management department, a traffic supervision center and a driver;
Step 504, monitoring the development execution condition of the early warning information and recording feedback information.
4. The intelligent traffic-based vehicle-road cooperative management system is used for realizing the intelligent traffic-based vehicle-road cooperative management method according to any one of claims 1-3, and comprises a traffic management center, and is characterized in that: the traffic management center is in communication connection with a data acquisition module, a data analysis module, a model construction module, a road early warning evaluation module, an early warning module and a traffic scheduling module, wherein the modules are in electrical signal connection;
The data acquisition module is used for acquiring real-time data of the road environment and acquiring historical data of the road environment from a public database of traffic management;
The data analysis module is used for preprocessing the road environment data acquired by the data acquisition module and extracting road safety interference factors;
The model construction module is used for constructing a road risk assessment model by combining the preprocessed road environment historical data and the road safety interference factors, acquiring a traffic flow interference index and a weather interference index, and analyzing the running speed and the traffic flow trend of the vehicle;
the road early warning evaluation module is used for combining the historical data of the road environment and the road risk evaluation model to obtain a road early warning evaluation coefficient to evaluate the safety and risk of the road;
The early warning module is used for determining the early warning grade of the current road dangerous condition according to the combination of the road early warning evaluation coefficient and the real-time data of the road environment, and generating corresponding early warning information based on the road early warning evaluation result;
and the traffic scheduling module is used for managing and scheduling road traffic according to the early warning information and the real-time traffic condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410971916.5A CN118506583B (en) | 2024-07-19 | 2024-07-19 | Vehicle-road collaborative management method and system based on intelligent transportation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410971916.5A CN118506583B (en) | 2024-07-19 | 2024-07-19 | Vehicle-road collaborative management method and system based on intelligent transportation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118506583A CN118506583A (en) | 2024-08-16 |
| CN118506583B true CN118506583B (en) | 2024-11-26 |
Family
ID=92241870
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410971916.5A Active CN118506583B (en) | 2024-07-19 | 2024-07-19 | Vehicle-road collaborative management method and system based on intelligent transportation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118506583B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119229643A (en) * | 2024-08-19 | 2024-12-31 | 无锡图茂网络科技有限公司 | Anomaly analysis method based on big data |
| CN119045455A (en) * | 2024-08-22 | 2024-11-29 | 上海汉强汽车零配件有限公司 | Cloud computing-based automobile controller management system |
| CN119068676B (en) * | 2024-09-03 | 2025-08-12 | 江苏中天交通工程有限公司 | Intelligent road traffic control safety management system |
| CN118824022B (en) * | 2024-09-20 | 2025-02-07 | 宁波东鸿信息技术有限公司 | A digital safety monitoring and early warning system and method based on big data |
| CN119918951B (en) * | 2025-04-01 | 2025-08-01 | 广东联合电子服务股份有限公司 | Traffic safety risk early warning method and system based on traffic flow characteristic data |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101110161A (en) * | 2007-08-31 | 2008-01-23 | 北京科技大学 | A vehicle type automatic identification and traffic flow automatic detection system and method thereof |
| CN117207022A (en) * | 2023-10-24 | 2023-12-12 | 江苏中天交通工程有限公司 | A kind of raw material polishing and grinding device for processing sign parts |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101060613B1 (en) * | 2009-04-27 | 2011-08-31 | 건국대학교 산학협력단 | Creation of flooding information using weather information and service method for navigation using it |
| CN204632105U (en) * | 2015-05-27 | 2015-09-09 | 北京中天星火智能交通科技有限公司 | A kind of traffic flow investigation apparatus |
| HRP20241547T1 (en) * | 2022-03-29 | 2025-03-14 | Alps Alpine Europe Gmbh | Method for assessing a risk of interference, data processing device, system for assessing a risk of interference, and bicycle assembly |
| CN114999228B (en) * | 2022-06-01 | 2022-12-16 | 北京易航远智科技有限公司 | Anti-collision method for automatic driving vehicle in severe weather |
| CN116524719A (en) * | 2023-05-22 | 2023-08-01 | 浪潮智慧科技有限公司 | A road congestion prediction method, device and medium |
| CN117995000B (en) * | 2024-04-02 | 2024-08-02 | 江苏中天交通工程有限公司 | Signal lamp structure for road traffic |
| CN118314751A (en) * | 2024-05-09 | 2024-07-09 | 长安大学 | A smart traffic management method and platform based on the Internet of Things cloud platform |
-
2024
- 2024-07-19 CN CN202410971916.5A patent/CN118506583B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101110161A (en) * | 2007-08-31 | 2008-01-23 | 北京科技大学 | A vehicle type automatic identification and traffic flow automatic detection system and method thereof |
| CN117207022A (en) * | 2023-10-24 | 2023-12-12 | 江苏中天交通工程有限公司 | A kind of raw material polishing and grinding device for processing sign parts |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118506583A (en) | 2024-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN118506583B (en) | Vehicle-road collaborative management method and system based on intelligent transportation | |
| CN103971523B (en) | A kind of mountain road traffic safety dynamic early-warning system | |
| US9111442B2 (en) | Estimating incident duration | |
| CN111680745A (en) | A method and system for sudden congestion discrimination based on multi-source traffic big data fusion | |
| CN108346288B (en) | Road section operation state early warning method and device and electronic equipment | |
| CN114363316B (en) | A cross-regional road infrastructure intelligent networking monitoring and supervision system | |
| CN102231231A (en) | Area road network traffic safety situation early warning system and method thereof | |
| CN111402586A (en) | Road meteorological environment forecasting and early warning control system and method based on Internet of vehicles | |
| CN113643539A (en) | Intelligent traffic control system based on big data | |
| CN119694138B (en) | Dynamic construction method for digital twin bodies of expressway | |
| CN109785627A (en) | A kind of crossroad access flux monitoring system | |
| CN114550445A (en) | Urban area traffic safety state evaluation method and device | |
| CN117315939A (en) | Expressway traffic safety situation early warning method and system | |
| CN120108182A (en) | Operational situation prediction system for high-speed traffic emergencies for travel services | |
| CN112989069B (en) | Traffic violation analysis method based on knowledge graph and block chain | |
| CN119513675A (en) | Vehicle driving behavior evaluation method and system based on ETC data of Internet of Things | |
| CN118097953B (en) | Urban main road network traffic accident risk assessment method based on multi-source risk | |
| CN102157061A (en) | Keyword-statistic-based traffic event identifying method | |
| CN119181242A (en) | Traffic data acquisition and real-time road condition analysis system, method, equipment and medium based on big data | |
| Dalgleish | Highway traffic monitoring and data quality | |
| CN118470996A (en) | Road safety operation reminding method and system combining meteorological cloud | |
| CN116978231A (en) | Road section emergency traffic situation influence evaluation analysis model and method | |
| Chen et al. | Automatic freeway bottleneck identification and visualization using image processing techniques | |
| CN120509620B (en) | Traffic toughness dynamic evaluation prediction system based on deep learning | |
| CN115035722B (en) | Road safety risk prediction method based on combination of space-time characteristics and social media |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |