CN119964373A - A highway traffic monitoring system and method based on the Internet - Google Patents
A highway traffic monitoring system and method based on the Internet Download PDFInfo
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Abstract
本发明公开了一种基于互联网的高速公路交通监控系统及方法，涉及高速公路交通监控技术领域，解决了现有技术在极端天气下，摄像头的成像质量会受到影响，从而降低了数据采集的准确性的技术问题；本发明通过采集目标路段的车辆数据以及交通数据；基于历史交通数据训练人工智能模型，得到交通数据识别模型；基于实时的环境数据通过交通数据识别模型识别得到目标交通数据；以及，基于车辆数据计算得到目标路段上车辆的平均速度；基于目标交通数据计算得到目标路段的最大限速；基于平均速度与预设速度阈值判断是否对目标路段进行拥堵预警；以及，基于目标路段的最大限速判断是否对目标路段上的车辆进行速度预警，解决了上述技术问题。
The present invention discloses an Internet-based highway traffic monitoring system and method, which relates to the technical field of highway traffic monitoring, and solves the technical problem that in the prior art, the imaging quality of a camera is affected in extreme weather conditions, thereby reducing the accuracy of data collection. The present invention collects vehicle data and traffic data of a target road section; trains an artificial intelligence model based on historical traffic data to obtain a traffic data recognition model; obtains target traffic data through the traffic data recognition model based on real-time environmental data; and calculates the average speed of vehicles on the target road section based on the vehicle data; calculates the maximum speed limit of the target road section based on the target traffic data; determines whether to issue a congestion warning to the target road section based on the average speed and a preset speed threshold; and determines whether to issue a speed warning to vehicles on the target road section based on the maximum speed limit of the target road section, thereby solving the above technical problems.
Description
Technical Field
The invention belongs to the field of highway traffic monitoring, and particularly relates to an internet-based highway traffic monitoring system and method.
Background
With the increasing of the quantity of the vehicles, the traffic flow of the expressway is increased, the traffic accidents are frequent, the traffic jam problem is serious, and the traditional traffic monitoring system has various defects such as untimely data update, obvious information island phenomenon, lack of intelligent analysis and the like when coping with the combat.
The prior art (patent of 2019110718868) discloses a traffic monitoring and early warning method and a traffic monitoring and early warning system for a highway, wherein a flight time TOF camera collects environmental images of a road section where the flight time TOF camera is located according to a received data collection instruction and a preset frequency, three-dimensional point cloud data are generated and sent to a monitoring processor, the monitoring processor carries out filtering processing, vehicle characteristic data extraction and counting processing on the three-dimensional point cloud data to obtain the total number of vehicles of the road section corresponding to the road section ID, the monitoring processor obtains a congestion threshold value and judges whether the total number of vehicles is larger than the congestion threshold value, when the total number of vehicles is larger than the congestion threshold value, the monitoring processor generates free release commands according to the road section ID and the total number of vehicles and sends the free release commands to each charging terminal and each user terminal of the road section corresponding to the road section ID, the user terminal receives and analyzes the release commands to obtain free release prompt information and displays the free release prompt information on the user terminal, and the charging terminal generates an opening control command according to the free release commands to open vehicle interception equipment. However, in extreme weather, such as heavy fog and snow, the imaging quality of the camera is not considered in the prior art, so that the accuracy of data acquisition is reduced, the friction coefficient and visibility of the road surface are obviously changed, and the running safety of the vehicle is further affected.
Therefore, the present invention solves the above-mentioned problems by providing an internet-based highway traffic monitoring system and method.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art, and provides an internet-based expressway traffic monitoring system and method, which are used for solving the technical problems that in extreme weather, such as heavy fog, heavy snow and the like, the imaging quality of a camera is influenced, the accuracy of data acquisition is reduced, the friction coefficient and visibility of a road surface are obviously changed, and the running safety of a vehicle is influenced in the prior art.
In order to achieve the above object, a first aspect of the present invention provides an internet-based highway traffic monitoring system, comprising a data acquisition module, a data analysis module and a traffic monitoring module;
the data acquisition module is used for acquiring vehicle data and traffic data of a target road section in real time;
The data analysis module is used for training an artificial intelligent model based on historical traffic data to obtain a traffic data identification model, identifying the traffic data based on real-time environment data through the traffic data identification model to obtain target traffic data, and the data analysis module is used for acquiring the traffic data identification model based on the real-time environment data
Calculating the average speed of the vehicle on the target road section based on the vehicle data;
The traffic monitoring module judges whether to perform congestion early warning on the target road section based on the average speed and a preset speed threshold value, and
And judging whether to perform speed early warning on the vehicle on the target road section or not based on the maximum speed limit of the target road section.
Preferably, the collecting vehicle data of the target road section includes:
the method comprises the steps of collecting speeds Vi of a plurality of vehicles through a microwave detector installed on a target road section, and marking the speeds Vi of the plurality of vehicles as vehicle data, wherein the target road section is a road section to be monitored on an expressway, and vi= { V1, V2, V3, & gt, vn }, and n is the total number of vehicles on the target road section.
Preferably, the traffic data includes:
Measuring the road surface friction coefficient of the target road section by a pendulum type friction meter;
Obtaining the visibility of a target road section through a weather prediction platform;
the road friction coefficient and visibility are marked as traffic data.
The road surface friction coefficient refers to a friction coefficient between a tire and an automobile tire and a road surface.
Preferably, the training the artificial intelligence model based on the historical traffic data includes:
Extracting road friction coefficients of a target road section corresponding to a plurality of environmental data in historical traffic data and visibility of the target road section corresponding to the plurality of environmental data, wherein the environmental data comprises environmental temperature, environmental humidity, snowfall, fog concentration and rainfall types, and the rainfall types comprise rainwater, snow water and hail;
Integrating the environmental data into standard input data, and integrating the road friction coefficient and visibility of a target road section corresponding to the environmental data into standard output data;
Training an artificial intelligent model based on standard input data and standard output data to obtain a traffic data identification model, wherein the artificial intelligent model comprises a convolutional neural network or a deep confidence network.
It should be noted that the data such as ambient temperature, humidity, snowfall, mist concentration and rainfall type have direct or indirect influence on the road friction coefficient, so that the data can be used for predicting the friction coefficient, specifically, whether the road is frozen or not is directly influenced by the ambient temperature, particularly, under the low-temperature condition, moisture is easy to freeze to form a layer of thin ice, the friction coefficient is obviously reduced, under the high-humidity condition, water vapor in air can be condensed into dew or frost on the road to increase the risk of wet skid, the snowfall not only influences the thickness of snow, but also can possibly cause compaction snow or refreezing to further deteriorate the friction condition, the mist not only reduces visibility, but also can increase the humidity of the road, particularly, at low temperature, the moisture in the mist can be condensed into frost or ice to reduce the friction coefficient, different types of rainfall (such as rain, snow water and hail) have different influences on the friction coefficient, namely, the rain can form an initial wet oil film at the early stage, the phenomenon of water accumulation and water skid can be initiated after the water is melted, the water is frozen again, a smooth ice surface can be formed, the hail can be rapidly fallen down, and the risk of ice particles can be greatly increased.
Preferably, the identifying the target traffic data based on the real-time environmental data through the traffic data identifying model includes:
Acquiring real-time environmental data through a weather prediction platform;
And inputting the real-time environment data into a traffic data identification model to obtain target traffic data, wherein the target traffic data comprises the road surface friction coefficient and the visibility of a target road section corresponding to the real-time environment data.
Preferably, the calculating, based on the vehicle data, the average speed of the vehicle on the target road section includes:
Extracting the speeds Vi of a plurality of vehicles on a target road section;
by the formula And calculating the average speed of a plurality of vehicles on the target road section, wherein Vp is the average speed of a plurality of vehicles on the target road section.
The method and the system for calculating the average speed Vp of the vehicles on the target road section based on the collected vehicle speed data Vi calculate the average speed Vp of the vehicles on the target road section through a formula, the average speed is not only a key index for measuring traffic smoothness, but also can reflect the actual traffic capacity of the road, through dynamic monitoring of the average speed, a traffic management department can timely know the traffic condition of the road section, find potential congestion risks and take corresponding measures for dredging, and compared with the traditional fixed threshold judgment, the dynamic evaluation based on the average speed is more flexible and accurate, and can be better suitable for traffic changes under different time periods and weather conditions.
Preferably, the calculating, based on the target traffic data, the maximum speed limit of the target road section includes:
The road friction coefficient of the target road section corresponding to the real-time environment data is marked as f, and the visibility is marked as D;
by the formula And calculating to obtain the maximum speed limit of the target road section, wherein Vmax is the maximum speed limit of the target road section, R is the maximum value of the normal reaction time interval of the driver, and g is the gravity acceleration.
The maximum value of the normal reaction time interval of the driver is used when calculating the maximum speed limit to ensure that even the slowest driver can cope with emergency situations in a safety range and avoid traffic accidents.
Preferably, the determining whether to perform congestion pre-warning on the target road section based on the average speed and the preset speed threshold includes:
judging whether the average speed is smaller than a preset speed threshold, if yes, generating congestion early warning information and sending the congestion early warning information to a client, and if no, continuously monitoring and judging.
The preset speed threshold is set according to the design speed of the target road section, wherein the design speed is an ideal running speed determined according to the geometric design of the road, traffic flow expectation, safety standard and other factors, and is used for ensuring that vehicles can safely and efficiently pass under normal conditions, and the speed is an important index of highway engineering construction and is used for determining and coordinating with the design index of the highway.
The method compares the calculated average speed with the preset speed threshold to judge whether to trigger congestion early warning, wherein the preset speed threshold is set according to the design speed of a target road section, the design speed comprehensively considers factors such as geometric design of a road, traffic flow expectation, safety standard and the like, the purpose is to ensure that vehicles can safely and efficiently pass under normal conditions, when the average speed is lower than the preset speed threshold, the system can generate congestion early warning information and send the congestion early warning information to a client through various channels (such as navigation equipment, a mobile phone APP, an electronic display screen and the like), the method not only reminds a driver that congestion exists in front of the vehicle, suggests to slow down running or select an alternative route, but also helps a traffic management department to timely take measures such as current limiting and current dividing, relieves traffic pressure and reduces traffic accidents.
Preferably, the determining whether to perform speed pre-warning on the vehicle on the target road section based on the maximum speed limit of the target road section includes:
Extracting the speeds Vi of a plurality of vehicles on a target road section;
Judging whether the speeds Vi of a plurality of vehicles are greater than the maximum speed limit of a target road section, if so, generating overspeed early warning information and sending the overspeed early warning information to a client, and if not, continuously monitoring and judging.
The system can dynamically calculate the maximum speed limit of a target road section by combining the maximum value of the real-time road friction coefficient, the visibility and the driver response time, and perform overspeed early warning according to the actual speed of a vehicle, so that the safety and the traffic efficiency of a highway are obviously improved, particularly, the maximum value of the normal response time interval of the driver is used for ensuring that even the slowest-response driver can cope with emergency in a safety range, traffic accidents are avoided, the system can timely find and warn overspeed behaviors to remind the driver to immediately decelerate and keep the safety distance by monitoring the vehicle speed Vi in real time and comparing with the calculated maximum speed limit Vmax, the dynamic speed limit adjustment mechanism based on the environmental condition and the driver response time not only improves the safety of the road, but also effectively reduces the traffic accidents caused by overspeed, ensures that all vehicles can safely run in complex and variable traffic environments, and in addition, the continuous monitoring and timely early warning functions enable traffic management departments to rapidly take measures to optimize traffic flow and improve the overall traffic efficiency.
A second aspect of the present invention provides an internet-based highway traffic monitoring method, comprising:
the method comprises the steps of firstly, collecting vehicle data and traffic data of a target road section in real time;
Training an artificial intelligent model based on historical traffic data to obtain a traffic data identification model;
Calculating the average speed of the vehicle on the target road section based on the vehicle data;
And judging whether to perform congestion early warning on the target road section based on the average speed and a preset speed threshold value, and judging whether to perform speed early warning on the vehicle on the target road section based on the maximum speed limit of the target road section.
Compared with the prior art, the invention has the beneficial effects that:
1. The method comprises the steps of collecting vehicle data and traffic data of a target road section, training an artificial intelligent model based on historical traffic data to obtain a traffic data identification model, identifying the target traffic data based on real-time environment data through the traffic data identification model, calculating the average speed of a vehicle on the target road section based on the vehicle data, calculating the maximum speed limit of the target road section based on the target traffic data, judging whether to perform congestion early warning on the target road section based on the average speed and a preset speed threshold value, judging whether to perform speed early warning on the vehicle on the target road section based on the maximum speed limit of the target road section, and solving the problem that in the prior art, in extreme weather such as heavy fog, heavy snow and the like, the imaging quality of a camera is influenced, so that the accuracy of data collection is reduced, the obvious change of the friction coefficient and the visibility of a road surface is caused, and the running safety of the vehicle is influenced.
2. The method ensures accurate grasp of traffic conditions of a target road section through real-time data acquisition, the microwave detector continuously acquires vehicle speed data, the problem of untimely data update in a traditional monitoring system is avoided, a solid foundation is provided for subsequent analysis and decision, the average speed calculation provides a scientific basis for evaluating traffic flow, the average speed is dynamically monitored to help discover potential congestion risks in time, and a dredging measure is adopted, the congestion early warning mechanism generates early warning information by comparing the average speed with a preset speed threshold value, reminds a driver, assists a traffic management department in relieving traffic pressure, and reduces accidents.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system module according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system workflow according to an embodiment of the invention;
FIG. 3 is a schematic diagram of steps of a method according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, an embodiment of a first aspect of the present invention provides an internet-based highway traffic monitoring system, which includes a data acquisition module, a data analysis module, and a traffic monitoring module;
the data acquisition module is used for acquiring vehicle data and traffic data of a target road section in real time;
The data analysis module is used for training an artificial intelligent model based on historical traffic data to obtain a traffic data identification model, identifying the traffic data based on real-time environment data through the traffic data identification model to obtain target traffic data, and calculating the average speed of a vehicle on a target road section based on the vehicle data;
and the traffic monitoring module is used for judging whether to perform congestion early warning on the target road section based on the average speed and a preset speed threshold value, and judging whether to perform speed early warning on the vehicle on the target road section based on the maximum speed limit of the target road section.
Collecting vehicle data of a target road segment, comprising:
the method comprises the steps of collecting speeds Vi of a plurality of vehicles through a microwave detector installed on a target road section, and marking the speeds Vi of the plurality of vehicles as vehicle data, wherein the target road section is a road section to be monitored on an expressway, and vi= { V1, V2, V3, & gt, vn }, and n is the total number of vehicles on the target road section.
Traffic data, comprising:
Measuring the road surface friction coefficient of the target road section by a pendulum type friction meter;
Obtaining the visibility of a target road section through a weather prediction platform;
the road friction coefficient and visibility are marked as traffic data.
Training an artificial intelligence model based on historical traffic data, comprising:
Extracting road friction coefficients of a target road section corresponding to a plurality of environmental data in historical traffic data and visibility of the target road section corresponding to the plurality of environmental data, wherein the environmental data comprises environmental temperature, environmental humidity, snowfall, fog concentration and rainfall types, and the rainfall types comprise rainwater, snow water and hail;
Integrating the environmental data into standard input data, and integrating the road friction coefficient and visibility of a target road section corresponding to the environmental data into standard output data;
Training an artificial intelligent model based on standard input data and standard output data to obtain a traffic data identification model, wherein the artificial intelligent model comprises a convolutional neural network or a deep confidence network.
The method for identifying and obtaining the target traffic data through the traffic data identification model based on the real-time environment data comprises the following steps:
Acquiring real-time environmental data through a weather prediction platform;
And inputting the real-time environment data into a traffic data identification model to obtain target traffic data, wherein the target traffic data comprises the road surface friction coefficient and the visibility of a target road section corresponding to the real-time environment data.
Calculating an average speed of the vehicle on the target road section based on the vehicle data, comprising:
Extracting the speeds Vi of a plurality of vehicles on a target road section;
by the formula And calculating the average speed of a plurality of vehicles on the target road section, wherein Vp is the average speed of a plurality of vehicles on the target road section.
Calculating the maximum speed limit of the target road section based on the target traffic data comprises the following steps:
The road friction coefficient of the target road section corresponding to the real-time environment data is marked as f, and the visibility is marked as D;
by the formula And calculating to obtain the maximum speed limit of the target road section, wherein Vmax is the maximum speed limit of the target road section, R is the maximum value of the normal reaction time interval of the driver, and g is the gravity acceleration.
Judging whether to perform congestion pre-warning on the target road section based on the average speed and a preset speed threshold value, including:
judging whether the average speed is smaller than a preset speed threshold, if yes, generating congestion early warning information and sending the congestion early warning information to a client, and if no, continuously monitoring and judging.
Judging whether to perform speed early warning on the vehicle on the target road section based on the maximum speed limit of the target road section comprises the following steps:
Extracting the speeds Vi of a plurality of vehicles on a target road section;
Judging whether the speeds Vi of a plurality of vehicles are greater than the maximum speed limit of a target road section, if so, generating overspeed early warning information and sending the overspeed early warning information to a client, and if not, continuously monitoring and judging.
For example, assuming that a certain highway (target road section) encounters heavy snow in winter, the system needs to monitor traffic conditions of the road section in real time, adjust speed limit and issue early warning information according to actual conditions. The method comprises the following steps:
1. a data acquisition module;
Vehicle data acquisition:
Microwave detector, the microwave detector installed on the target road segment collects vehicle speed data every 5 minutes. Assume that, over a period of time, the vehicle speed data collected is as follows:
vi= {70,68,65,72,69,67,71,66,64,73} km/h; where n=10 is the total number of vehicles on the target road segment.
Traffic data acquisition:
pendulum type friction meter, which is used for measuring road surface friction coefficient of target road section
And the weather prediction platform is used for obtaining the visibility of the target road section.
Environmental data the weather forecast platform provides the following real-time environmental data:
ambient temperature t= -2 ℃;
ambient humidity h=90%;
Snowfall s=5 mm/hr;
wind speed w=10 meters/second;
Rainfall type, namely snowwater.
2. A data analysis module;
training an artificial intelligence model:
and (3) historical data extraction, namely extracting road friction coefficients and visibility of a target road section corresponding to a plurality of environmental data (such as temperature, humidity, snowfall, fog concentration and the like) from the historical traffic data. For example, in the past year, under similar weather conditions (low temperature, high humidity, large snowfall), the friction coefficient and visibility data for this road segment are as follows:
Ambient temperature range-5 ℃ to 0 ℃;
The ambient humidity ranges from 80% to 95%;
Snowfall ranging from 3 to 10 mm/hr;
road surface friction coefficient range from 0.2 to 0.3;
visibility range from 30 to 80 meters;
And integrating the environment data into standard input data, and integrating the corresponding friction coefficient and visibility into standard output data. Training by using artificial intelligent models such as Convolutional Neural Network (CNN) or Deep Belief Network (DBN) to obtain a traffic data identification model.
Real-time environmental data analysis:
Inputting real-time environment data, namely inputting current environment data (temperature, humidity, snowfall, wind speed and the like) into a trained traffic data identification model to obtain a real-time friction coefficient f=0.25 and visibility D=50 meters of a target road section, wherein the friction coefficient f=0.25 indicates that the road surface is slippery, and the visibility D=50 meters indicates that the visibility is lower.
Calculating an average speed:
Based on the collected vehicle speed data Vi, the following formula is adopted Calculating an average speed Vp of the vehicle on the target road segment:
Vp=(70+68+65+72+69+67+71+66+64+73)/10=68.5km/h;
Calculating the maximum speed limit:
Calculating the maximum speed limit Vmax of the target road section based on the real-time friction coefficient f=0.25 and the visibility D=50 meters, and combining the reaction time of the driver (assuming that the reaction time interval of the driver is 0.7-1.5 seconds, then R=1.5 seconds) and the gravitational acceleration g=9.8 m/s 2;
the calculation formula is as follows:
Substituting data to obtain Vmax about 75.2km/h;
thus, the maximum speed limit of the target link is about 75km/h.
3. A traffic monitoring module;
congestion early warning:
the preset speed threshold is assumed to be 60km/h. If the average speed Vp is lower than 60km/h, congestion warning information is generated.
Judging that the current average speed Vp=68.5 km/h is higher than the preset speed threshold value by 60km/h, so that congestion early warning is not triggered. The system will continue to monitor and update the average speed periodically.
If Vp is less than a preset speed threshold (60 km/h), triggering congestion early warning.
When the system judges that the target road section has congestion, the monitoring processor can generate congestion early warning information and send the congestion early warning information to navigation equipment, mobile phone APP or a vehicle-mounted information system of a user. After receiving the information, the user terminal can display prompt information on a screen to remind a driver that the road section in front of the driver possibly has congestion, and the user terminal recommends to run at a reduced speed or select other routes.
The congestion early warning information can be synchronously released to a traffic information release platform (such as an electronic display screen, a broadcasting station and the like) of the expressway, and real-time road condition information is provided for all drivers passing through the road section.
Speed early warning:
And (3) overspeed judgment, namely extracting the speeds Vi of a plurality of vehicles on the target road section, and judging whether the maximum speed limit Vmax=75 km/h is exceeded.
As a result, from the acquired speed data vi= {70,68,65,72,69,67,71,66,64,73} km/h, the speeds of all vehicles do not exceed 75km/h, and therefore overspeed warning is not triggered. The system will continue to monitor and periodically update the vehicle speed.
If the speed Vi of one or more vehicles exceeds Vmax, overspeed early warning is triggered.
When the system detects that the speed of a certain vehicle exceeds the maximum speed limit, the monitoring processor can generate overspeed early warning information and send the overspeed early warning information to navigation equipment, a mobile phone APP or a vehicle-mounted information system of the vehicle. After receiving the information, the user terminal can display prompt information on a screen to remind a driver that the current speed exceeds the speed limit and recommend immediate speed reduction.
Overspeed early warning information can be synchronously released to traffic information release platforms (such as electronic display screens, broadcasting stations and the like) of the expressway, and real-time speed limiting information is provided for all drivers passing through the road section.
Through the above example, it can be seen that the highway traffic monitoring system of the invention can collect and analyze the vehicle data and traffic data of the target road section in real time, accurately predicts the road friction coefficient and visibility based on the artificial intelligence model trained by the historical data, and calculates the maximum speed limit of the target road section. The system can also respectively judge whether congestion early warning and speed early warning are needed according to the average speed and the maximum speed limit, and ensure the safe running of the vehicle under severe weather conditions. The intelligent monitoring mode not only improves the safety and the traffic efficiency of the road, but also can timely cope with emergency situations and reduce traffic accidents.
Referring to fig. 3, an embodiment of the second aspect of the present invention provides an internet-based highway traffic monitoring method, including:
the method comprises the steps of firstly, collecting vehicle data and traffic data of a target road section in real time;
Training an artificial intelligent model based on historical traffic data to obtain a traffic data identification model;
Calculating the average speed of the vehicle on the target road section based on the vehicle data;
And judging whether to perform congestion early warning on the target road section based on the average speed and a preset speed threshold value, and judging whether to perform speed early warning on the vehicle on the target road section based on the maximum speed limit of the target road section.
The method comprises the steps of obtaining a plurality of data, wherein part of data in the formula is obtained by removing dimensions and taking the numerical calculation, the formula is a formula closest to the actual situation by simulating a large amount of collected data through software, and preset parameters and preset thresholds in the formula are set by a person skilled in the art according to the actual situation or are obtained through simulating the large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
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