CN116978231A - Road section emergency traffic situation influence evaluation analysis model and method - Google Patents
Road section emergency traffic situation influence evaluation analysis model and method Download PDFInfo
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Abstract
The invention discloses a road section emergency traffic situation influence evaluation analysis model and a road section emergency traffic situation influence evaluation analysis method, which are based on a Bayesian network and other related mining algorithms, and are used for estimating related follow-up traffic road condition evolution situations by establishing a Bayesian network evaluation model, collecting vehicles, road condition information, traffic accident grades and other data in a period before and after the violation time of a violation vehicle from a traffic vehicle management system database, sorting the collected information, establishing a training set training model of the Bayesian network evaluation model, bringing a prediction grade into a model training range, identifying the violation misjudgment, evaluating the traffic road section situation, providing decision support capability for improving the emergency treatment of expressway emergency, and simultaneously estimating related follow-up traffic road condition evolution situations based on the Bayesian network and other related mining algorithms, and carrying out decision support on emergency events.
Description
Technical Field
The invention relates to the technical field of traffic violations, in particular to a road section emergency traffic situation influence evaluation analysis model and a road section emergency traffic situation influence evaluation analysis method.
Background
In order to maintain the management order of urban vehicles, the electronic monitoring equipment and other equipment are arranged to play an essential role in managing urban road traffic, and the intelligent traffic advocations also increase the electronic police equipment at urban traffic intersections. The electronic police captures traffic violations or traffic accidents by using an automatic detection and measurement technology, transmits the acquired information back to public security departments for analysis and processing by using a network, and penalizes the culprit by using the acquired information as evidence so as to reduce accident occurrence and assist traffic police. However, the electronic police is a machine, so that the electronic police is inevitably subjected to 'accidental injury', thereby causing erroneous judgment on the vehicle violations, and the road conditions are complex and changeable, such as the situation that the traffic signal lamp is inconsistent with the on-site traffic police command, the vehicle is covered by a license plate and the like in order to avoid 119 special vehicles, signal lamp faults, avoiding faults or accident vehicles and the like, the situation that the violations are caused can apply for canceling the violation records, and the situation that the traffic signal lamp is more complicated, such as the situation that a plurality of continuous vehicles run red lamps or line pressing violations at a red lamp intersection for avoiding the special vehicles for executing the emergency tasks, the vehicle closest to the special vehicles is easily identified as being capable of applying for canceling the violation records in a manual auditing link, and the vehicle farther from the special vehicles is more difficult to identify, thereby causing the illegal erroneous judgment, and the illegal behavior erroneous judgment and the erroneous judgment are generated in the manual auditing link due to huge workload and possibly being interfered by human factors or objective factors.
Reasonable resource allocation is required to have clearer and more accurate knowledge on a traffic system, traffic management departments are required to timely find traffic flow changes possibly causing traffic jams in allocation, vulnerable road sections in urban road networks, which are easy to cause the traffic jams, are identified, and the maximization of regulation and control benefits is ensured under limited traffic regulation and control investment. In response to these traffic control demands, a more intelligent and efficient traffic control system (ITS) has been developed. The intelligent traffic system is proposed by IBM for the first time in 2008, and it is desirable to be able to build a real-time, efficient and accurate integrated traffic control system in an urban road network or even in a larger range using advanced information, communication, sensing, control and computer technologies. Intelligent traffic systems, which have been proposed to receive widespread attention from countries around the world. Accurate traffic flow prediction is a key point of an intelligent traffic system in sensing the running state of the traffic system, making short-term traffic control measures, playing the efficacy of traffic infrastructure and improving the running efficiency of the traffic system. Short-term traffic flow prediction of roads is one of the core parts of intelligent traffic system research. Short-time traffic flow is a complex system of interaction of people, vehicles and roads, and all parts of the system interact and are mutually coupled, so that high nonlinearity, time variability and uncertainty are presented, and difficulty in short-time traffic flow prediction is increased.
Disclosure of Invention
Aiming at the problems, the invention provides a road section emergency traffic situation influence evaluation analysis model which is used for estimating the evolution situation of the related follow-up traffic road conditions based on the related mining algorithms such as a Bayesian network and the like.
In a first aspect, a method for evaluating and analyzing traffic situation impact of a road section emergency, which identifies violation misjudgment by establishing a bayesian network evaluation model, evaluates the situation of a high-speed traffic road section, includes the following steps:
traffic information collection: collecting vehicles, road condition information and traffic accident grade data in a period of time before and after the violation time of the violation vehicle, wherein the vehicle condition, the road section position of the accident, the accident occurrence time, the road design of the accident road section and other relevant factors causing accident influence;
information overall classification: sorting and classifying traffic information, and setting misjudgment information;
database establishment: establishing an initial data training set in a corresponding time period according to the obtained information data;
model training and situation prediction evaluation: and establishing a Bayesian network evaluation model, determining the model, training, and then predicting and evaluating the traffic situation of the emergency on the road section by utilizing the real-time information of the acquired traffic road section to predict the traffic situation of the next road section.
Further, the traffic information is collected from a traffic vehicle management system database, further comprising:
vehicle and road condition information: whether the illegal vehicles avoid special vehicle information, whether the traffic signal lamp fails or not, whether the failed vehicles or roadblock information is avoided, whether the traffic signal lamp is consistent with on-site traffic police command information, whether license plates are applied with information and whether the vehicles violate regulations or not are carried out in the front-back period section;
traffic flow information of traffic road network of the set time in the prediction range: the prediction range is the range of the traffic road network needing prediction.
Further, the collection of traffic flow information of the traffic road network with the set time in the prediction range specifically includes:
collecting traffic flow information of a traffic network section within a prediction horizon for a period of time, the traffic flow information comprising: traffic speed, traffic density, and traffic flow;
collecting longitude and latitude ranges of a traffic network to be predicted, the number of road sections in the longitude and latitude ranges, the topological structure among the road sections and the road information of the road sections; the road information comprises the length of a road section, the road grade and longitude and latitude coordinates of the starting point of the road section;
and collecting traffic flow information of traffic network road sections within a period of time within a prediction range, forming a time sequence of traffic state quantity of each road section within the prediction range, collecting traffic flow information within a certain period of time for each road section, and forming a time sequence with the same length of each traffic flow information quantity of each road section after compensation.
Further, the initial data training set content includes: whether the vehicles violate regulations or not, whether special vehicles are avoided or not, whether traffic signals are in fault or not, whether fault vehicles or roadblock information are avoided or not, whether the traffic signals are consistent with on-site traffic police command or not, and whether license plates are applied mechanically or not.
Further, the model training comprises the steps of:
the obtained information is used as the input of an identification model to obtain the probability distribution of whether to avoid special vehicle nodes, the probability distribution of whether to avoid fault nodes of traffic lights, the probability distribution of whether to avoid fault vehicles or roadblock nodes, the probability distribution of whether to agree with the traffic lights and the on-site traffic police command nodes and the probability distribution of whether to apply license plates to the nodes;
calculating the posterior probability distribution of whether the vehicle violates the regulation nodes according to the prior probability distribution of whether the vehicle violates the regulation nodes;
the result of misjudgement and identification of the vehicle rule violation in the time period is as follows: comparing the posterior probability of the actual violation of the vehicle with the posterior probability distribution of the false violation of the vehicle, and judging the actual violation of the vehicle if the posterior probability distribution of the actual violation of the vehicle is larger than or equal to the posterior probability of the false violation of the vehicle; if the posterior probability distribution of the actual violation of the vehicle is smaller than the posterior probability that the violation vehicle is misjudged, the violation vehicle is judged to be misjudged.
On the other hand, the road section emergency traffic situation influence assessment analysis model is used for realizing a road section emergency traffic situation influence assessment analysis method, the road section emergency traffic situation influence assessment analysis model collects data such as vehicles, road condition information, traffic accident grades and the like in a period before and after the violation time of a violation vehicle from a traffic vehicle management system database, sorts the collected information, builds a training set training model of a Bayesian network assessment model, brings a prediction grade into a model training range, identifies the violation misjudgment, and evaluates the traffic road section situation.
The invention has the beneficial effects that: the invention provides a road section emergency traffic situation influence evaluation analysis model and a road section emergency traffic situation influence evaluation analysis method, which are based on a Bayesian network and other related mining algorithms, and are used for estimating related follow-up traffic road condition evolution situations by establishing a Bayesian network evaluation model, collecting vehicles, road condition information, traffic accident grades and other data in a period before and after the violation time of a violation vehicle from a traffic vehicle management system database, sorting the collected information, establishing a training set training model of the Bayesian network evaluation model, bringing a prediction grade into a model training range, identifying the violation misjudgment, evaluating the traffic road section situation, providing decision support capability for improving the emergency treatment of expressway emergency, and simultaneously estimating related follow-up traffic road condition evolution situations based on the Bayesian network and other related mining algorithms, and carrying out decision support on emergency events.
Drawings
Fig. 1 is a flow chart of a road section emergency traffic situation influence evaluation and analysis method.
Detailed Description
For a clearer understanding of technical features, objects, and effects of the present invention, a specific embodiment of the present invention will be described with reference to the accompanying drawings.
The invention provides a road section emergency traffic situation impact assessment analysis model and a road section emergency traffic situation impact assessment analysis method. The method comprises the steps of establishing a Bayesian network assessment model, collecting data such as vehicles, road condition information, traffic accident grades and the like in a period before and after the violation time of a violation vehicle from an existing traffic vehicle management system database, sorting and classifying the collected information, establishing a training set training model of the Bayesian network assessment model, bringing a prediction grade into a model training range, identifying violation misjudgment, and evaluating traffic road section situation.
The first step, collecting information includes:
(1) Vehicle and road condition information; the method specifically comprises the steps of judging whether the illegal vehicles avoid special vehicle information Ak, judging whether the traffic signal lamp breaks down or not, judging whether the traffic signal lamp and the on-site traffic police command are consistent or not, judging whether the license plates are applied with information Ex and judging whether the vehicles break down or not in a section of a period of time.
(2) A traffic accident level; the relevant factors causing the accident influence are analyzed, such as the vehicle condition, the position of the road section where the accident occurs, the accident occurrence time, the road design of the accident road section and the like.
(3) Predicting traffic flow information of a traffic road network with set time in a range; the prediction range is the range of the traffic road network needing prediction. Traffic flow information of traffic network segments within a prediction horizon is collected, including traffic flow speed, traffic flow density, traffic flow.
(4) The longitude and latitude range of the traffic network to be predicted, the number of road sections, the topological structure among the road sections and the road information of the road sections, including the length of the road sections, the road grade and the longitude and latitude coordinates of the starting point of the road sections. And collecting traffic flow information of the traffic network road sections within a period of time within a prediction range, forming a time sequence of traffic state quantity of each road section within the prediction range, collecting traffic flow information including traffic flow speed, traffic flow density and traffic flow in a certain time for each road section, and carrying out data compensation on the road section with the missing road section. And forming a time sequence with the same length of each traffic information quantity of each road section after compensation.
Secondly, sorting the above information, and further setting the following misjudgment information: gi contains { G1, G2}, G1 represents the vehicle indeed violation information, and G2 represents the vehicle is erroneous judgment information. Ai contains { A1, A2, A3}, A1 indicating the absence of special vehicle information; a2 represents information that a special vehicle exists and the distance between the special vehicle and the offending vehicle is more than 5 m; a3 represents information that the distance between the special vehicle and the violation vehicle is smaller than 5 m. Bi contains { B1, B2}, B1 represents traffic signal normal information; b2 represents traffic light failure information. Ci contains { C1, C2, C3}, C1 indicating that no faulty truck or barrier information exists; c2 represents information that a faulty vehicle or roadblock exists and the distance between the faulty vehicle and the road-block vehicle is more than 5 m; c3 represents information that is less than 5m from the offending vehicle. Di contains { D1, D2, D3}, D1 indicates that traffic police command information is not present on site; d2 represents information caused by signal lamps and traffic police command; d3 represents inconsistent information of the signal lamp and the traffic police command. Ei contains { E1, E2}, E1 represents the actual vehicle coincidence information of the color and appearance of the offending vehicle and the license plate number thereof, and E2 represents the non-coincidence information. The 10 seconds before and after the violation time of the offending vehicle and the road condition information Ai, bi, ci, di, ei data are obtained from pictures shot by the electronic police by utilizing an image processing technology.
Third, an initial data training set is established for the period of time based on the information data obtained, where the training data set is referred to as an initial data training set. Specifically, the method comprises the steps of judging whether the vehicle violates the rule information Gi = { G1, G2 = {1,2}, judging whether special vehicle information Ai = { A1, A2, A3 = {1,2,3}, judging whether the traffic signal lamp breaks down the information Bi = { B1, B2 = {1,2}, judging whether the traffic signal lamp breaks down the information Ci = { C1, C2, C3 = {1,2,3}, judging whether the traffic signal lamp is consistent with the on-site traffic police or not = { D1, D2, D3 = {1,2}, judging whether the license plate is covered by the information Ei = { E1, E2 = {1,2}.
Fourth, the obtained information is used as the input of an identification model to obtain probability distribution P (Ai) of whether to avoid a special vehicle node A, probability distribution P (Bi) of whether to avoid a fault vehicle or a roadblock node C, probability distribution P (Ci) of whether to avoid the fault vehicle or the roadblock node C, probability distribution P (Di) of whether to accord with a traffic signal and a site traffic police command, and probability distribution P (Ei) of whether to apply a license plate to a node E. And combining the prior probability distribution Pr (Gi) of the vehicle violation node G to calculate the posterior probability distribution of the vehicle violation node G, wherein Pn (G1) represents the posterior probability of the vehicle violation indeed, and Pn (G2) represents the posterior probability of the violation vehicle being misjudged. The result of misjudgement and identification of the vehicle rule violation in the time period is as follows: comparing the sizes of Pn (G1) and Pn (G2), and if Pn (G1) is more than or equal to Pn (G2), namely the posterior probability of the actual violation of the vehicle is greater than or equal to the posterior probability of the false judgment of the violation vehicle, judging the violation vehicle as the actual violation; if Pn (G1) < Pn (G2), i.e., the posterior probability of the vehicle violation is less than the posterior probability that the violation vehicle is a false positive, then the violation vehicle is determined to be a false positive.
And fifthly, after the model is determined and trained, predicting and evaluating the traffic situation of the emergency of the road section, and predicting the traffic situation of the next road section.
The invention provides a road section emergency traffic situation influence evaluation analysis model and a road section emergency traffic situation influence evaluation analysis method, which are based on a Bayesian network and other related mining algorithms, and are used for estimating related follow-up traffic road condition evolution situations by establishing a Bayesian network evaluation model, collecting vehicles, road condition information, traffic accident grades and other data in a period before and after the violation time of a violation vehicle from a traffic vehicle management system database, sorting the collected information, establishing a training set training model of the Bayesian network evaluation model, bringing a prediction grade into a model training range, identifying the violation misjudgment, evaluating the traffic road section situation, providing decision support capability for improving the emergency treatment of expressway emergency, and simultaneously estimating related follow-up traffic road condition evolution situations based on the Bayesian network and other related mining algorithms, and carrying out decision support on emergency events.
The foregoing has shown and described the basic principles and features of the invention and the advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The road section emergency traffic situation influence evaluation analysis method is characterized by comprising the following steps of:
traffic information collection: collecting vehicles, road condition information and traffic accident grade data in a period of time before and after the violation time of the violation vehicle, wherein the vehicle condition, the road section position of the accident, the accident occurrence time, the road design of the accident road section and other relevant factors causing accident influence;
information overall classification: sorting and classifying traffic information, and setting misjudgment information;
database establishment: establishing an initial data training set in a corresponding time period according to the obtained information data;
model training and situation prediction evaluation: and establishing a Bayesian network evaluation model, determining the model, training, and then predicting and evaluating the traffic situation of the emergency on the road section by utilizing the real-time information of the acquired traffic road section to predict the traffic situation of the next road section.
2. The road segment emergency traffic situation impact assessment analysis method according to claim 1, wherein the traffic information is collected from a traffic vehicle management system database, further comprising:
vehicle and road condition information: whether the illegal vehicles avoid special vehicle information, whether the traffic signal lamp fails or not, whether the failed vehicles or roadblock information is avoided, whether the traffic signal lamp is consistent with on-site traffic police command information, whether license plates are applied with information and whether the vehicles violate regulations or not are carried out in the front-back period section;
traffic flow information of traffic road network of the set time in the prediction range: the prediction range is the range of the traffic road network needing prediction.
3. The method for evaluating and analyzing traffic situation effects of road section emergency according to claim 2, wherein the collection of traffic flow information of the traffic road network at a set time in the prediction range specifically comprises:
collecting traffic flow information of a traffic network section within a prediction horizon for a period of time, the traffic flow information comprising: traffic speed, traffic density, and traffic flow;
collecting longitude and latitude ranges of a traffic network to be predicted, the number of road sections in the longitude and latitude ranges, the topological structure among the road sections and the road information of the road sections; the road information comprises the length of a road section, the road grade and longitude and latitude coordinates of the starting point of the road section;
and collecting traffic flow information of traffic network road sections within a period of time within a prediction range, forming a time sequence of traffic state quantity of each road section within the prediction range, collecting traffic flow information within a certain period of time for each road section, and forming a time sequence with the same length of each traffic flow information quantity of each road section after compensation.
4. The method for analyzing the traffic situation impact evaluation of the road segment emergency according to claim 1, wherein the initial data training set content comprises: whether the vehicles violate regulations or not, whether special vehicles are avoided or not, whether traffic signals are in fault or not, whether fault vehicles or roadblock information are avoided or not, whether the traffic signals are consistent with on-site traffic police command or not, and whether license plates are applied mechanically or not.
5. The method for evaluating and analyzing the traffic situation impact of a road segment emergency according to claim 1, wherein the model training comprises the following steps:
the obtained information is used as the input of an identification model to obtain the probability distribution of whether to avoid special vehicle nodes, the probability distribution of whether to avoid fault nodes of traffic lights, the probability distribution of whether to avoid fault vehicles or roadblock nodes, the probability distribution of whether to agree with the traffic lights and the on-site traffic police command nodes and the probability distribution of whether to apply license plates to the nodes;
calculating the posterior probability distribution of whether the vehicle violates the regulation nodes according to the prior probability distribution of whether the vehicle violates the regulation nodes;
the result of misjudgement and identification of the vehicle rule violation in the time period is as follows: comparing the posterior probability of the actual violation of the vehicle with the posterior probability distribution of the false violation of the vehicle, and judging the actual violation of the vehicle if the posterior probability distribution of the actual violation of the vehicle is larger than or equal to the posterior probability of the false violation of the vehicle; if the posterior probability distribution of the actual violation of the vehicle is smaller than the posterior probability that the violation vehicle is misjudged, the violation vehicle is judged to be misjudged.
6. The road section emergency traffic situation impact assessment analysis model is used for realizing the road section emergency traffic situation impact assessment analysis method according to any one of claims 1-5, and is characterized in that the road section emergency traffic situation impact assessment analysis model collects data such as vehicles, road condition information, traffic accident grades and the like in a period of time before and after the violation time of a violation vehicle from an existing traffic vehicle management system database, sorts the collected information, establishes a training set training model of a Bayesian network assessment model, brings prediction grades into a model training range, identifies violation misjudgment and assesses traffic road section situations.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108280991A (en) * | 2017-01-05 | 2018-07-13 | 大唐高鸿信息通信研究院（义乌）有限公司 | The vehicle traffic accident prediction technique of vehicle-mounted short haul connection net |
| CN110288823A (en) * | 2019-05-13 | 2019-09-27 | 江苏大学 | A kind of break in traffic rules and regulations erroneous judgement recognition methods based on naive Bayesian network |
| CN113409576A (en) * | 2021-06-24 | 2021-09-17 | 北京航空航天大学 | Bayesian network-based traffic network dynamic prediction method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108280991A (en) * | 2017-01-05 | 2018-07-13 | 大唐高鸿信息通信研究院（义乌）有限公司 | The vehicle traffic accident prediction technique of vehicle-mounted short haul connection net |
| CN110288823A (en) * | 2019-05-13 | 2019-09-27 | 江苏大学 | A kind of break in traffic rules and regulations erroneous judgement recognition methods based on naive Bayesian network |
| CN113409576A (en) * | 2021-06-24 | 2021-09-17 | 北京航空航天大学 | Bayesian network-based traffic network dynamic prediction method and system |
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