CN111696347B - Method and device for automatically analyzing traffic incident information - Google Patents
Method and device for automatically analyzing traffic incident information Download PDFInfo
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- CN111696347B CN111696347B CN202010488582.8A CN202010488582A CN111696347B CN 111696347 B CN111696347 B CN 111696347B CN 202010488582 A CN202010488582 A CN 202010488582A CN 111696347 B CN111696347 B CN 111696347B
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- 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
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- 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
- G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
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- 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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/161—Decentralised systems, e.g. inter-vehicle communication
- G08G1/162—Decentralised systems, e.g. inter-vehicle communication event-triggered
Abstract
The invention provides a method and a device for automatically analyzing traffic incident information, which relate to the technical field of data processing, and are characterized in that first person flow information in a first traffic incident is obtained; obtaining first traffic information in the first traffic event; obtaining first people flow density information in a first time according to the first people flow information; obtaining first traffic density information within the first time according to the first traffic flow information; obtaining the first people stream density information and first influence factor information of the first traffic event; obtaining the first traffic density information and second influence factor information of the first traffic incident; determining a first comprehensive influence factor according to the first influence factor information and the second influence factor information; judging whether the first comprehensive influence factor meets a first preset condition or not; and when the first comprehensive influence factor accords with a first preset condition, obtaining first early warning information according to the first comprehensive influence factor.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for automatically analyzing traffic event information.
Background
Currently, china has become the first major world of global automobile consumption beyond the united states. The increase of various vehicles greatly improves the working efficiency of people, but also obviously increases traffic accidents. Traffic Accident (Traffic Accident) refers to an event that a vehicle causes personal injury or property loss on a road due to mistake or Accident. Traffic accidents are not only caused by unspecified persons violating road traffic safety regulations; or due to irresistible natural disasters such as earthquake, typhoon, mountain torrents, lightning stroke and the like. At present, technologies such as internet, database and the like are mature, have wide application in all aspects, and can be completely introduced into traffic accident treatment and accident prediction and early warning, so that traffic management departments can report and treat accidents more timely and conveniently.
However, the applicant of the present invention finds that the prior art has at least the following technical problems:
the existing traffic incident information early warning and incident handling are poor in effectiveness and low in maturity.
Disclosure of Invention
The embodiment of the invention provides a method and a device for automatically analyzing traffic incident information, solves the technical problems of poor effectiveness and low maturity of information early warning and incident handling of a traffic incident in the prior art, and achieves the technical effects of quickly and efficiently analyzing the traffic incident, improving the effectiveness and reliability of the information early warning, reducing the inducement of the traffic incident and improving the driving safety factor.
In view of the above, embodiments of the present application are provided to provide a method and apparatus for automatically analyzing traffic event information.
In a first aspect, the present invention provides a method for automatically analyzing traffic event information, the method comprising: obtaining first person traffic information in a first traffic event; obtaining first traffic information in the first traffic event; obtaining first people flow density information in a first time according to the first people flow information; obtaining first traffic density information in the first time according to the first traffic information; obtaining the first traffic density information and first influence factor information of the first traffic event; obtaining the first traffic density information and second influence factor information of the first traffic incident; determining a first comprehensive influence factor according to the first influence factor information and the second influence factor information; judging whether the first comprehensive influence factor meets a first preset condition or not; and when the first comprehensive influence factor accords with a first preset condition, obtaining first early warning information according to the first comprehensive influence factor.
Preferably, the method further comprises:
obtaining a second time period for the first traffic event; judging whether the second time period meets a second preset condition or not; when the second time period meets a second preset condition, obtaining third influence factor information of the second time period and the first traffic incident; and determining second early warning information according to the third influence factor information and the first comprehensive factor.
Preferably, the method further comprises:
acquiring first online public opinion information; judging whether the first network public opinion information and the first traffic incident have a first correlation; when the first network public opinion information has a first correlation with the first traffic event, obtaining a first popularity of the first public opinion information; judging whether the first heat exceeds a first preset threshold value or not; and when the first heat exceeds a first preset threshold value, third early warning information is obtained.
Preferably, the method further comprises:
obtaining first vehicle information in the first traffic event; obtaining first violation information of the first vehicle; obtaining second vehicle information in the first traffic event; judging whether the second vehicle has an illegal behavior; obtaining first driver information of the first vehicle when the second vehicle does not have the violation; determining first feature representation information according to the first driver information; judging whether the first characteristic portrait information meets a third preset condition or not; and when the first characteristic portrait information meets a third preset condition, obtaining fourth early warning information.
Preferably, the method further comprises:
obtaining first road information of the first traffic incident; determining a first road characteristic according to the first road information; judging whether the first road characteristic meets a fourth preset condition or not; when the first road characteristic meets a fourth preset condition, obtaining first road information of the first vehicle; obtaining first navigation routing information for the first vehicle; judging whether the first lane information is consistent with the first navigation route planning information or not; and when the first lane information is inconsistent with the first navigation route planning information, acquiring fifth early warning information.
Preferably, the method further comprises:
obtaining first road condition information according to the first passenger flow density information and the first traffic flow density information; acquiring first video information according to the first road condition information; obtaining second navigation routing information of a third vehicle; determining third navigation route planning information according to the first video information and the first navigation route planning information, wherein the third navigation route planning information comprises replacement route information; and sending first prompt information to a third driver of the third vehicle according to the third navigation route planning information.
In a second aspect, the present invention provides an apparatus for automatically analyzing traffic event information, the apparatus comprising:
a first obtaining unit, configured to obtain first person traffic information in a first traffic event;
a second obtaining unit, configured to obtain first traffic information in the first traffic event;
a third obtaining unit, configured to obtain first personal flow density information within a first time according to the first personal flow information;
a fourth obtaining unit configured to obtain first traffic density information in the first time according to the first traffic information;
a fifth obtaining unit, configured to obtain the first traffic density information and first influence factor information of the first traffic event;
a sixth obtaining unit, configured to obtain the first traffic density information and second influence factor information of the first traffic event;
a first determining unit, configured to determine a first comprehensive influence factor according to the first influence factor information and the second influence factor information;
the first judging unit is used for judging whether the first comprehensive influence factor meets a first preset condition or not;
a seventh obtaining unit, configured to obtain first warning information according to the first comprehensive influence factor when the first comprehensive influence factor meets a first preset condition.
Preferably, the apparatus further comprises:
an eighth obtaining unit to obtain a second time period of the first traffic event;
a second judging unit, configured to judge whether the second time period meets a second preset condition;
a ninth obtaining unit, configured to obtain third influence factor information of the second time period and the first traffic event when the second time period meets a second preset condition;
and the second determining unit is used for determining second early warning information according to the third influence factor information and the first comprehensive factor.
Preferably, the apparatus further comprises:
a tenth obtaining unit, configured to obtain first network public opinion information;
a third judging unit, configured to judge whether the first internet public opinion information and the first traffic event have a first association relationship;
an eleventh obtaining unit, configured to obtain a first popularity of the first public opinion information when the first internet public opinion information has a first association relation with the first traffic event;
a fourth judging unit configured to judge whether the first heat degree exceeds a first predetermined threshold;
a twelfth obtaining unit, configured to obtain third warning information when the first heat exceeds a first predetermined threshold.
Preferably, the apparatus further comprises:
a thirteenth obtaining unit configured to obtain first vehicle information in the first traffic event;
a fourteenth obtaining unit configured to obtain first violation information of the first vehicle;
a fifteenth obtaining unit configured to obtain second vehicle information in the first traffic event;
a fifth judging unit configured to judge whether there is an illegal act on the second vehicle;
a sixteenth obtaining unit configured to obtain first driver information of the first vehicle when there is no violation in the second vehicle;
a third determination unit configured to determine first feature representation information based on the first driver information;
a fifth judging unit configured to judge whether the first feature image information satisfies a third preset condition;
a seventeenth obtaining unit, configured to obtain fourth warning information when the first feature representation information satisfies a third preset condition.
Preferably, the apparatus further comprises:
an eighteenth obtaining unit configured to obtain first road information on occurrence of the first traffic event;
a fourth determining unit configured to determine a first road characteristic according to the first road information;
a sixth judging unit, configured to judge whether the first road characteristic satisfies a fourth preset condition;
a nineteenth obtaining unit configured to obtain first lane information of the first vehicle when the first road characteristic satisfies a fourth preset condition;
a twentieth obtaining unit for obtaining first navigation routing information of the first vehicle;
a seventh judging unit, configured to judge whether the first lane information is consistent with the first navigation route planning information;
a twenty-first obtaining unit, configured to obtain fifth warning information when the first lane information is inconsistent with the first navigation route planning information.
Preferably, the apparatus further comprises:
a twenty-second obtaining unit, configured to obtain first road condition information according to the first traffic density information and the first traffic density information;
a twenty-third obtaining unit, configured to obtain first video information according to the first road condition information;
a twenty-fourth obtaining unit for obtaining second navigation routing information of a third vehicle;
a fifth determining unit, configured to determine third navigation routing information according to the first video information and the first navigation routing information, where the third navigation routing information includes replacement routing information;
a first execution unit, configured to send a first prompt message to a third driver of the third vehicle according to the third navigation routing information.
In a third aspect, the present invention provides an apparatus for automated analysis of traffic event information, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when executing the program.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
according to the method and the device for automatically analyzing the traffic incident information, provided by the embodiment of the invention, the first person flow information in the first traffic incident is obtained; obtaining first traffic information in the first traffic event; obtaining first people flow density information in a first time according to the first people flow information; obtaining first traffic density information in the first time according to the first traffic information; obtaining the first traffic density information and first influence factor information of the first traffic event; obtaining the first traffic density information and second influence factor information of the first traffic event; determining a first comprehensive influence factor according to the first influence factor information and the second influence factor information; judging whether the first comprehensive influence factor meets a first preset condition or not; when the first comprehensive influence factor accords with a first preset condition, first early warning information is obtained according to the first comprehensive influence factor, so that the technical problems that the effectiveness of information early warning and event handling of a traffic event is poor and the maturity is low in the prior art are solved, the traffic event is analyzed quickly and efficiently, the effectiveness and the reliability of the information early warning are improved, the inducement of the traffic event is reduced, and the driving safety coefficient is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a method for automatically analyzing traffic event information in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of an apparatus for automatically analyzing traffic event information according to an embodiment of the present invention;
fig. 3 is a schematic diagram of another apparatus for automatically analyzing traffic event information according to an embodiment of the present invention.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a first determining unit 17, a first judging unit 18, a seventh obtaining unit 19, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the invention provides a method and a device for automatically analyzing traffic incident information, which are used for solving the technical problems of poor effectiveness and low maturity of information early warning and incident handling of traffic incidents in the prior art.
The technical scheme provided by the invention has the following general idea: obtaining first person traffic information in a first traffic event; obtaining first traffic information in the first traffic event; obtaining first people flow density information in a first time according to the first people flow information; obtaining first traffic density information in the first time according to the first traffic information; obtaining the first traffic density information and first influence factor information of the first traffic event; obtaining the first traffic density information and second influence factor information of the first traffic incident; determining a first comprehensive influence factor according to the first influence factor information and the second influence factor information; judging whether the first comprehensive influence factor meets a first preset condition or not; when the first comprehensive influence factor accords with a first preset condition, first early warning information is obtained according to the first comprehensive influence factor, so that the technical effects of quickly and efficiently analyzing a traffic incident, improving effectiveness and reliability of information early warning, reducing inducement of the traffic incident and improving driving safety factor are achieved.
The technical solutions of the present invention are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present invention are described in detail in the technical solutions of the present application, and are not limited to the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Example one
Fig. 1 is a flow chart illustrating a method for automatically analyzing traffic event information according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method for automatically analyzing traffic event information, the method including:
step 110: first person flow information in a first traffic event is obtained.
Step 120: first traffic volume information in the first traffic event is obtained.
Specifically, in the analysis and study of the traffic incident, the data information of the pedestrian flow and the traffic flow in the traffic incident can be collected, and whether the data information of the pedestrian flow and the traffic flow has influence on the traffic incident or not is analyzed. The traffic accident refers to an event that the vehicle causes personal injury or property loss on the road due to mistake or accident. Traffic accidents are not only caused by unspecified persons violating traffic regulations; or due to irresistible natural disasters such as earthquake, typhoon, mountain torrents, lightning stroke and the like. According to the traffic information processing method and device, first person flow information and first traffic flow information in a first traffic incident are obtained, wherein the first person flow information is the time period of the first traffic incident and the number of people passing through the road section. The first traffic information is a time period of occurrence of the first traffic event, the number of vehicles traveling on the road segment.
Step 130: and obtaining first people flow density information in a first time according to the first people flow information.
Step 140: and obtaining first traffic density information in the first time according to the first traffic information.
Specifically, the first passenger flow information and the first vehicle flow information obtained in step 110 and step 120 are used to calculate first passenger flow density information in a first time according to the first passenger flow information, and the first vehicle flow density information in the first time is calculated according to the first vehicle flow information. The first traffic density information refers to information of a ratio of the number of persons in a predetermined distance around the traffic event occurrence area to a floor area of the predetermined distance. The first traffic density information is information of a ratio of the number of vehicles in a predetermined distance around the traffic event occurrence area to a floor area of the predetermined distance, that is, the first traffic density information is traffic density information in road traffic.
Step 150: obtaining the first traffic density information and first influence factor information of the first traffic event.
Step 160: and obtaining the first traffic density information and second influence factor information of the first traffic event.
Step 170: and determining a first comprehensive influence factor according to the first influence factor information and the second influence factor information.
Specifically, the first influence factor information is the degree of influence of the first traffic event by the first traffic density information and the importance of the influence factor. The second influence factor information is the influence degree of the first traffic density information on the first traffic event and the importance of the influence factors. The first traffic event information and the first influence factor information of the first traffic event and the second traffic event information of the first traffic event are obtained. For example, in a traffic event near a subway station at work hours, the first people stream density reaches 7-9 persons/m2If the first traffic event is a traffic event, the first traffic density information occupies a first influence factor, and if the first traffic density information is more crowded, the first traffic density information occupies a second influence factor, and further determining a first degree of influence of the first traffic density information in the traffic event and a second degree of influence of the first traffic density information in the traffic event. According to the firstDetermining a first comprehensive influence factor by using the first influence factor information and the second influence factor information, wherein the first comprehensive influence factor is the comprehensive influence degree occupied by the first traffic density information and the first traffic density information in the first traffic event, for example, if the traffic density and the traffic density value at the entrance of a certain railway station exceed standard density values, the comprehensive influence degree of the first traffic density information and the first traffic density information in the starting traffic event is 60%, and the first comprehensive influence factor comprises the first traffic density information and the first traffic density information.
Step 180: and judging whether the first comprehensive influence factor meets a first preset condition.
Step 190: and when the first comprehensive influence factor accords with a first preset condition, obtaining first early warning information according to the first comprehensive influence factor.
Specifically, a first preset condition of the first comprehensive influence factor is set, for example, the first preset condition is that the first traffic density information and the first traffic density information account for important influence factors in the traffic event, and the magnitude of the comprehensive influence degree exceeds 50%. And judging whether the first comprehensive influence factor meets a first preset condition or not, and when the first comprehensive influence factor meets the first preset condition, obtaining first early warning information according to the first comprehensive influence factor. The first early warning information is set for early warning threshold values of first person flow, first vehicle flow, first person flow density and first vehicle flow density, when the first person flow, the first vehicle flow, the first person flow density and the first vehicle flow density of a certain place exceed the set early warning threshold values, the early warning information is initiated for a vehicle driver, and the early warning information is sent to a traffic control department to perform traffic control intervention, so that traffic accidents are avoided.
Therefore, the method for automatically analyzing the traffic event information in the embodiment can determine the first passenger flow density information and the first traffic flow density information according to the first passenger flow and the first traffic flow information in the traffic event occurrence area, and further analyze, study and judge the importance degree and the influence degree size relationship of the first passenger flow density information and the first traffic flow density information on the influence factors of the traffic event, so that the incentive in the traffic event can be quickly and efficiently analyzed, reliable data basis is provided for information early warning, the effectiveness and the reliability of the information early warning are improved, the incentive of the traffic event is reduced, the technical effect of the driving safety factor is improved, and the technical problems of poor effectiveness and low maturity of the information early warning and event handling of the traffic event in the prior art are solved.
Furthermore, the data fusion method in this embodiment may also be implemented by combining an Artificial Intelligence technology, wherein Artificial Intelligence (AI) is also called machine Intelligence, which is a subject for researching a computer to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, and the like) of a human, and mainly includes a principle that the computer realizes Intelligence, and a computer similar to human brain Intelligence is manufactured, so that the computer can realize higher-level application. The method comprises the following specific steps: obtaining a photograph of a first traffic event; inputting the picture of the first traffic event into a training model, wherein the training model is obtained by machine learning training using a plurality of sets of data, and each set of data in the plurality of sets of data comprises: a first traffic event, first identification information to identify a first traffic density, and second identification information to identify a first traffic density; acquiring output information of the model, wherein the output information is first early warning information, and the first early warning information in the output information of the model is obtained by respectively determining early warning thresholds of first traffic density and first traffic density by integrating first influence factor information of the first traffic density on a traffic event and second influence factor information of the first traffic density on the first traffic event.
Further, the training model in this embodiment is obtained by using machine learning training with multiple sets of data, where machine learning is a way to implement artificial intelligence, and has a certain similarity with data mining, and is also a multi-domain cross subject, and relates to multiple subjects such as probability theory, statistics, approximation theory, convex analysis, and computation complexity theory. Compared with the method for finding mutual characteristics among big data by data mining, the machine learning focuses on the design of an algorithm, so that a computer can learn rules from the data in a whitish manner, and unknown data can be predicted by using the rules.
Further, the method further comprises: obtaining a second time period for the first traffic event; judging whether the second time period meets a second preset condition or not; when the second time period meets a second preset condition, obtaining third influence factor information of the second time period and the first traffic incident; and determining second early warning information according to the third influence factor information and the first comprehensive factor.
Specifically, the incentive analysis for the traffic incident in the embodiment of the present application further includes analysis of a time period of occurrence of the traffic incident, for example, the time period of occurrence of the traffic incident is holiday or information of a large performance event in a certain time period in a certain area. And setting a second preset condition by obtaining second time period information of the first traffic incident, wherein the second preset condition is a holiday or a large performance event time period. And when the second time period meets a second preset condition, obtaining third influence factor information occupied by the second time period in the first traffic incident, namely the influence degree and the influence degree of the second time period on the first traffic incident. And determining second early warning information by integrating the third influence factor information and the first integrated factor in consideration of the people flow density and the traffic flow density information in the second time period, wherein the second early warning information is early warning threshold information of the people flow density and the traffic flow density on the special date.
Further, the method further comprises: obtaining first network public opinion information; judging whether the first online public opinion information and the first traffic event have a first association relation or not; when the first network public opinion information has a first association relation with the first traffic event, obtaining a first popularity of the first public opinion information; judging whether the first heat exceeds a first preset threshold value or not; and when the first heat exceeds a first preset threshold value, third early warning information is obtained.
Specifically, when people pay attention to a certain activity to retrieve or discuss related information, corresponding retrieval logs and communication contents, such as data of the IMSI and IMEI information of the mobile phone, an internet IP address, a login base station, retrieval contents, a user ID, discussion topics, discussion contents and the like, are generated on the ICP network server. Therefore, on the basis of protecting the individual privacy of citizens, multimedia data such as map search, internet search engines, news media, microblogs, WeChat friend circles, trembles, fast hands and the like in a certain interested area are obtained in real time, namely first network public opinion information, wherein the first network public opinion information comprises information such as keyword search quantity, large-scale activity access request quantity, thematic publishing and forwarding comment data and emotional tendency. Whether the first internet public opinion information and the first traffic incident have the first correlation or not is judged, in other words, whether the people flow density and the traffic flow density of the area are influenced by the first internet public opinion information and increased or not is judged before the first traffic incident occurs. When the first network public opinion information has a first correlation with the first traffic event, a first popularity of the first public opinion information is obtained. A first preset threshold value of the first popularity is set, and the first preset threshold value is a relative search index of the first public opinion information. And when the first popularity exceeds a first preset threshold value, third early warning information is obtained, wherein the third early warning information is early warning threshold value information for predicting people flow density and traffic flow density according to the popularity of the first public opinion information.
Further, the method further comprises: obtaining first vehicle information in the first traffic event; obtaining first violation information of the first vehicle; obtaining second vehicle information in the first traffic event; judging whether the second vehicle has an illegal behavior; obtaining first driver information of the first vehicle when the second vehicle does not have the violation; determining first feature representation information according to the first driver information; judging whether the first characteristic portrait information meets a third preset condition or not; and when the first characteristic portrait information meets a third preset condition, obtaining fourth early warning information.
Specifically, the first vehicle information in the first traffic event is obtained, and the first vehicle illegal driving, namely the first illegal driving information, such as the first vehicle overtaking into an opposite driving vehicle, is obtained. And obtaining the information of other vehicles in the first traffic incident, namely obtaining the information of other vehicles in the first traffic incident, which have traffic accidents with the first vehicle. And judging whether the second vehicle has the illegal behavior, and when the second vehicle does not have the illegal behavior, obtaining the first driver information of the first vehicle. The first characteristic portrait information is determined according to the first driver information, for example, the first characteristic portrait information includes age distribution, gender, occupation situation, personal preference, and the like. And setting a third preset condition, wherein the third preset condition is that the driving age of the driver is in the practice stage. And judging whether the first characteristic portrait information meets a third preset condition or not, and obtaining fourth early warning information when the first characteristic portrait information meets the third preset condition, wherein the fourth early warning information is the early warning information of the practice vehicle in the driving road section.
Further, the method further comprises: obtaining first road information of the first traffic incident; determining a first road characteristic according to the first road information; judging whether the first road characteristic meets a fourth preset condition or not; when the first road characteristic meets a fourth preset condition, obtaining first road information of the first vehicle; obtaining first navigation routing information for the first vehicle; judging whether the first lane information is consistent with the first navigation route planning information or not; and when the first lane information is inconsistent with the first navigation route planning information, acquiring fifth early warning information.
Specifically, the road condition characteristics of the road are also one of the inducement factors of traffic accidents, such as no traffic lights at the intersection, a Y-shaped intersection, a large curve road shape, and the like. The first road characteristics of the first road are obtained by obtaining first road information of the first traffic incident. And setting a fourth preset condition, wherein the fourth preset condition is that no traffic light, a Y-shaped intersection, a large-curve road shape and the like are arranged at the intersection. And judging whether the first road characteristic meets a fourth preset condition, and when the first road characteristic meets the fourth preset condition, obtaining first lane information of the first vehicle, namely obtaining lane information of the first vehicle. The first navigation route planning information of the first vehicle is the navigation route planning information sent by the navigation equipment on the first vehicle to the first vehicle, for example, traffic light slows down in the front of 100m or intersection lane change information in the front of the left. And judging whether the first lane information of the first vehicle running is consistent with the lane information planned in the first navigation route planning information. And when the first road information is inconsistent with the first navigation route planning information, acquiring fifth early warning information, wherein the fifth early warning information is warning board information and the like set according to road characteristics.
Further, the method further comprises: obtaining first road condition information according to the first passenger flow density information and the first traffic flow density information; acquiring first video information according to the first road condition information; obtaining second navigation routing information of a third vehicle; determining third navigation route planning information according to the first video information and the first navigation route planning information, wherein the third navigation route planning information comprises replacement route information; and sending first prompt information to a third driver of the third vehicle according to the third navigation route planning information.
Specifically, the first road condition information is information on the degree of congestion of vehicles on the first road and whether or not traffic is smooth. And obtaining first road condition information according to the first passenger flow density information and the first traffic flow density information, and obtaining first video information of the first road condition. When the first passenger flow density information and the first traffic flow density information are congested, video information of a congested road section is obtained, and if a traffic accident occurs on the congested road section. And obtaining second navigation route planning information of the third vehicle, wherein the second navigation route planning information comprises a first road for driving. And replanning the driving route according to the first video information and the first navigation route planning information, and determining third navigation route planning information. The third navigation route planning information contains the route replacement information and sends first prompt information to a third driver of a third vehicle to prompt the third driver to bypass a congested road section with a traffic accident, so that the vehicle passing efficiency is improved, and the road congestion is prevented from being aggravated.
Example two
Based on the same inventive concept as the method for automatically analyzing the traffic event information in the foregoing embodiment, the present invention further provides a method and apparatus for automatically analyzing the traffic event information, as shown in fig. 2, the apparatus comprising:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining first person flow information in a first traffic event;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain first traffic information in the first traffic event;
a third obtaining unit 13, wherein the third obtaining unit 13 is configured to obtain first personal flow density information in a first time according to the first personal flow information;
a fourth obtaining unit 14, wherein the fourth obtaining unit 14 is configured to obtain first vehicle flow density information in the first time according to the first vehicle flow information;
a fifth obtaining unit 15, the fifth obtaining unit 15 being configured to obtain the first traffic density information and the first influence factor information of the first traffic event;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to obtain the first traffic density information and the second influence factor information of the first traffic event;
a first determining unit 17, where the first determining unit 17 is configured to determine a first comprehensive influence factor according to the first influence factor information and the second influence factor information;
a first judging unit 18, where the first judging unit 18 is configured to judge whether the first comprehensive influence factor meets a first preset condition;
a seventh obtaining unit 19, where the seventh obtaining unit 19 is configured to obtain first warning information according to the first comprehensive influence factor when the first comprehensive influence factor meets a first preset condition.
Further, the apparatus further comprises:
an eighth obtaining unit to obtain a second time period of the first traffic event;
a second judging unit, configured to judge whether the second time period meets a second preset condition;
a ninth obtaining unit, configured to obtain third influence factor information of the second time period and the first traffic event when the second time period meets a second preset condition;
and the second determining unit is used for determining second early warning information according to the third influence factor information and the first comprehensive factor.
Further, the apparatus further comprises:
a tenth obtaining unit, configured to obtain first network public opinion information;
a third judging unit, configured to judge whether the first internet public opinion information and the first traffic event have a first association relationship;
an eleventh obtaining unit, configured to obtain a first popularity of the first public opinion information when the first internet public opinion information has a first association relation with the first traffic event;
a fourth judging unit configured to judge whether the first heat degree exceeds a first predetermined threshold;
a twelfth obtaining unit, configured to obtain third warning information when the first heat exceeds a first predetermined threshold.
Further, the apparatus further comprises:
a thirteenth obtaining unit configured to obtain first vehicle information in the first traffic event;
a fourteenth obtaining unit configured to obtain first violation information of the first vehicle;
a fifteenth obtaining unit configured to obtain second vehicle information in the first traffic event;
a fifth judging unit configured to judge whether there is an illegal act on the second vehicle;
a sixteenth obtaining unit configured to obtain first driver information of the first vehicle when there is no violation in the second vehicle;
a third determination unit configured to determine first feature representation information based on the first driver information;
a fifth judging unit configured to judge whether the first feature image information satisfies a third preset condition;
a seventeenth obtaining unit, configured to obtain fourth warning information when the first feature representation information satisfies a third preset condition.
Further, the apparatus further comprises:
an eighteenth obtaining unit, configured to obtain first road information on occurrence of the first traffic event;
a fourth determining unit configured to determine a first road characteristic according to the first road information;
a sixth judging unit, configured to judge whether the first road characteristic satisfies a fourth preset condition;
a nineteenth obtaining unit configured to obtain first lane information of the first vehicle when the first road characteristic satisfies a fourth preset condition;
a twentieth obtaining unit for obtaining first navigation routing information of the first vehicle;
a seventh judging unit, configured to judge whether the first lane information is consistent with the first navigation route planning information;
a twenty-first obtaining unit, configured to obtain fifth warning information when the first lane information is inconsistent with the first navigation route planning information.
Further, the apparatus further comprises:
a twenty-second obtaining unit, configured to obtain first road condition information according to the first traffic density information and the first traffic density information;
a twenty-third obtaining unit, configured to obtain first video information according to the first road condition information;
a twenty-fourth obtaining unit for obtaining second navigation routing information of a third vehicle;
a fifth determining unit, configured to determine third navigation routing information according to the first video information and the first navigation routing information, where the third navigation routing information includes replacement routing information;
a first execution unit, configured to send first prompt information to a third driver of the third vehicle according to the third navigation routing information.
Various modifications and embodiments of a method for automatically analyzing traffic event information in the first embodiment of fig. 1 are also applicable to an apparatus for automatically analyzing traffic event information in the present embodiment, and a method for automatically analyzing traffic event information in the present embodiment will be apparent to those skilled in the art from the foregoing detailed description of a method for automatically analyzing traffic event information, and therefore, for the sake of brevity of description, will not be described in detail herein.
EXAMPLE III
Based on the same inventive concept as one of the methods for automatically analyzing traffic event information in the foregoing embodiments, the present invention further provides an apparatus for automatically analyzing traffic event information, as shown in fig. 3, comprising a memory 304, a processor 302, and a computer program stored on the memory 304 and executable on the processor 302, wherein the processor 302 implements the steps of any one of the methods for automatically analyzing traffic event information as described above when executing the program.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
Example four
Based on the same inventive concept as the method of automatically analyzing traffic event information in the foregoing embodiment, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of: obtaining first person traffic information in a first traffic event; obtaining first traffic information in the first traffic event; obtaining first people flow density information in a first time according to the first people flow information; obtaining first traffic density information in the first time according to the first traffic information; obtaining the first traffic density information and first influence factor information of the first traffic event; obtaining the first traffic density information and second influence factor information of the first traffic incident; determining a first comprehensive influence factor according to the first influence factor information and the second influence factor information; judging whether the first comprehensive influence factor meets a first preset condition or not; and when the first comprehensive influence factor accords with a first preset condition, obtaining first early warning information according to the first comprehensive influence factor.
In a specific implementation, when the program is executed by a processor, any method step in the first embodiment may be further implemented.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
according to the method and the device for automatically analyzing the traffic incident information, provided by the embodiment of the invention, the first person flow information in the first traffic incident is obtained; obtaining first traffic information in the first traffic event; obtaining first people flow density information in a first time according to the first people flow information; obtaining first traffic density information in the first time according to the first traffic information; obtaining the first traffic density information and first influence factor information of the first traffic event; obtaining the first traffic density information and second influence factor information of the first traffic incident; determining a first comprehensive influence factor according to the first influence factor information and the second influence factor information; judging whether the first comprehensive influence factor meets a first preset condition or not; when the first comprehensive influence factor accords with a first preset condition, first early warning information is obtained according to the first comprehensive influence factor, so that the technical problems that the effectiveness of information early warning and event handling of traffic events is poor and the maturity is low in the prior art are solved, the traffic events are analyzed quickly and efficiently, the effectiveness and the reliability of the information early warning are improved, the inducement of the traffic events is reduced, and the driving safety coefficient is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (6)
1. A method for automatically analyzing traffic event information, the method comprising:
obtaining first person traffic information in a first traffic event;
obtaining first traffic information in the first traffic event;
obtaining first people flow density information in a first time according to the first people flow information;
obtaining first traffic density information in the first time according to the first traffic information;
obtaining the first traffic density information and first influence factor information of the first traffic event;
obtaining the first traffic density information and second influence factor information of the first traffic incident;
determining a first comprehensive influence factor according to the first influence factor information and the second influence factor information;
judging whether the first comprehensive influence factor meets a first preset condition or not;
when the first comprehensive influence factor meets a first preset condition, first early warning information is obtained according to the first comprehensive influence factor, wherein the obtaining of the first early warning information according to the first comprehensive influence factor comprises: obtaining a photograph of a first traffic event; inputting the picture of the first traffic event into a training model, wherein the training model is obtained by machine learning training using a plurality of sets of data, and each set of data in the plurality of sets of data comprises: a first traffic event, first identification information to identify a first traffic density, and second identification information to identify a first traffic density; acquiring output information of the model, wherein the output information is first early warning information, and the first early warning information in the output information of the model is an early warning threshold value of first traffic density and first traffic density respectively determined after first influence factor information of the first traffic density on a traffic event and second influence factor information of the first traffic density on the first traffic event are integrated;
obtaining first network public opinion information;
judging whether the first network public opinion information and the first traffic incident have a first correlation;
when the first internet public opinion information has a first correlation relation with the first traffic event, obtaining a first popularity of the first internet public opinion information;
judging whether the first popularity exceeds a first preset threshold value, wherein the first preset threshold value is a relative search index of first public opinion information;
when the first popularity exceeds a first preset threshold, obtaining third early warning information, wherein the third early warning information is early warning threshold information for predicting people flow density and traffic flow density according to the popularity of the first public opinion information;
the method further comprises the following steps:
obtaining a second time period for the first traffic event;
judging whether the second time period meets a second preset condition or not;
when the second time period meets a second preset condition, obtaining third influence factor information of the second time period and the first traffic incident;
determining second early warning information according to the third influence factor information and the first comprehensive influence factor, wherein the second early warning information is early warning threshold information of people flow density and traffic flow density on a special date;
obtaining first vehicle information in the first traffic event;
obtaining first violation information of the first vehicle;
obtaining second vehicle information in the first traffic event;
judging whether the second vehicle has an illegal behavior;
obtaining first driver information of the first vehicle when the second vehicle does not have the violation;
determining first feature representation information according to the first driver information;
judging whether the first characteristic portrait information meets a third preset condition, wherein the third preset condition is that the driving age of a driver is in a practice stage;
and when the first characteristic portrait information meets a third preset condition, obtaining fourth early warning information, wherein the fourth early warning information is early warning information of the practice vehicle in a driving road section.
2. The method of claim 1, wherein the method further comprises:
obtaining first road information of the first traffic incident;
determining a first road characteristic according to the first road information;
judging whether the first road characteristic meets a fourth preset condition or not;
when the first road characteristic meets a fourth preset condition, obtaining first road information of the first vehicle;
obtaining first navigation routing information for the first vehicle;
judging whether the first lane information is consistent with the first navigation route planning information or not;
and when the first lane information is inconsistent with the first navigation route planning information, acquiring fifth early warning information.
3. The method of claim 2, wherein the method further comprises:
obtaining first road condition information according to the first passenger flow density information and the first traffic flow density information;
acquiring first video information according to the first road condition information;
obtaining second navigation routing information of a third vehicle;
determining third navigation route planning information according to the first video information and the first navigation route planning information, wherein the third navigation route planning information comprises replacement route information;
and sending first prompt information to a third driver of the third vehicle according to the third navigation route planning information.
4. An apparatus for automated analysis of traffic event information, the apparatus comprising:
a first obtaining unit, configured to obtain first person traffic information in a first traffic event;
a second obtaining unit, configured to obtain first traffic information in the first traffic event;
a third obtaining unit, configured to obtain first personal flow density information within a first time according to the first personal flow information;
a fourth obtaining unit, configured to obtain first traffic density information within the first time according to the first traffic information;
a fifth obtaining unit, configured to obtain the first traffic density information and first influence factor information of the first traffic event;
a sixth obtaining unit, configured to obtain the first traffic density information and second influence factor information of the first traffic event;
a first determining unit, configured to determine a first comprehensive influence factor according to the first influence factor information and the second influence factor information;
the first judging unit is used for judging whether the first comprehensive influence factor meets a first preset condition or not;
a seventh obtaining unit, configured to obtain first warning information according to the first comprehensive influence factor when the first comprehensive influence factor meets a first preset condition, where obtaining first warning information according to the first comprehensive influence factor includes: obtaining a photograph of a first traffic event; inputting the picture of the first traffic event into a training model, wherein the training model is obtained by machine learning training using a plurality of sets of data, and each set of data in the plurality of sets of data comprises: a first traffic event, first identification information to identify a first traffic density, and second identification information to identify a first traffic density; acquiring output information of the model, wherein the output information is first early warning information, and the first early warning information in the output information of the model is early warning threshold values of first traffic density and first traffic density respectively determined after first influence factor information of the first traffic density on a traffic event and second influence factor information of the first traffic density on the first traffic event are synthesized;
a tenth obtaining unit, configured to obtain first network public opinion information;
a third judging unit, configured to judge whether the first internet public opinion information and the first traffic event have a first association relationship;
an eleventh obtaining unit, configured to obtain a first popularity of the first internet public opinion information when the first internet public opinion information has a first association relation with the first traffic event;
a fourth judging unit, configured to judge whether the first popularity exceeds a first predetermined threshold, where the first predetermined threshold is a relative search index of first public opinion information;
a twelfth obtaining unit, configured to obtain third early warning information when the first popularity exceeds a first predetermined threshold, where the third early warning information is early warning threshold information for predicting a people flow density and a traffic flow density according to popularity of the first public opinion information;
the device further comprises:
an eighth obtaining unit to obtain a second time period of the first traffic event;
a second judging unit, configured to judge whether the second time period meets a second preset condition;
a ninth obtaining unit, configured to obtain third influence factor information of the second time period and the first traffic event when the second time period meets a second preset condition;
a second determining unit, configured to determine second warning information according to the third influence factor information and the first comprehensive influence factor, where the second warning information is warning threshold information of people flow density and traffic flow density on a special date;
a thirteenth obtaining unit configured to obtain first vehicle information in the first traffic event;
a fourteenth obtaining unit configured to obtain first violation information of the first vehicle;
a fifteenth obtaining unit configured to obtain second vehicle information in the first traffic event;
a fifth judging unit configured to judge whether there is an illegal act on the second vehicle;
a sixteenth obtaining unit configured to obtain first driver information of the first vehicle when there is no violation in the second vehicle;
a third determination unit configured to determine first feature representation information based on the first driver information;
an eighth judging unit, configured to judge whether the first feature image information satisfies a third preset condition, where the third preset condition is a driver whose driving age is in a practice stage;
a seventeenth obtaining unit, configured to obtain fourth warning information when the first feature representation information meets a third preset condition, where the fourth warning information is warning information of a practice vehicle in a driving road segment.
5. An apparatus for automated analysis of traffic event information comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-3 when executing the program.
6. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 3.
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