CN117994983A - Highway network situation awareness method and system based on Internet of things - Google Patents
Highway network situation awareness method and system based on Internet of things Download PDFInfo
- Publication number
- CN117994983A CN117994983A CN202410272787.0A CN202410272787A CN117994983A CN 117994983 A CN117994983 A CN 117994983A CN 202410272787 A CN202410272787 A CN 202410272787A CN 117994983 A CN117994983 A CN 117994983A
- Authority
- CN
- China
- Prior art keywords
- traffic
- road network
- area
- network sub
- coefficient
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000012544 monitoring process Methods 0.000 claims abstract description 22
- 239000013618 particulate matter Substances 0.000 claims description 9
- 238000001556 precipitation Methods 0.000 claims description 9
- 230000036962 time dependent Effects 0.000 claims description 5
- 241000208340 Araliaceae Species 0.000 claims description 3
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims description 3
- 235000003140 Panax quinquefolius Nutrition 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 235000008434 ginseng Nutrition 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 3
- 206010039203 Road traffic accident Diseases 0.000 description 3
- 230000000903 blocking effect Effects 0.000 description 3
- 230000002411 adverse Effects 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000009960 carding Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035755 proliferation Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention relates to the field of traffic information management, and discloses a highway network situation awareness method and system based on the Internet of things, wherein the method comprises the following steps: firstly, dividing an urban road into a plurality of road network sub-areas, and acquiring traffic information of each road network sub-area in real time; secondly, weather information of each road network monitoring area is obtained; processing by combining the traffic information and the weather information to obtain traffic risk coefficients of each road network sub-area; secondly, comparing the traffic risk coefficient with a preset threshold value interval, distinguishing and marking each road network sub-area according to a comparison result, and early warning the road network sub-areas exceeding the preset threshold value interval; and finally, grading the early warning, and pushing the early warning to the portable terminals of traffic personnel in sequence according to the grading sequence. The traffic risk degree of each road network sub-area is comprehensively analyzed by combining traffic information and weather information, so that early warning is timely carried out, and traffic management personnel are informed of accurate and rapid processing.
Description
Technical Field
The invention relates to the field of traffic information management, in particular to a highway network situation awareness method and system based on the Internet of things.
Background
With the national economic development, the material conditions of people are steadily increased, so that the urban vehicle maintenance amount is gradually increased to meet the urban travel demand. The proliferation of vehicles presents a major challenge to urban traffic networks.
Because the construction of the existing traffic network generally falls behind the ever-increasing travel demands, traffic jam conditions generally exist in the rush hour of working days, and the duration of the traffic jam is closely related to weather conditions, large-scale vehicle driving quantity, accident rate and the like; however, the existing traffic management system cannot predict the above situation in time, and generally, after a traffic road network has a blocking point, the traffic road network is fed back to traffic management personnel, and the traffic management personnel dredges or commands and regulates traffic signals on site after reaching the blocking point; it is evident that the above-described approach has a significant hysteresis, which results in an adverse effect on the traffic efficiency.
Disclosure of Invention
The invention aims to provide a highway network situation awareness method and system based on the Internet of things, and the technical problems are solved.
The aim of the invention can be achieved by the following technical scheme:
a highway network situation awareness method based on the Internet of things comprises the following steps:
S1, acquiring real-time distribution conditions of urban roads based on unmanned aerial vehicles, dividing the urban roads into a plurality of road network sub-areas, and acquiring traffic information of each road network sub-area in real time based on traffic monitoring cameras;
S2, acquiring weather information of each road network monitoring area based on weather monitoring equipment;
S3, processing according to a preset processing rule after combining traffic information and weather information to obtain traffic danger estimated values of each road network sub-area;
S4, comparing the traffic danger estimated value with a preset threshold value interval, distinguishing and marking each road network sub-area according to a comparison result, and early warning the road network sub-areas exceeding the preset threshold value interval;
s5: the early warning is classified, and the early warning is sequentially pushed to the portable terminals of traffic personnel according to the classification sequence.
As a further technical scheme, the traffic information in S1 includes traffic flow data, vehicle speed data, vehicle type data, lane time occupancy data and intersection queuing length; the weather information in the step S2 comprises temperature, humidity, precipitation, particulate matter concentration and air pressure.
As a further technical solution, the processing in S3 the traffic information and the weather information according to the preset processing rule includes:
processing the traffic flow data, the vehicle speed data, the vehicle type data, the lane time occupancy data and the intersection queuing length to obtain a traffic risk coefficient O i;
processing temperature, humidity, precipitation, particulate matter concentration and air pressure to obtain a weather coefficient N i;
by the formula:
Calculating to obtain a traffic risk estimated value X i;
Wherein, O th、Nth is a reference value respectively of traffic risk coefficient and weather coefficient, and is selected and determined according to historical data and experience data; o 'and N' are the average value of traffic risk coefficient and weather coefficient in the current time period respectively;
Comparing the traffic risk estimate X i with a preset threshold interval [ X a,Xb ];
If X i＞Xb is detected, judging that the traffic pressure of the current road network sub-area exceeds the standard, and sending out red early warning;
If X i∈[Xa,Xb, judging that the traffic pressure of the current road network sub-area is in a critical state, and sending out yellow early warning;
If X i＜Xa is not found, judging that the current road network sub-area has no traffic pressure.
As a further technical solution, the process for obtaining the traffic risk coefficient O i is as follows:
by the formula:
calculating to obtain a traffic pressure coefficient mu i;
dividing the current road network sub-area into a rush hour and a common hour;
and respectively obtain the time-varying curves of traffic pressure coefficients during rush hour and rush hour And a time-dependent profile/>, over a common period of time
Obtaining a time-varying reference curve of traffic pressure coefficient during rush hour and rush hour based on historical dataAnd a reference curve/>, which varies with time during the normal period
Respectively to/>Analyzing to obtain a traffic state value D h during rush hours and a traffic state value D p during a common period;
by the formula:
Calculating to obtain a traffic risk coefficient O i;
wherein Q j is the vehicle flow of the jth vehicle type, omega j is the preset proportionality coefficient corresponding to the jth vehicle type, n is the total number of vehicle types, alpha 1、α2、α3、α4 is the weight coefficient according to the historical data and the empirical data, Is the ratio between the current actual vehicle speed V i and the reference vehicle speed V c, wherein the reference vehicle speed V c is obtained based on historical data,/>For the ratio between the current lane time occupancy R ot and the reference lane time occupancy R c, the reference lane time occupancy R c is obtained based on historical data, L i is the current intersection queuing length, and L max is the current intersection maximum queuing length; beta is a conversion coefficient, and is determined according to empirical data.
As a further technical scheme, the process of obtaining the traffic state value D h during rush hour and the traffic state value D p during the ordinary period is as follows:
By the following formula:
D h and D p were calculated separately.
As a further technical scheme, the method for acquiring the weather coefficient comprises the following steps:
Based on meteorological monitoring equipment, temperature, humidity, precipitation, particulate matter concentration and air pressure data are obtained in real time, the data are input into a neural network model trained in advance, and then a weather coefficient N i is output.
As a further technical scheme, the process of predicting and analyzing the road network sub-area sending out the yellow warning is as follows:
comparing the acquired rush hour traffic state value D h with a preset traffic state value D h0;
if D h∈[0,Dh0, judging that the traffic state of the current road network sub-area is normal in rush hour and rush hour;
If D h∈[Dh0, infinity ], judging that the traffic state of the current road network sub-area during rush hour is poor;
marking a road network sub-area with normal traffic state at rush hour as a green area, and marking a road network sub-area with poor traffic state at rush hour as a red area;
Acquiring the number of times that each road network sub-area is marked as a red area in the rush hour under a time period, and arranging the red areas according to descending order S 1、S2、......、Sm;
by the formula: calculating to obtain a traffic trend coefficient theta i;
Wherein y is the arrangement order of the ith road network sub-area; epsilon is used for eliminating ginseng.
As a further technical solution, the traffic trend coefficient θ i is compared with the traffic trend coefficient threshold value θ 0;
If the theta i exceeds the theta 0, judging that the probability of exceeding the standard of the traffic pressure in the sub-area of the road network is high, and sending out a first-level yellow early warning;
otherwise, judging that the probability of exceeding the traffic pressure of the road network sub-area is smaller, and sending out a secondary yellow early warning.
An expressway network situation awareness system based on the internet of things, comprising:
the traffic information acquisition module acquires real-time distribution conditions of urban roads based on unmanned aerial vehicles, divides the urban roads into a plurality of road network sub-areas and acquires traffic information of each road network sub-area in real time based on traffic monitoring cameras;
The weather information acquisition module is used for acquiring weather information of each road network monitoring area based on weather monitoring equipment;
The information data processing module is used for processing traffic information and weather information according to a preset processing rule to obtain traffic danger values of each road network sub-area;
The traffic state analysis module compares the traffic dangerous value with a preset threshold value interval, marks each road network sub-area in a distinguishing way according to a comparison result, and pre-warns the road network sub-areas exceeding the preset threshold value interval;
The traffic early warning module is used for grading early warning and pushing the early warning to the portable terminal of traffic personnel in sequence according to the grading sequence; the grading sequence is sequentially decreased to red early warning, primary yellow early warning and secondary yellow early warning.
The invention has the beneficial effects that:
(1) According to the method, the obtained traffic parameters and the weather parameters are combined to calculate the traffic risk estimation value corresponding to each pre-divided road network sub-area, the traffic risk estimation value can accurately reflect the traffic pressure in the road network sub-area, the road network sub-area can be judged to be in an ascending trend or a descending trend according to the change curve of the traffic risk estimation value in each road network sub-area along with time, so that references are provided for processing traffic jam conditions in the road network sub-area in advance, each road network sub-area is distinguished and marked according to the comparison result of the traffic risk estimation value and a preset threshold value interval, and the road network sub-area exceeding the preset threshold value interval is pre-warned, so that traffic management personnel can preferentially process the traffic conditions of the corresponding road network sub-area according to the traffic risk degree, stay in the road network without intervention of traffic management personnel, the traffic jam serious area is free of human intervention and traffic carding probability, and traffic efficiency is comprehensively improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a process step diagram of the present invention;
Fig. 2 is a schematic diagram of a system structure according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention is a highway network situation awareness method based on the internet of things, which comprises the following steps:
S1, acquiring real-time distribution conditions of urban roads based on unmanned aerial vehicles, dividing the urban roads into a plurality of road network sub-areas, and acquiring traffic information of each road network sub-area in real time based on traffic monitoring cameras;
S2, acquiring weather information of each road network monitoring area based on weather monitoring equipment;
S3, processing according to a preset processing rule after combining traffic information and weather information to obtain traffic danger estimated values of each road network sub-area;
S4, comparing the traffic danger estimated value with a preset threshold value interval, distinguishing and marking each road network sub-area according to a comparison result, and early warning the road network sub-areas exceeding the preset threshold value interval;
s5: the early warning is classified, and the early warning is sequentially pushed to the portable terminals of traffic personnel according to the classification sequence.
The traffic information in the S1 comprises vehicle flow data, vehicle speed data, vehicle type data, lane time occupancy data and crossing queuing length; the weather information in the step S2 comprises temperature, humidity, precipitation, particulate matter concentration and air pressure.
In order to solve the problem that the existing traffic management system cannot predict the situation in time, the traffic signal lamp is generally fed back to traffic management personnel after a traffic road network is blocked, and the traffic management personnel dredge or command and regulate the traffic signal lamp on site after reaching the blocking position; obviously, the adoption of the mode obviously has larger hysteresis, so that the traffic passing efficiency is adversely affected; therefore, in this embodiment, by combining the acquired traffic parameters with the weather parameters, the traffic risk estimation corresponding to each pre-divided road network sub-area is calculated, the traffic risk estimation can accurately reflect the traffic pressure in the road network sub-area, and by combining the time-dependent change curve of the traffic risk estimation in each road network sub-area, it can be determined whether the road network sub-area is in an ascending trend or a descending trend, so as to provide a reference for processing traffic jam conditions in the road network sub-area in advance, and according to the comparison result of the traffic risk estimation and the preset threshold interval, distinguish and mark each road network sub-area, and early warn the road network sub-area beyond the preset threshold interval, so that traffic management personnel can preferentially process traffic conditions of the corresponding road network sub-area according to the traffic risk degree, stop at the road network without intervention of traffic management personnel, and the occurrence probability of traffic jam points can be determined, and traffic efficiency is comprehensively improved.
The process of processing the traffic information and the weather information according to the preset processing rule in the S3 comprises the following steps:
Processing the traffic flow data, the vehicle speed data, the vehicle type data, the lane time occupancy data and the intersection queuing length to obtain a traffic pressure coefficient O i;
Processing temperature, humidity, precipitation, particulate matter concentration and air pressure to obtain a weather coefficient N i; acquiring temperature, humidity, precipitation, particulate matter concentration and air pressure data in real time based on meteorological monitoring equipment, inputting the data into a neural network model trained in advance, and then outputting a weather coefficient N i;
by the formula:
Calculating to obtain a traffic risk estimated value X i;
Wherein, O th、Nth is a reference value respectively of traffic risk coefficient and weather coefficient, and is selected and determined according to historical data and experience data; o 'and N' are the average value of traffic risk coefficient and weather coefficient in the current time period respectively;
Comparing the traffic risk estimate X i with a preset threshold interval [ X a,Xb ];
If X i＞Xb is detected, judging that the traffic pressure of the current road network sub-area exceeds the standard, and sending out red early warning;
If X i∈[Xa,Xb, judging that the traffic pressure of the current road network sub-area is in a critical state, and sending out yellow early warning;
If X i＜Xa is not found, judging that the current road network sub-area has no traffic pressure.
In this embodiment, a method for processing the traffic information and the weather information is provided, specifically, by the formula: Calculating to obtain a traffic risk estimated value X i; wherein, O th、Nth is a reference value respectively of traffic risk coefficient and weather coefficient, and is selected and determined according to historical data and experience data; o 'and N' are the average value of traffic risk coefficient and weather coefficient in the current time period respectively; /(I) As a weight coefficient, selecting and determining according to historical data; obviously, when the value of |O i-Oth | is larger, the ratio of |O i-Oth | to O' is larger, which indicates that the current traffic pressure is larger, that is, the traffic jam condition in the sub-area of the road network is more serious, and then the traffic danger estimated value is larger, it is required to be explained that when O i＜Oth, the value of |O i-Oth | is 0; at the moment, the higher the urgent degree of the on-site command of the personnel waiting for traffic management in the road network sub-area is; when the value of |N th-Ni | is larger, the deviation between the weather overall situation in the road network subarea and the weather overall situation in the conventional state is larger, the probability of traffic accidents is larger, for example, the traffic accidents are easier to occur under the condition of low visibility caused by strong wind, strong rainfall or haze, and the traffic jam situation in the area is more aggravated once the traffic accidents occur, so that the estimated traffic danger is larger; through the process, traffic factors and weather factors can be comprehensively considered, the traffic risk degree in each road network subarea is accurately and objectively reflected after the traffic factors and the weather factors are associated, an important basis is provided for distribution and on-site management and control of subsequent traffic management personnel, and the accuracy of traffic early warning in each area is improved.
The process for obtaining the traffic risk coefficient O i comprises the following steps:
by the formula:
calculating to obtain a traffic pressure coefficient mu i;
dividing the current road network sub-area into a rush hour and a common hour;
and respectively obtain the time-varying curves of traffic pressure coefficients during rush hour and rush hour And a time-dependent profile/>, over a common period of time
Obtaining a time-varying reference curve of traffic pressure coefficient during rush hour and rush hour based on historical dataAnd a reference curve/>, which varies with time during the normal period
Respectively to/>Analysis was performed by the following formula:
Acquiring a traffic state value D h during rush hours and a traffic state value D p during a common period;
by the formula:
Calculating to obtain a traffic risk coefficient O i;
wherein Q j is the vehicle flow of the jth vehicle type, omega j is the preset proportionality coefficient corresponding to the jth vehicle type, n is the total number of vehicle types, alpha 1、α2、α3、α4 is the weight coefficient according to the historical data and the empirical data, Is the ratio between the current actual vehicle speed V i and the reference vehicle speed V c, wherein the reference vehicle speed V c is obtained based on historical data,/>For the ratio between the current lane time occupancy R ot and the reference lane time occupancy R c, the reference lane time occupancy R c is obtained based on historical data, L i is the current intersection queuing length, and L max is the current intersection maximum queuing length; beta is a conversion coefficient, and is determined according to empirical data.
In this embodiment, a method for obtaining a traffic risk coefficient is provided, specifically, by the formula: Calculating to obtain a traffic pressure coefficient mu i, and respectively obtaining a time-varying curve/>, of the traffic pressure coefficient during rush hour and rush hour And a time-dependent profile/>, over a common period of timeRespectively pair/>/>Analysis was performed by the formula: /(I) Acquiring a traffic state value D h during rush hours and a traffic state value D p during a common period; substituting the formula into the following formula: /(I)Calculating to obtain a traffic risk coefficient O i; obviously, at/>The larger the value is, namely the larger the deviation between the traffic pressure coefficient and the reference curve is in the preset time period t a～tb, the larger the fluctuation is, the more unstable the traffic state of the road network sub-area is indicated during the rush hour, the greater the degree of traffic danger is, and the greater the traffic danger coefficient O i is; whileThe larger the traffic pressure coefficient is, the larger the deviation between the traffic pressure coefficient and the reference curve is in the preset time period t a～tb in the common time period is, the more unstable the traffic state of the road network sub-area is in the common time period, the greater the traffic danger degree is, and the greater the traffic danger coefficient O i is; the traffic transit time is divided into rush hours and common hours, so that the method can be suitable for the change of traffic pressure in different periods, and the traffic danger coefficient in the current period can be fed back more clearly and accurately, so that the rapid response to the traffic condition in the road network subarea is realized.
The process of predicting and analyzing the road network sub-area sending out the yellow early warning is as follows:
comparing the acquired rush hour traffic state value D h with a preset traffic state value D h0;
if D h∈[0,Dh0, judging that the traffic state of the current road network sub-area is normal in rush hour and rush hour;
If D h∈[Dh0, infinity ], judging that the traffic state of the current road network sub-area during rush hour is poor;
marking a road network sub-area with normal traffic state at rush hour as a green area, and marking a road network sub-area with poor traffic state at rush hour as a red area;
Acquiring the number of times that each road network sub-area is marked as a red area in the rush hour under a time period, and arranging the red areas according to descending order S 1、S2、......、Sm;
by the formula: calculating to obtain a traffic trend coefficient theta i;
Wherein y is the arrangement order of the ith road network sub-area; epsilon is used for eliminating ginseng.
In this embodiment, a method for analyzing a road network sub-area triggering yellow warning is provided; specifically, the acquired traffic state value D h in rush hours and rush hours is compared with a preset traffic state value D h0; if D h∈[0,Dh0, judging that the traffic state of the current road network sub-area is normal in rush hour and rush hour; if D h∈[Dh0, infinity ], judging that the traffic state of the current road network sub-area during rush hour is poor; marking a road network sub-area with normal traffic state at rush hour as a green area, and marking a road network sub-area with poor traffic state at rush hour as a red area; acquiring the times of marking each road network sub-area as a red area in the rush hour under a time period, and arranging the times according to a descending order S 1、S2、......、Sm; by the formula: Calculating to obtain a traffic trend coefficient theta i; comparing the traffic trend coefficient theta i with a traffic trend coefficient threshold theta 0; if the theta i exceeds the theta 0, judging that the probability of exceeding the standard of the traffic pressure in the sub-area of the road network is high, and sending out a first-level yellow early warning; otherwise, judging that the probability of exceeding the traffic pressure of the road network sub-area is smaller, and sending out a secondary yellow early warning; obviously, the technical scheme is used for predicting and analyzing the road network sub-area triggering the yellow early warning, timely finding and analyzing the trend of the road network sub-area towards red early warning deterioration, timely preventing and coping, for example, the green light time of certain intersections is short, the red light time is long, the green light passing time can be improved by remote control under the condition of initial congestion, and the traffic jam is not managed and controlled after reaching at least one intersection, so that the situation that the traffic condition is treated after being deteriorated, and the traffic condition is more costly and time consuming and is unfavorable for the overall passing efficiency can be effectively prevented.
An expressway network situation awareness system based on the internet of things, comprising:
the traffic information acquisition module acquires real-time distribution conditions of urban roads based on unmanned aerial vehicles, divides the urban roads into a plurality of road network sub-areas and acquires traffic information of each road network sub-area in real time based on traffic monitoring cameras;
The weather information acquisition module is used for acquiring weather information of each road network monitoring area based on weather monitoring equipment;
The information data processing module is used for processing traffic information and weather information according to a preset processing rule to obtain traffic danger values of each road network sub-area;
The traffic state analysis module compares the traffic dangerous value with a preset threshold value interval, marks each road network sub-area in a distinguishing way according to a comparison result, and pre-warns the road network sub-areas exceeding the preset threshold value interval;
the traffic early warning module is used for grading early warning and pushing the early warning to the portable terminal of traffic personnel in sequence according to the grading sequence; the grading sequence is sequentially decreased to red early warning, primary yellow early warning and secondary yellow early warning; and information transmission is realized among the modules based on the communication of the Internet of things.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (9)
1. The expressway network situation awareness method based on the Internet of things is characterized by comprising the following steps of:
S1, acquiring real-time distribution conditions of urban roads based on unmanned aerial vehicles, dividing the urban roads into a plurality of road network sub-areas, and acquiring traffic information of each road network sub-area in real time based on traffic monitoring cameras;
S2, acquiring weather information of each road network monitoring area based on weather monitoring equipment;
S3, processing according to a preset processing rule after combining traffic information and weather information to obtain traffic danger estimated values of each road network sub-area;
S4, comparing the traffic danger estimated value with a preset threshold value interval, distinguishing and marking each road network sub-area according to a comparison result, and early warning the road network sub-areas exceeding the preset threshold value interval;
s5: the early warning is classified, and the early warning is sequentially pushed to the portable terminals of traffic personnel according to the classification sequence.
2. The internet of things-based expressway situation awareness method according to claim 1, wherein the traffic information in S1 includes traffic flow data, vehicle speed data, vehicle type data, lane time occupancy data, and intersection queuing length; the weather information in the step S2 comprises temperature, humidity, precipitation, particulate matter concentration and air pressure.
3. The internet of things-based highway network situation awareness method according to claim 2, wherein the processing of the traffic information and the weather information according to the preset processing rule in S3 includes:
processing the traffic flow data, the vehicle speed data, the vehicle type data, the lane time occupancy data and the intersection queuing length to obtain a traffic risk coefficient O i;
processing temperature, humidity, precipitation, particulate matter concentration and air pressure to obtain a weather coefficient N i;
by the formula:
Calculating to obtain a traffic risk estimated value X i;
Wherein, O th、Nth is a reference value respectively of traffic risk coefficient and weather coefficient, and is selected and determined according to historical data and experience data; o 'and N' are the average value of traffic risk coefficient and weather coefficient in the current time period respectively;
Comparing the traffic risk estimate X i with a preset threshold interval [ X a,Xb ];
If X i＞Xb is detected, judging that the traffic pressure of the current road network sub-area exceeds the standard, and sending out red early warning;
If X i∈[Xa,Xb, judging that the traffic pressure of the current road network sub-area is in a critical state, and sending out yellow early warning;
If X i＜Xa is not found, judging that the current road network sub-area has no traffic pressure.
4. The expressway network situation awareness method based on the internet of things according to claim 3, wherein the acquiring process of the traffic risk coefficient O i is as follows:
by the formula:
calculating to obtain a traffic pressure coefficient mu i;
dividing the current road network sub-area into a rush hour and a common hour;
and respectively obtain the time-varying curves of traffic pressure coefficients during rush hour and rush hour And a time-dependent profile/>, over a common period of time
Obtaining a time-varying reference curve of traffic pressure coefficient during rush hour and rush hour based on historical dataAnd a reference curve/>, which varies with time during the normal period
Respectively to/>Analyzing to obtain a traffic state value D h during rush hours and a traffic state value D p during a common period;
by the formula:
Calculating to obtain a traffic risk coefficient O i;
wherein Q j is the vehicle flow of the jth vehicle type, omega j is the preset proportionality coefficient corresponding to the jth vehicle type, n is the total number of vehicle types, alpha 1、α2、α3、α4 is the weight coefficient according to the historical data and the empirical data, Is the ratio between the current actual vehicle speed V i and the reference vehicle speed V c, wherein the reference vehicle speed V c is obtained based on historical data,/>For the ratio between the current lane time occupancy R ot and the reference lane time occupancy R c, the reference lane time occupancy R c is obtained based on historical data, L i is the current intersection queuing length, and L max is the current intersection maximum queuing length; beta is a conversion coefficient, and is determined according to empirical data.
5. The internet of things-based highway network situation awareness method according to claim 4, wherein the process of obtaining the traffic state value D h during rush hour and the traffic state value D p during the ordinary period is:
By the following formula:
D h and D p were calculated separately.
6. The expressway network situation awareness method based on the internet of things according to claim 1, wherein the method for acquiring the weather coefficient is as follows:
Based on meteorological monitoring equipment, temperature, humidity, precipitation, particulate matter concentration and air pressure data are obtained in real time, the data are input into a neural network model trained in advance, and then a weather coefficient N i is output.
7. The internet of things-based expressway situation awareness method according to claim 3, wherein the predicting and analyzing the road network sub-area sending the yellow warning comprises the following steps:
comparing the acquired rush hour traffic state value D h with a preset traffic state value D h0;
if D h∈[0,Dh0, judging that the traffic state of the current road network sub-area is normal in rush hour and rush hour;
If D h∈[Dh0, infinity ], judging that the traffic state of the current road network sub-area during rush hour is poor;
marking a road network sub-area with normal traffic state at rush hour as a green area, and marking a road network sub-area with poor traffic state at rush hour as a red area;
Acquiring the number of times that each road network sub-area is marked as a red area in the rush hour under a time period, and arranging the red areas according to descending order S 1、S2、......、Sm;
by the formula: calculating to obtain a traffic trend coefficient theta i;
Wherein y is the arrangement order of the ith road network sub-area; epsilon is used for eliminating ginseng.
8. The internet of things-based highway network situation awareness method according to claim 7, wherein the traffic trend coefficient θ i is compared with a traffic trend coefficient threshold θ 0;
If the theta i exceeds the theta 0, judging that the probability of exceeding the standard of the traffic pressure in the sub-area of the road network is high, and sending out a first-level yellow early warning;
otherwise, judging that the probability of exceeding the traffic pressure of the road network sub-area is smaller, and sending out a secondary yellow early warning.
9. An expressway network situation awareness system based on the internet of things, which is suitable for the expressway network situation awareness method based on the internet of things according to any one of claims 1 to 8, and is characterized by comprising the following steps:
the traffic information acquisition module acquires real-time distribution conditions of urban roads based on unmanned aerial vehicles, divides the urban roads into a plurality of road network sub-areas and acquires traffic information of each road network sub-area in real time based on traffic monitoring cameras;
The weather information acquisition module is used for acquiring weather information of each road network monitoring area based on weather monitoring equipment;
The information data processing module is used for processing the traffic information and the weather information according to a preset processing rule to obtain traffic danger estimated values of each road network sub-area;
The traffic state analysis module compares the traffic danger estimated value with a preset threshold value interval, marks each road network sub-area in a distinguishing way according to a comparison result, and pre-warns the road network sub-areas exceeding the preset threshold value interval;
The traffic early warning module is used for grading early warning and pushing the early warning to the portable terminal of traffic personnel in sequence according to the grading sequence; the grading sequence is sequentially decreased to red early warning, primary yellow early warning and secondary yellow early warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410272787.0A CN117994983A (en) | 2024-03-11 | 2024-03-11 | Highway network situation awareness method and system based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410272787.0A CN117994983A (en) | 2024-03-11 | 2024-03-11 | Highway network situation awareness method and system based on Internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117994983A true CN117994983A (en) | 2024-05-07 |
Family
ID=90895362
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410272787.0A Pending CN117994983A (en) | 2024-03-11 | 2024-03-11 | Highway network situation awareness method and system based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117994983A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011186940A (en) * | 2010-03-10 | 2011-09-22 | Toshiba Corp | Road traffic information providing system and method |
| CN102231231A (en) * | 2011-06-16 | 2011-11-02 | 同济大学 | Area road network traffic safety situation early warning system and method thereof |
| KR20210086381A (en) * | 2019-12-30 | 2021-07-08 | 계명대학교 산학협력단 | System and Method for Predicting Traffic Accident Risk |
-
2024
- 2024-03-11 CN CN202410272787.0A patent/CN117994983A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011186940A (en) * | 2010-03-10 | 2011-09-22 | Toshiba Corp | Road traffic information providing system and method |
| CN102231231A (en) * | 2011-06-16 | 2011-11-02 | 同济大学 | Area road network traffic safety situation early warning system and method thereof |
| KR20210086381A (en) * | 2019-12-30 | 2021-07-08 | 계명대학교 산학협력단 | System and Method for Predicting Traffic Accident Risk |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107742418B (en) | Automatic identification method for traffic jam state and jam point position of urban expressway | |
| CN113066287B (en) | Active prevention and control method and system for highway traffic accident site risks | |
| CN104732762A (en) | Traffic abnormal road section probability identification method | |
| CN111882858B (en) | Multi-source data-based method for predicting queuing length of highway abnormal event | |
| CN111862598B (en) | Variable speed limit control method based on high-definition checkpoint data and accident risk | |
| CN117014473B (en) | Intelligent networking automobile monitoring system based on end cloud fusion technology | |
| CN114363316A (en) | Intelligent networking monitoring and supervision system for cross-regional road infrastructure | |
| CN115063990A (en) | Dynamic speed limit control method for bottleneck section of highway in mixed traffic flow environment | |
| CN105719510A (en) | Efficiency evaluation method of road traffic accident chain blocking system under IOV (Internet of Vehicles) environment | |
| CN113129610A (en) | Unfavorable weather expressway self-adaptive variable speed limit and information cooperative control system | |
| CN113112789A (en) | Method for predicting and controlling influence of urban expressway emergency | |
| CN115206115A (en) | Road network congestion area control method based on multi-source data edge calculation in intelligent networking environment | |
| CN115527382A (en) | Traffic control method based on intelligent multi-objective optimization algorithm | |
| CN110276951B (en) | Traffic jam early warning method based on mobile internet | |
| CN117198058B (en) | Road traffic intelligent supervision system based on remote sensing image | |
| CN116580565B (en) | Government affair big data analysis system based on cloud computing | |
| CN117994983A (en) | Highway network situation awareness method and system based on Internet of things | |
| CN117037485A (en) | Intelligent expressway active traffic flow control method and system | |
| CN111754790A (en) | Ramp entrance traffic control system and method based on radar | |
| CN114333336B (en) | Method for detecting and managing traffic flow among expressway cells | |
| CN115497342A (en) | Intelligent bridge ship collision prevention early warning method and system based on multi-source data fusion | |
| CN112950941B (en) | Attack early warning analysis system based on big data | |
| JP2000259976A (en) | Inflow lamp controller and its method | |
| CN213365789U (en) | Multifunctional highway monitoring and dispatching system | |
| CN114022050A (en) | Traffic road risk management and control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |