CN109448367B - Intelligent road traffic tracking management system based on big data image acquisition - Google Patents

Intelligent road traffic tracking management system based on big data image acquisition Download PDF

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CN109448367B
CN109448367B CN201811230362.4A CN201811230362A CN109448367B CN 109448367 B CN109448367 B CN 109448367B CN 201811230362 A CN201811230362 A CN 201811230362A CN 109448367 B CN109448367 B CN 109448367B
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road
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image acquisition
traffic
coefficient
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CN109448367A (en
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戚湧
卢伟涛
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Nanjing University of Science and Technology
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

The invention discloses an intelligent road traffic tracking management system based on big data image acquisition, which comprises a data storage database, an area division module, a road condition updating module, a cloud server, a vehicle storage database, a plurality of vehicle-mounted terminals, a plurality of image acquisition terminals and a display terminal, wherein the area division module is used for dividing the area of the road; the cloud server is respectively connected with the road condition updating module, the vehicle storage database, the data storage database, the display terminal, the vehicle-mounted terminals and the image acquisition terminals, the area dividing module is connected with the data storage database, and the vehicle-mounted terminals are connected with the vehicle storage database. According to the invention, through the vehicle-mounted terminal, the image acquisition terminal and the combination of the cloud server and the road state updating module, the traffic condition between adjacent road sections to be detected can be accurately analyzed, the congestion degree of the road sections to be detected can be conveniently displayed for managers, the tracking management of road traffic is realized, and a reliable reference basis is recommended for later vehicle driving routes.

Description

Intelligent road traffic tracking management system based on big data image acquisition
Technical Field
The invention belongs to the technical field of road traffic management, and relates to an intelligent road traffic tracking management system based on big data image acquisition.
Background
With the rapid development of social economy, the urbanization process is accelerated, the urban traffic demand is rapidly increased, the traffic problem becomes an urgent problem to be solved in the urban development process, and various intelligent traffic management and control technologies are applied to road traffic management to improve the use efficiency and traffic efficiency of roads and reduce traffic congestion and traffic accidents.
However, in the current road traffic management system, the number of vehicles on a road and vehicle images in each road area cannot be analyzed, only the current traffic condition can be obtained, the influence of the road traffic of the previous road section on the road traffic of the next road section cannot be counted, the problem of poor accuracy of road prediction analysis exists, and effective tracking of the road traffic cannot be realized.
Disclosure of Invention
The invention aims to provide an intelligent road traffic tracking management system based on big data, which can obtain a comprehensive traffic congestion coefficient between adjacent road sections by combining a road condition updating module, a vehicle-mounted terminal and an image acquisition terminal with a cloud server, and solves the problems that the traffic conditions of the two adjacent road sections cannot be predicted and the accuracy is poor in the existing road traffic management process, and the effective traffic tracking cannot be realized on each traffic section.
The purpose of the invention can be realized by the following technical scheme:
an intelligent road traffic tracking management system based on big data image acquisition comprises a data storage database, an area division module, a road condition updating module, a cloud server, a vehicle storage database, a plurality of vehicle-mounted terminals, a plurality of image acquisition terminals and a display terminal;
the cloud server is respectively connected with the road condition updating module, the vehicle storage database, the data storage database, the display terminal, the vehicle-mounted terminals and the image acquisition terminals, the area dividing module is connected with the data storage database, and the vehicle-mounted terminals are connected with the vehicle storage database;
the data storage database is used for storing serial numbers corresponding to road branches, the road branches are numbered according to a set sequencing sequence and are respectively 1,2, 1, i, 1, n, the road branches are road sections between adjacent intersections, at least one image acquisition point is arranged on each road branch, an image acquisition terminal is installed at each image acquisition point, the image acquisition points on the same road branch are numbered according to the driving direction of a vehicle and are respectively 1,2, 1, j, 1, m, and the standard vehicle speed of each image acquisition point is stored;
in addition, different rainwater blocking coefficients and haze blocking coefficients corresponding to different rainfall amounts and haze levels are stored, whether an instantaneous blocking coefficient G corresponding to construction or traffic accidents exists on the road surface is stored, if the construction or traffic accidents exist on the road surface, G is equal to 1, and if the construction or traffic accidents exist on the road surface, G is equal to 0;
the region dividing module is used for dividing each road branch into a plurality of road sections to be detected by using the length of a fixed road section, the road sections to be detected on the same road branch are numbered sequentially according to the advancing direction of a vehicle, the number of the road sections to be detected is 1,2, a.
The road state updating module is used for inputting the weather basic conditions of the day and whether construction or traffic accidents exist in each road section to be detected of each road branch, and sending the input weather basic conditions and road surface construction or traffic accident information of each road branch to the cloud server;
the vehicle-mounted terminal is arranged on a vehicle and used for monitoring the current speed and the position of the vehicle in real time and sending the detected speed and the detected position of the vehicle to a vehicle storage database;
the vehicle storage database is used for storing the road branch distribution map, receiving and storing the speed and position information of the vehicle which is sent by each vehicle-mounted terminal and is driven by the vehicle, and marking the position corresponding to the vehicle on the road branch distribution map;
in addition, the vehicle storage database is used for storing different standard vehicle images, each standard vehicle image has at least one characteristic different from the vehicle distribution position and the vehicle quantity, each standard vehicle image forms a standard vehicle image set Y which is { Y1, Y2,. multidot.,. yu.,. multidot.yq }, yu represents the u-th standard vehicle image, each standard vehicle image corresponds to a vehicle traffic judgment value, the vehicle traffic judgment value corresponding to each standard vehicle image forms a vehicle traffic judgment value set X which is { X1, X2,. multidot.,. multidot.xq }, and xu represents the u-th vehicle traffic judgment value, the vehicle traffic judgment numerical value represents an original vehicle judgment congestion coefficient, and each standard vehicle image in the standard vehicle image set and each vehicle traffic judgment numerical value in the vehicle traffic judgment numerical value set are in a one-to-one mapping relation;
the image acquisition terminal is installed at each image acquisition point on each road branch, the image acquisition terminal installed at the image acquisition point has the same number as that of the image acquisition point, the image acquisition terminal acquires image information of a road surface at a fixed time end, and simultaneously acquires position information corresponding to the image acquisition terminal and sends the acquired road surface image information to the cloud server;
the image acquisition terminal comprises a timing unit, a reset unit, an image acquisition unit, a controller and a data communication unit, wherein the controller is respectively connected with the timing unit, the reset unit, the image acquisition unit and the data communication unit, and the reset unit is connected with the timing unit;
the timing unit is used for performing accumulated timing and sending the accumulated time to the controller, the reset unit is used for receiving a control instruction sent by the controller and clearing the accumulated time of the timing unit, the image acquisition unit is a high-definition camera and is used for acquiring vehicle image information at an image acquisition point and sending the acquired vehicle image information to the controller, the controller receives the accumulated time sent by the timing unit and compares the received accumulated time with a set time threshold, if the accumulated time is equal to the set time threshold, the controller sends a reset control instruction to the reset unit and controls the reset unit to clear the accumulated time of the timing unit, meanwhile, a shooting control instruction is sent to the image acquisition unit to control the image acquisition unit to shoot images, and the controller receives the vehicle image information sent by the image acquisition unit, and sending the received vehicle image information to a data communication unit; the data communication unit is used for receiving the vehicle image information sent by the controller and sending the received road surface image information to the cloud server;
the cloud server counts the number of vehicles of each vehicle-mounted terminal in each road section to be detected in the vehicle storage database according to the road sections to be detected divided by the area dividing module, and the number of vehicles in each road section to be detected forms a set S of the number of vehicles to be detectedi(si1,si2,...,sij,...,sim)，sij is expressed as the number of vehicles in the jth road section to be tested on the ith road branch, and the number of vehicles in the road section to be tested on the same road branch is compared with the number of vehicles in the previous road section to be tested on the road section to be tested to obtain a set S 'of the number of vehicles to be tested'i(s′i1,s′i2,...,s′ij,...,s′i(m-1))，s′ij is expressed as the difference value between the number of vehicles in the jth road section to be tested on the ith road branch and the number of vehicles in the jth +1 road section to be tested, namely s'ij＝si(j+1)-sij, cloudThe server is used for collecting S 'according to the number of the vehicles to be tested'iAnd counting the traffic flow pressure coefficient in each road section to be measured
Figure GDA0002305440860000031
λ is expressed as an influence factor, and 10.6 s is takeni(j +1) represents the number of vehicles in the j +1 th road section to be detected on the ith road branch;
the cloud server acquires the corresponding speed of the vehicles in each road section to be detected from the vehicle storage database, the acquired speed of the vehicles in each road section to be detected is sequenced according to the sequence of the vehicles from front to back, and a real-time speed set V is formedij(vij1,vij2,...,vijt,....,vijk)，vijt is the speed of the t vehicle corresponding to the jth vehicle in the jth road section to be measured on the ith road branch road, vijk in k is expressed as the number of vehicles in the jth section to be measured on the ith road branch, namely k is sij, the cloud server counts the average speed of the road sections to be tested according to the real-time speed measurement set in the road sections to be tested
Figure GDA0002305440860000032
Figure GDA0002305440860000033
The average speed of the j to-be-measured road section on the ith road branch is represented, the average speed of each to-be-measured road section is compared with the minimum value of the standard speed of the vehicle at the image acquisition point in the to-be-measured road stored in the storage database, and a to-be-measured road section speed comparison set V 'is obtained'i(v′i1,v′i2,...,v′ij,...,v′im)，v′ij is the comparison condition between the average speed in the jth road section to be tested on the ith road branch and the minimum value of the vehicle standard speed corresponding to the road section to be tested, and the set V 'is compared according to the speed of the road section to be tested'iCounting the inferred congestion coefficient gamma corresponding to the road section to be detectedij, the cloud server obtains the actual congestion coefficient P of each road branch according to the inferred congestion coefficient corresponding to each road section to be detected on each road branchi；
Meanwhile, the cloud server receives road surface image information sent by each image acquisition terminal on each road branch, and the received road image information is sequenced according to the number sequence corresponding to each image acquisition terminal on the same road branch to form a road image information set Bi(bi1,bi2,...,bij,...,bim)，bij represents road surface image information sent by the jth image acquisition terminal on the ith road branch, each road surface image in the obtained road image information set is compared with each standard vehicle image stored in the vehicle storage database one by one to screen out the vehicle traffic judgment value corresponding to the standard vehicle image matched with each road surface image, and the obtained vehicle traffic judgment values acquired by each image acquisition terminal form a terminal traffic judgment value set Hi(hi1,hi2,...,hij,...,him)，hij is a vehicle traffic judgment value corresponding to the jth image acquisition terminal on the ith road branch, j is 1,2ij is equal to one vehicle traffic judgment value in the vehicle traffic judgment value set X { X1, X2.,. xu.,. xq }, and the terminal traffic judgment value set H is usediThe vehicle traffic judgment value corresponding to the middle and next image acquisition terminal is subtracted from the vehicle traffic judgment value corresponding to the previous image acquisition terminal to obtain a terminal comparison traffic judgment value set delta Hi(Δhi1,Δhi2,...,Δhij,...,Δhi(m-1))，Δhij is expressed as the difference value between the vehicle traffic judgment value corresponding to the j +1 th image acquisition terminal on the ith road branch and the vehicle traffic judgment value corresponding to the j image acquisition terminal;
the cloud server receives the weather basic conditions sent by the road state updating module and whether each road section to be detected of each road branch is constructed or has a traffic accident, obtains the rainfall and the haze grade according to the received weather basic conditions, extracts the rainwater blocking coefficient and the haze blocking coefficient corresponding to the rainfall in the data storage database,and extracting the instantaneous obstruction coefficients corresponding to construction or traffic accidents, and forming a rainwater obstruction coefficient set C by extracting the rainwater obstruction coefficients in each road section to be detectedi(ci1,ci2,...,cij,...,cim), the haze blocking coefficients in the road sections to be detected form a haze blocking coefficient set Di(di1,di2,...,dij,...,dim), and forming an instantaneous obstruction coefficient set F by using instantaneous obstruction coefficients corresponding to whether each road section to be detected is constructed or has a traffic accidenti(fi1,fi2,...,fij,...,fim) in which c)ij is expressed as a rainwater obstruction coefficient corresponding to the jth road section to be detected on the ith road branch, dij is expressed as a haze blocking coefficient, f, corresponding to the jth road section to be detected on the ith road branchij represents the instantaneous obstruction coefficient corresponding to the jth road section to be detected on the ith road branch, and m represents the number of the road sections to be detected on the ith road branch;
the cloud server is used for determining the traffic flow pressure coefficient Z in each road section to be measuredijActual congestion coefficient P of each road branchiAnd comparing the traffic judgment value set delta H by the terminaliAnd the rainwater obstruction coefficient, the haze obstruction coefficient and the instantaneous obstruction coefficient are integrated to count the comprehensive traffic jam coefficient between the previous road section to be tested and the next road section to be tested on each road branch
Figure GDA0002305440860000051
The higher the comprehensive traffic congestion coefficient is, the more serious the congestion degree between the last road section to be detected and the next road section on the same road branch is, and the cloud server sends the comprehensive traffic congestion coefficient between the last road section to be detected and the next road section to be detected on each road branch to the display terminal;
and the display terminal is used for receiving and displaying the comprehensive traffic congestion coefficient between the previous road section to be tested and the next road section to be tested on each road branch sent by the cloud server.
Further, the rain blocking coefficients corresponding to the rainfall levels are f1, f2, f3, f4, f5, f1 is more than f2 is more than f3 is more than f4 is more than f5, the haze blocking coefficients corresponding to the haze grades are r1, r2 and r3 respectively, and r1 is more than r2 is more than r3, f1 represents a rainwater obstruction coefficient corresponding to 0.3-1mm/h of rainfall, f2 represents a rainwater obstruction coefficient corresponding to 1-2mm/h of rainfall, f3 represents a rainwater obstruction coefficient corresponding to 2-3mm/h of rainfall, f4 represents a rainwater obstruction coefficient corresponding to 3-4mm/h of rainfall, f5 represents a rainwater obstruction coefficient corresponding to a rainfall larger than 4mm/h, r1 represents a moderate haze obstruction coefficient corresponding to mild haze, r2 represents a haze obstruction coefficient corresponding to haze, and r3 represents a haze obstruction coefficient corresponding to severe haze.
Further, the rainwater obstruction coefficient set Ci(ci1,ci2,...,cij,...,cim) is one of f1, f2, f3, f4 and f5, and the haze blocking coefficient set Di(di1,di2,...,dij,...,dim) is one of haze blocking coefficients r1, r2 and r3, and an instantaneous blocking coefficient set Fi(fi1,fi2,...,fij,...,fim) is equal to G, and G is equal to 1 or 0, wherein r1 represents a haze barrier coefficient corresponding to mild haze, r2 represents a haze barrier coefficient corresponding to moderate haze, r3 represents a haze barrier coefficient corresponding to severe haze, f1 represents a rainwater barrier coefficient corresponding to 0.3-1mm/h of rainfall, f2 represents a rainwater barrier coefficient corresponding to 1-2mm/h of rainfall, f3 represents a rainwater barrier coefficient corresponding to 2-3mm/h of rainfall, f4 represents a rainwater barrier coefficient corresponding to 3-4mm/h of rainfall, and f5 represents a rainwater barrier coefficient corresponding to a rainfall greater than 4mm/h of rainfall.
Further, the weather basic conditions include rainfall and haze level.
Furthermore, the vehicle-mounted terminal comprises a vehicle speed monitoring unit, a positioning unit, a processor and a data transmission unit, wherein the processor is respectively connected with the vehicle speed monitoring unit, the positioning unit and the data transmission unit;
the vehicle speed monitoring unit is a vehicle speed sensor and is used for detecting the vehicle speed of a vehicle in real time and sending the detected vehicle speed to the processor, the positioning unit is used for acquiring the position information of the vehicle in real time and sending the acquired vehicle position to the processor, the processor receives the vehicle speed sent by the vehicle speed monitoring unit and the vehicle position information sent by the positioning unit and sends the received vehicle speed and the received vehicle position information to the data transmission unit, and the data transmission unit sends the vehicle speed and the position information corresponding to the vehicle storage database.
Furthermore, the image acquisition terminal comprises a timing unit, a reset unit, an image acquisition unit, a controller and a data communication unit, wherein the controller is respectively connected with the timing unit, the reset unit, the image acquisition unit and the data communication unit, and the reset unit is connected with the timing unit;
the timing unit is used for performing accumulated timing and sending the accumulated time to the controller, the reset unit is used for receiving a control instruction sent by the controller and clearing the accumulated time of the timing unit, the image acquisition unit is a high-definition camera and is used for acquiring vehicle image information at an image acquisition point and sending the acquired vehicle image information to the controller, the controller receives the accumulated time sent by the timing unit and compares the received accumulated time with a set time threshold, if the accumulated time is equal to the set time threshold, the controller sends a reset control instruction to the reset unit and controls the reset unit to clear the accumulated time of the timing unit, meanwhile, a shooting control instruction is sent to the image acquisition unit to control the image acquisition unit to shoot images, and the controller receives the vehicle image information sent by the image acquisition unit, and sending the received vehicle image information to a data communication unit; the data communication unit is used for receiving the vehicle image information sent by the controller and sending the received road surface image information to the cloud server.
Further, any one of the standard vehicle images yu in the standard vehicle image set has a vehicle traffic determination value xu that best matches the standard vehicle image yu in the vehicle traffic determination value set, any one of the vehicle traffic determination value xu in the vehicle traffic determination value set has a standard vehicle image yu that best matches the vehicle traffic determination value xu in the standard vehicle image set.
Further, the statistical process of the actual congestion coefficient by the cloud server includes the following steps:
s1, obtaining the inferred congestion coefficients corresponding to the road sections to be detected on the road branches, and forming an inferred congestion coefficient set gammai(γi1,γi2,...,γij,...,γim)，γij represents an inferred congestion coefficient corresponding to the jth road section to be detected on the ith road branch;
s2, obtaining a transposed matrix gamma corresponding to the inferred congestion coefficient set in the step S1i T；
S3, calculating and obtaining the actual congestion coefficient of the road branch corresponding to the road section to be detected, wherein the formula is
Figure GDA0002305440860000075
m is expressed as the number of road sections to be measured on the ith road branch, gammai TExpressed as a transposed matrix, P, deducing the set of congestion coefficientsiAnd is expressed as an actual congestion coefficient corresponding to the ith road branch.
Further, the calculation formula of the inferred congestion coefficient is
Figure GDA0002305440860000072
Lambda is expressed as a comprehensive influence factor, and 0.68, v is takenij denotes maxExpressed as the maximum value of the standard vehicle speed of the vehicle in the jth road section to be measured on the ith road branch, vij denotes minAnd the minimum value is expressed as the vehicle standard speed in the jth road section to be measured on the ith road branch.
Further, the calculation formula of the comprehensive traffic congestion coefficient is
Figure GDA0002305440860000073
Figure GDA0002305440860000074
Expressed as the comprehensive traffic congestion coefficient from the jth-1 to-be-measured road section to the jth to-be-measured road section on the ith road branch, ZijDenoted as jth waiting on ith road branchMeasuring traffic flow pressure coefficient, Z, in a section of roadi(j-1)Expressed as the traffic flow pressure coefficient, delta h, in the j-1 th road section to be measured on the ith road branchij is the difference between the vehicle traffic judgment value corresponding to the j +1 th image acquisition terminal on the ith road branch and the vehicle traffic judgment value corresponding to the j image acquisition terminal, PiExpressed as the actual congestion coefficient, c, corresponding to the ith road branchi(j-1) and cij is respectively expressed as the rainwater obstruction coefficient corresponding to the j-1 th road branch and the j-th road section to be detected on the ith road branch, di(j-1) and dij is respectively expressed as haze blocking coefficients corresponding to the j-1 th road branch and the j-th road section to be detected, fi(j-1) and fij is respectively expressed as the instantaneous obstruction coefficient corresponding to the j-1 th road branch and the j-th road section to be detected.
The invention has the beneficial effects that:
the invention provides an intelligent road traffic tracking management system based on big data image acquisition, which acquires the speed and position information of a vehicle through a vehicle-mounted terminal and sends the information to a cloud server, acquires road vehicle images at each image acquisition point through an image acquisition terminal, and the vehicle-mounted terminal and the image acquisition terminal are combined with the cloud server and a road state updating module to analyze and process the speed and the position of the vehicle in a road section to be detected and the road vehicle images to obtain the traffic flow pressure coefficient in each road section to be detected, the actual congestion coefficient of each road branch, a terminal comparison traffic judgment value, a rainwater obstruction coefficient, a haze obstruction coefficient and an instantaneous obstruction coefficient, thereby obtaining the comprehensive traffic congestion coefficient between adjacent road sections to be detected, the system can accurately analyze the traffic condition between the adjacent road sections to be detected, and has the characteristic of intellectualization, the method and the system are convenient for displaying the congestion degree of the road section to be detected for managers, realize the tracking management of road traffic, and recommend reliable reference basis for later vehicle driving routes.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an intelligent road traffic tracking management system based on big data image acquisition according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an intelligent road traffic tracking management system based on big data image acquisition comprises a data storage database, a region division module, a road condition updating module, a cloud server, a vehicle storage database, a plurality of vehicle-mounted terminals, a plurality of image acquisition terminals and a display terminal; the cloud server is respectively connected with the road condition updating module, the vehicle storage database, the data storage database, the display terminal, the vehicle-mounted terminals and the image acquisition terminals, the area dividing module is connected with the data storage database, and the vehicle-mounted terminals are connected with the vehicle storage database.
The data storage database is used for storing serial numbers corresponding to road branches, the road branches are numbered according to a set sequencing sequence and are respectively 1,2, 1, i, 1, n, the road branches are road sections between adjacent intersections, at least one image acquisition point is arranged on each road branch, an image acquisition terminal is installed at each image acquisition point, the image acquisition points on the same road branch are numbered according to the driving direction of a vehicle and are respectively 1,2, 1, j, 1, m, and vehicle standard vehicle speeds of the image acquisition points are stored, and the standard vehicle speeds are between the highest vehicle speed allowed by the road branches and 40% of the highest vehicle speed allowed by the road branches;
in addition, rain blocking coefficients and haze blocking coefficients corresponding to different rainfall amounts and haze levels are stored, the rain blocking coefficients corresponding to the rainfall amounts are respectively f1, f2, f3, f4 and f5, f1 < f2 < f3 < f4 < f5, the haze blocking coefficients corresponding to the haze levels are respectively r1, r2 and r3, and r1 < r2 < r3, wherein f1 represents a rain blocking coefficient corresponding to a rainfall amount of 0.3-1mm/h, f2 represents a rain blocking coefficient corresponding to a rainfall amount of 1-2mm/h, f3 represents a rain blocking coefficient corresponding to a rainfall amount of 2-3mm/h, f4 represents a rain blocking coefficient corresponding to a rainfall amount of 3-4mm/h, f5 represents a rain blocking coefficient corresponding to a rainfall amount of more than 4mm/h, and r1 represents a light haze blocking coefficient corresponding to a haze level, r2 represents a haze blocking coefficient corresponding to moderate haze, r3 represents a haze blocking coefficient corresponding to severe haze, and the haze blocking coefficient is used for storing whether the road surface has an instantaneous blocking coefficient G corresponding to construction or traffic accidents, if the road surface has the construction or traffic accidents, G is equal to 1, and if the road surface has the construction or traffic accidents, G is equal to 0;
the region dividing module is used for dividing each road branch into a plurality of road sections to be detected by using the length of a fixed road section, the road sections to be detected on the same road branch are numbered sequentially according to the advancing direction of a vehicle, the number of the road sections to be detected is 1,2, a.
The road state updating module is used for inputting the weather basic conditions of the day and whether construction or traffic accidents exist in each road section to be detected of each road branch, and sending the input weather basic conditions and whether construction or traffic accident information exists in the road surface of each road branch to the cloud server, wherein the weather basic conditions comprise rainfall and haze levels;
the vehicle-mounted terminal is arranged on a vehicle and used for monitoring the current speed and the position of the vehicle in real time and sending the detected speed and the detected position of the vehicle to a vehicle storage database;
the vehicle-mounted terminal comprises a vehicle speed monitoring unit, a positioning unit, a processor and a data transmission unit, wherein the processor is respectively connected with the vehicle speed monitoring unit, the positioning unit and the data transmission unit, the vehicle speed monitoring unit is a vehicle speed sensor and is used for detecting the vehicle speed of a vehicle in real time and sending the detected vehicle speed to the processor, the positioning unit is used for acquiring the position information of the vehicle in real time and sending the acquired vehicle position to the processor, the processor receives the vehicle speed sent by the vehicle speed monitoring unit and the vehicle position information sent by the positioning unit and sends the received vehicle speed and the vehicle position information to the data transmission unit, and the data transmission unit sends the vehicle speed and the position information corresponding to the vehicle to a vehicle storage database;
the vehicle storage database is used for storing the road branch distribution map, receiving and storing the speed and position information of the vehicle which is sent by each vehicle-mounted terminal and is driven by the vehicle, and marking the position corresponding to the vehicle on the road branch distribution map;
in addition, the vehicle storage database is used for storing different standard vehicle images, each standard vehicle image has at least one characteristic different from the vehicle distribution position and the vehicle quantity, each standard vehicle image forms a standard vehicle image set Y which is { Y1, Y2,. once, yu.. once, yq }, yu represents the u-th standard vehicle image, each standard vehicle image corresponds to a vehicle traffic judgment value, the vehicle traffic judgment value corresponding to each standard vehicle image forms a vehicle traffic judgment value set X which is { X1, X2,. once, xu.., xq }, xu represents the u-th vehicle traffic judgment value, the vehicle traffic judgment value represents the original vehicle judgment congestion coefficient, and each standard vehicle image in the standard vehicle image set and each vehicle traffic judgment value in the vehicle traffic judgment value set are in a one-to-one mapping relationship, the method comprises the steps that any standard vehicle image yu in a standard vehicle image set is provided with a vehicle traffic judgment value xu which is most matched with the standard vehicle image yu in the vehicle traffic judgment value set, any vehicle traffic judgment value xu in the vehicle traffic judgment value set is provided with a standard vehicle image yu which is most matched with the vehicle traffic judgment value xu in the standard vehicle image set;
the image acquisition terminal is installed at each image acquisition point on each road branch, the image acquisition terminal installed at the image acquisition point has the same number as that of the image acquisition point, the image acquisition terminal acquires image information of a road surface at a fixed time end, and simultaneously acquires position information corresponding to the image acquisition terminal and sends the acquired road surface image information to the cloud server;
the image acquisition terminal comprises a timing unit, a reset unit, an image acquisition unit, a controller and a data communication unit, wherein the controller is respectively connected with the timing unit, the reset unit, the image acquisition unit and the data communication unit, and the reset unit is connected with the timing unit;
the timing unit is used for performing accumulated timing and sending the accumulated time to the controller, the reset unit is used for receiving a control instruction sent by the controller and clearing the accumulated time of the timing unit, the image acquisition unit is a high-definition camera and is used for acquiring vehicle image information at an image acquisition point and sending the acquired vehicle image information to the controller, the controller receives the accumulated time sent by the timing unit and compares the received accumulated time with a set time threshold, if the accumulated time is equal to the set time threshold, the controller sends a reset control instruction to the reset unit and controls the reset unit to clear the accumulated time of the timing unit, meanwhile, a shooting control instruction is sent to the image acquisition unit to control the image acquisition unit to shoot images, and the controller receives the vehicle image information sent by the image acquisition unit, and sending the received vehicle image information to a data communication unit; the data communication unit is used for receiving the vehicle image information sent by the controller and sending the received road surface image information to the cloud server;
the cloud server counts the number of vehicles of each vehicle-mounted terminal in each road section to be detected in the vehicle storage database according to the road sections to be detected divided by the area dividing module, and the number of vehicles in each road section to be detected forms a set S of the number of vehicles to be detectedi(si1,si2,...,sij,...,sim)，sij is expressed as the number of vehicles in the jth road section to be tested on the ith road branch, and the number of vehicles in the road section to be tested on the same road branch is compared with the number of vehicles in the previous road section to be tested on the road section to be tested to obtain a set S 'of the number of vehicles to be tested'i(s′i1,s′i2,...,s′ij,...,s′i(m-1))，s′ij is expressed as the difference value between the number of vehicles in the jth road section to be tested on the ith road branch and the number of vehicles in the jth +1 road section to be tested, namely s'ij＝si(j+1)-sij, the cloud server gathers S 'according to the number of the vehicles to be tested'iAnd counting the traffic flow pressure coefficient in each road section to be measured
Figure GDA0002305440860000101
λ is expressed as an influence factor, and 10.6 s is takeni(j +1) represents the number of vehicles in the j +1 th road section to be detected on the ith road branch;
the cloud server acquires the corresponding speed of the vehicles in each road section to be detected from the vehicle storage database, the acquired speed of the vehicles in each road section to be detected is sequenced according to the sequence of the vehicles from front to back, and a real-time speed set V is formedij(vij1,vij2,...,vijt,....,vijk)，vijt is the speed of the t vehicle corresponding to the jth vehicle in the jth road section to be measured on the ith road branch road, vijk in k is expressed as the number of vehicles in the jth section to be measured on the ith road branch, namely k is sij, the cloud server counts the average speed of the road sections to be tested according to the real-time speed measurement set in the road sections to be tested
Figure GDA0002305440860000111
Figure GDA0002305440860000112
The average speed of the j to-be-measured road section on the ith road branch is represented, the average speed of each to-be-measured road section is compared with the minimum value of the standard speed of the vehicle at the image acquisition point in the to-be-measured road stored in the storage database, and a to-be-measured road section speed comparison set V 'is obtained'i(v′i1,v′i2,...,v′ij,...,v′im)，v′ij is the average speed of the jth road section to be measured on the ith road branch and corresponds to the road section to be measuredComparing conditions among minimum values of standard vehicle speeds of vehicles according to the vehicle speed of the road section to be detected to compare the set V'iCounting the inferred congestion coefficient corresponding to the road section to be detected
Figure GDA0002305440860000113
Lambda is expressed as a comprehensive influence factor, and 0.68, v is takenij denotes maxExpressed as the maximum value of the standard vehicle speed of the vehicle in the jth road section to be measured on the ith road branch, vij denotes minThe traffic congestion coefficient is expressed as the minimum value of the standard vehicle speed of the vehicle in the jth road section to be detected on the ith road branch, and the cloud server counts the actual congestion coefficient P of each road branch according to the inferred congestion coefficient corresponding to each road section to be detected on each road branchiThe statistical process of the actual congestion coefficient specifically includes the following steps:
s1, obtaining the inferred congestion coefficients corresponding to the road sections to be detected on the road branches, and forming an inferred congestion coefficient set gammai(γi1,γi2,...,γij,...,γim)，γij represents an inferred congestion coefficient corresponding to the jth road section to be detected on the ith road branch;
s2, obtaining a transposed matrix gamma corresponding to the inferred congestion coefficient set in the step S1i T；
S3, calculating and obtaining the actual congestion coefficient of the road branch corresponding to the road section to be detected, wherein the formula is
Figure GDA0002305440860000114
m is expressed as the number of road sections to be measured on the ith road branch, gammai TExpressed as a transposed matrix, P, deducing the set of congestion coefficientsiAnd is expressed as an actual congestion coefficient corresponding to the ith road branch.
Meanwhile, the cloud server receives road surface image information sent by each image acquisition terminal on each road branch, and the received road image information is sequenced according to the number sequence corresponding to each image acquisition terminal on the same road branch to form a road image information set Bi(bi1,bi2,...,bij,...,bim)，bij represents road surface image information sent by the jth image acquisition terminal on the ith road branch, each road surface image in the obtained road image information set is compared with each standard vehicle image stored in the vehicle storage database one by one to screen out the vehicle traffic judgment value corresponding to the standard vehicle image matched with each road surface image, and the obtained vehicle traffic judgment values acquired by each image acquisition terminal form a terminal traffic judgment value set Hi(hi1,hi2,...,hij,...,him)，hij is a vehicle traffic judgment value corresponding to the jth image acquisition terminal on the ith road branch, j is 1,2ij is equal to one vehicle traffic judgment value in the vehicle traffic judgment value set X { X1, X2.,. xu.,. xq }, and the terminal traffic judgment value set H is usediThe vehicle traffic judgment value corresponding to the middle and next image acquisition terminal is subtracted from the vehicle traffic judgment value corresponding to the previous image acquisition terminal to obtain a terminal comparison traffic judgment value set delta Hi(Δhi1,Δhi2,...,Δhij,...,Δhi(m-1))，Δhij is expressed as the difference value between the vehicle traffic judgment value corresponding to the j +1 th image acquisition terminal on the ith road branch and the vehicle traffic judgment value corresponding to the j image acquisition terminal;
the cloud server receives the weather basic conditions sent by the road state updating module and whether each road section to be detected of each road branch is constructed or has a traffic accident, the rainfall and the haze grade are obtained according to the received weather basic conditions, the rainwater blocking coefficient and the haze blocking coefficient corresponding to the rainfall in the data storage database and the instantaneous blocking coefficient corresponding to the construction or traffic accident are extracted, and the rainwater blocking coefficients in each road section to be detected are extracted to form a rainwater blocking coefficient set Ci(ci1,ci2,...,cij,...,cim), the haze blocking coefficients in the road sections to be detected form a haze blocking coefficient set Di(di1,di2,...,dij,...,dim), and forming an instantaneous obstruction coefficient set F by using instantaneous obstruction coefficients corresponding to whether each road section to be detected is constructed or has a traffic accidenti(fi1,fi2,...,fij,...,fim) in which c)ij is expressed as a rainwater obstruction coefficient corresponding to the jth road section to be detected on the ith road branch, dij is expressed as a haze blocking coefficient, f, corresponding to the jth road section to be detected on the ith road branchij is the instantaneous obstruction coefficient corresponding to the jth road section to be detected on the ith road branch, and m is the number of the road sections to be detected on the ith road branch.
The cloud server is used for determining the traffic flow pressure coefficient Z in each road section to be measuredijActual congestion coefficient P of each road branchiAnd comparing the traffic judgment value set delta H by the terminaliAnd the rainwater obstruction coefficient, the haze obstruction coefficient and the instantaneous obstruction coefficient are integrated to count the comprehensive traffic jam coefficient between the previous road section to be tested and the next road section to be tested on each road branch
Figure GDA0002305440860000131
Figure GDA0002305440860000132
Expressed as the comprehensive traffic congestion coefficient from the jth-1 to-be-measured road section to the jth to-be-measured road section on the ith road branch, ZijExpressed as the traffic flow pressure coefficient, Z, in the jth road section to be measured on the ith road branchi(j-1)Expressed as the traffic flow pressure coefficient, delta h, in the j-1 th road section to be measured on the ith road branchij is the difference between the vehicle traffic judgment value corresponding to the j +1 th image acquisition terminal on the ith road branch and the vehicle traffic judgment value corresponding to the j image acquisition terminal, PiExpressed as the actual congestion coefficient, c, corresponding to the ith road branchi(j-1) and cij is respectively expressed as the rainwater obstruction coefficient corresponding to the j-1 th road branch and the j-th road section to be detected on the ith road branch, di(j-1) and dij is respectively expressed as haze blocking coefficients corresponding to the j-1 th road branch and the j-th road section to be detected, fi(j-1) and fij is respectively expressed as instantaneous obstruction coefficients corresponding to the j-1 th road section and the j-th road section to be detected on the ith road branch, the higher the comprehensive traffic congestion coefficient is, the more serious the congestion degree between the previous road section to be detected and the next road section to be detected is, and the cloud server sends the comprehensive traffic congestion coefficient between the previous road section to be detected and the next road section to be detected on each road branch to the display terminal;
the display terminal is used for receiving and displaying the comprehensive traffic jam coefficient between the previous road section to be detected and the next road section to be detected on each road branch sent by the cloud server, can effectively track road traffic through the comprehensive traffic jam coefficient, is convenient for managers to effectively analyze the jam degree between the two adjacent road sections to be detected, further realizes effective tracking management on traffic conditions, and provides valuable reference for pushing vehicle running routes in the later period.
The invention provides an intelligent road traffic tracking management system based on big data image acquisition, which acquires the speed and position information of a vehicle through a vehicle-mounted terminal and sends the information to a cloud server, acquires road vehicle images at each image acquisition point through an image acquisition terminal, and the vehicle-mounted terminal and the image acquisition terminal are combined with the cloud server and a road state updating module to analyze and process the speed and the position of the vehicle in a road section to be detected and the road vehicle images to obtain the traffic flow pressure coefficient in each road section to be detected, the actual congestion coefficient of each road branch, a terminal comparison traffic judgment value, a rainwater obstruction coefficient, a haze obstruction coefficient and an instantaneous obstruction coefficient, thereby obtaining the comprehensive traffic congestion coefficient between adjacent road sections to be detected, the system can accurately analyze the traffic condition between the adjacent road sections to be detected, and has the characteristic of intellectualization, the method and the system are convenient for displaying the congestion degree of the road section to be detected for managers, realize the tracking management of road traffic, and recommend reliable reference basis for later vehicle driving routes.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.

Claims (10)

1. The utility model provides an intelligent road traffic tracking management system based on big data image gathers which characterized in that: the system comprises a data storage database, an area division module, a road condition updating module, a cloud server, a vehicle storage database, a plurality of vehicle-mounted terminals, a plurality of image acquisition terminals and a display terminal;
the cloud server is respectively connected with the road condition updating module, the vehicle storage database, the data storage database, the display terminal, the vehicle-mounted terminals and the image acquisition terminals, the area dividing module is connected with the data storage database, and the vehicle-mounted terminals are connected with the vehicle storage database;
the data storage database is used for storing serial numbers corresponding to road branches, the road branches are numbered according to a set sequencing sequence and are respectively 1,2, 1, i, 1, n, the road branches are road sections between adjacent intersections, at least one image acquisition point is arranged on each road branch, an image acquisition terminal is installed at each image acquisition point, the image acquisition points on the same road branch are numbered according to the driving direction of a vehicle and are respectively 1,2, 1, j, 1, m, and the standard vehicle speed of each image acquisition point is stored;
in addition, different rainwater blocking coefficients and haze blocking coefficients corresponding to different rainfall amounts and haze levels are stored, whether an instantaneous blocking coefficient G corresponding to construction or traffic accidents exists on the road surface is stored, if the construction or traffic accidents exist on the road surface, G is equal to 1, otherwise, G is equal to 0;
the region dividing module is used for dividing each road branch into a plurality of road sections to be detected by using the length of a fixed road section, the road sections to be detected on the same road branch are numbered sequentially according to the advancing direction of a vehicle, the number of the road sections to be detected is 1,2, a.
The road state updating module is used for inputting the weather basic conditions of the day and whether construction or traffic accidents exist in each road section to be detected of each road branch, and sending the input weather basic conditions and road surface construction or traffic accident information of each road branch to the cloud server;
the vehicle-mounted terminal is arranged on a vehicle and used for monitoring the current speed and the position of the vehicle in real time and sending the detected speed and the detected position of the vehicle to a vehicle storage database;
the vehicle storage database is used for storing the road branch distribution map, receiving and storing the speed and position information of the vehicle which is sent by each vehicle-mounted terminal and is driven by the vehicle, and marking the position corresponding to the vehicle on the road branch distribution map;
in addition, the vehicle storage database is used for storing different standard vehicle images, each standard vehicle image has at least one characteristic different from the vehicle distribution position and the vehicle quantity, each standard vehicle image forms a standard vehicle image set Y which is { Y1, Y2,. multidot.,. yu.,. multidot.yq }, yu represents the u-th standard vehicle image, each standard vehicle image corresponds to a vehicle traffic judgment value, the vehicle traffic judgment value corresponding to each standard vehicle image forms a vehicle traffic judgment value set X which is { X1, X2,. multidot.,. multidot.xq }, and xu represents the u-th vehicle traffic judgment value, the vehicle traffic judgment numerical value represents an original vehicle judgment congestion coefficient, and each standard vehicle image in the standard vehicle image set and each vehicle traffic judgment numerical value in the vehicle traffic judgment numerical value set are in a one-to-one mapping relation;
the image acquisition terminal is installed at each image acquisition point on each road branch, the image acquisition terminal installed at the image acquisition point has the same number as that of the image acquisition point, the image acquisition terminal acquires image information of a road surface at a fixed time end, and simultaneously acquires position information corresponding to the image acquisition terminal and sends the acquired road surface image information to the cloud server;
the cloud server counts the number of vehicles of each vehicle-mounted terminal in each road section to be detected in the vehicle storage database according to the road sections to be detected divided by the area dividing module, and the number of vehicles in each road section to be detected forms a set S of the number of vehicles to be detectedi(si1,si2,...,sij,...,sim)，sij is expressed as the number of vehicles in the jth road section to be tested on the ith road branch, and the number of vehicles in the road section to be tested on the same road branch is compared with the number of vehicles in the previous road section to be tested on the road section to be tested to obtain a set S 'of the number of vehicles to be tested'i(s′i1,s′i2,...,s′ij,...,s′i(m-1))，s′ij is expressed as the difference value between the number of vehicles in the jth road section to be tested on the ith road branch and the number of vehicles in the jth +1 road section to be tested, namely s'ij＝si(j+1)-sij, the cloud server gathers S 'according to the number of the vehicles to be tested'iAnd counting the traffic flow pressure coefficient in each road section to be measured
Figure FDA0002305440850000021
λ is expressed as an influence factor, and 10.6 s is takeni(j +1) represents the number of vehicles in the j +1 th road section to be detected on the ith road branch;
the cloud server acquires the corresponding speed of the vehicles in each road section to be detected from the vehicle storage database, the acquired speed of the vehicles in each road section to be detected is sequenced according to the sequence of the vehicles from front to back, and a real-time speed set V is formedij(vij1,vij2,...,vijt,....,vijk)，vijt is the speed of the t vehicle corresponding to the jth vehicle in the jth road section to be measured on the ith road branch road, vijk in k is expressed as the number of vehicles in the jth section to be measured on the ith road branch, namely k is sij, the cloud server counts the average speed of the road sections to be tested according to the real-time speed measurement set in the road sections to be tested
Figure FDA0002305440850000022
Figure FDA0002305440850000023
Expressing the average speed of the jth road section to be tested on the ith road branch and storing the average speed of each road section to be tested in a databaseThe minimum value of the standard vehicle speed of the vehicle at the image acquisition point in the line to be tested is stored in the comparison set V, and a comparison set V of the vehicle speed of the road section to be tested is obtainedi′(v′i1,v′i2,...,v′ij,...,v′im)，v′ij is the comparison condition between the average speed in the jth road section to be tested on the ith road branch and the minimum value of the standard speed of the vehicle corresponding to the road section to be tested, and the set V is compared according to the speed of the road section to be testedi' counting the inferred congestion coefficient gamma corresponding to the road section to be detectedij, the cloud server obtains the actual congestion coefficient P of each road branch according to the inferred congestion coefficient corresponding to each road section to be detected on each road branchi；
Meanwhile, the cloud server receives road surface image information sent by each image acquisition terminal on each road branch, and the received road image information is sequenced according to the number sequence corresponding to each image acquisition terminal on the same road branch to form a road image information set Bi(bi1,bi2,...,bij,...,bim)，bij represents road surface image information sent by the jth image acquisition terminal on the ith road branch, each road surface image in the obtained road image information set is compared with each standard vehicle image stored in the vehicle storage database one by one to screen out the vehicle traffic judgment value corresponding to the standard vehicle image matched with each road surface image, and the obtained vehicle traffic judgment values acquired by each image acquisition terminal form a terminal traffic judgment value set Hi(hi1,hi2,...,hij,...,him)，hij is a vehicle traffic judgment value corresponding to the jth image acquisition terminal on the ith road branch, j is 1,2ij is equal to one vehicle traffic judgment value in the vehicle traffic judgment value set X { X1, X2.,. xu.,. xq }, and the terminal traffic judgment value set H is usediAnd the vehicle traffic judgment value corresponding to the middle and next image acquisition terminal is subtracted from the vehicle traffic judgment value corresponding to the last image acquisition terminal to obtain a terminal comparison traffic judgment valueSet of fraction values Δ Hi(Δhi1,Δhi2,...,Δhij,...,Δhi(m-1))，Δhij is expressed as the difference value between the vehicle traffic judgment value corresponding to the j +1 th image acquisition terminal on the ith road branch and the vehicle traffic judgment value corresponding to the j image acquisition terminal;
the cloud server receives the weather basic conditions sent by the road state updating module and whether each road section to be detected of each road branch is constructed or has a traffic accident, the rainfall and the haze grade are obtained according to the received weather basic conditions, the rainwater blocking coefficient and the haze blocking coefficient corresponding to the rainfall in the data storage database and the instantaneous blocking coefficient corresponding to the construction or traffic accident are extracted, and the rainwater blocking coefficients in each road section to be detected are extracted to form a rainwater blocking coefficient set Ci(ci1,ci2,...,cij,...,cim), the haze blocking coefficients in the road sections to be detected form a haze blocking coefficient set Di(di1,di2,...,dij,...,dim), and forming an instantaneous obstruction coefficient set F by using instantaneous obstruction coefficients corresponding to whether each road section to be detected is constructed or has a traffic accidenti(fi1,fi2,...,fij,...,fim) in which c)ij is expressed as a rainwater obstruction coefficient corresponding to the jth road section to be detected on the ith road branch, dij is expressed as a haze blocking coefficient, f, corresponding to the jth road section to be detected on the ith road branchij represents the instantaneous obstruction coefficient corresponding to the jth road section to be detected on the ith road branch, and m represents the number of the road sections to be detected on the ith road branch;
the cloud server is used for determining the traffic flow pressure coefficient Z in each road section to be measuredijActual congestion coefficient P of each road branchiAnd comparing the traffic judgment value set delta H by the terminaliAnd the rainwater obstruction coefficient, the haze obstruction coefficient and the instantaneous obstruction coefficient are integrated to count the comprehensive traffic jam coefficient between the previous road section to be tested and the next road section to be tested on each road branch
Figure FDA0002305440850000041
The higher the comprehensive traffic congestion coefficient is, the more serious the congestion degree between the last road section to be detected and the next road section on the same road branch is, and the cloud server sends the comprehensive traffic congestion coefficient between the last road section to be detected and the next road section to be detected on each road branch to the display terminal;
and the display terminal is used for receiving and displaying the comprehensive traffic congestion coefficient between the previous road section to be tested and the next road section to be tested on each road branch sent by the cloud server.
2. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: the rain blocking coefficients corresponding to the rainfall levels are f1, f2, f3, f4 and f5 respectively, f1 is more than f2 is more than f3 is more than f4 is more than f5, the haze blocking coefficients corresponding to the haze grades are r1, r2 and r3 respectively, and r1 is more than r2 is more than r3, f1 represents a rainwater obstruction coefficient corresponding to 0.3-1mm/h of rainfall, f2 represents a rainwater obstruction coefficient corresponding to 1-2mm/h of rainfall, f3 represents a rainwater obstruction coefficient corresponding to 2-3mm/h of rainfall, f4 represents a rainwater obstruction coefficient corresponding to 3-4mm/h of rainfall, f5 represents a rainwater obstruction coefficient corresponding to a rainfall larger than 4mm/h, r1 represents a moderate haze obstruction coefficient corresponding to mild haze, r2 represents a haze obstruction coefficient corresponding to haze, and r3 represents a haze obstruction coefficient corresponding to severe haze.
3. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: the rainwater obstruction coefficient set Ci(ci1,ci2,...,cij,...,cim) is one of f1, f2, f3, f4 and f5, and the haze blocking coefficient set Di(di1,di2,...,dij,...,dim) is one of haze blocking coefficients r1, r2 and r3, and an instantaneous blocking coefficient set Fi(fi1,fi2,...,fij,...,fim) is equal to G, and G is equal to 1 or 0, wherein r1 represents a light haze pairThe corresponding haze blocking coefficient, r2 represents the haze blocking coefficient corresponding to medium haze, r3 represents the haze blocking coefficient corresponding to severe haze, f1 represents the rainwater blocking coefficient corresponding to 0.3-1mm/h rainfall, f2 represents the rainwater blocking coefficient corresponding to 1-2mm/h rainfall, f3 represents the rainwater blocking coefficient corresponding to 2-3mm/h rainfall, f4 represents the rainwater blocking coefficient corresponding to 3-4mm/h rainfall, and f5 represents the rainwater blocking coefficient corresponding to rainfall greater than 4 mm/h.
4. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: the weather basic conditions include rainfall and haze level.
5. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: the vehicle-mounted terminal comprises a vehicle speed monitoring unit, a positioning unit, a processor and a data transmission unit, wherein the processor is respectively connected with the vehicle speed monitoring unit, the positioning unit and the data transmission unit;
the vehicle speed monitoring unit is a vehicle speed sensor and is used for detecting the vehicle speed of a vehicle in real time and sending the detected vehicle speed to the processor, the positioning unit is used for acquiring the position information of the vehicle in real time and sending the acquired vehicle position to the processor, the processor receives the vehicle speed sent by the vehicle speed monitoring unit and the vehicle position information sent by the positioning unit and sends the received vehicle speed and the received vehicle position information to the data transmission unit, and the data transmission unit sends the vehicle speed and the position information corresponding to the vehicle storage database.
6. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: the image acquisition terminal comprises a timing unit, a reset unit, an image acquisition unit, a controller and a data communication unit, wherein the controller is respectively connected with the timing unit, the reset unit, the image acquisition unit and the data communication unit, and the reset unit is connected with the timing unit;
the timing unit is used for performing accumulated timing and sending the accumulated time to the controller, the reset unit is used for receiving a control instruction sent by the controller and clearing the accumulated time of the timing unit, the image acquisition unit is a high-definition camera and is used for acquiring vehicle image information at an image acquisition point and sending the acquired vehicle image information to the controller, the controller receives the accumulated time sent by the timing unit and compares the received accumulated time with a set time threshold, if the accumulated time is equal to the set time threshold, the controller sends a reset control instruction to the reset unit and controls the reset unit to clear the accumulated time of the timing unit, meanwhile, a shooting control instruction is sent to the image acquisition unit to control the image acquisition unit to shoot images, and the controller receives the vehicle image information sent by the image acquisition unit, and sending the received vehicle image information to a data communication unit; the data communication unit is used for receiving the vehicle image information sent by the controller and sending the received road surface image information to the cloud server.
7. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: any one standard vehicle image yu in the standard vehicle image set has a vehicle traffic judgment value xu which is most matched with the standard vehicle image yu in the vehicle traffic judgment value set, any one vehicle traffic judgment value xu in the vehicle traffic judgment value set has a standard vehicle image yu which is most matched with the vehicle traffic judgment value xu in the standard vehicle image set.
8. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: the statistical process of the actual congestion coefficient by the cloud server comprises the following steps:
s1, obtaining the inferred congestion coefficients corresponding to the road sections to be detected on the road branches, and forming an inferred congestion coefficient set gammai(γi1,γi2,...,γij,...,γim)，γij represents an inferred congestion coefficient corresponding to the jth road section to be detected on the ith road branch;
s2, obtaining a transposed matrix gamma corresponding to the inferred congestion coefficient set in the step S1i T；
S3, calculating and obtaining the actual congestion coefficient of the road branch corresponding to the road section to be detected, wherein the formula is
Figure FDA0002305440850000065
m is expressed as the number of road sections to be measured on the ith road branch, gammai TExpressed as a transposed matrix, P, deducing the set of congestion coefficientsiAnd is expressed as an actual congestion coefficient corresponding to the ith road branch.
9. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: the calculation formula of the inferred congestion coefficient is
Figure FDA0002305440850000062
Lambda is expressed as a comprehensive influence factor, and 0.68, v is takenij denotes maxExpressed as the maximum value of the standard vehicle speed of the vehicle in the jth road section to be measured on the ith road branch, vij denotes minAnd the minimum value is expressed as the vehicle standard speed in the jth road section to be measured on the ith road branch.
10. The intelligent road traffic tracking management system based on big data image acquisition as claimed in claim 1, wherein: the calculation formula of the comprehensive traffic jam coefficient is
Figure FDA0002305440850000063
Figure FDA0002305440850000064
Expressed as the comprehensive traffic congestion coefficient from the jth-1 to-be-measured road section to the jth to-be-measured road section on the ith road branch, ZijTo representIs the traffic flow pressure coefficient, Z, in the jth road section to be measured on the ith road branchi(j-1)Expressed as the traffic flow pressure coefficient, delta h, in the j-1 th road section to be measured on the ith road branchij is the difference between the vehicle traffic judgment value corresponding to the j +1 th image acquisition terminal on the ith road branch and the vehicle traffic judgment value corresponding to the j image acquisition terminal, PiExpressed as the actual congestion coefficient, c, corresponding to the ith road branchi(j-1) and cij is respectively expressed as the rainwater obstruction coefficient corresponding to the j-1 th road branch and the j-th road section to be detected on the ith road branch, di(j-1) and dij is respectively expressed as haze blocking coefficients corresponding to the j-1 th road branch and the j-th road section to be detected, fi(j-1) and fij is respectively expressed as the instantaneous obstruction coefficient corresponding to the j-1 th road branch and the j-th road section to be detected.
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