CN110363985B - Traffic data analysis method, device, storage medium and equipment - Google Patents

Traffic data analysis method, device, storage medium and equipment Download PDF

Info

Publication number
CN110363985B
CN110363985B CN201910570898.9A CN201910570898A CN110363985B CN 110363985 B CN110363985 B CN 110363985B CN 201910570898 A CN201910570898 A CN 201910570898A CN 110363985 B CN110363985 B CN 110363985B
Authority
CN
China
Prior art keywords
traffic
traffic data
calculating
data
constructing
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.)
Active
Application number
CN201910570898.9A
Other languages
Chinese (zh)
Other versions
CN110363985A (en
Inventor
吕佳润
张继先
刘丰磊
钟添翼
王世彬
闫建华
秦智聪
刘晨
陈上
王锐锋
翟战强
张琦
宫正磊
章杰
胡伟
张淼
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing E Hualu Information Technology Co Ltd
Original Assignee
Beijing E Hualu Information Technology Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing E Hualu Information Technology Co Ltd filed Critical Beijing E Hualu Information Technology Co Ltd
Priority to CN201910570898.9A priority Critical patent/CN110363985B/en
Publication of CN110363985A publication Critical patent/CN110363985A/en
Application granted granted Critical
Publication of CN110363985B publication Critical patent/CN110363985B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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

Abstract

The invention discloses a traffic data analysis method, a device, a storage medium and equipment, wherein the method comprises the following steps: acquiring traffic data; classifying and calculating the traffic data according to a blood relationship management algorithm, and constructing a theme library containing different types of traffic data; analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm, and constructing a subject database containing different-dimensional traffic data; and generating a traffic data analysis result according to the subject database and the thematic database. The traffic data analysis method fuses and utilizes information of different sources and different types, and improves the accuracy and reliability of data acquisition. Meanwhile, original traffic data are analyzed and calculated by means of a blood margin management algorithm and a multi-dimensional analysis algorithm, and finally a theme and special topic database is formed in a gathering mode, so that a data basis is provided for urban traffic development decision making and road traffic information service. By implementing the invention, data support can be provided for traffic management, the data use efficiency is improved, and the informatization overall benefit is fully exerted.

Description

Traffic data analysis method, device, storage medium and equipment
Technical Field
The invention relates to the technical field of data processing, in particular to a traffic data analysis method, a traffic data analysis device, a traffic data analysis storage medium and traffic data analysis equipment.
Background
In recent years, with the improvement of the motor vehicle ownership rate of residents in China and the high-speed development of economy, urban traffic congestion becomes a hot problem with extremely high attention of the public in society, and is a difficult problem to be overcome by government decision and traffic management. The social cost caused by traffic jam increases in a geometric progression, and the sustainable development and the overall health level of a city are directly influenced. To address this problem, traditional methods that rely solely on expediting the construction of traffic infrastructure are not able to solve. With the development of the internet of things, the emergence and rapid popularization of novel hardware such as intelligent electronic terminals and intelligent mobile terminals, and the rapid development of mobile internet, internet of vehicles, cloud and the like, the accumulation of traffic data also shows the growth speed of geometric progression.
Although mass traffic data is acquired through the Internet of things and various traffic devices, the management and service efficiency is low due to the fact that a data sharing system is not established at present, information island conditions exist, comprehensive utilization of traffic resources is insufficient, and information fault is caused. These reasons result in the waste of a large amount of traffic data, and huge data cannot be captured, managed, processed and organized into effective data for relieving traffic congestion within a reasonable time through a software system.
Disclosure of Invention
In view of this, embodiments of the present invention provide a traffic data analysis method, apparatus, storage medium, and device, so as to solve the problem that in the prior art, huge traffic data cannot be captured, managed, processed, and organized into effective data for alleviating traffic congestion in a reasonable time through a software system.
The technical scheme provided by the invention is as follows:
a first aspect of an embodiment of the present invention provides a traffic data analysis method, where the traffic data analysis method includes: acquiring traffic data, wherein the traffic data comprises special traffic network data, Internet data and public security special network data; classifying and calculating the traffic data according to a blood relationship management algorithm, and constructing a theme library containing different types of traffic data; analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm, and constructing a special question bank containing different-dimensional traffic data; and generating a traffic data analysis result according to the subject database and the special subject database.
Optionally, analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm, and constructing a subject database containing traffic data of different dimensions, including: analyzing and calculating the traffic data according to a traffic management dimension analysis algorithm, and constructing a special subject library containing traffic management calculation results; analyzing and calculating the traffic data according to a traffic organization dimension analysis algorithm, and constructing a special question bank containing a traffic organization calculation result; analyzing and calculating the traffic data according to an informatization engineering dimension analysis algorithm, and constructing a thematic library containing the calculation result of the informatization engineering; analyzing and calculating the traffic data according to a road network form dimension analysis algorithm, and constructing a special subject library containing road network form calculation results; analyzing and calculating the traffic data according to a flow operation dimension analysis algorithm, and constructing a thematic library containing flow operation calculation results; analyzing and calculating the traffic data according to an accident prevention dimension analysis algorithm, and constructing a special subject library containing accident prevention calculation results; and analyzing and calculating the traffic data according to an illegal early warning dimension analysis algorithm, and constructing a special question bank containing illegal early warning calculation.
Optionally, analyzing and calculating the traffic data according to a traffic management dimension analysis algorithm, and constructing a topic library including a traffic management calculation result, including: acquiring the police strength number and the traffic condition number within a first preset time and the police strength number and the traffic condition number within a second preset time according to the traffic data; calculating to obtain an average police strength number within a first preset time according to the police strength number within the first preset time and the traffic condition number; calculating to obtain an average police strength number in a second preset time according to the police strength number and the traffic condition number in the second preset time; calculating the police strength matching degree according to the average police strength number in the first preset time and the average police strength number in the second preset time; and constructing a special subject library containing traffic management calculation results according to the police strength matching degree.
Optionally, analyzing and calculating the traffic data according to a traffic organization dimension analysis algorithm, and constructing a topic library including a traffic organization calculation result, including: acquiring traffic conditions of different road sections according to the traffic data; calculating according to the traffic condition to obtain a traffic deadlock factor, an overflow index factor, an unbalance index factor, an equal lamp frequency factor, a travel delay time factor and a green lamp idle time factor; calculating a reasonable signal timing rate according to the traffic deadlock factor, the overflow index factor, the unbalance index factor, the equal lamp frequency factor, the travel delay time factor and the green lamp idle time factor; and constructing a special question bank containing a traffic organization calculation result according to the signal timing reasonable rate.
Optionally, analyzing and calculating the traffic data according to an informatization engineering dimension analysis algorithm, and constructing a thematic library including the calculation result of the informatization engineering, including: acquiring the quantity of various types of traffic equipment in different areas according to the traffic data; calculating the coverage rate of the traffic equipment according to the number of various traffic equipment in different areas; and constructing a subject library containing the calculation result of the informatization project according to the coverage rate.
Optionally, analyzing and calculating the traffic data according to a road network form dimension analysis algorithm, and constructing a topic library including a road network form calculation result, including: acquiring the queuing length of vehicles in each direction of the intersection according to the internet data; acquiring a first intersection with the longest queuing length and a second intersection with the shortest queuing length according to the queuing length; calculating an imbalance index according to the queuing lengths of the first intersection and the second intersection; calculating the unbalance duration of each intersection according to the unbalance index; and constructing a special question bank containing the road network form calculation result according to the unbalance duration.
Optionally, analyzing and calculating the traffic data according to a traffic operation dimension analysis algorithm, and constructing a special topic library including a traffic operation calculation result, including: acquiring traffic flow of each road section according to the traffic data; calculating the average speed of the vehicle, the traffic flow density and the traffic flow growth rate according to the vehicle flow; and constructing a special question bank containing a flow operation calculation result according to the average speed of the vehicles, the traffic flow density and the traffic flow increase rate.
Optionally, analyzing and calculating the traffic data according to an accident prevention dimension analysis algorithm, and constructing a topic library including an accident prevention calculation result, including: acquiring accident starting number according to the traffic data; calculating to obtain the number of the ten-thousand-vehicle accidents according to the accident starting number; and constructing a special subject library containing accident prevention calculation results according to the number of the ten-thousand-vehicle accidents.
Optionally, analyzing and calculating the traffic data according to an illegal early warning dimension analysis algorithm, and constructing a special topic library including an illegal early warning calculation result, including: acquiring an illegal high-speed road section according to the traffic data, wherein the illegal high-speed road section forms a point section sample set; calculating the distance between the sample points of each illegal high-speed road section in the point section sample set according to the Dijkstra algorithm; classifying the point segment sample set according to an agglomeration hierarchical clustering algorithm to obtain a classification result; calculating the number of different types of sample sets according to the distance between the sample points in the sample sets of each point segment in the classification result; and constructing a special subject library containing the illegal early warning calculation result according to the classification result of the number of the different types of sample sets.
A second aspect of an embodiment of the present invention provides a traffic data analysis device, including: the data acquisition module is used for acquiring traffic data, and the traffic data comprises special traffic network data, Internet data and public security special network data; the subject database construction module is used for carrying out classification calculation on the traffic data according to a blood relationship management algorithm and constructing a subject database containing different types of traffic data; the special question bank building module is used for analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm and building a special question bank containing different-dimensional traffic data; and the analysis result forming module is used for forming an analysis result according to the subject database and the thematic database.
A third aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores computer instructions for causing a computer to execute the traffic data analysis method according to any one of the first aspect and the first aspect of the embodiments of the present invention.
A fourth aspect of an embodiment of the present invention provides a traffic data analysis device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the traffic data analysis method according to any one of the first aspect and the first aspect of the embodiments of the present invention.
The technical scheme provided by the embodiment of the invention has the following effects:
the traffic data analysis method, the traffic data analysis device, the storage medium and the equipment provided by the embodiment of the invention fully utilize the existing data resources, such as traffic private network data, internet data and public security private network data, fuse and utilize different sources and different types of information, and improve the accuracy and reliability of data acquisition. Meanwhile, original traffic data are analyzed and calculated by means of a blood margin management algorithm and a multi-dimensional analysis algorithm, and finally a theme and special topic database is formed in a gathering mode, so that a data basis is provided for urban traffic development decision making and road traffic information service. By implementing the invention, data support can be provided for traffic management, the data use efficiency is improved, and the informatization overall benefit is fully exerted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method of traffic data analysis according to an embodiment of the invention;
FIG. 2 is a flow chart of a traffic data analysis method according to another embodiment of the present invention;
FIG. 3 is a flow chart of a traffic data analysis method according to another embodiment of the present invention;
FIG. 4 is a flow chart of a traffic data analysis method according to another embodiment of the present invention;
FIG. 5 is a flow chart of a traffic data analysis method according to another embodiment of the present invention;
FIG. 6 is a flow chart of a traffic data analysis method according to another embodiment of the present invention;
FIG. 7 is a flow chart of a traffic data analysis method according to another embodiment of the present invention;
FIG. 8 is a flow chart of a traffic data analysis method according to another embodiment of the present invention;
fig. 9 is a block diagram of a structure of a traffic data analysis apparatus according to an embodiment of the present invention;
fig. 10 is a schematic hardware configuration diagram of a traffic data analysis device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
An embodiment of the present invention provides a traffic data analysis method, as shown in fig. 1, the traffic data analysis method includes the following steps:
step S101: and acquiring traffic data, wherein the traffic data comprises special traffic network data, Internet data and public security special network data. The special traffic network data comprises data from equipment facilities such as signal machines, video monitoring, guide screens, parking spaces, anti-collision facilities and road monitoring; the internet data mainly come from relevant data about road conditions, warning situations and the like; the public security private network data comprises key vehicle data, public security traffic management comprehensive application platform data, integrated command platform data, data of an internet information issuing system and the like.
Step S102: and carrying out classification calculation on the traffic data according to a blood relationship management algorithm, and constructing a subject library containing different types of traffic data. Specifically, before the traffic data is classified and calculated according to the blood margin management algorithm, the acquired traffic data may be firstly cleaned to form a standard library. The blood margin management algorithm can classify data in the standard library, and the classification can be performed according to the blood margin relation between the data, for example, if two data are obtained from one data through complex transformation, the three data can be classified into the same category through the blood margin management algorithm, so that a theme library comprising a motor vehicle theme domain, a driver theme domain, a road theme domain, an event theme domain and an illegal theme domain can be formed according to different categories of the data after classification calculation.
Step S103: and analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm, and constructing a subject database containing different-dimensional traffic data. Specifically, the traffic data can be analyzed and calculated according to the dimensions such as a traffic management dimension analysis algorithm, a traffic organization dimension analysis algorithm, an information-based engineering dimension analysis algorithm, a road network form dimension analysis algorithm, a traffic operation dimension analysis algorithm, an accident prevention dimension analysis algorithm, an illegal early warning dimension analysis algorithm and the like, and a special question bank containing different dimensions of traffic data is constructed.
Step S104: and generating a traffic data analysis result according to the subject database and the thematic database. Specifically, a data analysis platform based on a Hadoop storage calculation frame architecture can be formed by the standard library, the subject library, the special subject library and the like formed by the analysis algorithm, and analysis results can be displayed in a data billboard mode according to the analysis and calculation of the data by the data analysis platform. Meanwhile, according to the special item library in the data analysis platform, a special item data analysis chart can be formed for each dimension and pushed to related users in a weekly report mode, a monthly report mode and a quarterly report mode.
The traffic data analysis method provided by the embodiment of the invention fully utilizes the existing data resources, such as traffic private network data, internet data and public security private network data, fuses and utilizes different sources and different types of information, and improves the accuracy and reliability of data acquisition. Meanwhile, original traffic data are analyzed and calculated by means of a blood margin management algorithm and a multi-dimensional analysis algorithm, and finally a theme and special topic database is formed in a gathering mode, so that a data basis is provided for urban traffic development decision making and road traffic information service. By implementing the invention, data support can be provided for traffic management, the data use efficiency is improved, and the informatization overall benefit is fully exerted.
As an optional implementation manner of the embodiment of the invention, when analyzing and calculating traffic data according to a traffic management dimension analysis algorithm, the traffic data can be analyzed and calculated according to two indexes of duty enforcement and science and technology enforcement, wherein the duty enforcement comprises calculation algorithms in nine aspects of accident and police force matching degree, congestion and police force matching degree, law violation and police force matching degree, police force putting rate, number of ten thousand cars of police, number of police average police cars, number of per-person enforcement times and number of per-car enforcement times; the scientific and technological law enforcement comprises calculation algorithms of the illegal parking rate of the trunk road, the illegal rate of key vehicles, the informatization law enforcement and check rate, the illegal rate of motor vehicles on the trunk road and the like.
Specifically, in the calculation algorithms of the above several aspects, the calculation processes of the calculation algorithms of three aspects of the accident and police force matching degree, the congestion and police force matching degree, and the law violation and police force matching degree in the law enforcement on duty are first described, as shown in fig. 2, the calculation algorithms of the three aspects include the following steps:
step S201: acquiring the police strength number and the traffic condition number within a first preset time and the police strength number and the traffic condition number within a second preset time according to the traffic data; the accident and police strength matching degree calculation algorithm can obtain the police strength number at the current moment, the accumulated number of the accidents not at the knot at the current moment, the police strength number at the same moment every day in the last 30 days and the accumulated number of the accidents not at the knot in the same moment every day in the last 30 days; the congestion and police strength matching degree calculation algorithm can acquire the police strength number at the current moment, the total mileage of the congested road section at the current moment, the police strength number at the same moment every day in the last 30 days and the total mileage of the congested road section at the same moment every day in the last 30 days; the law violation and police strength matching degree calculation algorithm can obtain the average police strength number in nearly 30 days, the average accumulated illegal event number in nearly 30 days, the police strength number in nearly 180 days at the same moment every day and the accumulated illegal event number in nearly 180 days at the same moment every day.
Step S202: calculating to obtain an average police strength number within a first preset time according to the police strength number and the traffic condition number within the first preset time; the accident and police strength matching degree calculation algorithm can calculate the average police strength number of each unsettled accident at the current moment according to the police strength number at the current moment/the accumulated number of unsettled accidents at the current moment; the calculation algorithm of the congestion and police strength matching degree can calculate the average police strength number of each kilometer of the congested road section at the current moment according to the police strength number at the current moment/the total mileage of the congested road section at the current moment; the law violation and police force matching degree calculation algorithm can calculate the average police force number of each violation event in near 30 days according to the average police force number in near 30 days/the average accumulated violation event number in near 30 days.
Step S203: calculating to obtain an average police strength number in a second preset time according to the police strength number and the traffic condition number in the second preset time; wherein the calculation algorithm of the matching degree of the accident and the police force can be based on
Figure BDA0002109680630000081
(i is more than or equal to 1 and less than or equal to 30) to obtain the average alarm force number of each incomplete accident in near 30 days, and the calculation algorithm of the matching degree of the congestion and the alarm force can be based on
Figure BDA0002109680630000082
(i is more than or equal to 1 and less than or equal to 30) to obtain the average police force number of the congested road sections per kilometer in nearly 30 days, and the calculation algorithm of the illegal and police force matching degree can be based on
Figure BDA0002109680630000083
(1≤i≤180) And calculating to obtain the average police force number of each illegal event in nearly 180 days.
Step S204: calculating according to the average police strength within the first preset time and the average police strength within the second preset time to obtain the police strength matching degree; the accident and police strength matching degree calculation algorithm can calculate the accident and police strength matching degree according to the average police strength number of each unsettled accident at the current moment/the average police strength number of each unsettled accident at nearly 30 days; the congestion and police strength matching degree calculation algorithm can calculate the congestion and police strength matching degree according to the average police strength number of each congested road section at the current moment/the average police strength number of each congested road section in nearly 30 days; the law violation and police strength matching degree calculation algorithm can calculate the law violation and police strength matching degree according to the average police strength number of each violation event in 30 days or the average police strength number of each violation event in 180 days.
Step S205: and constructing a special subject library containing traffic management calculation results according to the police strength matching degree. Specifically, a thematic library including traffic management calculation results can be constructed according to the matching degree of accidents and police force, the matching degree of congestion and police force, and the matching degree of violation and police force. In addition, the thematic library containing the traffic management calculation results can also comprise calculation results of other aspects of calculation algorithms.
The police force putting rate can be calculated based on police member positioning data and duty scheduling data in traffic data, and specifically can be calculated according to the number of police members which are on line for more than 30 minutes and have displacement and have duty time for more than 4 hours/the total number of police members recorded in the system to obtain the police force putting rate; meanwhile, the number of the warning situations of every ten thousand motor vehicles is calculated to obtain the number of the warning situations of every ten thousand motor vehicles; the number of police officers distributed by every ten thousand persons can be calculated to obtain the number of the ten thousand persons; calculating the ratio of the number of police cars working normally to the number of police officers on duty to obtain the number of police average police cars; in addition, on the basis of the data of the commanding and scheduling system in the traffic data, the number of times of policing and law enforcement for each on-duty policeman in a single day is calculated, so that the per-capita law enforcement number can be obtained; and calculating the times of police and law enforcement within a single day of each police car based on the data of the command and dispatch system to obtain the times of law enforcement on average car.
The calculation algorithm of the four aspects in the science and technology law enforcement index can comprise the following steps: calculating the illegal parking rate of the main road according to the illegal parking number (the illegal vehicle data of the main road is divided by the kilometer number of the main road) of each kilometer of the main road; calculating according to the ratio of the illegal quantity of the key vehicles in the area range and in unit time to obtain the illegal rate of the key vehicles; calculating law enforcement occupation ratios of illegal snapshot according to the electric police and checkpoint data in the traffic data so as to obtain informatization law enforcement and check rate; and calculating the illegal rate of the motor vehicles on the main road according to the ratio of the motor vehicles on the main road and the motor vehicles on the main road junction in traffic.
As an optional implementation manner of the embodiment of the invention, when analyzing and calculating traffic data according to a traffic organization dimension analysis algorithm, analyzing and calculating can be performed according to two indexes of an equipment organization and a facility organization, wherein the equipment organization comprises calculation algorithms in three aspects of traffic information service induction rate, construction lane occupation measure rate, signal timing reasonable rate and the like; the facility organization comprises a calculation algorithm of three aspects of marking rate, anti-collision facility setting rate, warning facility setting rate and the like.
Specifically, in the calculation algorithm of the above aspects, firstly, a calculation process of a signal timing reasonableness rate calculation algorithm in the device organization index is described, as shown in fig. 3, the calculation algorithm includes the following steps:
step S301: and acquiring traffic conditions of different road sections according to the traffic data. Specifically, the traffic condition includes a traffic deadlock condition, a traffic overflow condition, a traffic imbalance condition, a lamp waiting condition, a trip delay condition, a green lamp emptying condition, and the like.
Step S302: and calculating according to the traffic condition to obtain a traffic deadlock factor, an overflow index factor, an unbalance index factor, an equal lamp frequency factor, a travel delay time factor and a green lamp free time factor. Specifically, when a traffic deadlock occurs, the value is 1; when the deadlock does not occur, the value is 0, and when the deadlock value is 0, the deadlock factor is 1; the deadlock factor is 0 when the deadlock value is 1. When traffic overflow occurs, the average value x of straight overflow indexes in all directions of the intersection can be obtained, and the value of x is [0, 1 ]]The overflow index factor y is-10 x +10, and y is [0, 10 ]]. When traffic imbalance occurs, the straight movement of each direction of the intersection can be acquiredThe average value of the unbalance indexes x, x is taken as [0, 1 ]]The imbalance index factor y is-10 x +10, y is taken to be [0, 10 ]]. When the equal lamp frequency factor is calculated, the average value x of the equal lamp frequency of straight going in each direction of the intersection can be obtained, x is taken as [0, plus infinity ], the equal lamp frequency factor y is-10 x +10, and y is taken as (10, plus infinity). When the travel delay time factor is calculated, the average value x of the straight travel delay time of each direction of the intersection can be obtained, wherein the value of x is [0, infinity ], and the travel delay time factor
Figure BDA0002109680630000101
y is (0, 10)]. When the green light idle time factor is calculated, the average value x of the idle time of the green light in each direction of the intersection can be obtained, wherein the value of x is [0, + ∞ ], and the idle time factor of the green light
Figure BDA0002109680630000102
y is (0, 10)]. The traffic deadlock factor, the overflow index factor, the imbalance index factor, the lamp waiting time factor, the travel delay time factor, and the green light idle time factor may be updated within a preset time, for example, may be updated every 5 min.
Step S303: and calculating a reasonable signal timing rate according to the traffic deadlock factor, the overflow index factor, the unbalance index factor, the equal lamp frequency factor, the travel delay time factor and the green lamp idle time factor. Specifically, the signal timing reasonable rate may be calculated according to a "deadlock factor (a + overflow index factor + b + imbalance index factor + c + and other lamp number factor + d + travel delay time factor + e + green lamp idle time factor)," where a, b, c, d, and e are weights, and may be assigned according to the actual value, for example, a is 0.3, b is 0.25, c is 0.2, d is 0.15, and e is 0.1.
Step S304: and constructing a subject database containing the calculation result of the traffic organization according to the signal timing reasonable rate. Specifically, after the signal timing reasonable rate is obtained through calculation, the traffic condition can be classified into four grades according to the calculation result of the signal timing reasonable rate, for example, the four grades can be classified according to the calculation result of the signal timing reasonable rate, wherein the four grades represent health in 8.0-10.0; scores of 6.0 to 7.9 indicate good; 4.0 to 5.9 represent normal; scores of 0 to 3.9 indicate poor results. In addition, the thematic library containing the calculation results of the traffic organization can also comprise the calculation results of other aspects of calculation algorithms.
The traffic information service induction rate can be calculated according to the following method: acquiring the number of the road sections subjected to traffic guidance release according to the guidance information release system log; acquiring the total number of congested and severely congested road sections in an area according to the road condition data of the Internet; and calculating to obtain the traffic information service induction rate according to the ratio of the number of the sections where the traffic induction is published to the total number of the sections which are congested and heavily congested. In addition, the construction lane occupation measure rate can be calculated based on the construction lane occupation and the basic data of the traffic control system, and specifically, the ratio of the number of the road sections on which the construction lane occupation and control information is executed to the number of the road sections on which the construction lane occupation and control information which should be executed at present is approved can be calculated, so that the construction lane occupation measure rate is obtained.
The calculation algorithm of three aspects in the facility organization index can comprise: and calculating the number of accident black points based on a six-in-one system, and analyzing the ratio of the set number of the anti-collision facilities to the number of the accident black points by combining the infrastructure data of the transportation equipment to calculate the set rate of the anti-collision facilities. The number of accident black spots is calculated based on a six-in-one system, and the ratio of the set number of the warning facilities to the number of the accident black spots is analyzed by combining infrastructure data of the traffic equipment to calculate the set rate of the warning facilities. In addition, the marking rate of the marker line can be calculated according to the following method: calculating according to the road mileage/all road mileage of the clear traffic sign to obtain the sign marking rate; calculating according to the road mileage/all road mileage of clear traffic markings to obtain marking rate; calculating the marking rate of the mark and the marked line according to the mean value of the marking rate and the marked line rate; wherein, the total road mileage is the total length of the selected road.
As an optional implementation manner of the embodiment of the invention, when analyzing and calculating traffic data according to an informatization engineering dimension analysis algorithm, the analysis and calculation can be performed according to two indexes of equipment construction and equipment operation and maintenance, wherein the equipment construction comprises calculation algorithms in five aspects of vehicle-average informatization parking space, guidance screen coverage rate, unit road length monitoring equipment coverage rate, parking space supply rate and the like; the equipment operation and maintenance comprises calculation algorithms of equipment health rate, operation and maintenance service closing rate and the like.
Specifically, in the calculation algorithms of the above aspects, firstly, the calculation process of the calculation algorithm in two aspects of the coverage rate of the guidance screen and the coverage rate of the monitoring device per unit road length in the device construction is explained, as shown in fig. 4, the calculation algorithm in the two aspects includes the following steps:
step S401: acquiring the quantity of various traffic equipment in different areas according to the traffic data; specifically, the number of traffic guidance screens and the number of monitoring facilities in different areas can be obtained.
Step S402: and calculating the coverage rate of the traffic equipment according to the number of various traffic equipment in different areas. Specifically, the coverage rate of the guidance screen can be calculated by taking the number of traffic guidance screens in different areas as a basis, and calculating the ratio of the coverage area of the traffic guidance screen to the total area. The coverage rate of the monitoring equipment for the unit road length can be calculated according to the obtained number of the monitoring facilities, and the coverage ratio of the monitoring facilities in the area in the unit road length is calculated.
Step S403: and constructing a special question bank containing the calculation result of the informatization project according to the coverage rate. Specifically, a special question bank containing the calculation results of the informatization engineering can be constructed according to the coverage rate of the induction screen and the coverage rate of the unit road length monitoring equipment. In addition, the thematic library containing the calculation results of the informatization engineering can also comprise the calculation results of other aspects of calculation algorithms. For example, the number of the vehicle-averaged informationized parking spaces can be calculated based on the driving management data and the parking lot data, so that the vehicle-averaged informationized parking spaces are obtained; and obtaining basic data of the parking lot according to road planning basic data of a traffic administration, and calculating to obtain the parking space supply rate according to the total number of parking spaces in the selected jurisdiction/the automobile holding amount in the selected jurisdiction.
The calculation algorithm of the two aspects in the equipment operation and maintenance index can comprise the following steps: calculating the health degree of the traffic management facility equipment based on the operation and maintenance data so as to obtain the health rate of the equipment; and calculating the ratio of the number of the operation and maintenance service solving events to the total number of the events based on the operation and maintenance data so as to obtain the closing rate of the operation and maintenance service.
As an optional implementation manner of the embodiment of the invention, when analyzing and calculating traffic data according to a road network morphological dimension analysis algorithm, the analysis and calculation can be performed according to four indexes of an intersection, a road section, an area and a whole dimension, wherein the intersection comprises a calculation algorithm for the number of times of equal lights of an entrance road, the unbalance duration of the intersection, the loss duration of green lights and the like; the road section comprises a calculation algorithm of key road section average travel speed, a normal block point road section, average mileage reachable in one hour, trunk line one-way average unbalance duration and the like; the region comprises calculation algorithms in four aspects of average travel speed, road network density, congestion mileage proportion, congestion duration and the like of a key region; the full dimension comprises calculation algorithms of the abnormal congestion duration, the congestion delay and the like.
Specifically, in the calculation algorithms in the several aspects, firstly, a calculation process of the calculation algorithm in two aspects of the intersection imbalance duration and the trunk line one-way equipartition imbalance duration in the intersection index is described, as shown in fig. 5, the calculation algorithm in the two aspects includes the following steps:
step S501: acquiring the queuing length of vehicles in each direction of the intersection according to the internet data; specifically, the vehicle queuing length of each inlet or intersection direction on the trunk line of the intersection can be obtained according to the internet data, and each inlet direction D is usediIs recorded as Li(i＝1，2，3，…，n)。
Step S502: acquiring a first intersection with the longest queuing length and a second intersection with the shortest queuing length according to the queuing length; specifically, assuming that there are n entrance directions, the direction D with the longest queuing length can be selected from the n directionspThen L ismax＝max1≤i≤n(Li) Selecting the direction D with the minimum queuing length in n directions simultaneouslyqThen L ismin＝min1≤i≤n(Li)，LiIndicating the queue length of each intersection.
Step S503: calculating an imbalance index according to the queuing lengths of the first intersection and the second intersection; in particular, the imbalance index may be based on
Figure BDA0002109680630000141
And calculating, and judging that the intersection is unbalanced when the unbalance index is greater than 0.5.
Step S504: calculating the unbalance duration of each intersection according to the unbalance index; specifically, when the intersection or the trunk is determined to be out of balance according to the out-of-balance index, the current time t may be recordedmCalculating unbalance index at an interval of 5min, and recording time t when judging that the intersection is not in the unbalance statenAccording to tmAnd tnAnd calculating the time difference to obtain the unbalance time length, wherein the unbalance time length is the unbalance time length of the intersection or the unidirectional average unbalance time length of the trunk line.
Step S505: and constructing a special question bank containing the road network form calculation result according to the unbalance duration. Specifically, a special question bank containing the road network form calculation result can be constructed according to the calculated intersection unbalance duration and the trunk one-way average unbalance duration. In addition, the topic library including the road network morphology calculation result may further include calculation results of other aspect calculation algorithms.
The number of equal-average lamps of the entrance way can be calculated based on the bayonet data and the timing scheme data of the signal control platform, and specifically the number of equal-average lamps of the entrance way can be calculated according to the ratio of the driving delay time to the period of the traffic signal lamp. Furthermore, the green light loss time can also be calculated according to the following method: acquiring the number of the inlet directions of the current intersection based on regional road network data in the traffic data; calculating green initial loss time for each inlet direction in sequence, specifically, calculating the difference between the first vehicle time passing through the gate and the green light starting time after the green light is started; summing the green initial loss time of each inlet direction to obtain the green initial loss time of the intersection; sequentially calculating the idle time of the green light for each entrance direction, specifically the difference value between the red light turning-on time and the last vehicle time passing through the gate and the red light turning-on time; summing the idle discharge time of the green lights in each entrance direction to obtain the idle discharge time of the green lights at the intersection; and summing the idle time of the green light of the intersection and the green initial loss time of the intersection to obtain the green light loss time of the intersection.
The calculation algorithm of three aspects in the road section index can comprise: and acquiring travel time required by vehicle running from the starting point to the end point of the key road section based on the bayonet vehicle passing data, and averaging the travel time of all vehicles to obtain the average travel speed of the key road section. And calculating to obtain the average mileage which can reach one hour based on the internet data and the road network basic data. In addition, the frequent point road section can be calculated according to the following method: determining a daily congestion road according to a preset threshold value (for example, the daily congestion road may be a road whose congestion time exceeds 1 hour in 4 days within 5 days on average); and clustering daily congested roads according to a condensation hierarchical clustering method to obtain the number of sections of the frequent congestion points.
The calculation algorithm of three aspects in the regional indicator can comprise: and calculating the average value of the travel speeds of all vehicles on each road section in the area based on the checkpoint data to obtain the average travel speed of the key area. And calculating to obtain the road network density based on the ratio of the total length of the roads in the district to the total area of the district. And calculating the traffic congestion index of the road section based on the checkpoint data and the road network data, wherein the time length of the traffic congestion index in the range of 1.8-2.2 is the congestion duration. In addition, the frequent point road section can be calculated according to the following method: acquiring the total length of a regional road network according to basic map data; judging the total mileage of traffic jam according to the internet data and the checkpoint data; and obtaining the congestion mileage proportion according to the ratio of the total congestion mileage to the total length of the regional road network.
The calculation algorithm of two aspects in the full-dimensional index can comprise: an abnormal congestion duration calculation algorithm and a congestion delay calculation algorithm.
The abnormal congestion duration calculation algorithm comprises the following steps: calculating a traffic congestion index of an evaluation road section based on the gate data and the regional road network data; obtaining the checkpoint data of an evaluation road section in a certain historical time period (one day/week/month), and calculating the historical average speed v, the standard deviation sigma and the mean value mu of the evaluation road section; obtaining the average speed of the vehicle within 5 minutes of the current evaluation road section
Figure BDA0002109680630000151
Judging whether the average velocity is [ mu-3 sigma, [ mu-3 sigma ]]Within the range if notIf the current road section is within the range, the next step is carried out, otherwise, the current road section is judged not to belong to the abnormal congestion road section; acquiring traffic event data of a three-in-one system and a traffic event detector, judging whether a traffic event condition occurs on a current road section, if so, judging that the current road section belongs to an abnormal congestion road section, and recording the current time as t1(ii) a Judging the current traffic jam condition of the current road section at intervals of 5 minutes, and recording the current time as t when the current road section does not belong to the abnormal jam state any more2(ii) a The abnormal congestion time is t2-t1。
The congestion delay calculation algorithm comprises the following steps: acquiring gate data, and calculating the actual travel time of the vehicle passing through the road; determining the free flow speed of the road section according to the road network basic data; determining the free stream journey time of the road according to the ratio of the length of the road to the speed of the free stream; and determining congestion delay according to the difference value between the actual travel time and the free flow speed.
As an optional implementation manner of the embodiment of the present invention, when analyzing and calculating traffic data according to a traffic flow operation dimension analysis algorithm, analysis and calculation may be performed according to five indexes, such as a travel speed, a traffic flow density, a traffic flow same-ratio growth rate, a month unevenness coefficient, a travel structure analysis, and the like, where the occurrence structure analysis includes four aspects of calculation algorithms, such as a peak/flat time interval flow analysis, a vehicle type flow analysis, a travel migration analysis, and a regional driving direction analysis.
Specifically, among the above-mentioned several indexes, the calculation process of three indexes, i.e., travel speed, traffic flow density, and traffic flow proportional increase rate, is first described, and as shown in fig. 6, the calculation process of these three indexes includes the following steps:
step S601: acquiring traffic flow of each road section according to the traffic data;
step S602: calculating the average speed of the vehicle, the traffic flow density and the traffic flow growth rate according to the vehicle flow;
step S603: and constructing a special question bank containing the calculation result of the flow operation according to the average speed of the vehicles, the traffic flow density and the traffic flow increase rate.
Specifically, the average travel speed of the vehicle in transit in 10 minutes at the present time can be calculated based on the gate data to obtain the vehicle average speed (travel speed); the traffic flow density is the density of vehicles on a road section with unit length; the traffic flow increase rate (traffic flow increase rate) is the ratio of the traffic flow at the current moment to the traffic flow at the same moment in the last year.
In addition, the thematic library containing the calculation results of the flow operation can also comprise the calculation results of other aspects of calculation algorithms. If the monthly unevenness coefficient is the ratio of the annual average daily traffic volume to the monthly average daily traffic volume; calculating the running quantity of the vehicles travelling in different periods of the region based on the checkpoint data to obtain a calculation result of flow analysis in a peak period/peak-off period; analyzing the service property distribution condition of the traveling vehicle based on the six-in-one system and the checkpoint data to obtain a calculation result of vehicle type flow analysis; analyzing the departure place and the destination of each vehicle through the checkpoint data, recording the departure place and the destination as one-time migration between two places and cities, summarizing and obtaining the vehicle traffic conditions of the vehicles between all jurisdictions to obtain a calculation result of travel migration analysis; and calculating the number of vehicles entering and exiting the region based on the gate data to obtain a calculation result of region driving direction analysis.
As an optional implementation manner of the embodiment of the invention, when analyzing and calculating the traffic data according to the accident prevention dimension analysis algorithm, the analysis and calculation can be performed according to five indexes, such as the number of accidents of ten thousands of cars, the number of accidents, the number of casualties, economic loss, the number of key vehicles and the like.
Specifically, among the above indexes, the calculation process of two indexes, i.e., the number of accidents of ten thousands of cars and the number of accidents at the beginning, is first described, and as shown in fig. 7, the calculation process of the two indexes includes the following steps:
step S701: acquiring accident starting numbers according to the traffic data; specifically, the number of slight, general, great and extra accidents and the total number of accidents can be respectively obtained from the six-in-one system.
Step S702: calculating to obtain the number of the ten-thousand-vehicle accidents according to the number of the accidents; wherein, the number of the ten-thousand-vehicle accidents is 10000 multiplied by the accumulated weighted number of the accidents/(the total number of the motor vehicles is the motor vehicle activity), and the accident weight can be distributed by adopting the following weight: the accident of the big accident/4 is a major accident/3 is a common accident/3 is a minor accident/1.
Step S703: and constructing a special subject library containing accident prevention calculation results according to the number of the accidents of the ten-thousand cars. In addition, the topic library containing the accident prevention calculation results can also comprise the calculation results of other aspects of calculation algorithms. For example, casualties are those of minor, general, major accidents taken from the six-in-one system, and the total number of deaths for all accidents; the economic loss is the economic loss of a slight, general, heavy and extra-large accident obtained from a six-in-one system and the total economic loss of all accidents; the number of key vehicles is the number of two passengers and one dangerous goods, school buses and rural minibuses obtained from the six-in-one system, and the total number of all key vehicles.
As an optional implementation manner of the embodiment of the invention, when analyzing and calculating the traffic data according to the violation early warning dimension analysis algorithm, the traffic data can be analyzed and calculated according to three indexes, namely the number of illegal starting points, the number of illegal vehicle violations, the number of illegal high-speed distribution points, and the like.
Specifically, among the above indexes, firstly, it is explained that the calculation process of the illegal high-incidence point index is shown in fig. 8, and the calculation process includes the following steps:
step S801: acquiring an illegal high-speed road section according to the traffic data, wherein the illegal high-speed road section forms a point section sample set; wherein, a single illegal high-speed road section is used as a sample point X of the point section sample setiAll illegal high-speed road sections form a point section sample set, and the specific position of a single sample point is the coordinate recorded at the midpoint of the illegal high-speed road section on the map.
Step S802: calculating the distance between the sample points of each illegal high-speed road section in the point section sample set according to the Dijkstra algorithm; in particular, when calculating the distance dN between two sample points p, q according to the dijkstra algorithm, it is possible to use
Figure BDA0002109680630000181
Calculation of viAnd vjThe vertex representing the shortest path traversed between p and q.
Step S803: classifying the point sample set according to an agglomeration hierarchical clustering algorithm to obtain a classification result; specifically, the segment sample set may be classified according to a first preset threshold, for example, one sample point may be randomly selected as a reference point in the segment sample set, sample points whose distance from the reference point is within a first preset threshold range are classified into one class, and other sample points are continuously classified according to the rule, that is, one sample point is continuously randomly selected as the reference point in other sample points, and sample points whose distance from the reference point is within the first preset threshold range are searched for and classified into one class again until all sample points in the segment sample set are classified, so as to obtain a classification result of the sample set. Optionally, the first preset threshold may be 300 meters, or may be other values, which is not limited in this application.
Step S804: judging the point/segment attribute of the sample set according to the distance between the sample points in the sample set of each point segment in the classification result; specifically, all sample sets in the classification result may be respectively determined, one of the sample sets is selected, the type of the sample set is determined according to a second preset threshold and a third preset threshold, for example, the second preset threshold is 500 meters, the third preset threshold is 1000 meters, when the maximum distance between all sample points in the sample set is less than 500 meters, the sample set is determined to be a "point", when the maximum distance between sample points in the sample set is greater than 500 meters and less than 1000 meters, the sample set is determined to be a "segment", and the number of the "point" or "segment" sample sets may be calculated after the determination of all sample sets is completed.
Step S805: and constructing a special subject library containing the illegal early warning calculation result according to the classification result containing the number of the different types of sample sets. In addition, the special subject library containing the illegal warning calculation result can also comprise the calculation result of other calculation algorithms. For example, the starting number of the violation with different score values and the total starting number of the violation can be respectively obtained from the six-in-one system as the starting number of the violation; the number of illegal vehicle can be obtained by 10000 times the number of illegal vehicle/(total number of motor vehicle and motor vehicle activity).
According to the traffic data analysis method provided by the embodiment of the invention, according to the acquired traffic data of different types and different sources, a traffic management dimension analysis algorithm, a traffic organization dimension analysis algorithm, an information engineering dimension analysis algorithm, a road network form dimension analysis algorithm, a flow operation dimension analysis algorithm, an accident prevention dimension analysis algorithm and an illegal early warning dimension analysis algorithm are specifically adopted to divide specific calculation indexes of each dimension, so that the standardization, normalization and abnormal value calculation of the traffic data are realized. Meanwhile, calculation results of different dimensions can be pushed to related users, and decision support is provided for traffic management personnel to integrally master and analyze road traffic health situations.
An embodiment of the present invention further provides a traffic data analysis device, as shown in fig. 9, the traffic data analysis device includes:
the data acquisition module 1 is used for acquiring traffic data, wherein the traffic data comprises traffic private network data, internet data and public security private network data; for details, refer to the related description of step S101 in the above method embodiment.
The subject database construction module 2 is used for carrying out classification calculation on the traffic data according to the blood relationship management algorithm and constructing a subject database containing different types of traffic data; for details, refer to the related description of step S102 in the above method embodiment.
The special topic database construction module 3 is used for analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm and constructing a special topic database containing different-dimensional traffic data; for details, refer to the related description of step S103 in the above method embodiment.
And the analysis result forming module 4 is used for forming an analysis result according to the subject database and the thematic database. For details, refer to the related description of step S104 in the above method embodiment.
The traffic data analysis device provided by the embodiment of the invention makes full use of the existing data resources, such as traffic private network data, internet data and public security private network data, and fuses and utilizes different sources and different types of information, thereby improving the accuracy and reliability of data acquisition. Meanwhile, original traffic data are analyzed and calculated by means of a blood margin management algorithm and a multi-dimensional analysis algorithm, and finally a theme and special topic database is formed in a gathering mode, so that a data basis is provided for urban traffic development decision making and road traffic information service. By implementing the invention, data support can be provided for traffic management, the data use efficiency is improved, and the informatization overall benefit is fully exerted.
An embodiment of the present invention further provides a traffic data analysis device, as shown in fig. 10, the traffic data analysis device may include a processor 51 and a memory 52, where the processor 51 and the memory 52 may be connected by a bus or in another manner, and fig. 10 illustrates an example of connection by a bus.
The processor 51 may be a Central Processing Unit (CPU). The Processor 51 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 52 is a non-transitory computer-readable storage medium, and can be used for storing non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the traffic data analysis device in the embodiment of the present invention (for example, the data acquisition module 1, the theme library construction module 2, the theme library construction module 3, and the analysis result formation module 4 shown in fig. 9). The processor 51 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 52, that is, implements the traffic data analysis method in the above method embodiment.
The memory 52 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 51, and the like. Further, the memory 52 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 52 may optionally include memory located remotely from the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 52 and, when executed by the processor 51, perform the traffic data analysis method in the embodiment shown in fig. 1-8.
The details of the traffic data analysis device may be understood by referring to the corresponding descriptions and effects in the embodiments shown in fig. 1 to fig. 8, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims (11)

1. A traffic data analysis method, comprising:
acquiring traffic data, wherein the traffic data comprises special traffic network data, Internet data and public security special network data;
classifying and calculating the traffic data according to a blood relationship management algorithm, and constructing a theme library containing different types of traffic data;
analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm, and constructing a special question bank containing different-dimensional traffic data;
generating a traffic data analysis result according to the subject database and the special subject database;
analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm, and constructing a special question bank containing different-dimensional traffic data, wherein the method comprises the following steps:
analyzing and calculating the traffic data according to a traffic management dimension analysis algorithm, and constructing a thematic library containing traffic management calculation results, wherein the traffic management dimension analysis algorithm comprises the step of calculating the police force matching degree according to the police force number and the traffic state;
analyzing and calculating the traffic data according to a traffic organization dimension analysis algorithm to construct a special question bank containing a traffic organization calculation result, wherein the traffic organization dimension analysis algorithm comprises a signal timing reasonable rate calculated according to a traffic deadlock factor, an overflow index factor, an unbalance index factor, an equal light frequency factor, a travel delay time factor and a green light free time factor;
analyzing and calculating the traffic data according to an information engineering dimension analysis algorithm, and constructing a thematic library containing information engineering calculation results, wherein the information engineering dimension analysis algorithm comprises the step of calculating the coverage rate of traffic equipment;
analyzing and calculating the traffic data according to a road network form dimension analysis algorithm, and constructing a thematic library containing road network form calculation results, wherein the road network form dimension analysis algorithm comprises the step of calculating the unbalance duration of each intersection according to the queuing length and the unbalance index;
analyzing and calculating the traffic data according to a flow operation dimension analysis algorithm, and constructing a special subject library containing flow operation calculation results, wherein the flow operation dimension analysis algorithm comprises the steps of calculating the average speed of vehicles, the density of traffic flow and the growth rate of the traffic flow;
analyzing and calculating the traffic data according to an accident prevention dimension analysis algorithm, and constructing a special subject library containing accident prevention calculation results, wherein the accident prevention dimension analysis algorithm comprises the step of calculating the number of thousands of accidents;
and analyzing and calculating the traffic data according to an illegal early warning dimension analysis algorithm, and constructing a special question bank containing illegal early warning calculation, wherein the illegal early warning dimension analysis algorithm comprises the steps of calculating classification results and quantity of illegal high-incidence points and illegal high-incidence road sections.
2. The traffic data analysis method according to claim 1, wherein analyzing and calculating the traffic data according to a traffic management dimension analysis algorithm, and constructing a thematic library containing traffic management calculation results comprises:
acquiring the police strength number and the traffic condition number within a first preset time and the police strength number and the traffic condition number within a second preset time according to the traffic data;
calculating to obtain an average police strength number within a first preset time according to the police strength number within the first preset time and the traffic condition number;
calculating to obtain an average police strength number in a second preset time according to the police strength number and the traffic condition number in the second preset time;
calculating the police strength matching degree according to the average police strength number in the first preset time and the average police strength number in the second preset time;
and constructing a special subject library containing traffic management calculation results according to the police strength matching degree.
3. The traffic data analysis method according to claim 1, wherein the analyzing and calculating the traffic data according to a traffic organization dimension analysis algorithm, and constructing a thematic library containing traffic organization calculation results comprises:
acquiring traffic conditions of different road sections according to the traffic data;
calculating according to the traffic condition to obtain a traffic deadlock factor, an overflow index factor, an unbalance index factor, an equal lamp frequency factor, a travel delay time factor and a green lamp idle time factor;
calculating a reasonable signal timing rate according to the traffic deadlock factor, the overflow index factor, the unbalance index factor, the equal lamp frequency factor, the travel delay time factor and the green lamp idle time factor;
and constructing a special question bank containing a traffic organization calculation result according to the signal timing reasonable rate.
4. The traffic data analysis method according to claim 1, wherein analyzing and calculating the traffic data according to an informatization engineering dimension analysis algorithm, and constructing a thematic library containing the calculation results of the informatization engineering, comprises:
acquiring the quantity of various types of traffic equipment in different areas according to the traffic data;
calculating the coverage rate of the traffic equipment according to the number of various traffic equipment in different areas;
and constructing a subject library containing the calculation result of the informatization project according to the coverage rate.
5. The traffic data analysis method according to claim 1, wherein the analyzing and calculating the traffic data according to a road network form dimension analysis algorithm to construct a topic library including road network form calculation results comprises:
acquiring the queuing length of vehicles in each direction of the intersection according to the internet data;
acquiring a first intersection with the longest queuing length and a second intersection with the shortest queuing length according to the queuing length;
calculating an imbalance index according to the queuing lengths of the first intersection and the second intersection;
calculating the unbalance duration of each intersection according to the unbalance index;
and constructing a special question bank containing the road network form calculation result according to the unbalance duration.
6. The traffic data analysis method according to claim 1, wherein the analyzing and calculating the traffic data according to a traffic operation dimension analysis algorithm, and constructing a thematic library containing traffic operation calculation results comprises:
acquiring traffic flow of each road section according to the traffic data;
calculating the average speed of the vehicle, the traffic flow density and the traffic flow growth rate according to the vehicle flow;
and constructing a special question bank containing a flow operation calculation result according to the average speed of the vehicles, the traffic flow density and the traffic flow increase rate.
7. The traffic data analysis method according to claim 1, wherein the analyzing and calculating the traffic data according to an accident prevention dimension analysis algorithm, and constructing a thematic library containing accident prevention calculation results comprises:
acquiring accident starting number according to the traffic data;
calculating to obtain the number of the ten-thousand-vehicle accidents according to the accident starting number;
and constructing a special subject library containing accident prevention calculation results according to the number of the ten-thousand-vehicle accidents.
8. The traffic data analysis method according to claim 1, wherein analyzing and calculating the traffic data according to an illegal early warning dimension analysis algorithm, and constructing a special topic library containing an illegal early warning calculation result comprises:
acquiring an illegal high-speed road section according to the traffic data, wherein the illegal high-speed road section forms a point section sample set;
calculating the distance between the sample points of each illegal high-speed road section in the point section sample set according to the Dijkstra algorithm;
classifying the point segment sample set according to an agglomeration hierarchical clustering algorithm to obtain a classification result;
calculating the number of different types of sample sets according to the distance between the sample points in the sample sets of each point segment in the classification result;
and constructing a special subject library containing the illegal early warning calculation result according to the classification result containing the number of the different types of sample sets.
9. A traffic data analysis apparatus, comprising:
the data acquisition module is used for acquiring traffic data, and the traffic data comprises special traffic network data, Internet data and public security special network data;
the subject database construction module is used for carrying out classification calculation on the traffic data according to a blood relationship management algorithm and constructing a subject database containing different types of traffic data;
the special question bank building module is used for analyzing and calculating the traffic data according to a multi-dimensional analysis algorithm and building a special question bank containing different-dimensional traffic data;
and the analysis result forming module is used for forming an analysis result according to the subject database and the thematic database.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the traffic data analysis method according to any one of claims 1 to 8.
11. A traffic data analysis device, comprising: a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the traffic data analysis method of any of claims 1-8.
CN201910570898.9A 2019-06-27 2019-06-27 Traffic data analysis method, device, storage medium and equipment Active CN110363985B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910570898.9A CN110363985B (en) 2019-06-27 2019-06-27 Traffic data analysis method, device, storage medium and equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910570898.9A CN110363985B (en) 2019-06-27 2019-06-27 Traffic data analysis method, device, storage medium and equipment

Publications (2)

Publication Number Publication Date
CN110363985A CN110363985A (en) 2019-10-22
CN110363985B true CN110363985B (en) 2020-12-29

Family

ID=68215934

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910570898.9A Active CN110363985B (en) 2019-06-27 2019-06-27 Traffic data analysis method, device, storage medium and equipment

Country Status (1)

Country Link
CN (1) CN110363985B (en)

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111540204B (en) * 2020-05-08 2021-05-11 青岛海信网络科技股份有限公司 Intersection problem diagnosis-oriented traffic running state assessment method and device
CN111899506B (en) * 2020-06-12 2023-04-11 上海应用技术大学 Traffic overflow judging method based on electronic police data
CN111932877B (en) * 2020-08-07 2022-06-17 公安部交通管理科学研究所 Road section traffic abnormal state identification method based on license plate data
CN111986482B (en) * 2020-08-24 2021-10-01 安徽科力信息产业有限责任公司 Urban road intersection control efficiency evaluation method, system and storage medium
CN112988783A (en) * 2021-03-12 2021-06-18 李涛 Public opinion occurrence time sequence analysis method based on multidimensional data model
CN113870564B (en) * 2021-10-26 2022-09-06 安徽百诚慧通科技股份有限公司 Traffic jam classification method and system for closed road section, electronic device and storage medium
CN114724355B (en) * 2021-12-23 2023-03-21 韶关学院 Road traffic intelligent analysis device and analysis system based on internet
CN116129640B (en) * 2023-01-09 2024-03-15 浪潮智慧科技有限公司 Data management method and device for road data

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5917280B2 (en) * 2012-05-07 2016-05-11 住友電気工業株式会社 Traffic data analysis apparatus and computer program
CN106354887A (en) * 2016-10-21 2017-01-25 上海延华智能科技（集团）股份有限公司 Operation method and system thereof for interconnection sharing of urban transportation information
CN107274667A (en) * 2017-08-14 2017-10-20 公安部交通管理科学研究所 Urban transportation intelligence managing and control system networking joint control framework and implementation
CN109493620B (en) * 2017-09-11 2022-05-24 阿里巴巴集团控股有限公司 Traffic road condition analysis system, method and device
CN108010317A (en) * 2017-11-29 2018-05-08 天津易华录信息技术有限公司 A kind of traffic administration big data system
CN107945517A (en) * 2017-12-29 2018-04-20 迈锐数据（北京）有限公司 A kind of traffic data processing apparatus
CN108108859A (en) * 2018-01-29 2018-06-01 北京易华录信息技术股份有限公司 A kind of traffic administration duties optimization method based on big data analysis
CN108648451B (en) * 2018-05-15 2021-07-30 北京数行健科技有限公司 Traffic data processing equipment and traffic situation management system
CN108806258A (en) * 2018-07-09 2018-11-13 北方工业大学 Urban road intersection operation evaluation index system based on floating car data
CN109189760A (en) * 2018-08-16 2019-01-11 北京易华录信息技术股份有限公司 A kind of building of traffic element portrait and analysis method based on big data technology
CN109345823B (en) * 2018-09-19 2021-11-05 连云港杰瑞电子有限公司 Intelligent traffic data analysis system based on big data

Also Published As

Publication number Publication date
CN110363985A (en) 2019-10-22

Similar Documents

Publication Publication Date Title
CN110363985B (en) Traffic data analysis method, device, storage medium and equipment
Ericsson et al. Optimizing route choice for lowest fuel consumption–potential effects of a new driver support tool
CN104200649B (en) Based on the rush-hour path resource dispatching distribution System and method for of pre-first to file
CN108346288B (en) Road section operation state early warning method and device and electronic equipment
CN111028505B (en) Traffic jam treatment method and device
CN106504522A (en) A kind of wisdom traffic management system based on big data
Weijermars Analysis of urban traffic patterns using clustering
CN107240264B (en) A kind of non-effective driving trace recognition methods of vehicle and urban road facility planing method
CN113870564B (en) Traffic jam classification method and system for closed road section, electronic device and storage medium
CN113868492A (en) Visual OD (origin-destination) analysis method based on electric police and checkpoint data and application
Chepuri et al. Travel time reliability analysis on selected bus route of mysore using GPS data
Pindarwati et al. Measuring performance level of smart transportation system in big cities of Indonesia comparative study: Jakarta, Bandung, Medan, Surabaya, and Makassar
CN113870559B (en) Traffic flow calculation method based on big data Internet of vehicles
CN116258342A (en) Parking management service system
Chepuri et al. Examining performance of an urban corridor using microscopic traffic simulation model under mixed traffic environment in India
CN114519500A (en) Method, device and equipment for determining road safety risk level and storage medium
CN114627643A (en) Expressway accident risk prediction method, device, equipment and medium
Schneider et al. Traveller information service based on real-time toll data in Austria
Ou et al. Big-data-based analysis on the relationship between taxi travelling patterns and taxi drivers' incomes
Liu Bi-level optimization algorithm for dynamic reversible lane control based on short-term traffic flow prediction
Wundersitz et al. Identifying and improving exposure measures
Kaushik et al. Analysis on Different Roads of Meerut City
Chen et al. Comprehensive evaluation of operational efficiency of intersections in arterial considering pedestrians yield rule
CN113140107B (en) Traffic information processing method and device, electronic equipment and computer storage medium
Reddy et al. Measurement of delay using travel time reliability statistics in an urban outer corridor

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
GR01 Patent grant
GR01 Patent grant