CN116682270A - Traffic flow monitoring method and system based on big data - Google Patents
Traffic flow monitoring method and system based on big data Download PDFInfo
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- CN116682270A CN116682270A CN202211438695.2A CN202211438695A CN116682270A CN 116682270 A CN116682270 A CN 116682270A CN 202211438695 A CN202211438695 A CN 202211438695A CN 116682270 A CN116682270 A CN 116682270A
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- 230000010354 integration Effects 0.000 claims description 2
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Classifications
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- 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
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The application is applicable to the technical field of traffic, and provides a traffic flow monitoring method and system based on big data, wherein the method comprises the following steps: receiving traffic flow data uploaded by flow monitoring equipment of each traffic road section, wherein the traffic flow data comprises time information and corresponding traffic road sections; classifying traffic flow data of each traffic road section according to the incoming main road section to obtain a plurality of road section categories, wherein each road section category corresponds to one incoming main road section; carrying out predictive analysis on traffic flow data in each road section category to generate main road section predictive information, wherein the main road section predictive information comprises an incoming main road section, a congestion level and a congestion time; and generating coordination information according to the main road section prediction information, wherein the coordination information comprises coordination places and coordination time, and each coordination place corresponds to a coordination person number. Therefore, related departments can arrange work in advance and conduct coordinated deployment so as to reduce inconvenience brought to owners and passengers by road blockage.
Description
Technical Field
The application relates to the technical field of traffic, in particular to a traffic flow monitoring method and system based on big data.
Background
At present, the traffic flow of roads in many cities is large, and traffic congestion and blocking phenomena occur frequently. In order to acquire traffic flow data, flow monitoring devices are installed on a plurality of road sections in a city, and can acquire real-time road traffic flow to remind passengers and drivers of current road congestion, but at present, the traffic flow monitoring devices are not well utilized for carrying out advanced prediction of road blocking, so that related departments are inconvenient to deploy in advance. Accordingly, there is a need to provide a traffic flow monitoring method and system based on big data, which aims to solve the above problems.
Disclosure of Invention
Aiming at the defects existing in the prior art, the application aims to provide a traffic flow monitoring method and system based on big data so as to solve the problems existing in the background art.
The application is realized in such a way that a traffic flow monitoring method based on big data comprises the following steps:
receiving traffic flow data uploaded by flow monitoring equipment of each traffic road section, wherein the traffic flow data comprises time information and corresponding traffic road sections;
classifying traffic flow data of each traffic road section according to the incoming main road section to obtain a plurality of road section categories, wherein each road section category corresponds to one incoming main road section;
carrying out predictive analysis on traffic flow data in each road section category to generate main road section predictive information, wherein the main road section predictive information comprises an incoming main road section, a congestion level and a congestion time;
and generating coordination information according to the main road section prediction information, wherein the coordination information comprises coordination places and coordination time, and each coordination place corresponds to a coordination person number.
As a further scheme of the application: the step of classifying the traffic flow data of each traffic road section according to the converged main road section to obtain a plurality of road section categories specifically comprises the following steps:
inputting all traffic flow data into a road section classification library, wherein the road section classification library comprises a plurality of incoming main road sections, each incoming main road section corresponds to a plurality of traffic road sections, and each traffic road section corresponds to an incoming proportion value;
and automatically outputting all the traffic flow data of each incoming main road section, classifying the traffic flow data of each traffic road section according to the incoming main road section, and marking an incoming proportion value on each traffic flow data.
As a further scheme of the application: the step of performing predictive analysis on the traffic flow data in each road section category to generate the main road section predictive information specifically includes:
determining the number of vehicles and the moving speed of the corresponding traffic road section according to the traffic flow data;
determining a time period when the vehicle of the traffic road section reaches the main road section according to the moving speed, and marking the time period on the vehicle flow data;
classifying the traffic flow data according to time periods, wherein the time periods of the traffic flow data in each class are overlapped;
and calculating the sum of the traffic flow data in each class, and generating the main road section prediction information.
As a further scheme of the application: the step of calculating the sum of the traffic flow data in each class and generating the prediction information of the main road section specifically comprises the following steps:
calculating the sum of the traffic flow data in each class, wherein the sum of the traffic flow data is equal to the sum of the traffic flow data multiplied by the corresponding import proportion value and then accumulated;
taking the maximum sum of the traffic flow data as the predicted traffic flow in the road section category, wherein the predicted traffic flow corresponds to a time period, and the time period is the blocking time;
and obtaining the blockage grade according to the predicted traffic flow, and integrating the blockage grade, the blockage time and the imported main road section to generate main road section prediction information.
As a further scheme of the application: the step of generating coordination information according to the main road section prediction information specifically includes:
determining a coordination place and a coordination time according to the incoming main road section and the blocking time in the main road section prediction information;
determining the number of coordinated people according to the congestion level in the main road section prediction information;
and integrating the coordination place, the coordination time and the coordination number of people to generate coordination information.
Another object of the present application is to provide a traffic flow monitoring system based on big data, the system comprising:
the traffic flow data receiving module is used for receiving traffic flow data uploaded by the flow monitoring equipment of each traffic road section, and the traffic flow data comprises time information and corresponding traffic road sections;
the traffic flow data classification module is used for classifying traffic flow data of each traffic road section according to the converging main road section to obtain a plurality of road section categories, and each road section category corresponds to one converging main road section;
the system comprises a main road section prediction information module, a traffic flow information analysis module and a traffic flow information analysis module, wherein the main road section prediction information module is used for performing prediction analysis on traffic flow data in each road section category to generate main road section prediction information, and the main road section prediction information comprises an incoming main road section, a congestion level and a congestion time;
and the coordination information module is used for generating coordination information according to the main road section prediction information, wherein the coordination information comprises coordination places and coordination time, and each coordination place corresponds to a coordination number of people.
As a further scheme of the application: the traffic flow data classification module comprises:
the traffic flow data input unit is used for inputting all traffic flow data into the road section classification library, wherein the road section classification library comprises a plurality of incoming main road sections, each incoming main road section corresponds to a plurality of traffic road sections, and each traffic road section corresponds to an incoming proportion value;
and the traffic flow data classification unit is used for automatically outputting all traffic flow data of each converging main road section, classifying the traffic flow data of each traffic road section according to the converging main road section, and marking the converging proportion value on each traffic flow data.
As a further scheme of the application: the main road section prediction information module includes:
the information determining unit is used for determining the number of vehicles and the moving speed of the corresponding traffic road section according to the traffic flow data;
a time period determining unit for determining a time period when the vehicle of the traffic road section arrives at the junction main road section according to the moving speed, and marking the time period on the vehicle flow data;
the secondary classification unit is used for classifying the traffic flow data according to time periods, and the time periods of the traffic flow data in each class are overlapped;
and the main road section prediction information unit is used for calculating the sum of the traffic flow data in each class and generating main road section prediction information.
As a further scheme of the application: the main road segment prediction information unit includes:
the vehicle flow data summation subunit is used for calculating the sum of the vehicle flow data in each class, wherein the sum of the vehicle flow data is equal to the sum of the vehicle flow data multiplied by the corresponding import proportion value and then accumulated;
the predicted traffic flow subunit is used for taking the predicted traffic flow in the road section category as the maximum sum of traffic flow data, wherein the predicted traffic flow corresponds to a time period, and the time period is a blocking time;
and the information integration subunit is used for obtaining the blockage grade according to the predicted traffic flow, and integrating the blockage grade, the blockage time and the imported main road section to generate main road section prediction information.
As a further scheme of the application: the coordination information module comprises:
the location time determining unit is used for determining a coordination location and a coordination time according to the incoming main road section and the blocking time in the main road section prediction information;
the coordinated number determining unit is used for determining the coordinated number according to the congestion level in the main road section prediction information;
and the coordination information unit is used for integrating the coordination place, the coordination time and the coordination number of people to generate coordination information.
Compared with the prior art, the application has the beneficial effects that:
according to the traffic road section traffic prediction method and system, traffic flow data of each traffic road section can be classified according to the incoming main road section to obtain a plurality of road section categories, prediction analysis is carried out on the traffic flow data in each road section category, main road section prediction information is automatically generated, the main road section prediction information comprises the incoming main road section, the blockage level and the blockage time, then coordination information is generated according to the main road section prediction information, the coordination information comprises coordination places and coordination time, each coordination place corresponds to a coordination number of people, and therefore related departments can arrange work in advance to conduct coordination deployment so as to reduce inconvenience brought to owners and passengers by road blockage.
Drawings
Fig. 1 is a flow chart of a traffic flow monitoring method based on big data.
Fig. 2 is a flow chart of classifying traffic flow data of each traffic road section according to a collected main road section to obtain a plurality of road section categories in the traffic flow monitoring method based on big data.
Fig. 3 is a flowchart of a method for monitoring traffic flow based on big data, which performs predictive analysis on traffic flow data in each road section category to generate prediction information of a main road section.
Fig. 4 is a flowchart of calculating the sum of traffic flow data in each class to generate main road section prediction information in the traffic flow monitoring method based on big data.
Fig. 5 is a flowchart of generating coordination information according to main road prediction information in a traffic flow monitoring method based on big data.
Fig. 6 is a schematic structural diagram of a traffic flow monitoring system based on big data.
Fig. 7 is a schematic structural diagram of a traffic flow data classification module in a traffic flow monitoring system based on big data.
Fig. 8 is a schematic structural diagram of a main road section prediction information module in a traffic flow monitoring system based on big data.
Fig. 9 is a schematic structural diagram of a main road prediction information unit in a traffic flow monitoring system based on big data.
Fig. 10 is a schematic structural diagram of a coordination information module in a traffic flow monitoring system based on big data.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, the present application will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Specific implementations of the application are described in detail below in connection with specific embodiments.
As shown in fig. 1, an embodiment of the present application provides a traffic flow monitoring method based on big data, the method including the following steps:
s100, receiving vehicle flow data uploaded by flow monitoring equipment of each traffic road section, wherein the vehicle flow data comprises time information and corresponding traffic road sections;
s200, classifying traffic flow data of each traffic road section according to the incoming main road section to obtain a plurality of road section categories, wherein each road section category corresponds to one incoming main road section;
s300, carrying out predictive analysis on traffic flow data in each road section category to generate main road section predictive information, wherein the main road section predictive information comprises an incoming main road section, a congestion level and a congestion time;
s400, generating coordination information according to the main road section prediction information, wherein the coordination information comprises coordination places and coordination time, and each coordination place corresponds to a coordinated person number.
It should be noted that, at present, the traffic flow of roads in many cities is large, and traffic congestion and blocking phenomenon occur frequently. In order to acquire traffic flow data, traffic monitoring devices are installed on many road sections in a city, and the traffic monitoring devices can acquire real-time road traffic flow to remind passengers and drivers of current road congestion, but at present, no good advanced prediction of road congestion is performed by using the traffic monitoring devices.
In the embodiment of the application, after receiving the traffic flow data uploaded by the traffic monitoring equipment of each traffic road section in the city, the cloud server automatically classifies the traffic flow data of each traffic road section according to the incoming main road section to obtain a plurality of road section categories, each road section category corresponds to one incoming main road section, that is, the traffic flows of all traffic road sections in the same road section category are likely to flow into the same incoming main road section, so that the traffic flow data of all traffic road sections in the same road section category can be predicted, after the traffic flow data in each road section category is predicted and analyzed, the main road section prediction information comprising the incoming main road section, the blocking level and the blocking time is automatically generated, and the blocking level reflects the traffic congestion condition.
As shown in fig. 2, as a preferred embodiment of the present application, the step of classifying traffic flow data of each traffic road segment according to the incoming main road segment to obtain a plurality of road segment categories specifically includes:
s201, inputting all traffic flow data into a road section classification library, wherein the road section classification library comprises a plurality of incoming main road sections, each incoming main road section corresponds to a plurality of traffic road sections, and each traffic road section corresponds to an incoming proportion value;
s202, all traffic flow data of each incoming main road section are automatically output, the traffic flow data of each traffic road section are classified according to the incoming main road section, and an incoming proportion value is marked on each traffic flow data.
In the embodiment of the application, a road section classification library is established in advance, the road section classification library comprises a plurality of incoming main road sections, each incoming main road section corresponds to a plurality of traffic road sections, each traffic road section corresponds to an incoming proportion value, the incoming proportion value reflects the proportion of vehicles in the traffic road section entering the incoming main road section, it is easy to understand that the traffic road section possibly has a plurality of exits, the vehicles in the traffic road section possibly cannot enter the corresponding incoming main road section, the incoming proportion value is obtained according to historical monitoring data, after all traffic data are input into the road section classification library, the embodiment of the application automatically outputs all traffic data of each incoming main road section, classifies the traffic data of each traffic road section according to the incoming main road section, and marks the incoming proportion value on each traffic data.
As shown in fig. 3, as a preferred embodiment of the present application, the step of performing predictive analysis on traffic data in each road segment class to generate main road segment predictive information specifically includes:
s301, determining the number of vehicles and the moving speed of a corresponding traffic road section according to traffic flow data;
s302, determining a time period when a vehicle of the traffic road section reaches a main road section according to the moving speed, and marking the time period on the traffic flow data;
s303, classifying the traffic flow data according to time periods, wherein the time periods of the traffic flow data in each class are overlapped;
s304, calculating the sum of the traffic flow data in each class, and generating the prediction information of the main road section.
In the embodiment of the application, firstly, the number of vehicles and the moving speed of a corresponding traffic road section are required to be determined according to traffic flow data, the number of vehicles can be directly obtained, the moving speed can be indirectly obtained through the traffic flow data, for example, a plurality of traffic flow intervals are established, each interval corresponds to one moving speed, for example, when the traffic flow data is between a first traffic flow and a second traffic flow, the moving speed is 50km/h, the larger the corresponding moving speed is, the smaller the corresponding moving speed is, then, the time period that the vehicles of the traffic road section reach a main road section is determined according to the moving speed, the time period is marked on the traffic flow data, for example, the time period that the vehicles of the first traffic road section reach the main road section is 10:20-10:30, the traffic flow data is classified according to the time period, and the time periods of the traffic flow data in each class overlap, that is, the traffic flow in each class can reach the same main road section in a similar time, and the total sum of the traffic flow data in each class is calculated, so that the main prediction information can be obtained.
As shown in fig. 4, as a preferred embodiment of the present application, the step of calculating the sum of the traffic data in each class to generate the main road segment prediction information specifically includes:
s3041, calculating the sum of the traffic flow data in each class, wherein the sum of the traffic flow data is equal to the sum of the traffic flow data multiplied by the corresponding import proportion value and then accumulated;
s3042, taking the predicted traffic flow in the road section category as the maximum sum of the traffic flow data, wherein the predicted traffic flow corresponds to a time period, and the time period is a blocking time;
s3043, obtaining a blockage grade according to the predicted traffic flow, and integrating the blockage grade, the blockage time and the imported main road section to generate main road section prediction information.
In the embodiment of the present application, it is required to calculate the sum of traffic data in each class, for example, one class includes a first traffic, a second traffic and a third traffic, all of which enter the same entry main road segment, and the entry proportion value entering the entry main road segment is the first proportion value, the second proportion value and the third proportion value, respectively, and then the sum of traffic data=the first traffic+the second proportion value+the third traffic, and since the traffic data is classified in advance according to a time period, the same entry main road segment corresponds to a plurality of classes, each class has a sum of traffic data, preferably, the sum of the traffic data is taken as the predicted traffic in the road segment class, the predicted traffic corresponds to a time period, and the time period is a time period for blocking, where the time period is a time period for the predicted traffic to reach the entry main road segment, and the traffic class corresponds to a predicted traffic class, and the traffic class is obtained according to a predicted traffic class.
As shown in fig. 5, as a preferred embodiment of the present application, the step of generating coordination information according to the main road segment prediction information specifically includes:
s401, determining coordination sites and coordination time according to the incoming main road sections and the blocking time in the main road section prediction information;
s402, determining the number of coordinated persons according to the congestion level in the main road section prediction information;
s403, integrating the coordination place, the coordination time and the number of coordination people to generate coordination information.
In the embodiment of the application, after the main road section prediction information is determined, the coordination place and the coordination time can be directly determined according to the incoming main road section and the blocking time in the main road section prediction information, then the coordination number is determined according to the blocking level in the main road section prediction information, each blocking level corresponds to the formulated coordination number, and finally the coordination information is generated.
As shown in fig. 6, the embodiment of the present application further provides a traffic flow monitoring system based on big data, the system includes:
the traffic flow data receiving module 100 is configured to receive traffic flow data uploaded by the traffic flow monitoring devices of each traffic road section, where the traffic flow data includes time information and a corresponding traffic road section;
the traffic flow data classification module 200 is configured to classify traffic flow data of each traffic road section according to an incoming main road section to obtain a plurality of road section categories, where each road section category corresponds to one incoming main road section;
the main road section prediction information module 300 is configured to perform prediction analysis on traffic flow data in each road section category, and generate main road section prediction information, where the main road section prediction information includes an incoming main road section, a congestion level, and a congestion time;
the coordination information module 400 is configured to generate coordination information according to the main road section prediction information, where the coordination information includes a coordination location and a coordination time, and each coordination location corresponds to a coordinated person number.
As shown in fig. 7, as a preferred embodiment of the present application, the traffic data classification module 200 includes:
the traffic flow data input unit 201 is configured to input all traffic flow data into a road segment classification library, where the road segment classification library includes a plurality of incoming main road segments, each incoming main road segment corresponds to a plurality of traffic road segments, and each traffic road segment corresponds to an incoming proportion value;
the traffic data classification unit 202 is configured to automatically output all traffic data of each incoming main road segment, classify the traffic data of each traffic road segment according to the incoming main road segment, and mark an incoming proportion value on each traffic data.
As shown in fig. 8, as a preferred embodiment of the present application, the main link prediction information module 300 includes:
an information determining unit 301 for determining the number of vehicles and the moving speed of the corresponding traffic segment according to the traffic flow data;
a time period determining unit 302, configured to determine a time period when the vehicle of the traffic road section arrives at the entry main road section according to the moving speed, and mark the time period on the traffic flow data;
a secondary classification unit 303, configured to classify traffic flow data according to time periods, where there is an overlap between the time periods of the traffic flow data in each class;
and a main road section prediction information unit 304 for calculating the sum of the traffic flow data in each class to generate main road section prediction information.
As shown in fig. 9, as a preferred embodiment of the present application, the main road segment prediction information unit 304 includes:
the traffic flow data summation subunit 3041 is configured to calculate a sum of traffic flow data in each class, where the sum of traffic flow data is equal to the sum of traffic flow data multiplied by a corresponding import proportion value and accumulated;
a predicted traffic flow subunit 3042, configured to obtain a predicted traffic flow in the road section category, where the sum of the traffic flow data is the maximum, and the predicted traffic flow corresponds to a time period, and the time period is a blocking time;
the information integrating subunit 3043 is configured to obtain a congestion level according to the predicted traffic flow, integrate the congestion level, the congestion time, and the incoming main link, and generate main link prediction information.
As shown in fig. 10, as a preferred embodiment of the present application, the coordination information module 400 includes:
a location time determining unit 401 for determining a coordination location and a coordination time according to the incoming main link and the congestion time in the main link prediction information;
a coordinated number determining unit 402 for determining a coordinated number of people according to the congestion level in the main road section prediction information;
and the coordination information unit 403 is configured to integrate the coordination place, the coordination time and the coordination number of people to generate coordination information.
The foregoing description of the preferred embodiments of the present application should not be taken as limiting the application, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A traffic flow monitoring method based on big data, the method comprising the steps of:
receiving traffic flow data uploaded by flow monitoring equipment of each traffic road section, wherein the traffic flow data comprises time information and corresponding traffic road sections;
classifying traffic flow data of each traffic road section according to the incoming main road section to obtain a plurality of road section categories, wherein each road section category corresponds to one incoming main road section;
carrying out predictive analysis on traffic flow data in each road section category to generate main road section predictive information, wherein the main road section predictive information comprises an incoming main road section, a congestion level and a congestion time;
and generating coordination information according to the main road section prediction information, wherein the coordination information comprises coordination places and coordination time, and each coordination place corresponds to a coordination person number.
2. The traffic flow monitoring method based on big data according to claim 1, wherein the step of classifying traffic flow data of each traffic road section according to the incoming main road section to obtain a plurality of road section categories specifically comprises:
inputting all traffic flow data into a road section classification library, wherein the road section classification library comprises a plurality of incoming main road sections, each incoming main road section corresponds to a plurality of traffic road sections, and each traffic road section corresponds to an incoming proportion value;
and automatically outputting all the traffic flow data of each incoming main road section, classifying the traffic flow data of each traffic road section according to the incoming main road section, and marking an incoming proportion value on each traffic flow data.
3. The traffic flow monitoring method according to claim 2, wherein the step of performing predictive analysis on traffic flow data in each road segment class to generate main road segment predictive information specifically comprises:
determining the number of vehicles and the moving speed of the corresponding traffic road section according to the traffic flow data;
determining a time period when the vehicle of the traffic road section reaches the main road section according to the moving speed, and marking the time period on the vehicle flow data;
classifying the traffic flow data according to time periods, wherein the time periods of the traffic flow data in each class are overlapped;
and calculating the sum of the traffic flow data in each class, and generating the main road section prediction information.
4. The traffic flow monitoring method according to claim 3, wherein the step of calculating the sum of the traffic flow data in each class to generate the main road section prediction information specifically comprises:
calculating the sum of the traffic flow data in each class, wherein the sum of the traffic flow data is equal to the sum of the traffic flow data multiplied by the corresponding import proportion value and then accumulated;
taking the maximum sum of the traffic flow data as the predicted traffic flow in the road section category, wherein the predicted traffic flow corresponds to a time period, and the time period is the blocking time;
and obtaining the blockage grade according to the predicted traffic flow, and integrating the blockage grade, the blockage time and the imported main road section to generate main road section prediction information.
5. The traffic flow monitoring method based on big data according to claim 1, wherein the step of generating coordination information according to the main road section prediction information specifically comprises:
determining a coordination place and a coordination time according to the incoming main road section and the blocking time in the main road section prediction information;
determining the number of coordinated people according to the congestion level in the main road section prediction information;
and integrating the coordination place, the coordination time and the coordination number of people to generate coordination information.
6. A traffic flow monitoring system based on big data, the system comprising:
the traffic flow data receiving module is used for receiving traffic flow data uploaded by the flow monitoring equipment of each traffic road section, and the traffic flow data comprises time information and corresponding traffic road sections;
the traffic flow data classification module is used for classifying traffic flow data of each traffic road section according to the converging main road section to obtain a plurality of road section categories, and each road section category corresponds to one converging main road section;
the system comprises a main road section prediction information module, a traffic flow information analysis module and a traffic flow information analysis module, wherein the main road section prediction information module is used for performing prediction analysis on traffic flow data in each road section category to generate main road section prediction information, and the main road section prediction information comprises an incoming main road section, a congestion level and a congestion time;
and the coordination information module is used for generating coordination information according to the main road section prediction information, wherein the coordination information comprises coordination places and coordination time, and each coordination place corresponds to a coordination number of people.
7. The big data based traffic flow monitoring system of claim 6, wherein the traffic flow data classification module comprises:
the traffic flow data input unit is used for inputting all traffic flow data into the road section classification library, wherein the road section classification library comprises a plurality of incoming main road sections, each incoming main road section corresponds to a plurality of traffic road sections, and each traffic road section corresponds to an incoming proportion value;
and the traffic flow data classification unit is used for automatically outputting all traffic flow data of each converging main road section, classifying the traffic flow data of each traffic road section according to the converging main road section, and marking the converging proportion value on each traffic flow data.
8. The big data based traffic flow monitoring system of claim 7, wherein the main link prediction information module comprises:
the information determining unit is used for determining the number of vehicles and the moving speed of the corresponding traffic road section according to the traffic flow data;
a time period determining unit for determining a time period when the vehicle of the traffic road section arrives at the junction main road section according to the moving speed, and marking the time period on the vehicle flow data;
the secondary classification unit is used for classifying the traffic flow data according to time periods, and the time periods of the traffic flow data in each class are overlapped;
and the main road section prediction information unit is used for calculating the sum of the traffic flow data in each class and generating main road section prediction information.
9. The big data based traffic flow monitoring system of claim 8, wherein the main road segment prediction information unit includes:
the vehicle flow data summation subunit is used for calculating the sum of the vehicle flow data in each class, wherein the sum of the vehicle flow data is equal to the sum of the vehicle flow data multiplied by the corresponding import proportion value and then accumulated;
the predicted traffic flow subunit is used for taking the predicted traffic flow in the road section category as the maximum sum of traffic flow data, wherein the predicted traffic flow corresponds to a time period, and the time period is a blocking time;
and the information integration subunit is used for obtaining the blockage grade according to the predicted traffic flow, and integrating the blockage grade, the blockage time and the imported main road section to generate main road section prediction information.
10. The big data based traffic flow monitoring system of claim 6, wherein the coordination information module comprises:
the location time determining unit is used for determining a coordination location and a coordination time according to the incoming main road section and the blocking time in the main road section prediction information;
the coordinated number determining unit is used for determining the coordinated number according to the congestion level in the main road section prediction information;
and the coordination information unit is used for integrating the coordination place, the coordination time and the coordination number of people to generate coordination information.
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