CN109993974B - Intelligent traffic-induced chain type progressive network construction method based on sliding window - Google Patents

Abstract

The invention discloses a sliding window-based intelligent traffic-induced chain type progressive network construction method. The method comprises the following steps: first, the present invention detects, describes and stores the traffic status information of vehicles in connected blocks of a highway by constructing a chain progressive network. And secondly, forming a chain type progressive network of the vehicles on the highway, and realizing real-time traffic state modeling, instantiation description, chain storage and iterative analysis of dynamic traffic flow. Thirdly, the GNSS system is used for carrying out synchronous time service on each block, and the synchronism and the accuracy of the information of each block are guaranteed. The invention constructs the intelligent traffic-induced chain type progressive network based on the sliding window principle, realizes the dynamic monitoring of vehicles and the real-time updating of the induction mode, and has wide application prospect.

Description

A method for constructing a chained progressive network for intelligent traffic guidance based on sliding windows

technical field

The invention belongs to the field of intelligent transportation, and in particular relates to a method for constructing a chain-type progressive network for intelligent traffic guidance based on a sliding window. Real-time dynamic monitoring of the state to achieve intelligent induction.

Background technique

The existing traffic flow detection mostly counts vehicles through automatic traffic flow observation instruments. These instruments are generally arranged at key intersections. However, due to the complexity of road conditions and the intricate complexity of roads, it is often difficult to carry out accurate and dynamic monitoring. This may result in the fact that the large traffic flow in a certain section cannot be fed back to the following vehicles, resulting in traffic congestion. Therefore, it is very necessary to combine the radar block technology to establish a sliding-window-based intelligent traffic guidance chain progressive network construction method to dynamically monitor the traffic flow and realize intelligent guidance.

SUMMARY OF THE INVENTION

In order to overcome the shortcomings of the prior art, the present invention provides a method for constructing a chain-type progressive network for intelligent traffic guidance based on a sliding window.

The present invention adopts following technical scheme to realize:

A sliding window-based intelligent traffic guidance chain progressive network construction method, comprising the following steps:

Step 1: Initialize the size of the sliding window: a pair of radars on both sides of the road that are aimed at each other is defined as a radar pair, and the number of consecutive and adjacent radar pairs is defined as the size of the sliding window, that is, the size of the radar block; during the intelligent induction process , initialize the number of radar pairs according to the actual environment, and complete the radar block number, which is convenient for radar block induction;

Step 2: Initialization of the chained progressive network structure: According to the defined sliding window size, initialize the database structure of the cloud platform to form a dynamic chained progressive network structure based on the current traffic state, wherein the chained progressive network structure satisfies the relational expression

In the formula, W _N is the comprehensive information state of radar block N, Wi is the comprehensive information state of radar block _i , and P _i is the comprehensive information impact factor of radar block i;

Step 3, the radar block captures vehicle information: the radar in each sliding window counts the number of vehicles, license plate number, driving speed, etc. in the radar block, and uploads the information to the cloud platform;

Step 4, chain progressive network update: based on the data information of the radar block, update the database of the chain progressive network to realize real-time traffic state modeling, instantiation description, chain storage and iterative analysis of dynamic traffic flow;

Step 5, intelligent induction: According to the database information of the chain progressive network, intelligent induction is carried out for vehicles in different radar blocks.

A further improvement of the present invention is that, it is characterized in that, the concrete realization method of step 1 is as follows:

101) Collect the six elements of weather information and upload it to the cloud platform;

102) Determine the size of the sliding window based on the weather conditions. If the visibility is less than 100 meters, the vehicle speed is generally slower, and the vehicle conditions within the unit length are more complicated. In order to accurately and effectively describe the traffic flow information, the window size selected at this time is 2~4, the actual distance is 40m~80m; on the contrary, if there is a weather condition with visibility greater than 100m, the selected window size is 5~8, and the actual distance is 100m~160m.

A further improvement of the present invention is that the concrete realization method of step 2 is as follows:

201) Numbering different radar blocks, establishing a chained database structure, and defining the name of each database according to the numbering to facilitate external access;

202) Determine the name, type and size of the data in each radar block database, and complete the initialization configuration.

A further improvement of the present invention is that the concrete realization method of step 4 is as follows:

401) Based on the traffic flow data information of different radar blocks, access the database by name, and update the database of the chain progressive network;

402) According to the data information of different databases, combined with GIS technology, complete the modeling and instantiation description of the real-time traffic state.

A further improvement of the present invention is that the concrete realization method of step 5 is as follows:

501) Based on the data information of the chain progressive network, automatically adjust the induction mode of different radar blocks;

502) When the traffic flow in a certain radar block is too large and congestion occurs, a prompt message is sent to vehicles in other adjacent radar blocks.

The present invention has following beneficial technical effect:

The invention provides a method for constructing an intelligent traffic guidance chain progressive network based on a sliding window. The method divides the traffic road into several radar blocks, and realizes the detection, description and storage of the vehicle traffic state information of the radar blocks connected to the highway. And realize the information access platform, improve the management ability of traffic. At the same time, a chain progressive network of expressway vehicles is formed, which realizes real-time traffic state modeling, instantiation description, chain storage and iterative analysis of dynamic traffic flow, which is helpful for rational traffic planning.

Further, the induction system (radar, induction light) can wake up and update the chain progressive network in real time, and use GPS and Beidou to synchronize the timing of each block, which ensures the synchronization and accuracy of the information of each block.

Description of drawings

FIG. 1 is a flow chart of the present invention.

FIG. 2 is a schematic diagram of the physical layer construction of the chain-type progressive network structure of the present invention when the weather conditions are poor.

FIG. 3 is a schematic diagram of the physical layer construction of the chain-type progressive network structure of the present invention when the weather conditions are good.

FIG. 4 is a structural model of the physical layer and the application layer of a chained progressive network according to the present invention.

Description of reference numbers:

1 is radar block N+1, 2 is radar block N, 3 is radar block N-1, 4 is radar and camera, and 6 is radar block.

Detailed ways

In order to make those skilled in the art better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only The embodiments are part of the present invention, not all of the embodiments, and are not intended to limit the scope of the present disclosure. Furthermore, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Various schematic diagrams in accordance with the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not to scale, some details have been exaggerated for clarity, and some details may have been omitted. The shapes of various regions and layers shown in the figures and their relative sizes and positional relationships are only exemplary, and in practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art should Regions/layers with different shapes, sizes, relative positions can be additionally designed as desired.

The present invention is further illustrated below in conjunction with accompanying drawing and embodiment:

Example

Referring to Figure 2, 1 is the radar network block N+1, 2 is the radar network block N, 3 is the radar network block N-1, 4 is the radar and camera, and 5 is the car. Since the number of radar pairs in each block is 2, the size of the sliding window is 2. The traffic flow status at a certain moment is shown in the figure. The number of vehicles in the radar network block N+1 is 2, the number of vehicles in the radar network block N is 0, and the number of vehicles in the radar network block N-1 is 1. The radar and camera in each block upload the number of vehicles, vehicle speed, and license plate number information to the database corresponding to the block name through comprehensive judgment. Since the physical layers are arranged like chains of a bicycle, the construction of the present invention is called a chained progressive network.

Referring to Figure 3, if the weather conditions improve, the traffic department will no longer limit the speed, and the number of vehicles within the same length will decrease. At this time, the size of the sliding window can be appropriately increased, and the number of radar pairs becomes 6. At this time, the chain The progressive network will complete the update and record data according to the new sliding window.

Referring to FIG. 4, it is a structural model of the physical layer and application layer of the chain progressive network of the invention. At the physical layer, radar blocks 1, 2, 3....N based on weather conditions are constructed, and the information exchange with the application layer is completed through the transmission protocol of the network layer and the transport layer. The application layer is a server based on B/S structure. It builds the database list of each radar block through the database, and realizes its dynamic modification and real-time access.

Refer to Table 1 and Table 2, which are part of a certain block of the cloud platform database. By establishing a database, the vehicle speed information and speed limit information can be stored, which is convenient for invocation and instantiation analysis. During the construction of the chain progressive network, information such as the license plate number and the number of vehicles can be added to realize the dynamic monitoring of the traffic flow.

Table 1 is the database of the cloud platform chain progressive network of the present invention.

Table 2 is the data type information of the cloud platform chain progressive network of the present invention.

Claims (5)

1. a kind of intelligent traffic guidance chain progressive network construction method based on sliding window, is characterized in that, comprises the steps: Step 1: Initialize the size of the sliding window: a pair of radars on both sides of the road that are aimed at each other is defined as a radar pair, and the number of consecutive and adjacent radar pairs is defined as the size of the sliding window, that is, the size of the radar block; during the intelligent induction process , initialize the number of radar pairs according to the actual environment, and complete the radar block number, which is convenient for radar block induction; Step 2: Initialization of the chained progressive network structure: According to the defined sliding window size, initialize the database structure of the cloud platform to form a dynamic chained progressive network structure based on the current traffic state, wherein the chained progressive network structure satisfies the relational expression

In the formula, W _N is the comprehensive information state of radar block N, Wi is the comprehensive information state of radar block _i , and P _i is the comprehensive information impact factor of radar block i; Step 3, the radar block captures vehicle information: the radar in each sliding window counts the number of vehicles, license plate numbers, and driving speeds in the radar block, and uploads the information to the cloud platform; Step 4, chain progressive network update: based on the data information of the radar block, update the database of the chain progressive network to realize real-time traffic state modeling, instantiation description, chain storage and iterative analysis of dynamic traffic flow; Step 5, intelligent induction: According to the database information of the chain progressive network, intelligent induction is carried out for vehicles in different radar blocks. 2. a kind of intelligent traffic guidance chain progressive network construction method based on sliding window according to claim 1, is characterized in that, it is characterized in that, the concrete realization method of step 1 is as follows: 101) Collect the six elements of weather information and upload it to the cloud platform; 102) Determine the size of the sliding window based on the weather conditions. If the visibility is less than 100 meters, the vehicle speed is generally slower, and the vehicle conditions within the unit length are more complicated. In order to accurately and effectively describe the traffic flow information, the window size selected at this time is 2~4, the actual distance is 40m~80m; on the contrary, if there is a weather condition with visibility greater than 100m, the selected window size is 5~8, and the actual distance is 100m~160m. 3. a kind of intelligent traffic guidance chain progressive network construction method based on sliding window according to claim 2, is characterized in that, the concrete realization method of step 2 is as follows: 201) Numbering different radar blocks, establishing a chained database structure, and defining the name of each database according to the numbering to facilitate external access; 202) Determine the name, type and size of the data in each radar block database, and complete the initialization configuration. 4. a kind of intelligent traffic guidance chain progressive network construction method based on sliding window according to claim 3, is characterized in that, the concrete realization method of step 4 is as follows: 401) Based on the traffic flow data information of different radar blocks, access the database by name, and update the database of the chain progressive network; 402) According to the data information of different databases, combined with GIS technology, complete the modeling and instantiation description of the real-time traffic state. 5. a kind of intelligent traffic guidance chain progressive network construction method based on sliding window according to claim 4, is characterized in that, the concrete realization method of step 5 is as follows: 501) Based on the data information of the chain progressive network, automatically adjust the induction mode of different radar blocks; 502) When the traffic flow in a certain radar block is too large and congestion occurs, a prompt message is sent to vehicles in other adjacent radar blocks.

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