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Road network traffic flow data restoration method and system based on feature pyramid network

A feature pyramid and traffic flow technology, applied in the field of intelligent transportation, can solve problems such as slow repair speed and inaccurate repair results, and achieve the effects of good ease of use, fast and accurate repair, and high-quality data repair accuracy

Active Publication Date: 2022-07-29
SHANDONG JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The inventor found that the existing road network traffic data repair method has the defects of slow repair speed and inaccurate repair results

Method used

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  • Road network traffic flow data restoration method and system based on feature pyramid network
  • Road network traffic flow data restoration method and system based on feature pyramid network
  • Road network traffic flow data restoration method and system based on feature pyramid network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] This embodiment provides a road network traffic flow data restoration method based on a feature pyramid network;

[0039] like figure 1 As shown, the road network traffic flow data restoration method based on feature pyramid network includes:

[0040] S101: Obtain road network traffic flow data to be repaired; divide the road network traffic flow data to be repaired into p periods; where p is a positive integer;

[0041] S102: Establish a granularity rule network of road space in the ith time period; based on the granularity rule network of road space in the ith time period and the traffic flow data of the road network to be repaired in the ith time period, calculate the unit grid single lane in the ith time period The average traffic volume, and the average traffic volume of a single lane in the unit grid in the ith period is regarded as the fine-scale traffic flow characteristics of road traffic in the ith period; where, the value of i ranges from 1 to p; i is a posi...

Embodiment 2

[0110] This embodiment provides a road network traffic flow data restoration system based on a feature pyramid network;

[0111] Road network traffic flow data restoration system based on feature pyramid network, including:

[0112] an obtaining module, which is configured to: obtain the traffic flow data of the road network to be repaired; divide the traffic flow data of the road network to be repaired into p time periods; wherein, p is a positive integer;

[0113] The fine-scale traffic feature extraction module is configured to: establish a granular rule network such as road space in the ith period; Calculate the average single-lane traffic volume of the unit grid in the ith period, and regard the average traffic volume of the single-lane in the unit grid in the ith period as the fine-scale flow characteristics of road traffic in the ith period; where, the value range of i is 1 to p; i is a positive integer;

[0114] A coarse-scale prior knowledge base building module, wh...

Embodiment 3

[0120] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are Stored in the memory, when the electronic device runs, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in the first embodiment.

[0121] It should be understood that, in this embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors, DSPs, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices. , discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or th...

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Abstract

The invention discloses a road network traffic flow data restoration method and system based on a feature pyramid network. The method comprises the following steps: acquiring road network traffic flow data to be restored; dividing the road network traffic flow data to be repaired into p time periods; establishing a road space equal-granularity rule network; based on the equal-granularity rule network and to-be-repaired road network traffic flow data in a certain time period, calculating a single-lane average traffic volume in a unit grid in the current time period, and regarding the single-lane average traffic volume in the unit grid in the current time period as a road traffic fine-scale flow feature in the current time period; the method comprises the following steps: obtaining a road coarse-scale grid structure based on road land utilization attributes, and establishing a road network traffic coarse-scale prior knowledge base by using traffic flow historical same-period similarity; inputting the fine-scale flow characteristics and the prior knowledge base into the trained multi-scale characteristic pyramid model of the current time period to obtain repaired road network traffic flow data of the current time period; and similarly, obtaining the repaired road network traffic flow data of all time periods.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a road network traffic flow data restoration method and system based on a feature pyramid network. Background technique [0002] The statements in this section merely provide background related to the present disclosure and do not necessarily constitute prior art. [0003] Under the background of the increasingly mature modern automatic detection technology, the urban road traffic information detection equipment is increasingly diversified, and the traffic data sources present multiple characteristics, including fixed road-end detection data (such as coil, geomagnetic, video, bayonet, etc.), There are also mobile vehicle-end detection data (such as floating cars, electronic tags, bus cards, mobile phones, etc.). Multi-source and multi-dimensional traffic data are often interfered by multiple factors, resulting in the lack of spatiotemporal dimensions or sparse...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G08G1/065
CPCG08G1/0125G08G1/065Y02T10/40
Inventor 郭亚娟
Owner SHANDONG JIAOTONG UNIV
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