Traffic exhaust emission prediction method based on deep residual network

A technology of exhaust emissions and emissions, applied in the direction of forecasting, neural learning methods, biological neural network models, etc., can solve problems such as low prediction accuracy and inability to predict traffic exhaust emissions at the same time

Active Publication Date: 2020-01-31
东北大学秦皇岛分校
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Problems solved by technology

However, the existing traffic exhaust emission prediction methods focus on the construction of future urban traffic emission inventory, or focus on the traffic emission prediction under different working conditions and road conditions. Traffic emissions in a sub-region

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  • Traffic exhaust emission prediction method based on deep residual network
  • Traffic exhaust emission prediction method based on deep residual network
  • Traffic exhaust emission prediction method based on deep residual network

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Embodiment Construction

[0039] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0040] Such as figure 1 Shown is a schematic diagram of the traffic exhaust emission prediction method based on the deep residual network of the present invention. The traffic tail gas emission prediction method based on deep residual network of the present invention comprises the following steps:

[0041] Step 1: Divide the area to be predicted into grid areas

[0042] Among them, A ij is the grid in row i and column j in the grid area, i∈{1,2,…,I}, j∈{1,2,…,J}, each grid in the grid area is square and equal in area.

[0043] In this embodiment, the region to be predicted is Beijing, such as figure 2 As shown, Beijing is divided into 32×32 grid areas, and the area of ​​each grid is 1 square kilometer.

[0044] Step 2: Collect vehicle GPS trajectory data in the area to be predicted in the tth time period to form a vehicle GPS trajector...

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Abstract

The invention relates to the technical field of traffic exhaust emission prediction, and provides a traffic exhaust emission prediction method based on a deep residual network. The method comprises the following steps: firstly, dividing a to-be-predicted region into grid regions; acquiring a vehicle GPS trajectory data set, holiday and festival conditions and weather condition data of a to-be-predicted area in each time period, and calculating the traffic exhaust emission of each grid in each time period; constructing a holiday-weather-emission sample set, a nearest time sample set, an adjacent time sample set and a remote time sample set, and preprocessing each sample set; constructing a traffic emission prediction model based on a deep residual network, and training the model; and finally, predicting the traffic exhaust emission of each grid in the to-be-predicted time period by utilizing the trained traffic emission prediction model. According to the method, the accuracy of trafficexhaust emission prediction can be improved, and the traffic exhaust emission of each subarea in the whole city can be predicted at the same time.

Description

technical field [0001] The invention relates to the technical field of traffic tail gas emission prediction, in particular to a traffic tail gas emission prediction method based on a deep residual network. Background technique [0002] Traffic pollution is one of the main sources of air pollution. With the transformation of my country's economy and the development of urbanization, the problem of urban air pollution has become more complicated, and the task of preventing and controlling urban air pollution has become more arduous. And due to the continuous increase of the number of urban motor vehicles, traffic pollution has increasingly become the main source of urban air pollution. Therefore, traffic emission prediction is of great significance to the prevention and control of urban air pollution. [0003] Traffic exhaust refers to harmful pollutants produced by the engine during the incomplete combustion of gasoline or diesel, mainly including nitrogen oxides, hydrocarbo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06K9/62G06N3/04G06N3/08
CPCG06Q10/04G06Q50/26G06N3/08G06N3/045G06F18/25G06F18/10G06F18/214
Inventor 曹松松李连江吴朝霞
Owner 东北大学秦皇岛分校
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