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A Multi-factor Short-term Traffic Flow Forecasting Method Based on Neural Network LSTM

A technology of neural network and forecasting method, applied in the field of multi-factor short-term traffic flow forecasting based on neural network LSTM, can solve the problems of poor forecasting effect of LSTM forecasting model, achieve accurate and effective forecasting, improve forecasting accuracy, and improve forecasting accuracy Effect

Active Publication Date: 2021-03-26
CHANGAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The object of the present invention is to provide a kind of method based on neural network LSTM model multi-factor traffic flow forecasting, in order to solve the problem that the LSTM forecasting model in the prior art predicts poor effect

Method used

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  • A Multi-factor Short-term Traffic Flow Forecasting Method Based on Neural Network LSTM
  • A Multi-factor Short-term Traffic Flow Forecasting Method Based on Neural Network LSTM
  • A Multi-factor Short-term Traffic Flow Forecasting Method Based on Neural Network LSTM

Examples

Experimental program
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Effect test

Embodiment 1

[0079] In this example, the data required for the example are provided by the toll station in Xi’an. Xi’an belongs to the temperate zone semi-humid continental monsoon climate, with four distinct seasons, mild climate and moderate rainfall. Most of the rainy seasons are concentrated in spring and summer, so in the example The data in June 2018 is counted every 15 minutes. Observing the data, from June 16th to June 18th, the light rain turned cloudy for three consecutive days. At this time, the data from June 1st to 18th is used as the training set, and June 19th is used as the test set to predict the traffic flow on June 19th. According to the previous weather data, it is known that June 19th is also cloudy , the weather conditions are different from the previous three days, and the test results are shown in Figure 2. Then delete the data from June 16th to June 18th to exclude the case of three consecutive days of light rain turning cloudy, and then use the data after excludi...

example 2

[0081] In this example, in order to consider the impact of holidays on traffic flow forecasting, the data in February of the 2018 Spring Festival is selected as the training set, because according to the local climate conditions, winter is cold and less rainy and snowy, which minimizes the impact of weather factors. Because the Spring Festival in February is the most important day of the year, and about 15 days before and after the Spring Festival is the peak period of Spring Festival travel, the selection of data for the Spring Festival in February fully illustrates the impact of holidays on traffic flow. Use the data of February as the training set to train in the LSTM model, and use the data of March 1 as the test set to make predictions. The results are shown in Figure 4(a) and Figure 4(b). The mse and rmse are relatively large. And the R square is less than 0.92. According to the neural network LSTM model, the prediction effect of this model is better, and the R square is ...

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Abstract

The invention belongs to the field of traffic engineering, and discloses a multi-factor short-term traffic flow prediction method based on neural network LSTM. Step 1: Obtain traffic flow data within a period of time, and preprocess the traffic flow data to obtain short-term traffic flow data; step 2: filter short-term traffic flow data according to weather records and holiday records, and divide the data set; step 3: perform data cleaning, data reconstruction and normalization; step 4: establish LSTM neural network model, according to the Select the data set for the weather conditions and holiday conditions on the forecast date, use the selected data set to train the LSTM neural network model and adjust the LSTM parameters, and obtain the traffic flow on the forecast date according to the established LSTM neural network model. The present invention proposes a more detailed idea, eliminates the influence of other factors, such as weather factors, holiday factors, etc. on traffic flow, relatively improves the prediction accuracy, and makes the traffic flow prediction for a certain period of time in the future more accurate and effective.

Description

technical field [0001] The invention belongs to the field of traffic engineering, in particular to a multi-factor short-term traffic flow prediction method based on neural network LSTM. Background technique [0002] Traffic management and intelligent traffic control and other upper-level applications are based on traffic flow. Research on short-term traffic flow prediction models based on time series has always been a research hotspot for scholars at home and abroad. Therefore, traffic control and guidance systems are hot topics in ITS research. Main course. The key to realizing traffic guidance is to accurately predict traffic flow, that is, to use the real-time and historical traffic flow data of existing lanes to predict the traffic flow situation in the next period by establishing a suitable model. Traffic flow forecasting can be divided into long-term forecasting, medium-term forecasting and short-term forecasting, which can serve different research fields. The short-...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G08G1/065G06K9/62G06N3/04
CPCG08G1/0125G08G1/065G06N3/044G06N3/045G06F18/214G06F18/24
Inventor 赵祥模程鑫王钰周洲赵怀鑫周经美张立成郝茹茹尚旭明韩睿之陈宇轩常惠
Owner CHANGAN UNIV
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