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Traffic missing data completion method based on bidirectional recurrent neural network

A neural network and two-way loop technology, applied in the field of transportation, can solve problems such as missing input data, poor completion effect, and data loss

Inactive Publication Date: 2020-02-25
DALIAN UNIV OF TECH
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Problems solved by technology

This method of completion based on historical data makes good use of the temporal characteristics of data for completion, and the completion results are relatively good, but this method has limitations
When a special event occurs, the current missing point is preceded by a series of missing points, such as a power outage, which will cause a continuous loss of data. When the last missing point is completed, due to the serious lack of input data, the complement Full effect is very poor in this case

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  • Traffic missing data completion method based on bidirectional recurrent neural network
  • Traffic missing data completion method based on bidirectional recurrent neural network
  • Traffic missing data completion method based on bidirectional recurrent neural network

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Embodiment Construction

[0065] The technical solutions of the present invention will be further described below in conjunction with specific embodiments and accompanying drawings.

[0066] A method for complementing missing traffic data based on a bidirectional cyclic neural network, the steps are as follows:

[0067] The first step is to preprocess the traffic flow data

[0068] (1) Time granularity division: all traffic flow data is processed into traffic flow data every 5 minutes according to the time granularity of 5 minutes;

[0069] (2) Standardize the data: use the minimum and maximum values ​​to standardize the traffic flow data, the formula is as follows:

[0070]

[0071] Among them, x represents the original value, and x min represents the minimum value of the original value, x max Represents the maximum value of the original value, max is the upper limit of normalization, min is the lower limit of normalization, [min,max] represents the interval after normalization, x * is the stan...

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Abstract

The invention provides a traffic missing data completion method based on a bidirectional recurrent neural network, and belongs to the field of traffic. The method comprises the following steps: firstly, the time sequence characteristic of data in time are utilized; meanwhile, the influence of data before and after the completion time point on the current time point is considered, the utilization and completion precision of the data is greatly improved, and secondly, the influence of external features and adjacent sensor data on the current sensor data is considered and added into the completion model, so that the completion precision is greatly improved. According to the method, the completion precision under the condition that the data missing rate is low is greatly improved, and the completion precision under the condition that the data missing rate is high is also improved.

Description

technical field [0001] The invention belongs to the field of traffic, and in particular relates to a method for complementing traffic missing data based on a bidirectional cyclic neural network. Background technique [0002] Road coil traffic flow data has periodicity, time series and trend. At this stage, the method of complementing traffic flow data is mainly based on its timing. [0003] Based on the sequential traffic flow data completion, the data of a period of time before the current missing point is taken, and the data of the missing point is completed through the neural network. For example, if you want to complete the traffic flow data at 16:00 today, then take the data from 8:00 to 15:00 that day as input, and use the recurrent neural network to get the data at the next time point—16:00. This method of completion based on historical data makes good use of the time-series characteristics of data for completion, and the completion results are relatively good, but ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06Q50/26
CPCG06N3/049G06N3/08G06N3/084G06Q50/26G06N3/045
Inventor 申彦明徐文权齐恒尹宝才
Owner DALIAN UNIV OF TECH
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