Traffic missing data completion method based on bidirectional recurrent neural network

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 ...

AI Technical Summary

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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