LSTM neural network short-term traffic flow prediction method based on ant colony algorithm optimization
An ant colony algorithm and neural network technology, which is applied in the field of short-term traffic flow prediction based on LSTM neural network optimization based on ant colony algorithm, can solve the problems of large deviation of predicted data, difficult to accurately predict traffic flow at intersections, etc. The effect of improving prediction accuracy and eliminating dimensional relations
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[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention.
[0049] Such as figure 1 , in an embodiment of the present invention, a kind of LSTM neural network short-term traffic flow prediction method based on ant colony algorithm optimization is provided, specifically comprising steps as follows:
[0050] Step 1) Import historical data from an intersection traffic database to Python's Pandas module for preprocessing, and group and aggregate according to time periods to eliminate data disorder, missing data and data errors in the original data; combine figure 2 , the inventive method at first calls the Pandas module of Python to carry out data preprocessing to original crossing traffic counter data, utilizes time series, car number matching to carry out repair operation to redundant, wrong data, and redundant data is de...
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