Taxi passenger flow prediction method

Abstract

The invention provides a taxi passenger flow prediction method, which comprises the following steps of: firstly, splicing get-on passenger flow and get-off passenger flow into the same dimension, performing spatial node serialization, normalizing data to obtain original data, and dividing the original data into training data and test data; secondly, performing spatial attention feature extraction on the training data by adopting a spatial multi-head attention algorithm to obtain global spatial features; flattening the global spatial features and the training data into the same dimension, and performing time feature extraction by adopting a first multi-layer perceptron to obtain global time features; and inputting the global time features and the training data into a second multi-layer perceptron for iterative training to obtain a trained flow prediction model, and inputting the test data into the prediction model to obtain a prediction result. The method can effectively extract the global spatial features and the global time features, and can achieve the high-precision prediction of the taxi passenger flow.

Description

technical field [0001] The invention relates to the technical field of traffic spatio-temporal data prediction, in particular to a taxi passenger flow prediction method. Background technique [0002] Traffic spatio-temporal data prediction is an important issue in the field of intelligent transportation. Traffic spatio-temporal data is an important indicator of traffic status. Short-term traffic spatio-temporal data prediction is an important content of traffic management and guidance. By predicting the passenger flow of taxis in various areas of the city in advance, traffic guidance can be effectively carried out, and vehicles in places with abundant taxi resources can be allocated to places with insufficient taxi resources, reducing passenger waiting time and improving urban traffic operation efficiency. [0003] Due to the complexity of traffic spatio-temporal data prediction, there are already many prediction models, and graph neural network is often used to extract spat...

AI Technical Summary

Problems solved by technology

[0004] The invention provides a taxi passenger flow forecasting method. The invention aims to solve the problem that it is difficult to effectively extract the correlation of data spatial features due to the fixed prior knowledge of the graph neural network in the traffic spatio-temporal data forecasting, so as to improve the prediction Accuracy, get better prediction results

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