Prediction model construction method, traffic flow prediction method, device and electronic equipment

Abstract

The embodiment of the invention provides a prediction model construction method, a traffic flow prediction method, a traffic flow prediction device and electronic equipment.. The prediction model construction method comprises the steps of training a first network based on road network data to be tested and source road network data; wherein the to-be-detected road network data represents a road network structure of a to-be-detected field and flow information of a road section corresponding to the road network structure, and the source road network data represents the road network structure of a source field and the flow information of the road section corresponding to the road network structure at a plurality of time nodes; training a second network based on a second data feature of the source domain; wherein the second data feature represents the characteristics of the road network structure in the source field, and the second data feature is consistent with the dimension of the first data feature representing the characteristics of the road network structure in the field to be detected; and taking the output of the first network as the input of the second network to obtain a prediction model of the traffic flow in the to-be-tested field. The technical effects that the obtained prediction model has the migration characteristic and the prediction cost of the traffic flow is reduced are achieved.

Description

technical field [0001] The present application relates to the field of transportation, in particular, to a method for constructing a prediction model, a method for predicting traffic flow, a device, and electronic equipment. Background technique [0002] Traffic flow prediction, especially short-term traffic flow prediction, is the basis of urban traffic control and guidance. Accurate prediction of road traffic congestion in a short period of time (within 10-30 minutes) has strong practical significance for traffic management. The occurrence of traffic flow is affected by many factors, including weather conditions, holidays, rush hours, large-scale urban activities, urban road maintenance, traffic accidents, etc., which are characterized by complexity and uncertainty. Accurate prediction of traffic flow is a A technical problem. [0003] At present, the prediction model based on time series training is mainly used to predict traffic flow. However, the prediction of traffic ...

AI Technical Summary

Problems solved by technology

[0003] At present, the prediction model based on time series training is mainly used to predict traffic flow. However, the prediction of traffic flow in this way is highly dependent on the road network, and the prediction model does not have migration characteristics, that is, it is not suitable for road networks with new structures. , the traffic flow prediction accuracy for the new structured road network is low

For a new road network, a large amount of historical data of the road network is required to retrain the prediction model, which is expensive

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