Model Training and Road Condition Prediction Method, Device, Equipment, Medium and Program Product
A technology of road conditions and prediction models, applied in neural learning methods, biological neural network models, traffic flow detection, etc., can solve the problems of insufficient description of change law information in the time domain dimension, poor timeliness, and low congestion recall rate
Active Publication Date: 2022-05-31
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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Problems solved by technology
However, the existing methods are insufficient to describe the change information of road conditions in the time domain dimension, and there are problems such as poor timeliness and low congestion recall rate, which urgently need to be improved.
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[0040] Exemplary embodiments of the present application are described below with reference to the accompanying drawings, including various embodiments of the present application.
[0042] S101, according to the sample road information, determine the time series feature representation of the sample road and the road condition label of the sample road.
[0043] Among them, the so-called sample road is a road as a training sample, and the sample road can be one or more
[0050] The so-called original model may be a road condition prediction model that has been constructed but not trained. optional, as shown in Figure 1B
[0061] FIG. 2 is a flowchart of another method for training a road condition prediction model provided according to an embodiment of the present application. the truth
[0067] S203, according to the sample road information, determine the road condition label of the sample road.
[0069] Wherein, the coding temporal neural network includes at least two temporally c...
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The application discloses a model training and road condition prediction method, device, equipment, medium, and program product, which relate to the field of artificial intelligence, especially to the technical fields of intelligent transportation and deep learning. The specific implementation plan is: according to the sample road information, determine the time series feature representation of the sample road and the road condition label of the sample road; input the time series feature representation of the sample road into the encoding time series neural network in the original model to obtain the encoding transfer parameters; Input the decoded temporal neural network in the original model to obtain the decoded feature representation; and input the decoded feature representation into the predicted neural network in the original model to obtain the predicted road conditions of the sample road; wherein the encoded temporal neural network includes at least two temporally connected sub-networks Encoding network; according to the predicted road conditions and road condition labels of the sample roads, the original model is trained to obtain a road condition prediction model. In order to improve the accuracy of road condition prediction.
Description
Model training and road condition prediction method, device, equipment, medium and program product technical field [0001] This application relates to the field of computer technology, in particular to the technical fields of artificial intelligence, intelligent transportation and deep learning. Specifically, it relates to a model training and road condition prediction method, device, equipment, medium and program product. Background technique With the development of artificial intelligence technology, broadcasting real-time road conditions and predicting road conditions to users has become the field of intelligent transportation. an integral part of. At present, when the prior art predicts road conditions, a machine learning model is usually used according to the historical road conditions of the road to be predicted. state (such as congested, slow or smooth), to predict the current road state, for example, using a machine learning model based on road A near one Th...
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IPC IPC(8): G08G1/01G08G1/052G08G1/065G06N3/04G06N3/08G06F17/16
CPCG08G1/0129G08G1/0137G08G1/052G08G1/065G06N3/08G06F17/16G06N3/045
Inventor 暴雨梁海金杨玲玲李成洲刘子昊宋雨坤
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD