Training method of road condition prediction model, and road condition prediction method and device
A prediction model and training method technology, applied in neural learning methods, biological neural network models, traffic flow detection, etc., can solve the problem of single prediction results, achieve the effect of improving model prediction accuracy and enriching model prediction results
Pending Publication Date: 2022-04-05
广州海格星航信息科技有限公司
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[0004] This application provides a training method for a road condition prediction model, a road condition prediction met
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[0053] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.
[0054]As described in the background art, the current data-driven solution uses historical traffic condition information as training data, such as historical road travel time and traffic flow. However, with the traffic state information as the training data, the features that the model can learn are too single, and what it predicts is only the road travel time between the departure point and the destination point. However, urban road conditions a...
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The invention discloses a training method of a road condition prediction model and a road condition prediction method and device, and the method comprises the steps: carrying out the data transformation of a historical road condition training set, obtaining a plurality of target image data, carrying out the feature extraction of the plurality of target image data through a preset residual image convolution model, obtaining a plurality of residual images, and carrying out the feature extraction of the plurality of target image data. Capturing data characteristics with direction relevance by utilizing a spatial dependency relationship of the graph data so as to combine time relevance characteristics of historical data of a plurality of time periods; and performing fusion and full connection on the plurality of residual images, outputting a visual road condition prediction value, calculating a loss function of the residual image convolution model according to the visual road condition prediction value, and updating model parameters of the residual image convolution model based on the loss function until the residual image convolution model reaches a preset convergence condition, thereby obtaining a road condition prediction model. Therefore, global features such as time and space are fully utilized, a prediction model with visual road condition features is constructed, model prediction results are enriched, and model prediction accuracy is improved.
Description
technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to a training method for a road condition prediction model, a road condition prediction method and a device. Background technique [0002] Before the user travels, by predicting the city's road conditions, it can help the user judge the convenience and safety of travel, so that the user can obtain a better travel experience. Road condition prediction schemes mainly include knowledge-driven schemes and data-driven schemes. Data-driven schemes are to establish road condition data sets, and predict road conditions by training model statistics and learning the relationship between historical road conditions and future road conditions. [0003] The current data-driven scheme uses historical traffic condition information as training data, such as historical road passing time and traffic volume. However, with the traffic status information as the training dat...
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Login to View More IPC IPC(8): G06V20/54G06V10/62G06V10/80G06V10/82G06N3/04G06N3/08G08G1/01
Inventor 林钢鑫金吉成陈文浩曾繁玉刘圣阳周炜高山
Owner 广州海格星航信息科技有限公司