Personalized traffic accident risk prediction and recommendation method based on depth learning

Abstract

The invention discloses a personalized traffic accident risk prediction recommendation method based on depth learning, which comprises the following steps: dividing a city into grid regions; calculating traffic accident data, traffic flow data and weather characteristic data of each grid region and each period; using the depth learning method to train the model, the traffic accident risk prediction model being obtained. According to the traffic accident data, traffic flow data and weather characteristics data input at the present time, the traffic accident risk prediction model is used to calculate the traffic accident risk prediction at the next time. The invention utilizes the depth learning method to learn the non-linear, high-dimensional and complex correlation relationship between thetraffic accident influence factor and the traffic accident, predicts the traffic accident risk at the city level, and improves the accuracy of the prediction result.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a personalized traffic accident risk prediction and recommendation method based on deep learning. Background technique [0002] In recent years, with the development of social economy, the national car ownership has been increasing. Although the increase in car ownership has brought convenience to transportation, it has also caused road congestion and an increase in traffic accidents. , Not only will it cause loss of personnel and property, but also further bring traffic congestion. By predicting the risk of traffic accidents in urban areas, it can provide timely route suggestions for people to travel and avoid areas prone to traffic accidents. [0003] In the existing work, people have done a lot of research on the analysis of traffic accidents. These works can be roughly divided into two categories: one is to study the causative factors of urban traffic accidents (driver...

AI Technical Summary

Problems solved by technology

[0004] The existing traffic accident risk prediction methods have the following defects: 1) the existing methods are mainly based on traditional machine learning methods, and it is difficult to learn the high-dimensional nonlinear relationship between the causative factors of traffic accidents; 2) the existing Most of the methods only focus on the prediction of traffic accidents on partially closed roads, and the prediction of traffic accidents at the city level is less; 3) The existing methods lack consideration of the strong correlation between the adjacent areas of traffic flow in space, and the weak correlation between distant areas specialty

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