Traffic Accident Prediction Based On Deep Learning

In recent years,the number of cars in my country has continued to rise,the load pressure on the road network has continued to increase,and traffic accidents have occurred frequently,which has posed a serious threat to the safety of people’s lives and properties.Therefore,the problem of traffic accident prediction is studied based on deep learning technology.Aiming at the problem of insufficient accident feature learning caused by the imperfect design of the traffic accident prediction model,a traffic accident risk prediction model MVST-RiskNet based on multi-view spatial-temporal feature learning is proposed.The features of traffic accidents are learned by constructing multiple modules,the dynamic spatiotemporal correlation of accidents is modeled by the combination of deformable convolutional network and gated recurrent neural network,and the premonition of secondary accidents caused by traffic events are learned by using flow difference features.Two spatially heterogeneous features are generated to model the traffic pattern differences between regions at the spatial level;at the same time,learnable vectors are introduced to enhance the high-level semantics of weather condition information to improve the ability to learn road conditions.Experiments show that MVST-RiskNet outperforms the baseline model on two datasets and can effectively extract traffic accident occurrence patterns.Aiming at the zero-inflation problem of model training caused by the sparsity of accident data,a federated learning based zero-inflation optimization strategy FZOS is proposed.Firstly,the accident features are transformed by means of data augmentation,so that the features are smoothed and the learning ability of the model is improved.Secondly,different weights are assigned to accident samples with different risk levels through a heuristic method,which guides the model to learn accident patterns and avoids the problem of error diffusion caused by prior assignment methods.At the same time,combining the advantages of the squared absolute error and the mean squared error loss function,the robustness of the model is improved by setting a multi-objective learning task.Experiments show that the MVST-RiskNet using the FZOS is better than other zero-inflated problem solving strategies,and FZOS has a certain generalization ability.Finally,based on the above algorithm,a regional traffic accident risk early warning system is implemented.The system consists of four modules,namely user login module,data management module,data analysis module,and accident early warning module.By displaying the functions of the realized modules,it is proved that the system can quickly and effectively carry out traffic accident early warning.