近年来，我国汽车保有量不断攀升，路网负载压力持续增大，交通事故频发，这对人民的生命财产安全造成了严重的威胁。因此，基于深度学习技术对交通事故预测问题展开研究。

针对交通事故预测模型设计不完善带来的事故特征学习不充足问题，提出一种基于多视图时空特征学习的交通事故风险预测模型MVST-RiskNet。通过构建多模块学习交通事故特征，利用可变形卷积网络和门控循环神经网络组合的方式建模事故动态时空相关性，使用流量差值特征学习交通事件带来的二次事故先兆性，并生成两个空间异质特征从空间层面建模区域之间的交通模式差异；同时引入可学习向量对天气状况信息进行高层语义增强，提高路况学习能力。实验表明，MVST-RiskNet在两个数据集上优于基准模型，能有效提取交通事故发生模式。

针对交通事故数据稀疏引起的模型零膨胀问题，提出一种基于联合学习的零膨胀优化策略FZOS。首先利用数据增强手段对事故特征进行变换，使得特征平滑化，提高模型学习能力。其次通过启发式的方法给不同风险等级的事故样本分配不同权重，引导模型偏向于学习事故模式，避免基于先验分配方法带来的误差扩散问题。同时结合平方绝对误差和均方误差损失函数的优点，通过设置多目标学习任务辅助提高模型鲁棒性。实验表明，使用FZOS策略的MVST-RiskNet模型效果优于其他零膨胀问题解决策略，FZOS具有一定的泛化能力。

最后在上述模型基础上，实现了区域网格级交通事故风险预警系统。系统由四个模块组成，分别是用户登录模块、数据管理模块、数据分析模块、事故预警模块。通过对已实现模块的功能测试，证明该系统可以快速有效的进行交通事故预警。

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.