基于边缘计算的城市交通事故风险深度预测

Urban Traffic Accident Risk Depth Prediction Based on Edge Computing

为了综合道路实时车辆信息和历史交通事故数据信息两个角度来预测交通事故风险,设计了一种基于边缘计算的城市交通事故风险深度预测模型。模型采用边缘计算技术对路面异常车辆信息和车流信息进行收集,并以此分析道路实时风险水平,同时采用LSTM深度神经网络预测模型对历史事故数据进行训练并预测城市不同区域未来时刻交通事故发生的频率,结合两者的预测结果得出区域内实时的交通风险情况。仿真结果显示,模型可以较为准确地预测出某一时间段城市交通事故多发区域,具有一定的可行性。

In order to predict traffic accident risk from the perspectives of real-time vehicle information and historical traffic accident data, a deep prediction model of urban traffic accident risk based on edge computing was designed. This model uses edge computing technology to collect the information of abnormal vehicles on the road and the traffic information for analysis on the real-time risk level of the road. At the same time, a deep neural network prediction model based on LSTM is adopted to train the historical accident data and predict thefrequency of traffic accidentoccurrence in different areas of the city. Thus, the real-time traffic risk prediction in a region can be obtained.The simulation results show that the model can accurately predict the urban traffic accident-prone areas in a certain period of time, which has a certain feasibility.