Urban Road Traffic Accidents Prediction Based on Image Sequence Analysis
To further improve the accuracy of traffic accident prediction in road networks,a short-term traffic accident prediction method based on sequential image analysis is proposed.First,an oversampling technique is applied to interpolate traffic accident data collected from a WeChat mini-program to mitigate the impact of extensive zero values within the data on model training accuracy.These data are then integrated with road network traffic flow and accident-related attributes to generate stable time series as input for the model.A Bidirectional ConvLSTM U-Net with densely connected convolutions(BCDU-Net)is constructed.In this model,bidirectional ConvLSTM structures are used to integrate the features from the encoder and decoder layers,comprehensively capturing spatiotemporal correlations in the sequential data.Additionally,densely connected convolutions are employed to concatenate feature maps in the depth dimension,ensuring that each layer can directly access gradients from the loss function.Finally,the performance of the proposed model is evaluated by comparing the predicted results with actual traffic accident data.The results show that,compared to the Fully Connected Long Short-Term Memory(FC-LSTM)model,the Convolutional LSTM(ConvLSTM)model,and the U-Net model,the proposed model achieves reductions in cross-entropy loss of 65.96%,15.70%,and 3.47%,reductions in root mean square error of 21.48%,3.13%,and 1.71%,and increases in precision of 75.06%,11.82%,and 3.08%,respectively.It is demonstrated that the proposed method offers superior performance in predicting urban road traffic accidents.
intelligent transportationtraffic accident predictiondeep learningtraffic safetyBCDU-Net model
万方数据
维普
