Object detection is the fundamental task of vision-based sensors in environmental perception and sensing. To leverage the full potential of roadside 4D MMW radars, an innovative traffic detection method is proposed based on their distinctive data characteristics. First, velocity-based filtering and region of interest (ROI) extraction were employed to filter and associate point data by merging the point cloud frames to enhance the point relationship. Then, the Louvain algorithm was used to divide the graph into modularity by converting the point cloud data into graph structure and amplifying the differences with the Gaussian kernel function. Finally, a detection augmentation method is introduced to address the problems of over-clustering and under-clustering based on the object ID characteristics of 4D MMW radar data. The experimental results showed that the proposed method obtained the highest average precision and F1 score: 98.15% and 98.58%, respectively. In addition, the proposed method showcased the lowest over-clustering and under-clustering errors in various traffic scenarios compared with the other detection methods.

The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet, Conv_LSTM, and TCN (temporal convolutional network). This paper proposed the WDA-DBN (water drop algorithm-Deep Belief Network) model and employed DEEPSHAP to identify factors with higher ASS information content. TCN and GRU performed well in the prediction. Compared to the other models, WDA-DBN exhibited the best performance in terms of MSE and MAE. Overall, deep learning models outperform econometric models in terms of information processing. The total time spent processing alarms positively influences ASS, while variables such as fatigue driving occurrences, abnormal driving occurrences, and nighttime driving alarm occurrences have a negative impact on ASS.