| dc.description.abstract | With urbanization, many traffic problems have resulted in casualties and economic losses. Therefore, traffic risk prediction and prevention have become important issues. Due to the development of the Internet of Things(IoT)and Artificial Intelligence (AI), Intelligent Transportation System(ITS)have become the trend for improving transporta- tion. People can not only use wireless communication devices to communicate in real-time to obtain the latest road condition information but also predict future traffic conditions through big data analysis.
However, many studies have used traffic spatio-temporal data to achieve traffic ac- cident prediction, but have not considered the spatio-temporal complexity, which leads to the inability to effectively extract traffic features. Given this, this study first uses the Pearson correlation coefficient to explore and prove that traffic accidents are highly cor- related with time and space. Next, we use multi-attribute traffic spatio-temporal data to predict traffic risks based on the CNN-LSTM model, which considers historical accidents, weather, traffic condition, time, and so on. And then we combined the feature embedding method to design and train the “CNN-LSTM Road Traffic Risk Prediction Model”, making the model suitable for the actual traffic environment. In terms of experimental simulation, this study analyzes and compares the performance of “single traffic accident data”and“multi-attribute traffic spatio-temporal data ”with different prediction models. The results show that the CNN-LSTM Road Traffic Risk Prediction Model can converge quickly and effectively with a lower loss value, Our model has better road traffic risk prediction performance; Finally, to further explore the relationship between different ac- cident factors and the severity of traffic accidents. This study compares three classifiers: decision tree, random forest, and XGBoost to classify and analyze the personal traffic accident data of drivers, and then explore the importance of traffic accident factors. | en_US |