The Relationship Between Traffic Flow Forecasting and Traffic Accident Forecasting and the Possible Combination Points - Wu Haoyu (ITM Web of Conferences)
This paper mainly studies the relationship between traffic flow forecasting and traffic accident forecasting and the possible combination points. Firstly, through the introduction of the background, the necessity of combining the two is analyzed. Secondly, the differences between neural networks and traditional machine learning are compared in terms of model structure, feature extraction, applicability, computational resources and time, and model complexity'. This paper emphasizes the importance of neural networks for traffic flow forecasting and traffic accident forecasting and introduces the commonly used neural networks. Then, the concept of temporal and spatial characteristics in traffic data is expounded, which opens the relationship between traffic flow forecasting and traffic accident forecasting and the analysis of possible combination points. Through the analysis of the existing research, it is found that the relationship between the two is mainly divided mto data complementarity, common goal, and common method, and the possible combination points of the two are analyzed and prospected. Finally, the data and methods of improving traffic flow forecasting are summarized, which is conducive to better traffic flow forecasting and the combination of the two.