Traffic Accident Prediction Based on LSTM-GBRT Model

• Published in: Journal of control science and engineering Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Zhang, Zhihao, Wushour, Silamu, Yang, Wenzhong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Fatalities; GNP; Gross National Product; Level (quantity); Level indicators; Methods; Neural networks; Population; Prediction models; Railroad accidents & safety; Regression analysis; Regression models; Research methodology; Roads & highways; Short term; System theory; Time series; Traffic accidents; Traffic accidents & safety; Traffic management; Traffic models; Traffic safety
• ISSN: 1687-5249; 1687-5257
• DOI: 10.1155/2020/4206919

Road traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help make scientific decisions. This paper establishes a traffic accident prediction model based on LSTM-GBRT (long short-term memory, gradient boosted regression trees) and predicts traffic accident safety level indicators by training traffic accident-related data. Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. The LSTM-GBRT model can accurately predict the safety level of traffic accidents, so that the traffic management department can better grasp the situation of traffic safety levels.