Unsupervised clustering ensemble for traffic level prediction

• Affiliation of Author(s):西南交通大学
• Journal:Machine Learning, Multi Agent and Cyber Physical Systems: Proceedings of the 15th International FLINS Conference (FLINS 2022)
• Place of Publication:Tianjin, China
• Key Words:Clustering ensemble; traffic accident prediction; machine learning
• Abstract:Traffic accidents are still an important cause of death. Predicting the severity of possible traffic accidents is helpful to speed up the decision-making of accident treatment plans and reduce casualties. Therefore, it is expected to establish a sufficient and reliable severity prediction model in traffic accidents. Up to now, there are a lot of research about traffic accident prediction. But traditional methods are susceptible to noise and not efficient enough. To conquer this challenge, we utilized a multi-diversified clustering ensemble approach to predict traffic accidents. Finally, 12 real datasets and 7 algorithms are used to carry out extensive comparison experiments, whose results show that clustering results generated by MDEC HC have better robustness and accuracy.
• Co-author:Jin Guo,Yueying Li,Ran Hao
• First Author:Jian Wang
• Indexed by:学术论文
• Correspondence Author:Hongjun Wang
• Discipline:Engineering
• Document Type:C
• Page Number:560-570
• Translation or Not:no