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Predicting duration of traffic accidents based on cost-sensitive Bayesian network and weighted K-nearest neighbor
| 题名： | Predicting duration of traffic accidents based on cost-sensitive Bayesian network and weighted K-nearest neighbor |
| 正文语种： | 英文 |
| 作者： | Li Kuang, Han Yan, Yujia Zhu, Shenmei Tu; Xiaoliang Fan |
| 作者单位： | School of Software, Central South University, Changsha, China; Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, Xiamen, China; Digital Fujian Institute of Urban Traffic Big Data Research, Xiamen University, Xiamen, China |
| 关键词： | Accident duration prediction; Bayesian network; cost-sensitive; KNN regression |
| 摘要： | With the development of urbanization, road congestion has become increasingly serious, and an important cause is the traffic accidents. In this article, we aim to predict the duration of traffic accidents given a set of historical records and the feature of the new accident, which can be collected from the vehicle sensors, in order to help guide the congestion and restore the road. Existing work on predicting the duration of accidents seldom consider the imbalance of samples, the interaction of attributes, and the cost-sensitive problem sufficiently. Therefore, in this article, we propose a two-level model, which consists of a cost-sensitive Bayesian network and a weighted K-nearest neighbor model, to predict the duration of accidents. After data preprocessing and variance analysis on the traffic accident data of Xiamen City in 2015, the model uses some important discrete attributes for classification, and then utilizes the remaining attributes for K-nearest neighbor regression prediction. The experiment results show that our proposed approach to predicting the duration of accidents achieves higher accuracy compared with classical models. |
| 出版年： | 2019 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 23 |
| 期： | 2 |
| 页码： | 161-174 |
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