A Risk Prediction Model Based on Crash History Data for Railway Trams
作者机构：School of Computer Science and Engineering Xi’an University of Technology
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2023年第32卷第5期
页 面：963-971页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 082304[工学-载运工具运用工程] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程]
基 金：supported by the Joint Funds of the National Natural Science Foundation of China (U1934222, U20B2050, 62120106011) the National Natural Science Foundation of China (62072368) the Key Research and Development Program of Shaanxi Province (2019GY-032, 2022GY-040, 2021ZDLGY05-09)
主 题：Solid modeling Clustering algorithms Feature extraction Prediction algorithms Rail transportation Safety History
摘 要：Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network(LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%,which can greatly enhances the tram driving safety.
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