Volume 32 Issue 5
Sep.  2023
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Article Contents
JI Wenjiang, YANG Jiangcheng, WANG Yichuan, et al., “A Risk Prediction Model Based on Crash History Data for Railway Trams,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 963-971, 2023, doi: 10.23919/cje.2022.00.231
Citation: JI Wenjiang, YANG Jiangcheng, WANG Yichuan, et al., “A Risk Prediction Model Based on Crash History Data for Railway Trams,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 963-971, 2023, doi: 10.23919/cje.2022.00.231

A Risk Prediction Model Based on Crash History Data for Railway Trams

doi: 10.23919/cje.2022.00.231
Funds:  This work was supported by the Joint Funds of the National Natural Science Foundation of China (U1934222, U20B2050, 62120106011), the National Natural Science Foundation of China (62072368), and the Key Research and Development Program of Shaanxi Province (2019GY-032, 2022GY-040, 2021ZDLGY05-09).
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  • Author Bio:

    Wenjiang JI was born in 1984. He received the B.S. degree from Xidian University in 2006 and the Ph.D. degree from Xidian University in 2013. He is currently a Lecturer in Xi’an University of Technology. His current research interests includes intelligent and safety-critical system, safety and security in railway transportation system. (Email: wjj@xaut.edu.cn)

    Jiangcheng YANG was born in 1997. He is currently studying at the School of Computer Science and Engineering, Xi’an University of Technology. His research interests include intelligent transportation system and data mining. (Email: 18161856106@163.com)

    Yichuan WANG was born in 1983. He received the Ph.D. degree in computer system architecture from Xidian University of China in 2014. Now he is a Lecturer in Xi’an University of Technology. His research interests include cloud computing and networks security. (Email: chuan@xaut.edu.cn)

    Lei ZHU was born in 1983. He received the Ph.D. degree in computer science and technology from Xi’an Jiaotong University in 2014. He is currently working at Xi’an University of Technology. His research interests include intelligent transportation system, data mining, and graph mining. (Email: leizhu@xaut.edu.cn)

    Yuan QIU was born in 1983. He received the B.S. degree from Xidian University in 2006 and Ph.D. degree from the University of Electro-Communications in 2017. He is currently a Lecturer in Xi’an University of Technology. His research interests include spatiotemporal data retrieval and data mining. (Email: qiuyuan@xaut.edu.cn)

    Xinhong HEI (corresponding author) was born in 1976. He received the B.S. degree and M.S. degree in computer science and technology from Xi’an University of Technology in 1998 and 2003, respectively, and the Ph.D. degree from Nihon University, Japan, in 2008. He is currently a Professor with the Faculty of Computer Science and Engineering, Xi’an University of Technology. His current research interests include intelligent transportation systems, and safety-critical system. (Email: heixinhong@xaut.edu.cn)

  • Received Date: 2022-07-25
  • Accepted Date: 2022-11-07
  • Available Online: 2023-01-18
  • Publish Date: 2023-09-05
  • 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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