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22 May 2024 Assessment of road safety performance based on CRITIC-TOPSIS-Kmeans model
Sophia Ding, Yuyang He, Haoyuan Sun, Kunyu Song, Zehong Xuan, Jianwei Zhang, Mingshu Zhang
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Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317634 (2024) https://doi.org/10.1117/12.3029065
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China
Abstract
The study assesses road safety performance in the Southeast Asian region by using the CRITIC-TOPSIS-Kmeans model. First, the weight of each indicator is obtained by CRITIC. Then, the obtained weights are embedded into the TOPSIS model. Furthermore, the TOPSIS sores are put into the K-means unsupervised machine learning model. The proposed CRITIC-TOPSIS-Kmeans model is utilized to rank and group the road safety performance of Southeast Asian countries. Finally, radar and bar plots are utilized to deconstruct indicators and TOPSIS scores, providing valuable references for policymakers. Overall, the development of the CRITIC-TOPSIS-Kmeans model not only establishes a fresh foundation for evaluating road safety achievements but also offers potential solutions for other MCDM challenges.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Sophia Ding, Yuyang He, Haoyuan Sun, Kunyu Song, Zehong Xuan, Jianwei Zhang, and Mingshu Zhang "Assessment of road safety performance based on CRITIC-TOPSIS-Kmeans model", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317634 (22 May 2024); https://doi.org/10.1117/12.3029065
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KEYWORDS
Safety
Roads
Performance modeling
Machine learning
Transportation
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