Abstract
Road traffic accidents, especially expressway traffic accidents, have become a severe problem in China. Under this condition, identification of road traffic accident prone locations is in urgent need. This work proposes a modification of DBSCAN clustering algorithm with parameters \(\varepsilon \) and \(\text {minPts}\) carefully chosen for identifying traffic accident prone locations. Experimental results on traffic accident datasets of three national expressways are given, demonstrating the effectiveness of the proposed algorithm.
Y. Bao—This research was supported by Central Public-interest Scientific Institution Basal Research Fund 2016SJA07 and National Key Technologies R&D Program 2014BAG01B04.
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Qiu, C., Xu, H., Bao, Y. (2016). Modified-DBSCAN Clustering for Identifying Traffic Accident Prone Locations. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_11
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DOI: https://doi.org/10.1007/978-3-319-46257-8_11
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