Attention-Based Residual Dilated Network for Traffic Accident Prediction
نویسندگان
چکیده
Traffic accidents directly influence public safety and economic development; thus, the prevention of traffic is great importance in urban transportation. The accurate prediction can assist departments to better control prevent accidents. Thus, this paper proposes a deep learning method named attention-based residual dilated network (ARDN), extract essential information from multi-source datasets enhance accident accuracy. utilizes bidirectional long short-term memory model sequential incorporates an attention mechanism recalibrate weights. Furthermore, layer adopted capture term effectively. Feature encoding also employed incorporate natural language descriptions point-of-interest data. Experimental evaluations collected Austin Houston demonstrate that ARDN outperforms range machine methods, such as logistic regression, gradient boosting, Xgboost, methods. ablation experiments further confirm indispensability each component proposed method.
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ژورنال
عنوان ژورنال: Mathematics
سال: 2023
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math11092011