Improving Traffic Safety through Traffic Accident Risk Assessment
نویسندگان
چکیده
The continuous development of sensors and the Internet Things has produced a large amount traffic data with location information. improvement safety benefits from availability accident data. Managers can patrol control relevant areas in advance limited police resources, according to short-term predictions. As result, possibility accidents be reduced, level improved. traditional approach prediction relies too much on subjective experience managers. Inspired by deep learning technology field computer vision, this study first divides road network into regular grids takes number each grid as pixel value an image. Then, based bi-directional ConvLSTM U-Net densely connected convolutions (BCDU-Net) is proposed. This method mines information hidden introduces further extract spatial-temporal features contained sequence. Thus, issues gradient disappearance model over-fitting caused training avoided. Finally, simulation experiment carried out historical Yinzhou District, Ningbo City. Results show that BCDU-Net better accuracy precision than other models three sets: motor vehicle accidents, non-motor single-vehicle accidents. Therefore, more suitable for good application prospects improving safety.
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ژورنال
عنوان ژورنال: Sustainability
سال: 2023
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su15043748