International Journal of Injury Control and Safety Promotion This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework
Int J Inj Contr Saf Promot. 2023 Jul 26:1-10. doi: 10.1080/17457300.2023.2239211. Online ahead of print.ABSTRACTWith the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering processing of data sets, a recurrent neural network (RNN) model based on Keras framework is proposed to predict the injury severity in urban areas. First, with crash data from 2014 to 2017 in Nevada, OPTICS clustering algorithm is employed to extract the crash injury in L...
Source: International Journal of Injury Control and Safety Promotion - July 26, 2023 Category: Accident Prevention Authors: Dajie Zuo Cheng Qian Daiquan Xiao Xuecai Xu Hui Wang Source Type: research

Construction of injury process from Japanese consumer product narrative injury data using an ontology-based method
In this study, the descriptive framework of injury data (DFID) is expanded and combined with accident causation models used to elaborate on the causality of each injury factor. Subsequently, the injury process description ontology (IPD-Onto) based on DFID (extension) is established through a seven-step method developed by Stanford University. The IPD-Onto divides injury cases into five unified classes and constructs the injury process through the object properties. The ontology-based description of the injury process (with causal relationships) provides additional description and interpretation capabilities that are unders...
Source: International Journal of Injury Control and Safety Promotion - July 25, 2023 Category: Accident Prevention Authors: Xiaodong Feng Kun Zhang Fang Jiang Yoshiki Mikami Source Type: research

Clustering and pedestrian crashes prediction modelling: Amman case
This study analyses the spatial distribution of pedestrian casualties to define contributory factors and delineate the means for their prediction. Three years of crash data were collected along with other factors and analysed using kernel density estimation (KDE), spatial autocorrelation (Moran's I), cluster K-Means, spatial regression, and general linear regressions (GLM). Kernel density estimate defines a cluster of pedestrian deaths within 1250 meters. According to Moran's I, 17/22 attributes about casualties, road networks, demographics, and land use have positive values, indicating similar importance clustering. The s...
Source: International Journal of Injury Control and Safety Promotion - June 25, 2023 Category: Accident Prevention Authors: Lina Shbeeb Source Type: research