Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China.
Division of Biosciences, University College London, London WC1E 6BT, UK.
Int J Environ Res Public Health. 2021 Feb 3;18(4):1430. doi: 10.3390/ijerph18041430.
Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear relationships between traffic accident characteristics and land use properties, which are further interpreted to form local and general rules. More variables are taken into account as the input land use properties and the output traffic accident characteristics. Five types of traffic accident characteristics are simultaneously predicted with higher accuracy, and three levels of interpretation, including the hidden factor-traffic potential, the potential-determine factors, which varies between grid cells, and the general rules across the whole study area are analyzed. Based on the model, some interesting insights were revealed including the division line in the potential traffic accidents in Shenyang (China). It is also purposed that the relationship between land use and accidents differ from previous researches in the neighboring and regional aspects. Neighboring grids have strong spatial connections so that the relationship of accidents in a continuous area is relatively similar. In a larger region, the spatial location is found to have a great influence on the traffic accident and has a strong directionality.
预测和解释交通事故的空间位置和原因是当前交通安全领域的热门话题之一。本研究提出了一种多维长短时记忆神经网络模型（MDLSTM），以拟合交通事故特征与土地利用属性之间的非线性关系，并进一步解释形成局部和一般规则。更多的变量被视为输入的土地利用属性和输出的交通事故特征。该模型能够更准确地同时预测五种类型的交通事故特征，并分析了三个层次的解释，包括隐藏因素——交通潜力、在网格单元之间变化的潜力决定因素以及整个研究区域的一般规则。基于该模型，揭示了一些有趣的见解，包括在中国沈阳潜在交通事故的分界线。还提出了土地利用与事故之间的关系在邻域和区域方面与以往研究不同。相邻的网格具有很强的空间连接，因此连续区域的事故关系相对相似。在更大的区域中，空间位置被发现对交通事故有很大的影响，并且具有很强的方向性。
