Abstract
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.
Highlights
IntroductionRes. Public Health 2021, According to the report published by the World Health Organization (WHO), road traffic crashes result in the deaths of approximately 1.35 million people around the world each year and leave between 20 and 50 million people with non-fatal injuries [1]
The land use dataset is compiled from the point of interest (POI) data, the evening peak traffic flow data and road maps, which are collected from Open Street Map (OSM)
The premise of this study is is that traffic accidents have significant spatial auto-correlation, The premise premise of of this this study study that traffic accidents have significant spatial auto-correThe is that traffic accidents have significant spatial auto-correwhich give risegive to the assumption that the multiple causes ofcauses traffic accidents are spalation, which rise to the assumption that the multiple of traffic accidents are lation, which give rise to the assumption that the multiple causes of traffic accidents are tial aggregates, and the spatial influence of such traffic accidents contains many valuable spatial aggregates, and the spatial influence of such traffic accidents contains many spatial aggregates, and the spatial influence of such traffic accidents contains many factors that are not
Summary
Res. Public Health 2021, According to the report published by the World Health Organization (WHO), road traffic crashes result in the deaths of approximately 1.35 million people around the world each year and leave between 20 and 50 million people with non-fatal injuries [1]. Factors affecting traffic accidents can be divided into subjective and objective aspects at the macroscopic level. The objective aspects mainly include regional characteristics, road network characteristics, climate characteristics and so on. The subjective aspects mainly include human operation errors, violations of regulations, negligence, vehicle technical reasons and so on. The involvement of multiple influencing factors complicates the prediction and analysis of traffic accidents, and makes it difficult to strip out the influence of any one of these factors. Current research is centred on quantitatively analyzing the conditions of different influencing factors and elucidating the most influential factors [2], gaps in this area of knowledge remain
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