Predicting and Interpreting Spatial Accidents through MDLSTM

Tianzheng Xiao, Huapu Lu, Jianyu Wang and Katrina Wang
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Tianzheng Xiao: Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China
Huapu Lu: Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China
Jianyu Wang: Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China
Katrina Wang: Division of Biosciences, University College London, London WC1E 6BT, UK

IJERPH, 2021, vol. 18, issue 4, 1-18

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.

Keywords: traffic accident; MDLSTM; spatial; interpretation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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