Open AccessMultivariate Forecasting of Road Accidents Based on Geographically Separated Data
Author(s) -
Katherina Meißner,
Julia Rieck
Publication year - 2021
Publication title -
vietnam journal of computer science
Language(s) - English
Resource type - Journals
eISSN - 2196-8888
pISSN - 2196-8896
DOI - 10.1142/s2196888821500196
Subject(s) - autoregressive integrated moving average , computer science , multivariate statistics , accident (philosophy) , interdependence , time series , road accident , operations research , transport engineering , machine learning , engineering , philosophy , epistemology , political science , law
As road accidents are the leading cause of death for young adults all over the world, it is necessary for the police to evaluate the accident circumstances carefully in order to take appropriate prevention measures. The circumstances of an accident vary in their frequency over time and depend on the local conditions at the accident site. An evaluation under geographical and temporal aspects is therefore necessary. On the basis of the time series, we investigate the various accident circumstances, which show interdependencies with each other, and their influence on the number of accidents. Moreover, a multivariate forecasting is used to indicate the future progression of accidents in different geographical regions. Forecast values are determined with a special extension of the ARIMA method. In order to identify geographical regions of interest, we present two different concepts for segmentation of accident data, which allow the adaptation of police measures to local characteristics.