Abstract
Abstract. The application of Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF) in recent years has been improved in analyzing big traffic data, modelling traffic collisions and decreasing processing time in finding collision patterns. Accident prediction models for short and long time can help in designing and programming traffic plans and decreasing road accidents. Based on the above details, in this paper, the Karaj-Qazvin highway accident data (1097 samples) and its patterns from 2009 to 2013 have been analyzed using time series methods.In the first step, using auto correlation function (ACF) and partial auto correlation function (PACF), the rank of time series model supposed to be autoregressive (AR) model and in the second stage, its coefficients were found. In order to extract the accident data, ArcGIS software was run. Furthermore, MATLAB software was used to find the model rank and its coefficients. In addition, Stata SE software was used for statistical analysis. The simulation results showed that on the weekly scale, based on the trend and periodic pattern of data, the model type and rank, ACF and PACF values, an accurate weekly hybrid model (time series and PACF) of an accident can be created. Based on simulation results, the investigated model predicts the number of accident using two prior week data with the Root Mean Square Error (RMSE) equal to three.
Highlights
The growing trend of traffic accidents is considered a serious public health problem in most countries of the world (World Health Organization, 2013 ; World Health Organization, 2004)
The causes of accidents in some countries are proportional to the rapid economic growth, which include the rapid increase in the number of motor vehicles, increased exposure to hazardous factors such as speed and alcohol consumption, as well as inadequate traffic safety laws (Chisholm et al, 2012)
The novelty of this study is to provide a model of traffic collision data model with mean square error of incidents, using Geographic Information System (GIS) on a weekly scale, taking into account the trend and period components, Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF) in a combined method
Summary
The growing trend of traffic accidents is considered a serious public health problem in most countries of the world (World Health Organization, 2013 ; World Health Organization, 2004). Many studies have so far been conducted in Iran and the world on the factors affecting the deaths from traffic accidents. These studies have been in areas such as human behavior, road safety, vehicle safety, traffic management, all of which reflect the requirements for reviewing and drafting laws and regulations and their implementation. Since 2007, losses due to traffic accidents on the roads in Iran have been declining (Bahadorimonfared et al, 2013) This decreasing trend is considerable when 15% of growth and increase in the production of vehicles is taken into account. The main objective of this research was to investigate the traffic accidents and consequent traffic accidents using Karaj-Qazvin highway data (study area) and time series
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