| The Traffic Collision Time Series Analysis Based on GIS Case study: Karaj-Qazvin Highway |
| Paper ID : 1217-SMPR |
| Authors: |
|
reza sanayei *1, Alireza Vafaeinejad2, JALAL KARAMI3, HOSSEIN AGHAMOHAMMADI4 1department of Remote Sensing and Geographic Information System,faculty of natural resources and environment, Science and Research Branch, Islamic Azad University, Tehran, Iran, 2Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran 3Department of Remote Sensing and Geographic Information System,Faculty of Humanities, Remote Sensing, Tarbiat Modares University, Tehran, 4Department of Remote Sensing and Geographic Information System, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran, |
| Abstract: |
| The application of Geographic Information Systems (GIS) in recent years have made an improvement in analyzing big traffic data, modelling traffic collisions and decreasing processing time in finding collision patterns. Accident predictions models for short and long time can help designing and programming traffic plans and decrease road accidents. Based on the above details, in this paper the Karaj-Qazvin highway accident data (1097 samples) and its patterns between 2009 and 2013 has 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. Also, Stata SE software is used for statistical analysis. The simulation results show in weekly scale, considering 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 accident can be created. Based on simulation results, the investigated model predicts the number of accident using two weeks ago data with the Root Mean Square Error (RMSE) equal to 3. |
| Keywords: |
| GIS, PACF, safety, time series, traffic collisions |
| Status : Conditional Accept (Poster) |
