A Mobile Based Expert System to Estimate the
Travel Risk Applying Imbalance Data Classification
Sima Sharifirad, Master student of computer science
Amirkabir University of Technology, Tehran, Iran
s.sharifirad@yahoo.com
Mehdi Ghatee, Assistant Professor,
Department of Computer Science, Amirkabir University of
Technology, Tehran, Iran
ghatee@aut.ac.ir
In this paper the data of transportation and accidents in Tehran-Bazargan highway is considered. This dataset is
imbalance, i.e., there are two classes that one of them is outnumbered the second one. In these cases the minority class has
the highest value but the accuracy of algorithms for this class is very low and in fact they are incapable of classifying the
minority one. This study inspects firstly, traffic of the accidents in Tehran-Bazargan road that have been gathered by the
police between 2010 and 2013. Secondly, we propose a mobile based expert system that receives the GPS information of
mobile phone inside the vehicle and some properties of the road and based on the trained imbalanced classification
algorithms and important information which gathered from different stakeholders such as road and construction
organization, police and municipality. The proposed system predicts the risk of accidents for different segments based on
the real conditions. It is worth mentioning that in the algorithmic level of this system, ensemble and famous decision tree
algorithms are applied based on the different pre-processing methods and the suitable metrics for measurements of
superiority for each algorithm are evaluated using WEKA software. The results show that Random Forest, Decorate and
Bagging algorithms produce the best results.
Keywords—Data imbalance, GPS, accidents.