:: Volume 3, Issue 2 (9-2015) ::
jgit 2015, 3(2): 43-59 Back to browse issues page
Forecasting Traffic Load using GPS data, a data mining approach
Zahra Mahdavian *, Aliakbar Niknafs
Graduate University of Advanced Technology
Abstract:   (4424 Views)

In today’s world,  rapid increase of urbanization and traffic challenges have led to the profound need of traffic control systems with the highest possible productivity and efficiency. Time loss and increased fuel consumption as well as air and noise pollutions have made traffic control to be one of the most important current issues in the world. One of the best possible methods for reaching this objective is to predict the directions and the final destination of the car. If the future position of a car can be predicted, traffic estimation in an urban zone will be a simple task. Route prediction is possible based on the previous routes of the car as well as parameters such as the starting point, time, day, month, and duration utilizing data mining methods and artificial neural networks .The current paper uses real GPS data obtained from different cars in order to carry out prediction operation of the route and final destination. One of the propoesd methods in this study is to establish a database for the previous routes of the cars using ArcGIS software The high accuracy of recording the previous routes of the cars in this database increases the accuracy of the route prediction process.In this study, two distinct databases were established. The first involves general database, where only the more challenging sections of the roads including intersections and crossroads are considered in the second which is the more complex database.Moreover, in order to carry out the prediction operation, association rules algorithms as well as artificial neural network algorithms have been used. The obtained results indicate the high accuracy of the prediction.  Artificial Neutral Network (ANN) algorithms used on the general database and the GRI algorithm used on the more complex one provide better results, respectively. Both algorithms  acquires  precision greater than 95%. The results obtained from the prediction process can be used for traffic planning and the optimization of car movements.

Keywords: Traffic, Data Mining, Prediction, GPS
Full-Text [PDF 1202 kb]   (2176 Downloads)    
Type of Study: Research |
Received: 2016/03/11 | Accepted: 2016/03/11 | Published: 2016/03/11



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Volume 3, Issue 2 (9-2015) Back to browse issues page