An Urban Road Segment Determination Method Based on Cellular Floating Vehicle Data for Tracking Mobile Stations

The rise of traffic information requirements has led to improve Intelligent Transportation System (ITS) which is developed to collect real-time traffic information based on various techniques. Cellular Floating Vehicle Data (CFVD) which is one of these techniques analyzes the cellular network data to estimate real-time traffic information with the higher coverage and the lower cost. Therefore, this study proposes an urban road segment determination method based on CFVD for tracking Mobile Stations (MSs). First, the cell ID and timestamp of cellular network signals (e.g., Handovers) which are generated by MS are recorded. Data mining technique is used to analyze the data for determining the road segment which is driven by MS user. The experiment results show the average accuracy of proposed method which is 93% is better than naive Bayes classification, decision tree, support vector machine, and back-propagation neural network. Therefore, the proposed road segment determination method based on CFVD can be used to track MSs and estimate traffic information for ITS.