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ISPRS Int. J. Geo-Inf. 2016, 5(10), 181; doi:10.3390/ijgi5100181

Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads

Department of Computer Science and Engineering, National Sun Yat Sen University, Kaohsiung 804, Taiwan
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Author to whom correspondence should be addressed.
Academic Editors: Chi-Hua Chen, Kuen-Rong Lo and Wolfgang Kainz
Received: 20 June 2016 / Revised: 11 September 2016 / Accepted: 28 September 2016 / Published: 2 October 2016
(This article belongs to the Special Issue Applications of Internet of Things)
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Abstract

In recent years, cellular floating vehicle data (CFVD) has been a popular traffic information estimation technique to analyze cellular network data and to provide real-time traffic information with higher coverage and lower cost. Therefore, this study proposes vehicle positioning and speed estimation methods to capture CFVD and to track mobile stations (MS) for intelligent transportation systems (ITS). Three features of CFVD, which include the IDs, sequence, and cell dwell time of connected cells from the signals of MS communication, are extracted and analyzed. The feature of sequence can be used to judge urban road direction, and the feature of cell dwell time can be applied to discriminate proximal urban roads. The experiment results show the accuracy of the proposed vehicle positioning method, which is 100% better than other popular machine learning methods (e.g., naive Bayes classification, decision tree, support vector machine, and back-propagation neural network). Furthermore, the accuracy of the proposed method with all features (i.e., the IDs, sequence, and cell dwell time of connected cells) is 83.81% for speed estimation. Therefore, the proposed methods based on CFVD are suitable for detecting the status of urban road traffic. View Full-Text
Keywords: intelligent transportation system; cellular networks; vehicle positioning; speed estimation; machine learning intelligent transportation system; cellular networks; vehicle positioning; speed estimation; machine learning
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Lai, W.-K.; Kuo, T.-H. Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads. ISPRS Int. J. Geo-Inf. 2016, 5, 181.

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