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

Abstract

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.

Applications of Internet of Things

“…Three papers on ITS are as follows: (1) "Vehicle positioning and speed estimation based on cellular network signals for urban roads," by Lai and Kuo [41]; (2) "A method for traffic congestion clustering judgment based on grey relational analysis," by Zhang et al [42]; and (3) "Smartphone-based pedestrian's avoidance behavior recognition towards opportunistic road anomaly detection," by Ishikawa and Fujinami [43].…”

“…The location and vehicle speed can be estimated by the k-nearest neighbor algorithm in accordance with the CFVD. In experimental environments, six urban road segments in Kaohsiung and Pingtung in Taiwan were driven in 27 runs for the evaluation of the proposed methods, and the results showed that the accuracies of vehicle positioning and speed estimation were 100% and 83.81%, respectively [41].…”

“…Therefore, the aim of this special issue is to introduce the readers a number of papers on various aspects of IoT applications. This special issue has received a total of 23 submitted papers with only 8 papers [41][42][43][44][45][46][47][48] accepted. A high rejection rate of 65.21% of this issue from the review process is to ensure that high-quality papers with significant results are selected and published.…”