A battery-friendly data acquisition model for vehicular speed estimation. (February 2016) - Kaya, Sevgi; Kilic, Necati; Kocak, Taskin; Gungor, Cagri
Highlights: Evaluate the effectiveness of using cellular positioning techniques along with GPS-sensor based data collection via performing experiments on energy consumption and location accuracy for various data acquisition modes where only GPS positioning, only cellular positioning and both of the techniques are executed. Our proposed system is designed to be used for average velocity calculation which is an essential parameter in traffic monitoring systems. Therefore, we perform experiments to compute the average velocity by extracting location data with the hybrid data acquisition model. We assume average speed measurements obtained from GPS positioning as the ground truth data to assess the performance of our model. Assess the feasibility of cellular positioning in cases where the GPS sensors are not available for location estimation. In this paper, we propose a hybrid acquisition model which applies cellular positioning techniques to obtain the raw location data where GPS sensor is not available or battery of a particular smart phone is too low for location lookup. Abstract: Modeling traffic flow and gathering accurate traffic congestion information are two challenging problems in smart transportation systems. Most of the traffic flow models and velocity estimation methodologies that have been proposed so far gather the data from GPS-equipped smart phones and extract the flow model based on GPS sampling. However, these approaches tend to fail in real life scenarios due to the insufficient vehicle data and unpredictable dynamics of the flow. Furthermore, utilization of GPS sensor leads to a battery drainage and hence reduces the overall system performance. In this paper, we propose a new battery-friendly data acquisition model to obtain the raw data. We then evaluate our model under various traffic conditions to determine its feasibility in vehicle speed estimation. The proposed model results in 88% location accuracy whereas it reduces the battery consumption by half.