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. 
A battery-friendly data acquisition model for vehicular speed estimation