Aggregating and Sampling Methods for Processing GPS Data Streams for Traffic State Estimation

Because of significant improvements in cost, accuracy, and coverage over dedicated traffic infrastructures, GPS-enabled mobile devices are preferred for continuous collection of traffic data. Estimating traffic states accurately from the obtained GPS data streams has great potential to increase efficiency of the existing traffic systems and to help reduce commuting time and fuel consumption. In this paper, first we propose a novel method to reasonably process GPS data by increasing weights of recent records and high velocity, rather than employing the current two extreme and popular approaches: the naive method aggregating all records with equal weights and the sliding-window (SW) sampling method preserving only the most recent records. Then, in line with the existing works, the proposed weighted approach is explored in two ways: aggregate-based and sampling-based ways. The aggregate-based way is classical but somewhat specific to the particular goal of traffic state estimation, whereas the sampling-based way is somewhat complicated but provides a universal set of samples for performing a variety of analyses. In the sampling-based way, a heuristic method is proposed to accurately estimate traffic states using preserved samples. Both ways are leveraged to evaluate performance of the novel weighed method and the heuristic method for estimating traffic states using samples. Finally, the feasibility and effectiveness of these methods is experimentally validated using a field-experiment data set (Mobile Century) and three simulated data sets.