Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.read more read lessĪbstract: "Harman's style often evokes that of a William James merged with the spirit of H.P. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. The experiments are performed both on synthetic and real dataset. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10-30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2-5 minutes) GPS trajectories. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. Abstract: Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map.
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