GPS (Global Positioning System) trajectories with low sampling rates are prevalent in many applications. However, current map matching methods do not perform well for low-sampling-rate GPS trajectories due to the large uncertainty between consecutive GPS points. In this paper, a collaborative map matching method (CMM) is proposed for low-sampling-rate GPS trajectories. CMM processes GPS trajectories in batches. First, it groups similar GPS trajectories into clusters and then supplements the missing information by resampling. A collaborative GPS trajectory is then extracted for each cluster and matched to the road network, based on longest common subsequence (LCSS) distance. Experiments are conducted on a real GPS trajectory dataset and a simulated GPS trajectory dataset. The results show that the proposed CMM outperforms the baseline methods in both, effectiveness and efficiency.

中文翻译：

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低采样率GPS轨迹的基于轨迹协作的地图匹配方法。

低采样率的GPS（全球定位系统）轨迹在许多应用中很普遍。但是，由于连续GPS点之间的不确定性较大，当前的地图匹配方法在低采样率GPS轨迹上效果不佳。本文提出了一种针对低采样率GPS轨迹的协作地图匹配方法（CMM）。CMM分批处理GPS轨迹。首先，它将相似的GPS轨迹分组为簇，然后通过重新采样补充缺失的信息。然后，根据最长公共子序列（LCSS）距离，为每个群集提取一个协作GPS轨迹并将其与道路网络匹配。实验是在真实的GPS轨迹数据集和模拟的GPS轨迹数据集上进行的。