路径推理过滤器：探测车辆数据的基于模型的低延迟地图匹配
The path inference filter: model-based low-latency map matching of probe vehicle data
摘要：
我们考虑的问题，重建车辆轨迹从稀疏序列的GPS点，其中采样间隔为10秒和2分钟之间。我们介绍了一类新的算法，称为路径推理过滤器（PIF），地图GPS数据在实时的，各种权衡和方案，并具有高吞吐量。地图匹配中的许多先前方法可以被证明是本文中提出的路径推理过滤器的特殊情况。我们提出了一个有效的程序，自动训练过滤器的新数据，有或没有地面实况观察。该框架在旧金山的一个大型出租车数据集上进行了评估，并显示出对当前技术水平的改进。该过滤器还提供了有关驾驶员驾驶模式的见解。路径推理过滤器已部署在移动千年交通信息系统内的工业规模，并用于映射旧金山，萨克拉门托，斯德哥尔摩和波尔图的数据舰队。
英文摘要：
We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.
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