Comparison, validation and calibration of map-matching algorithms for GPS data

自2013年底以来，Intact Insurance提供基于使用的汽车保险计划，旨在存储数据驱动的技术并改进保险产品。在这些程序下，Intact使用连接到车载计算机或智能手机应用程序的GPS加密狗收集客户的驾驶数据。这些数据用于描述驾驶行为并检测风险事件。为了对收集到的大量数据进行预处理，需要实现地图匹配算法。地图匹配是估计用户在道路段上的位置的过程。具体地，GPS位置仅以纬度和经度的形式提供，并且在空间上不与道路网络链接。这在城市中心尤其令人担忧，在那里，高层建筑可以完全阻挡GPS信号或产生虚假信号，从而导致数米量级的位置噪声。如果目标是确定旅行模式，那么有必要明确地将每个旅行与旅行的网络链接相匹配。虽然在文献中已经开发了几种地图匹配程序，但在准确性和计算速度方面还没有正式的比较。这些问题对于Intact Insurance来说非常重要，他们需要在计算成本和精度之间找到最佳权衡。此外，现有方法的性能可能根据环境类型（城市、郊区、高速公路等）而有很大差异。并且使用单一方法可能不是最佳解决方案。此外，在大数据的背景下，很少研究GPS和传感器数据的组合;这可以提高地图匹配的准确性，但增加了计算复杂性。因此，本研究项目的目标是：1）评估现有地图匹配方法在效率和准确性方面的性能，2）调查结合GPS和传感器数据在地图匹配准确性方面的好处，以及3）将最佳方法校准到Intact Insurance的分析管道中。

Since the end of 2013, Intact Insurance offers usage-based car insurance programs designed to leveragedata-driven technologies and improve insurance products. Under these programs, Intact collects driving datafrom customers using either a GPS dongle connected to the car on-board computer or a smartphoneapplication. This data is used to characterize driving behavior and to detect risky events. In order to pre-processthis massive amount of data collected, a map-matching algorithm needs to be implemented. Map matching isthe process of estimating a user's position on a road segment. Concretely, GPS positions are provided only interms of latitude and longitude and are not linked spatially to the road network. This is particularly of concernin urban centers, where tall buildings can completely block GPS signals or create spurious signals, leading topositional noise of several meters in magnitude. If the goal is to determine travelling patterns, then it isnecessary to explicitly match each trip to the travelled network links. Although several map-matchingprocedures have been developed in the literature, no formal comparison exists in terms of accuracy andcomputational speed. These issues are extremely important for Intact Insurance, who needs to find the optimaltrade-off between computational cost and precision. Furthermore, the performance of existing methods mayvary greatly depending on the type of environment (urban, suburban, highways, etc.) and the use of a singlemethod might not be the optimal solution. Moreover, the combination of GPS and sensor data has been rarelyinvestigated in the context of big data; this could improve the map-matching accuracy but increases thecomputing complexity. Therefore, the objective of this research project is to: 1) evaluate the performance ofexisting map-matching methods both in terms of efficiency and accuracy, 2) investigate the benefits ofcombining GPS and sensor data in the map-matching accuracy, and 3) calibrate the optimal method(s) intoIntact Insurance's analytical pipeline.