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

GPS 数据地图匹配算法的比较、验证和校准

• 批准号: 514125-2017
• 负责人: Labbe, Aurélie
• 金额: $ 1.82万
• 依托单位: HEC Montréal
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639629
• 关键词: Comparison; validation; calibration; map; matching

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.

自2013年底以来，完整保险提供基于使用情况的汽车保险计划，旨在利用数据驱动的技术并改进保险产品。在这些程序下，INTENAL使用连接到车载电脑或智能手机应用程序的GPS加密狗从客户那里收集驾驶数据。这些数据被用来描述驾驶行为和检测危险事件。为了对收集到的海量数据进行预处理，需要实现地图匹配算法。地图匹配是估计用户在路段上的位置的过程。具体地说，GPS定位只提供经纬度范围内的位置，并不在空间上链接到公路网。这在城市中心尤其令人担忧，那里的高层建筑可以完全阻挡GPS信号或产生虚假信号，导致几米量级的地面噪声。如果目标是确定旅行模式，那么有必要明确地将每次旅行与旅行的网络链接相匹配。尽管文献中已经开发了几种地图匹配程序，但在精度和计算速度方面还没有正式的比较。这些问题对于完整保险公司来说是极其重要的，他们需要在计算成本和精度之间找到最佳的权衡。此外，现有方法的性能可能因环境类型(城市、郊区、高速公路等)的不同而有很大差异。而单一方法的使用可能不是最佳解决方案。此外，GPS和传感器数据的结合在大数据环境下的研究还很少，这可以提高地图匹配的精度，但增加了计算复杂性。因此，本研究项目的目标是：1)从效率和精度两个方面评估现有地图匹配方法的性能；2)考察GPS和传感器数据结合在地图匹配精度上的好处；3)将最优方法(S)校准到Intact Insurance的分析管道中。