Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada.
Intactlab - Data Science, Intact Insurance, Suite 100, 2020 Boulevard Robert-Bourassa, Montréal, Québec H3T 2A7, Canada.
Accid Anal Prev. 2021 Sep;159:106232. doi: 10.1016/j.aap.2021.106232. Epub 2021 Jun 26.
Mobile sensors are a useful data source with applications in several transportation fields. Though cost of collection, transmission, and storage has limited studies on driving data and safety, this can be overcome through usage-based insurance (UBI). In UBI programs, drivers are monitored, and their premiums are adjusted based on driver-level surrogate safety measures (SSMs) related to exposure and driving style. Contextual link-level SSMs (volume, speed, or density) could further improve discount calibration. This study quantifies relationships between contextual SSMs and crashes and includes the validation of previous results (correlations between SSMs and crashes and statistical models estimated using smartphone-collected data from Quebec City) and the comparison of three Canadian cities (using UBI data from Quebec City, Montreal, and Ottawa). Extracted SSMs were compared to large volumes of historical crash frequency data using Spearman's Rank Correlation Coefficient and then implemented into spatial Bayesian crash models. Results from the UBI data generally matched those from the previous study, with observed correlations mirroring previous results in direction (braking, congestion, and speed variation are positively associated with crash frequency while mean speed is negatively associated) while correlation strength was slightly higher. Furthermore, these results were consistent between cities. For the crash modelling, repeatability of previous results in Quebec City was moderately good for the UBI data. Importantly for large-scale implementation, models estimated using UBI data were largely consistent between cities. This work provides an important contribution to the existing literature, clearly demonstrating how contextual safety measures could be applied to benefit UBI practices.
移动传感器是一种有用的数据来源，可应用于多个交通领域。尽管采集、传输和存储成本限制了对驾驶数据和安全的研究，但这可以通过基于使用情况的保险（UBI）来克服。在 UBI 计划中，会对驾驶员进行监控，并根据与暴露和驾驶风格相关的驾驶员级替代安全措施（SSM）来调整其保费。上下文链路级 SSM（数量、速度或密度）可以进一步提高折扣校准的准确性。本研究量化了上下文 SSM 与事故之间的关系，并验证了先前的结果（SSM 与事故之间的相关性以及使用智能手机从魁北克市收集的数据估计的统计模型），并比较了三个加拿大城市（使用来自魁北克市、蒙特利尔和渥太华的 UBI 数据）。使用 Spearman 秩相关系数将提取的 SSM 与大量历史碰撞频率数据进行比较，然后将其纳入空间贝叶斯碰撞模型中。UBI 数据的结果通常与先前的研究结果相匹配，观察到的相关性在方向上与先前的结果相吻合（制动、拥堵和速度变化与碰撞频率呈正相关，而平均速度与碰撞频率呈负相关），而相关性强度略高。此外，这些结果在各个城市之间是一致的。对于碰撞建模，UBI 数据在魁北克市的先前结果的可重复性较好。对于大规模实施而言，重要的是，使用 UBI 数据估计的模型在各个城市之间基本一致。这项工作对现有文献做出了重要贡献，清楚地表明了如何将上下文安全措施应用于 UBI 实践以从中受益。
