Department of Civil Engineering and Applied Mechanics, McGill University, Room 391, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec, H3A 0C3, Canada.
Department of Civil Engineering and Applied Mechanics, McGill University, Room 268, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec, H3A 0C3, Canada.
Accid Anal Prev. 2018 Nov;120:174-187. doi: 10.1016/j.aap.2018.07.013. Epub 2018 Aug 22.
Improving road safety requires accurate network screening methods to identify and prioritize sites in order to maximize the effectiveness of implemented countermeasures. In screening, hotspots are commonly identified using statistical models and ranking criteria derived from observed crash data. However, collision databases are subject to errors, omissions, and underreporting. More importantly, crash-based methods are reactive and require years of crash data. With the arrival of new technologies including Global Positioning System (GPS) trajectory data, proactive surrogate safety methods have gained popularity as an alternative approach for screening. GPS-enabled smartphones can collect reliable and spatio-temporally rich driving data from regular drivers using an inexpensive, simple, and user-friendly tool. However, few studies to date have analyzed large volumes of smartphone GPS data and considered surrogate-safety modelling techniques for network screening. The purpose of this paper is to propose a surrogate safety screening approach based on smartphone GPS data and a Full Bayesian modelling framework. After processing crash data and GPS data collected in Quebec City, Canada, several surrogate safety measures (SSMs), including vehicle manoeuvres (hard braking) and measures of traffic flow (congestion, average speed, and speed variation), were extracted. Then, spatial crash frequency models incorporating the extracted SSMs were proposed and validated. A Latent Gaussian Spatial Model was estimated using the Integrated Nested Laplace Approximation (INLA) technique. While the INLA Negative Binomial models outperformed alternative models, incorporating spatial correlations provided the greatest improvement in model fit. Relationships between SSMs and crash frequency established in previous studies were generally supported by the modelling results. For example, hard braking, congestion, and speed variation were all positively linked to crash counts at the intersection level. Network screening based on SSMs presents a substantial contribution to the field of road safety and works towards the elimination of crash data in evaluation and monitoring.
提高道路安全需要准确的网络筛选方法来识别和优先考虑地点，以最大限度地提高实施措施的效果。在筛选中，通常使用统计模型和基于观察到的碰撞数据得出的排名标准来识别热点。然而，碰撞数据库存在错误、遗漏和漏报。更重要的是，基于碰撞的方法是被动的，需要多年的碰撞数据。随着包括全球定位系统 (GPS) 轨迹数据在内的新技术的出现，作为筛选的替代方法，主动替代安全方法已变得流行。GPS 功能的智能手机可以使用一种廉价、简单、用户友好的工具，从普通驾驶员那里收集可靠且时空丰富的驾驶数据。然而，迄今为止，很少有研究分析大量智能手机 GPS 数据，并考虑替代安全建模技术用于网络筛选。本文的目的是提出一种基于智能手机 GPS 数据和全贝叶斯建模框架的替代安全筛选方法。在处理了加拿大魁北克市收集的碰撞数据和 GPS 数据之后，提取了几个替代安全措施 (SSM)，包括车辆操纵（急刹车）和交通流措施（拥堵、平均速度和速度变化）。然后，提出并验证了纳入提取的 SSM 的空间碰撞频率模型。使用集成嵌套拉普拉斯逼近 (INLA) 技术估计潜在高斯空间模型。虽然 INLA 负二项式模型优于替代模型，但纳入空间相关性可最大程度地提高模型拟合度。先前研究中建立的 SSM 与碰撞频率之间的关系通常得到了建模结果的支持。例如，急刹车、拥堵和速度变化与交叉口级别的碰撞计数呈正相关。基于 SSM 的网络筛选对道路安全领域做出了重大贡献，并朝着在评估和监测中消除碰撞数据的方向努力。
