Vulnerable road user safety evaluation using probe vehicle data with collision warning information
Vulnerable road user safety evaluation using probe vehicle data with collision warning information
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DOI：
10.1016/j.aap.2021.106528
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发表时间：
2022-02-01
影响因子：
5.9
通讯作者：
Sugiki, Nao
中科院分区：
文献类型：
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作者：
Matsuo, Kojiro;Chigai, Naoki;Sugiki, Nao
最近，互联车辆（CV）和高级驾驶员辅助系统（ADAS）技术，包括改装ADAS产品，已被引入现实市场。本研究的重点是行人碰撞警告（PCW）作为ADAS的一个强化功能，当车辆与弱势道路使用者（VRU）发生碰撞时，ADAS会运行。尽管已经进行了几项关于VRU碰撞的替代安全措施的研究，但这些研究都没有使用带有碰撞警告信息的真实CV数据。因此，目前的研究旨在i）开发一个安全性能函数（SPF）的碰撞对VRU在无信号交叉口，其中的PCW信息是使用连接的先进探测车（APV）获得的，和ii）评估的有效性，在无信号交叉口实施的交通安全处理的基础上开发的SPF。特别是，本研究提出了一个两步的经验贝叶斯估计的SPF模型（2步EB-SPF）的基础上，考虑的问题，关于有限的数量和车辆类型的APV，可以获得PCW信息。基于APV数据，分别使用实际PCW发生率和PCW发生率的EB估计来估计车辆-VRU碰撞计数负二项（NB）模型。虽然在前一个模型中，实际PCW发生率没有统计学意义，但在后一个模型中，PCW发生率的EB估计值具有统计学意义，并且与碰撞次数呈正相关。此外，交通安全处理实施在一个无信号交叉口，随后评估的基础上估计的两步EB-SPF模型的案例研究。因此，具有PCW发生率的EB估计的模型显示，车辆-VRU碰撞风险降低了约70%，并且在99%置信水平下具有统计学显著性，与没有PCW发生率的模型相比，这减小了置信区间。因此，包括碰撞警告信息的APV数据可以提高确定交通安全处理的效果的估计精度，这可以大大有助于交通安全评估，特别是对于短的后处理时间段，如在本案例研究中普遍存在的。
Recently, connected vehicle (CV) and advanced driver assistance system (ADAS) technologies, including retrofit ADAS products, have been introduced in the real-world market. This study focuses on pedestrian collision warning (PCW) as an intensive function of the ADAS, which operates when a vehicle is at a collision risk with a vulnerable road user (VRU). Although several studies have been conducted on surrogate safety measures for crashes against VRUs, none of these studies used real-world CV data with collision warning information. Thus, the current study aims to i) develop a safety performance function (SPF) for crashes against VRUs at unsignalized intersections, where the PCW information was acquired using connected advanced probe vehicles (APVs), and ii) assess the effectiveness of a traffic-safety treatment implemented at an unsignalized intersection based on the developed SPF. In particular, this study proposes a two-step empirical Bayesian estimation based on the SPF model (2-step EB-SPF) to consider the issue regarding the limited number and vehicle types of APVs that can obtain PCW information. Based on the APV data, the vehicle-VRU crash-count negative binomial (NB) models were separately estimated using the actual PCW incidence rate and the EB estimate of PCW incidence rate, respectively. Although the actual PCW incidence rate was not statistically significant in the former model, the EB estimate of the PCW incidence rate was statistically significant and positively related to the crash count in the latter model. Moreover, a traffic-safety treatment was implemented at an unsignalized intersection and subsequently assessed as a case study based on the estimated 2-step EB-SPF model. Consequently, the model with the EB estimate of PCW incidence rate revealed that the vehicle-VRU crash risk was reduced by approximately 70%, and it was statistically significant at the 99% confidence level, which diminished the confidence interval in comparison to the model without the PCW incidence rate. Thus, the APV data including collision warning information can improve the estimation accuracy of determining the effect of the traffic-safety treatment, which can considerably contribute toward traffic safety assessment, especially for short after-treatment periods such as that prevailing in this case study.