一种实时评估极端冲突安全的多变量方法

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Chuanyun Fu , Tarek Sayed
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引用次数: 18

摘要

一些研究主张使用极值理论(EVT)交通冲突模型,利用碰撞风险(RC)和循环返回水平(RLC)等实时安全指标进行实时碰撞风险预测。这种方法提供了一个逻辑框架,通过从观察到的级别(例如,交通冲突)外推到未观察到的级别(例如,崩溃)来估计崩溃风险。在这些研究中,仅使用了只考虑一个冲突指标(如修正碰撞时间,MTTC)的单变量EVT模型,这影响了模型估计碰撞风险的准确性和精度。由于现有的安全分析多变量EVT模型在描述多个冲突指标之间复杂的依赖结构以应用于实时安全评估方面的能力有限,因此有可能使用单变量模型。本文提出了一种多变量冲突极端事件实时安全评价方法,该方法由新颖的多变量EVT模型组成,该模型灵活整合了多个冲突指标和多个多维度综合表征道路设施安全水平的联合安全指标。该方法具有以下优点:1)采用倾斜Dirichlet、成对beta、Husler-Reiss和extreme -t四种参数模型作为角密度函数,充分描述了多个冲突极值之间的依赖程度;2)创新地从多变量联合分布中推导出碰撞风险、联合回报水平、伴随回报水平等重要的实时安全指标,用于多维度的安全评估。提出了一种基于近似似然的七步贝叶斯推理方法。将该模型估计方法应用于不列颠哥伦比亚省萨里市四个信号交叉口的周期级实时安全评估,并结合多个冲突指标。使用三个冲突指标:MTTC、侵占后时间(PET)和避免碰撞的减速率(DRAC)。建立了四种多变量EVT模型。在这些模型中,无论是二元框架还是三元框架,Husler-Reiss模型的拟合优度都最好,因为它更好地捕捉了三个冲突指标之间的依赖程度。结果表明,多变量模型比其相应的单变量模型识别出更多的崩溃风险周期。此外,大多数碰撞风险周期至少有一个联合回报水平高于冲突和碰撞之间的阈值(MTTC和PET均为0,DRAC为8.5 m/s2)。对于大多数周期的联合回报水平,一个回报水平超过阈值,而其他回报水平低于阈值。在二元框架下,正收益水平的所有伴随物都低于其各自的阈值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multivariate method for evaluating safety from conflict extremes in real time

Several studies have advocated the use of extreme value theory (EVT) traffic conflict models for real-time crash risk prediction using real-time safety indices such as the risk of crash (RC) and return level of a cycle (RLC). This approach provides a logical framework to estimate crash risk by extrapolating from the observed level (i.e., traffic conflict) to the unobserved level (i.e., crash). In these studies, only univariate EVT models that consider only one conflict indicator (e.g. modified time to collision, MTTC) were used which affects the models’ accuracy and precision in estimating crash risk. The use of univariate models is likely due to that existing safety analysis multivariate EVT models have limited capability of delineating the complex dependence structure between multiple conflict indicators for application to real-time safety evaluation. This study proposes a multivariate method for evaluating real-time safety from conflict extremes which consists of novel multivariate EVT models that flexibly integrate multiple conflict indicators and several joint safety indices that comprehensively characterize the safety level of a road facility from multiple dimensions. The proposed approach has several advantages including: 1) it uses four parametric models (tilted Dirichlet, pairwise beta, Husler-Reiss, and extremal-t) for the angular density function for fully describing the dependence level between multiple conflict extremes; and 2) it innovatively develops several important real-time safety indices (e.g., crash risk, joint return levels, and return level concomitant) from the multivariate joint distribution for multidimensionally assessing safety. A seven-step approximate likelihood-based Bayesian inference method for model development is proposed. The proposed model estimation method is applied for cycle-level real-time safety evaluation by combining several conflict indicators at four signalized intersections in the city of Surrey, British Columbia. Three conflict indicators are used: MTTC, post encroachment time (PET), and deceleration rate to avoid a crash (DRAC). Four types of multivariate EVT models were developed. Among these models, for both bivariate and trivariate framework, the Husler-Reiss model has the best goodness-of-fit as it better captures the dependence level among the three conflict indicators. The results indicate that multivariate models identify higher numbers of crash-risk cycles than their corresponding univariate models. Further, most of crash-risk cycles have at least one of joint return levels higher than the threshold (0 for both MTTC and PET, 8.5 m/s2 for DRAC) between a conflict and a collision. For joint return levels from most cycles, one return level exceeds the threshold, while others are lower than the threshold. Under the bivariate framework, all the concomitants of positive return levels are below their own thresholds.

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来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
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