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Bowtie Analysis Without Expert Acquisition for Safety Effect Assessments of Cooperative Intelligent Transport Systems

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Bowtie Analysis Without Expert Acquisition for Safety Effect Assessments of Cooperative Intelligent Transport Systems Bowtie Analysis without Expert Acquisition for Safety Effect Assessments of Cooperative Intelligent Transport Systems Ute Christine Ehlers1; Eirin Olaussen Ryeng, Dr.Eng.2; Edward McCormack, Ph.D.3; Faisal Khan, D.Sc., M.ASCE4; and Sören Ehlers, D.Sc.5 Abstract: Estimating the safety effects of emerging or future technology based on expert acquisitions is challenging because the accu- mulated judgment is at risk to be biased and imprecise. Therefore, this semiquantitative study is proposing and demonstrating an upgraded bowtie analysis for safety effect assessments that can be performed without the need for expert acquisition. While bowtie analysis is com- monly used in, for example, process engineering, it is novel in road traffic safety. Four crash case studies are completed using bowtie analysis, letting the input parameters sequentially vary over the entire range of possible expert opinions. The results suggest that only proactive safety measures estimated to decrease the probability of specific crash risk factors to at least “very improbable” can perceptibly decrease crash probability. Further, the success probability of a reactive measure must be at least “moderately probable” to reduce the probability of a serious or fatal crash by half or more. This upgraded bowtie approach allows the identification of (1) the sensitivity of the probability of a crash and its consequences to expert judgment used in the bowtie model and (2) the necessary effectiveness of a chosen safety measure allowing adequate changes in the probability of a crash and its consequences. DOI: 10.1061/AJRUA6.0000986. © 2018 American Society of Civil Engineers. Author keywords: Bowtie; Fuzzy sets; Expert judgment; Safety effect; Cooperative intelligent transport systems. Introduction among experts in various disciplines of forensic science. Confirma- tion bias is a psychological phenomenon “by which people tend to Cooperative intelligent transport systems (C-ITSs) are an emerging seek, perceive, interpret, and create new evidence in ways that technology in automotive and transportation engineering, and verify their preexisting beliefs” (Kassin et al. 2013, p.44). In expert expectations are high that their application will positively influence judgment in traffic safety, additional types of bias can be problem- road traffic safety, among other transport-related issues. As with atic, such as hindsight bias and publication bias (Shinar 2017). any other new or future technology, it is challenging to reliably Hindsight bias, also called the “knew-it-all-along” effect, is the estimate the effects C-ITSs might have. Facing this uncertainty and tendency to increase the perceived likelihood of an event or its a lack of knowledge, research is often based on expert judgment. outcome after the event has occurred. This bias embodies “beliefs Unfortunately, this form of data, its elicitation as well as its about events’ objective likelihoods, or subjective beliefs about interpretation, is prone to a large number of biases for various rea- one’s own prediction abilities” (Roese and Vohs 2012, p.411) sons (e.g., Eddy 1982; Meyer and Booker 1991; Tetlock 2005; and thus can be problematic in the reconstruction and causation Kirkebøen 2009; Kahneman 2011; Lees 2012; Morgan 2014). analysis of crashes (Dilich et al. 2006). Publication bias is the ten- For instance, Kassin et al. (2013) provided a comprehensive over- dency not to publish negative results, which seems likely to also view of recent research indicating not uncommon confirmation bias have an impact on the judgment of experts. Overviews of bias- reducing strategies and techniques that aim for high accuracy 1Ph.D. Candidate, Dept. of Civil and Environmental Engineering, in expert judgment are provided in a number of publications NTNU Norwegian Univ. of Science and Technology, 7491 Trondheim, (e.g., Meyer and Booker 1991; Kirkebøen 2009; Morgan 2014). Norway (corresponding author). Email: [email protected] 2 For example, the use of explicit decision rules like the Bayes theo- Associate Professor, Dept. of Civil and Environmental Engineering, rem and the training for it, or the incorporation of specific group NTNU Norwegian Univ. of Science and Technology, 7491 Trondheim, Norway. Email: [email protected] decision processes, have been shown to reduce bias (e.g., Meyer 3Research Associate Professor and Director of the Master of Sustainable and Booker 1991; Rowe and Wright 2001; Surowiecki 2004). Plus, Transportation Program, Dept. of Civil and Environmental Engineering, in the absence of empirical data, the data accumulated in expert Downloaded from ascelibrary.org by CASA Institution Identity on 11/02/19. Copyright ASCE. For personal use only; all rights reserved. Univ. of Washington, Seattle, WA 98195. Email: [email protected] acquisitions seem to be the only data on which research can pos- 4Professor and Canada Research Chair of Offshore Safety and Risk sibly be based. However, not even the best expert can exactly fore- Engineering, Faculty of Engineering and Applied Science, Memorial Univ., cast the future performance of a specific novel system or the St. John’s, NL, Canada A1B 3X5. Email: [email protected] system’s effects and their likelihood. 5 Professor and Head of the Institute for Ship Structural Design and Another challenge comes in estimating the actual safety effects Analysis, Hamburg Univ. of Technology, 21073 Hamburg, Germany. of C-ITSs in terms of their influence on crashes. Apart from Email: [email protected] implementing various C-ITSs with different levels of maturity in Note. This manuscript was submitted on January 4, 2018; approved on May 9, 2018; published online on August 6, 2018. Discussion period open many different ways, long periods of exposure to real traffic are until January 6, 2019; separate discussions must be submitted for indivi- necessary to collect significant crash data. The majority of C-ITSs dual papers. This paper is part of the ASCE-ASME Journal of Risk are still in the testing phase. Even applications whose deployment and Uncertainty in Engineering Systems, Part A: Civil Engineering, has already begun to a limited extent are far from providing enough © ASCE, ISSN 2376-7642. real traffic and crash data to estimate safety effects in a statistically © ASCE 04018036-1 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., 2018, 4(4): 04018036 reliable manner. Further, a substantial number of vehicles will have Wireless short-range radio communication between the road in- to be equipped with C-ITSs before the anticipated and actual safety frastructure, vehicles, and personal electronic devices allows ve- effects will show. Due to this current lack of empirical data, re- hicle-to-vehicle communication (V2V) and vehicle-to-infrastructure search has been based on “by proxy” or surrogate methods to assess communication (V2I). These information and communication links the effects of safety-related C-ITSs that are not yet implemented in can be one way or two way. Cooperative vehicles (V2V) can “see” real traffic or have been implemented for a relatively short time. one another through wireless high-speed communication in real By proxy or surrogate methods can be in-depth analyses of crash time and receive relevant data, such as position, speed, course, and reports (Virtanen et al. 2006), ex ante estimate studies based on, vehicle type. Compared with noncooperative vehicles and transport say, crash data and statistics (Wilmink et al. 2008; Schirokoff systems, in cooperative vehicles information and warning timing et al. 2012), traffic simulation modeling, driving simulator and field are improved. System users receive information and warnings in test studies, or a combination of one or more of these (Harding et al. real time, enhancing their situation awareness and providing them 2014). Ex ante estimate studies are based on in-depth crash inves- with additional reaction time. In addition, V2V-systems can aug- tigations and analyze whether crashes or fatalities could have been ment sensor-based intelligent transport systems, thereby improving prevented if a specific safety measure had been used (e.g., Vaa et al. accuracy and support vehicle control (OECD 2003; Bayly et al. 2014). These studies usually involve numerous assumptions re- 2007; Harding et al. 2014). garding vehicle fleet penetration rates and infrastructure coverage, The focus of this study is on safety-related C-ITSs that are future trends, anticipated driver behavior, and functional and tech- expected to directly improve road traffic safety by reducing nological features of the studied system. Hardly any surrogate the probability of crashes and their consequences. Examples of methods allow practical and fast safety effect estimation of new (potential) applications are intelligent speed adaptation, emer- or future C-ITSs while allowing for the various factors associated gency call systems, and various incident detection and warning with crashes and their consequences. Moreover, surrogate methods systems (local danger warning, red light violation warning, curve have one important disadvantage: crash risk is not measured di- speed warning, and the like). The “road traffic safety problem”— rectly. Instead, road safety is measured indirectly through perfor- that is, the number of injuries and fatalities resulting from mance indicators such as speed or driver behavior. Although crashes—can be understood as a function of three variables: their relation to or even correlation
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