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Connected and autonomous vehicle injury loss events: Potential risk and actuarial considerations for primary insurers
Abstract and Figures
The introduction of connected and autonomous vehicles (CAVs) to the road transport ecosystem will change the manner of collisions. CAVs are expected to optimize the safety of road users and the wider environment, while alleviating traffic congestion and maximizing occupant comfort. The net result is a reduction in the frequency of motor vehicle collisions, and a reduction in the number of injuries currently seen as “preventable.” A changing risk ecosystem will introduce new challenges and opportunities for primary insurers. Prior studies have highlighted the economic benefit provided by reductions in the frequency of hazardous events. This economic benefit, however, will be offset by the economic detriment incurred by emerging risks and the increased scrutiny placed on existing risks. We posit four plausible scenarios detailing how an introduction of these technologies could result in a larger relative rate of injury claims currently characterized as tail‐risk events. In such a scenario, the culmination of these losses will present as a second “hump” in actuarial loss models. We discuss how CAV risk factors and traffic dynamics may combine to make a second “hump” a plausible reality, and discuss a number of opportunities that may arise for primary insurers from a changing road environment.
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... Predictive modeling and virtual simulation algorithms, ambient sound recognition software, Internet of Things connected sensors, and movement and behavior tracking tools (Nair and Bhat, 2021;Shannon et al., 2021) cut down crashes and casualties. Cloud computing and image recognition technologies, sensor fusion algorithms, data mining and mobility simulation tools, and Internet of Things connected devices articulate urban network infrastructures. ...
... Predictive modeling and virtual simulation algorithms, ambient sound recognition software, Internet of Things connected sensors, and movement and behavior tracking tools cut down crashes and casualties. Nair and Bhat, 2021;Shannon et al., 2021 Urban big data analytics develops on virtual and augmented reality tools, sensing and navigation systems, cognitive automation technologies, and autonomous driving algorithms and behaviors. Penmetsa et al., 2021;Yang et al., 2021 Deep learning and edge intelligence technologies, data tracking apps, virtual data modeling tools, remote sensor networks, and simulation optimization and motion planning algorithms reduce motor vehicle collision frequency. ...
Based on an in-depth survey of the literature, the purpose of the paper is to explore autonomous vehicle routing and navigation, mobility simulation and traffic flow prediction tools, and deep learning object detection technology in smart sustainable urban transport systems. We contribute to the literature by indicating that multi-sensor environmental data fusion, environment perception systems, and deep convolutional neural networks are pivotal in connected autonomous vehicles. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “smart sustainable urban transport systems” + “autonomous vehicle routing and navigation,” “mobility simulation and traffic flow prediction tools,” and “deep learning object detection technology.” As research published between 2021 and 2022 was inspected, only 89 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 15 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.
... The overall picture is that ADAS/ICV technologies have the potential to reduce crashes caused by human error significantly [14,[26][27][28][29][30][31], albeit findings are not always consistent (see, for instance, [32]). Studies predict that Connected and Automated Vehicles (CAVs) will greatly decrease crash frequency and severity due to improved environmental sensing and hazard anticipation [33,34]. Scholliers et al. [35] studied the feasibility, costs, and benefits of retrofitting ADASs in the EU and estimated that the full penetration of an ADAS bundle, including forward collision warning, speed limit information, and lane departure warning, could potentially reduce between 12.9% to 27.2% of road-related fatalities and 8.4% to 23.4% of road-related injuries. ...
This paper focuses on the acceptance of ADASs in the traffic safety and human factor domain. More specifically, it examines the predictive validity of the Unified Model of Driver Acceptance (UMDA) for an ADAS bundle that includes forward collision warning, headway monitoring and warning, and lane-keeping assistance in Belgium and Vietnam, two substantially different geographical, socio-cultural, and macroeconomic settings. All systems in the studied ADAS bundle are located at the Society of Automotive Engineer (SAE)-level 0 of automation. We found moderate acceptance towards such an ADAS bundle in both countries, and respondents held rather positive opinions about system-specific characteristics. In terms of predictive validity, the UMDA scored quite well in both countries, though better in Belgium than in Vietnam. Macroeconomic factors and socio-cultural characteristics could explain these differences between the two countries. Policymakers are encouraged to prioritise initiatives that stimulate the purchase and use of the ADAS, rather than on measures meant to influence the underlying decisional balance.
... For example, monitoring energy consumption in government agencies, such as water and electricity use, has become common practice. The smart grid, a concept intertwined with the Internet of Things, plays a key role in achieving efficient energy consumption, especially in the face of challenges such as population growth and increased energy demand from new equipment [6]. ...
The merging of big data and the Internet of Things (IoT) has brought both exceptional difficulties and possibilities in data management. This study offers a thorough and methodical examination of the current literature on large data management approaches inside the Internet of Things framework. The study encompasses a broad spectrum of inquiry, ranging from fundamental notions to sophisticated approaches. The Internet of Things (IoT) is a powerful force that seeks to improve user experience and lifestyle. It incorporates several essential technologies including human-machine and machine-to machine communications, networking technologies, and sensor technologies. Fundamental to the success of the Internet of Things is the effective management of data transmitted through these technologies. This article explores the issues and challenges associated with data management in the context of the Internet of Things and examines different aspects of data, including its sources, collection processes, processing methods, and transmission devices. The article identifies and discusses problems arising from the need to process huge amounts of heterogeneous data in different systems. In relation to these issues, the logical and physical aspects of data management and communications networks are discussed. Additionally, the article takes an in-depth look at the data models used in IoT and explores data management, cleansing, and indexing techniques that take into account the unique characteristics of IoT data. The final sections of the paper comprehensively discuss the benefits and limitations associated with data management in IoT.
... Whether it is meant for pricing, streamlining business operations, or conducting market analysis, this context not only shapes the environment but also provides insights into the probability and potential extent of harm that might occur. Therefore, a thorough examination of AI ethics requires the adoption of a comprehensive viewpoint that encompasses the fundamental AI technology embedded in the an algorithms, the datasets that are used in the application's design & continuous improvement, and the broader business and societal contexts in which the technologies operates [6]. A significant sector within the insurance industry in which ethical and governmental values like fairness, anti-discriminations, & minimizing harm intersect is motor insurances. ...
In both the operational and customer-facing facets of the insurance sector, big data analytics and ever-evolving machine learning and artificial intelligence (AI) capabilities have become indispensable. Insurance-related goods and services, which cover things like business property insurance, auto insurance, and personal health insurance, are essential for advancing the economy and society. The significance of artificial intelligence and machine learning in preserving this equilibrium within the insurance industry has already been established. However, as AI and machine learning are used more frequently, it creates new issues and calls into question the industry's moral standards. In order to shed light on any potential new conflicts and concerns, it is vital to look into the ethical ramifications and conundrums surrounding the use of data in insurance innovation. In order to examine and gain a deeper understanding of the ethical landscape relating to the tensions surrounding the use of big data analytics, AI, and machine learning methodologies to sustain the operations of the insurance industry, this study combines the insights of insurance professionals with the expertise of an AI ethics specialist.
... Many scholars, experts, technical communities and media have already warned about the newer risks and hazards that AVs can impose on the safety of roads (e.g., Maurer et al., 2016, p.343;Bellet et al., 2019;Shannon et al., 2021). Deep uncertainties and lack of historical data complicate risk analysis and planning for safe deployment of such technology. ...
Intelligent Transportation Systems (ITS) with the aim of enhancing mobility and sustainability are gaining momentum across public policy sector. Connected and Autonomous Vehicles (CAVs) constitute an integral element of ITS. The rapid advances in the realm of Artificial Intelligence (AI) and relevant disciplines have accelerated the development and evolution of CAVs which are believed to thoroughly transform the transportation landscape in coming decades or even years. There are manifold potential benefits (e.g., increased safety and accessibility, convenience, saving time and energy, reducing traffic congestion, etc.) perceived for this disruptive technology. Nevertheless, there is a considerable extent of uncertainties over the safe and secure performance of intelligent self-driving cars in urban environments. These uncertainties can deteriorate the existing driving risks and incur new risks which can undermine the functional safety and technical reliability of those vehicles. The interdependencies between risk factors have neither been yet studied within an integrative framework nor from the sociotechnical perspective. In this study, an interdisciplinary approach was adopted to construct a Bayesian Belief Network (BBN) in order to capture influential risk factors in urban settings as well as the interdependencies between them, thereby providing estimates for the risk indices under varying and volatile circumstances. This will enable us to estimate the collision risk for intelligent self-driving cars in urban environments and evaluate the impact of risk mitigation actions. Furthermore, such a model can be used to classify the urban districts based on the estimated risks and serve policymakers in allocating resources to maximise the benefits of CAVs and avoid potential safety consequences. Sociotechnical theory as an interdisciplinary approach was adopted to form the foundation of BBN model. The factors were accordingly divided into four blocks and the intersection of these blocks represent collision risk index to quantify the safety risk in urban environments. To identify the risk factors, integrative literature review together with thematic analysis (TA) were used. A new technique was formulated to populate the node probability tables (NPTs) and generate uniform distributions. Afterwards, nine domain experts assigned weights to the identified links between the nodes and influence of the probability distributions. Sensitivity analysis was conducted to examine the influence of the incorporated nodes on the collision risk index. The outcome of the model (i.e., collision risk index) showed the highest sensitivity to traffic control infrastructure, weather conditions and traffic composition, respectively. Six scenarios were also devised to investigate the fluctuations of collision risk index due to variations in input nodes. The results of this research can provide insights for policymakers in contemplating policy choices such as investing in new or upgrading existing infrastructure, introducing new legislations, imposing regulatory requirements, licensing, and technology standardisation.
... Connected and automated vehicles (CAV) are expected to introduce a substantial improvement in road safety, with many researchers forecasting a significant reduction in accident frequency and severity (Scanlon et al., 2021;Shannon et al., 2021;Sheehan et al., 2017). The potential safety benefits are mainly attributed to reducing human error and the capability of new technologies to sense the environment and anticipate hazards with better accuracy and speed than the human eye (Fagnant and Kockelman, 2015). ...
Advanced Driver Assistance Systems (ADAS) have introduced several benefits in the vehicular industry, and their proliferation presents potential opportunities to decrease road accidents. The reasons are mainly attributed to the enhanced perception of the driving environment and reduced human errors. However, as environmental and infrastructural conditions influence the performance of ADAS, the estimation of accident reductions varies across geographical regions. This study presents an interdisciplinary methodology that integrates the literature on advanced driving technologies and road safety to quantify the expected impact of ADAS on accident reduction across combinations of road types, lighting, and weather conditions. The paper investigates the safety effectiveness of ADAS and the distribution of frequency and severity of road accidents across 18 driving contexts and eight accident types. Using road safety reports from the United Kingdom (UK), it is found that a high concentration of accidents (77%) occurs within a small subset of contextual conditions (4 out of 18) and that the most severe accidents happen in dark conditions on rural roads or motorways. The results of the safety effectiveness analysis show that a full deployment of the six most common ADAS would reduce the road accident frequency in the UK by 23.8%, representing an annual decrease of 18,925 accidents. The results also show that the most frequent accident contexts, urban-clear-daylight and rural-clear-daylight, can be reduced by 29%, avoiding 7,020 and 3,472 accidents, respectively. Automatic Emergency Braking (AEB) is the most impactful technology, reducing three out of the four most frequent accident categories – intersection (by 28%), rear-end (by 27.7%), and pedestrian accidents (by 28.4%). This study helps prioritise resources in ADAS research and development focusing on the most relevant contexts to reduce the frequency and severity of road accidents. Furthermore, the identified contextual accident hotspots can assist road safety stakeholders in risk mitigation programs.
... Further work should look at the impact of regulations and the noninsurable risks linked to M&R requirements as these factors were not covered by the current analysis and were not discussed during the interviews with the experts since they had a more technical background. Such factors are important for M&R of BEVs, and would be particularly relevant for AVs considering the expected shift and increase in liability costs as presented in Shannon et al. (2021) and in responsibilitybased allocation of liability costs according to Pütz et al. (2018). ...
Automation and electrification in road transport are trends that will influence several economic sectors of the European economy. The automotive maintenance and repair (M&R) sector will experience the effects of such transitions in the long term. This paper assesses the research in the road transport to derive the factors that may influence the M&R demand based on Battery Electric Vehicles (BEVs) and Autonomous Vehicles (AVs) uptake. Starting from current scientific research and grounded on interviews with experts, the paper reviews major drivers influencing M&R demand and provides indications on possible future effects. While for BEVs, previous work has been conducted to estimate the M&R cost variations, the research addressing the impacts of AVs deployment on the M&R sector is at its incipient stage, hence the views of experts were paramount to shed light on this topic. We identified a scientific consensus that BEVs have less M&R requirements compared with Conventional Vehicles (CVs). For AVs, our analysis and expert views identify some important factors influencing M&R requirements: hardware components, software that enables autonomy, the rise in vehicle kilometres travelled leading to higher wear and tear of replaceable parts, the need for adequate cleaning services, especially for fleets and shared vehicles. Further work should look at the impact of regulations and the non-insurable risks linked to M&R requirements.
... During the last year or so, members of the group have pointed to many upsides of BDA. These have included improvements in road safety offered by the use of telematics data [4][5][6][7][8] as well as how improved analytics have allowed groups that were previously difficult to insure to be brought under the insurance umbrella, by offering new usagebased insurance based on telematics. [9][10][11][12][13] We have also had interventions from those members concerned over the consequences of the roll-out of this new technology for vulnerable groups and for the possible impacts in terms of financial inclusion. ...
The European Union (EU) has a strong reputation and track record for the development of guidelines for the ethical use of artificial intelligence (AI) generally. In this paper, we discuss the development of an AI and ethical framework by the European Insurance and Occupational Pensions Authority (EIOPA), for the European insurance market. EIOPA's earlier report on big data analytics (EIOPA, 2019) provided a foundation to analyze the complex range of issues associated with AI being deployed in insurance, such as behavioral insurance, parametric products, novel pricing and risk assessment algorithms, e-service, and claims management. The paper presents an overview of AI in insurance applications throughout the insurance value chain. A general discussion of ethics, AI, and insurance is provided, and a new hierarchical model is presented that describes insurance as a complex system that can be analyzed by taking a layered, multi-level approach that maps ethical issues directly to specific level(s).
Road traffic collisions are the leading cause of death for those between the ages of 15–29, according to the World Health Organisation. This study investigates one of the primary reasons for the high fatality rate amongst Young Novice Drivers (YNDs) – their use of smartphones while driving. We gathered responses from a representative sample of YNDs on their behaviour while driving using an updated version of the ‘Behaviour of Young Novice Drivers Scale’. Survey responses totalled 700 YNDs situated throughout Germany. From these responses, we examined the prevalence of certain driving behaviours that are described as ‘distracting’ and compared these driving behaviours to the respondents’ use of specific smartphone features. The responses report that music-related activities (e.g. changing music on a smartphone) are most common amongst YNDs. Speaking on the phone is seldom-reported, although more males than females indicated engagement in this behaviour. We further carried out a correlation analysis and correspondence analysis. On that basis we found that those who report speaking on a smartphone are significantly more likely to engage in driving behaviours with potentially fatal consequences, such as speeding and driving while impaired by prohibited substances (drugs, alcohol). We propose that the results could be used by policymakers for public information implications and to tailor financial penalties for those engaging in smartphone behaviours that are linked to harmful driving behaviours. In addition, our findings can also be used in a Usage-based Insurance (UBI) context to financially incentivise safer driving.
Among the trends impacting most industries are new mobility concepts, digitalization, urbanization, rising environmental awareness, and demographic change. The automobile insurance industry, in particular, is strongly affected by new mobility concepts, including autonomous, shared, and electric vehicles, which are expected to increasingly impact the risk exposure and insurance demand in the future. Identifying and assessing the resulting risk and opportunity landscape from these trends thus becomes a major strategic challenge for insurers. The aim of this paper is to analyze the trends that impact the field of mobility and thus automobile insurers. Based on this, we derive a set of strategic response measures for insurers to enable them to be prepared for the future of mobility.
Motor Vehicle Collisions (MVCs) accounted for an economic cost of $242 billion in the United States in 2010. A significant portion (42%) was associated with factors considered for compensation estimates – medical costs, lost earnings and reduced household productivity. This study proposes a methodology that accounts for these costs by using expected compensation costs (ECCs). Our approach differs from prior studies as we consider all injuries suffered by an individual in the MVC, rather than only the ‘most severe’ injury. We estimate ECCs for each injured occupant by linking the injuries suffered with guidelines on injury compensations, allowing for ECCs to be linked directly with collision factors. To demonstrate the ECC system, we conduct a cross-sectional mediation analysis to study the relationships between collisions and compensation. delta-V (the change in a vehicle's speed pre- and post-crash) remains a primary factor in the severity of MVCs and so it serves as a focal point in our study. We find that some collision factors influence compensation estimates because of the effects of delta-V, while others influence ECCs regardless of delta-V. The ECC system we introduce can mitigate litigation risk and highlight future approaches to road safety, as it bridges the gap between crash characteristics, injuries suffered, and economic damage. Our results support policy recommendations that promote seatbelt use and warn against alcohol-impaired driving, and support the proliferation of safety-enabled vehicles whose technology can mitigate the economic damage associated with detrimental crash types.
Road traffic accidents are largely driven by human error; therefore, the development of connected automated vehicles (CAV) is expected to significantly reduce accident risk. However, these changes are by no means proven and linear as different levels of automation show risk-related idiosyncrasies. A lack of empirical data aggravates the transparent evaluation of risk arising from CAVs with higher levels of automation capability. Nevertheless, it is likely that the risks associated with CAV will profoundly reshape the risk profile of the global motor insurance industry. This paper conducts a deep qualitative analysis of the impact of progressive vehicle automation and interconnectedness on the risks covered under motor third-party and comprehensive insurance policies. This analysis is enhanced by an assessment of potential emerging risks such as the risk of cyber-attacks. We find that, in particular, primary insurers focusing on private retail motor insurance face significant strategic risks to their business model. The results of this analysis are not only relevant for insurance but also from a regulatory perspective as we find a symbiotic relationship between an insurance-related assessment and a comprehensive evaluation of CAV’s inherent societal costs.
Objective: The objective of this research study was to estimate the number of left turn across path/opposite direction (LTAP/OD) crashes and injuries that could be prevented in the United States if vehicles were equipped with an intersection advanced driver assistance system (I-ADAS).
Methods: This study reconstructed 501 vehicle-to-vehicle LTAP/OD crashes in the United States that were investigated in the NHTSA National Motor Vehicle Crash Causation Survey (NMVCCS). The performance of 30 different I-ADAS system variations was evaluated for each crash. These variations were the combinations of 5 time-to-collision (TTC) activation thresholds, 3 latency times, and 2 different response types (automated braking and driver warning). In addition, 2 sightline assumptions were modeled for each crash: One where the turning vehicle was visible long before the intersection and one where the turning vehicle was only visible within the intersection. For resimulated crashes that were not avoided by I-ADAS, a new crash delta-V was computed for each vehicle. The probability of Abbreviated Injury Scale 2 or higher injury in any body region (Maximum Abbreviated Injury Scale [MAIS] 2+F) to each front-row occupant was computed.
Results: Depending on the system design, sightline assumption, I-ADAS variation, and fleet penetration, an I-ADAS system that automatically applies emergency braking could avoid 18–84% of all LTAP/OD crashes. Only 0–32% of all LTAP/OD crashes could have been avoided using an I-ADAS system that only warns the driver. An I-ADAS system that applies emergency braking could prevent 47–93% of front-row occupants from receiving MAIS 2 + F injuries. A system that warns the driver in LTAP/OD crashes was able to prevent 0–37% of front-row occupants from receiving MAIS 2 + F injuries. The effectiveness of I-ADAS in reducing crashes and number of injured persons was higher when both vehicles were equipped with I-ADAS.
Conclusions: This study presents the simulated effectiveness of a hypothetical intersection active safety system on real crashes that occurred in the United States. This work shows that there is a strong potential to reduce crashes and injuries in the United States.
Autonomous vehicles (AV) have advanced considerably over the past decade and their potential to reduce road accidents is without equal. That said, the evolution towards fully automated driving will be accompanied by new and unfamiliar risks. The deployment of AVs hinges on the premise that they are considerably safer than human drivers. However, the ability of manufacturers, insurers and regulators to quantifiably demonstrate this risk reduction, relative to humans, presents a major barrier. Based on accident rates, it will likely take hundreds of millions of autonomous miles to derive statistically meaningful results. This paper addresses this issue and proposes a novel means of quantifying AV accident risks by benchmarking against a more familiar and quantifiable risk - Human Behaviour. This method is used to proactively quantify AV safety relative to human drivers. Currently, anomalous driving behaviour stems from human susceptibilities such as fatigue or aggression. We exploit this observation and explore AV driving behaviour where driving anomalies are symptoms of technology errors. The comparative behaviours of AV and safe human driving can be used to measure AV accident risk. An end-to-end model AV is simulated using Convolutional Neural Networks (CNN) to compare human and AV driving behaviours. Using a machine learning technique called Gaussian Processes (GP), contextual driving anomalies are detected, the frequency and severity of which are used to derive a risk score. This paper offers a starting point for addressing the challenges surrounding AV risk modelling.
The objective of this paper was to develop an injury risk model relating real world injury outcomes in near-side crashes with U.S. New Car Assessment Program (NCAP) test performance, crash, and occupant properties. The study was motivated by the longer-term goal of predicting injury outcomes in a future fleet in which all vehicles are expected to have passive safety performance equivalent to a 5-star NCAP rating level (the highest star rating and lowest risk of injury).
The dataset used to evaluate injury risk was the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS). Case years 2010–2015 were used. An injured occupant was defined as a vehicle occupant who experienced an injury of maximum Abbreviated Injury Scale (AIS) of 2 or greater, or who were fatally injured. Injury severity was scored using AIS-2005 (2008 update). Cases were selected in which front-row occupants of late-model vehicles were exposed to a near-side crash. Logistic regression was used to develop an injury model with delta-v, belt status, age, and gender as predictor variables. The side crash performance of each vehicle was identified and added to the model by matching each case with the associated performance in the NCAP moving deformable barrier side impact crash test.
NCAP MDB test performance, delta-v, and occupant age, sex, and BMI were found to be significant predictors of injury risk. The effect of a 5 % higher risk in the MDB test (approximately one star rating worse) was comparable to a 2.84 km/h increase in delta-v. This model informs the development of active safety systems in a future fleet where vehicle passive safety performance is higher, quantified by the NCAP MDB test.
This paper presents a model to predict the market penetration of autonomous cars for passenger transportation, focussing on autonomous taxis without a steering wheel. For this, a discrete system dynamics model was created and evaluated where the input parameters have been taken from the literature and a survey. In this survey, user demands were investigated among 873 participants. It showed that gender, age, job situation, city size and monthly income have an impact on the trust in the technology of autonomous driving as well as on the willingness to use autonomous taxis. In contrast, no evidence of an impact was found for the respondent’s education or the numbers of adults or children in the household.
The results indicate that the majority of autonomous driving vehicles will be private and will have a steering wheel within the scope of this study, which covers the time frame until 2040. These are expected to reach 12.4 million units. At the same time, autonomous taxis will enter the market. These are expected to be mainly non-controllable (without a steering wheel), reaching a maximum of 2.4 million units by 2038. This maximum mainly depends on the trust in the technology as well as on people’s willingness to give up their own car due to cost or usage factors. As an autonomous taxi supports more people on average, the total number of vehicles is expected to drop from 45.1 million to around 41.7 million by 2040. Furthermore, the percentage of people travelling primarily by taxis and public transport is expected to increase from today’s 20.0% to about 32% in 2040. Then, about 19% will use autonomous taxis at least occasionally. Lastly, the vehicle miles travelled are expected to increase by 25% with people switching from public transport to autonomous taxis.
Objective: Though the mortality rate for motor vehicle collisions (MVCs) has been decreasing since the 1960s with the advent of the first federal seat belt laws in 1968, MVC remains a leading cause of death for individuals aged 1 to 44 years. The purpose of this study is to examine the effects of frontal (FABs) and side airbags (SABs) and electronic stability control (ESC) on the components of the MVC mortality rate.
Methods: The MVC mortality rate from 1994 to 2015 was separated into its components of exposure of vehicles, exposure of travel, collision density, injury incidence, and case fatality rate. Year was categorized on the availability of safety technology in vehicles: 1994–1997 (first-generation FABs mandated), 1998–2001 (sled-certified, second-generation FABs mandated), 2002–2006 (increasing prevalence of SABs and ESC), 2007–2011 (advanced airbags mandated), and 2012–2015 (ESC mandated, SAB in over 90% of vehicles, introduction of advanced safety systems). Relative contributions (RCs) of the components to changes in the MVC-related mortality rate were calculated as the absolute value of the component’s beta coefficient divided by the sum of the absolute values of all components’ beta coefficients. Negative binomial regression–estimated rate ratios (RRs) for the changes in the rate of each component by year category compared to the prior year category.
Results: Significant decreases in the MVC mortality rate were observed for 2007–2011 and 2012–2015. The decrease in 2007–2011 was due in most part to an 18% decrease in the injury incidence (RR = 0.82, P < .0001, RC = 63%), though there was a noted contribution by the decrease in vehicle miles traveled (RR = 0.95, P < .0001, RC = 15%). The continued decrease in mortality in 2012–2015 was due is most part to the 10% decreased case fatality rate (RR = 0.90, P < .0001, RC = 66%) because there was no significant change in the vehicle miles traveled and injury incidence.
Conclusions: The results of this study highlight the effects of vehicle safety technologies on the MVC-related mortality rate and can help direct prevention efforts. Through the study period, there was no meaningful contribution to decreases in the MVC-related mortality rate due to components related to exposure (i.e., vehicles per population and the rate of vehicle miles traveled), suggesting that prevention efforts at decreasing exposure prevalence would have little effect on the MVC-related mortality rate. Instead, prevention efforts should continue to focus on event-phase methods to decrease injury occurrence and mitigate injury severity during the collision.