Abstract
Most light-duty vehicle (LDV) crashes occur due to human error. The National Highway Safety Administration (NHTSA) reports that eight percent of fatal crashes in 2018 were distraction-affected crashes, while close to ninety-four percent of all crashes occur in part due to human error. Crash avoidance features could reduce both the frequency and severity of light and heavy-duty vehicle crashes, primarily caused by distracted driving behaviors and/or human error by assisting in maintaining control or issuing alerts if a potentially dangerous situation is detected. As the automobile industry transitions to partial vehicle automation, newer crash avoidance technologies are beginning to appear more frequently in non-luxury vehicles such as the Honda Accord and Mazda CX-9. Additionally, the market penetration of electric vehicles (EVs) is increasing, in turn increasing the weight and size of vehicles on the road. This project develops a replicable, open, deployable model that can: 1) estimate the upper-bound crash avoidance potential that could be achieved as the effectiveness of warning and partial automation systems improve and adoption increases, 2) estimate the societal costs and benefits of fleet-wide deployment of crash avoidance technologies considering technology costs and benefits from avoided and less severe crashes, 3) estimate the number of lives that have been saved by forward collision warning, lane departure warning, and blind spot monitoring, and 4) estimate the effects of vehicle weight and size on crash frequency and severity.
Our hypothesis is that crash avoidance features are becoming more effective over time and helping to reduce the severity and frequency of crashes. However, there are not many frameworks and tools to help state and local agencies assess the private and societal cost and benefits of increased market adoption and how many lives have been saved by these technologies. We also hypothesize that the trend of increasing vehicle weight and size increases the severity of crashes. This paper builds off previous UTC research by starting with a method similar to Harper et al. (2016) and Khan et al. (2019) but uses more recent insurance and crash data, contributes estimates of lives saved in addition to private net benefits and overall societal net benefits, and conducts exploratory analysis on the role that EVs and heavier vehicles play in crash safety.
In the first part of this project, we will compile insurance institute data on crashes and crash severity, helping us to better understand observed changes in crash frequency and severity in vehicles that are equipped with warning and partial automation systems. Second, we will conduct a cost-benefit analysis to estimate the net-private and net-societal benefits of fleet-wide deployment of existing partial automation and warning systems. Third, we will develop a method to estimate the number of lives saved by crash avoidance technologies. Finally, we will conduct exploratory analysis on the role that EVs and heavier vehicles play in crash safety. To do this analysis, we will utilize several publicly available datasets such as 2022 Fatality Analysis Reporting System and observed insurance data from the Insurance Institute for Highway Safety.
In this phase of this project, we will mainly focus on automation and its effects on crash severity and frequency and will use similar datasets to conduct a more in-depth analysis on how EVs and heavier vehicles affect crash safety in future iterations. By focusing on both technologies independently (i.e., non-EVs with partial automation and EVs without partial automation), we can better understand the different ways they are impacting crash safety and how to mitigate any negative effects through policy.
References
Harper, C. D., Hendrickson, C. T., & Samaras, C. (2016). Cost and benefit estimates of partially-automated vehicle collision avoidance technologies. Accident Analysis & Prevention, 95, 104-115.
Khan, A., Harper, C. D., Hendrickson, C. T., & Samaras, C. (2019). Net-societal and net-private benefits of some existing vehicle crash avoidance technologies. Accident Analysis & Prevention, 125, 207-216.
Description
Timeline
Strategic Description / RD&T
This proposal addresses the USDOT’s research priority of “Data-driven System Safety” with the research objective of advancing transportation safety through “safe technology” (pg. 19). Specifically, this project will provide policymakers with a better understanding of the effect that AVs and EVs have on overall road safety, including those in vulnerable populations (e.g., pedestrians and cyclists). Critical research topics (found on pg. 16) such as vehicle and aircraft safety, automation, and connectivity and safety risk analysis methods are also addressed by this project. Finally, the project focuses upon the safety and intelligent vehicle systems goals of the Safety21 University Transportation Center.
Deployment Plan
Quarter 1: Begin compiling insurance and safety data and interact with City of Pittsburgh on local road safety issues and needs.
Quarter 2: Cost benefit analysis and lives saved estimation framework outlined. Interact with PennDOT on state road safety issues and needs
Quarter 3: Compile initial results of analysis. Present initial results to deployment partners. Presentation at the Transportation Research Board with results of our initial modeling efforts.
Quarter 4: Policy brief providing recommendations to policymakers on ways promote a smooth transition to a safe and sustainable transportation system with EVs and AVs. Journal paper on AV safety submitted for review. Exploratory analysis on the impacts of heavier light-duty vehicles on road safety.
Expected Outcomes/Impacts
This project will have a variety of outcomes and impacts:
1. Designing the framework to assess the safety impacts of EVs and AVs lays the foundation for federal, state, and local organizations to better understand cost and benefits of wide-scale deployment of warning and partial automation systems as well as how heavier vehicles on the road could affect road safety.
2. Working closely with the City of Pittsburgh and PennDOT could help inform local and state crash safety policy as the number of EVs and AVs on the road increase. Additionally, this will provide an opportunity for students to network and learn the missions and goals of the City of Pittsburgh and PennDOT, providing them with pathways to obtain internships and higher paying jobs after graduation (e.g., director of planning, policy analyst).
3. Journal papers and policy briefs can inform decision makers and researchers about pathways to help achieve safety goals such as zero roadway fatalities.
Expected Outputs
Anticipated outputs from this proposal include:
1. Novel methods to develop to evaluate the safety implications of EVs and AVs
2. Data sets, modeling results, and online/offline tools to deploy and validate the proposed methods.
3. Memorandum to the City of Pittsburgh and PennDOT and the development of a policy brief to inform future policy-making.
4. Journal paper to disseminate results to research community.
TRID
A TRID search was conducted for the different aspects of the project.
First, a search with the keywords ‘crash avoidance technologies cost benefit analysis’ was done using the TRID database which yielded 23 results. Out of the 23 results there were six projects sponsored by USDOT. Some of the studies explored the cost and benefits of crash avoidance technologies in transit and heavy-duty vehicles (Hickman et al. 2013; Dunn et al. 2007). The most relevant research project was conducted by Hendrickson and Harper (2016) and estimated the societal benefits of fleet-wide deployment of three different crash avoidance technologies in light-duty vehicles. This paper builds off previous UTC research by starting with a method similar to Hendrickson and Harper (2016) but uses more recent insurance and crash data and contributes estimates of lives saved and private benefits in addition to overall societal net benefits.
Second, we conducted a search with the keywords ‘lives saved crash avoidance technologies’ using the TRID database and yielded 4 results. Out of the 4 results there was one project sponsored by USDOT. This study done by (Glassbrenner 2011) focuses on how newer cars in general have impacted road safety but does not assess the effects of any specific in-vehicle technologies. This proposal makes a contribution to the literature by assessing how specific warning technologies (e.g., forward collision warning) and partial automation systems (e.g., automatic emergency braking) have impacted the number of fatalities.
Finally, we conducted a search with the keywords ‘vehicle weight and size crash severity’ using the TRID database and yielded 90 results. Out of the 90 results there were 19 projects sponsored by USDOT. Most studies were done more than 20 years ago (O’Neil et al. 1974; Carlson 1978) or focus on heavy duty trucks (USDOT 2015). We make a contribution to the literature by using more recent crash data and focusing on crashes involving light-duty vehicles.
References
Carlson, W. L. (1978). Empirical Crash Injury Modeling and Vehicle-size Mix (No. DOT-HS-803 349).
Dunn, T. P., Laver, R., Skorupski, D., & Zyrowski, D. (2007). Assessing the Business Case for Integrated Collision Avoidance Systems on Transit Buses.
Glassbrenner, D. (2011). An Analysis of Improvements to Vehicle Safety and Their Contribution to Recent Declines in Fatalities and Injury Rates. In 22nd International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Traffic Safety Administration (No. 11-0228).
Hendrickson, C. T., & Harper, C. (2018). Safety and Cost Assessment of Connected and Automated Vehicles.
Hickman, J. S., Guo, F., Camden, M. C., Flintsch, A. M., Hanowski, R. J., & Mabry, J. E. (2013). Onboard Safety Systems Effectiveness Evaluation.
O'neill, B., Hoksch, H., & Haddon, W. (1974). Empirical relationships between car size, car weight and crash injuries in car-to-car crashes. In Proceedings: International Technical Conference on the Enhanced Safety of Vehicles (Vol. 1974, pp. 362-368). National Highway Traffic Safety Administration.
USDOT (2015). Highway Safety and Truck Crash Comparative Analysis Technical Report: Comprehensive Truck Size and Weight Limits Study.
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| hcain@andrew.cmu.edu |
Cain, Heather |
College of Engineering |
Other |
Staff - Business Manager |
| cdharper@andrew.cmu.edu |
Harper, Corey |
College of Enigneering/Heinz |
PI |
Faculty - Untenured, Tenure Track |
| cth@cmu.edu |
Hendrickson, Chris |
College of Engineering |
Co-PI |
Faculty - Tenured |
| bethannh@andrew.cmu.edu |
Hockenberry, Beth |
College of Engineering |
Other |
Staff - Business Manager |
Budget
Amount of UTC Funds Awarded
$98000.00
Total Project Budget (from all funding sources)
$200000.00
Documents
Match Sources
No match sources!
Partners
| Name |
Type |
| City of Pittsburgh |
Deployment & Equity Partner Deployment & Equity Partner |
| Pennsylvania Department of Transportation |
Deployment & Equity Partner Deployment & Equity Partner |
