Abstract
Most light-duty vehicle (LDV) crashes occur due to human error. The National Highway Safety Administration (NHTSA) reports that about nine percent of fatal crashes in 2019 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. Although there has been research done to better understand the effects of warning and partial automation systems and EVs on traffic safety at the national level, we are not aware of any analysis that has been at the state or local level. While conducting research at the national level helps to inform federal policy, case studies are needed at the state and local levels to help better inform state and local policy and infrastructure investment decision-making. This project develops a replicable, open, deployable model that can: 1) identify crashes that occurred in Pennsylvania (PA) involving EVs or vehicles with warning or Level 1 systems, 2) assess the distribution of crashes involving EVs or vehicles with warning or Level 1 systems across factors such as weather conditions, vehicle speed, crash severity, pre-crash movement, facility type, and time of day, 3) estimate the relationship between contributing factors and the severity of crashes involving EVs or vehicles with warning or Level 1 features using regression analysis, and 4) make recommendations to the Pennsylvania Department of Transportation (PennDOT) and the City of Pittsburgh on policy and infrastructure improvements to improve traffic safety.
Our hypothesis is that current and past vehicles with warning systems and Level 1 features perform well in certain driving scenarios, facility types, and weather conditions but not others. However, there are not many frameworks and tools to help state and local agencies better understand the causes of crashes involving partially automated features, the locations of crash hotspots, and the infrastructure improvements and policies that could enhance road safety with automated vehicles (AVs). Additionally, this study should help technology providers better understand under which scenarios and conditions further testing and technology development is needed. We also hypothesize that the characteristics and patterns of crashes involving EVs differ from crashes not involving this technology.
Most studies that have assessed the role of warning and partial automation systems on traffic safety have focused on national-level analysis (Harper et al., 2016; Khan et al., 2019). This study builds off of previous UTC projects and add to the literature by being the first analysis we are aware of to provide a framework for state and local agencies to assess factors contributing to crashes involving vehicles with warning systems and Level 1 features and identify locations and the infrastructure upgrades needed to support today’s sensing systems.
In the first part of this project, we will compile crash data to better understand the frequency of crashes with warning systems and Level 1 features across contributing factors (e.g., weather or vehicle speed). Second, we will incorporate this data into a regression model to better understand the relationship between the contributing factors and the frequency and severity of crashes. Third, we will conduct an exploratory analysis on the contributing factors for crashes involving EVs. Finally, we will make recommendations to PennDOT and the City of Pittsburgh on policy and infrastructure improvements to improve traffic safety. To do this analysis, we will use the PennDOT crash dataset, which contains information on all police-reported crashes of all severities for PA.
In this phase of this project, we will mainly focus on contributing factors for crashes involving vehicles with warning systems and Level 1 features and will use similar datasets to conduct a more in-depth analysis on the contributing factors for EV crashes in future iterations. Because of Tesla’s dominance in both the EV and AV market space it’s important to consider both the role that EVs and AVs play in crash safety. By focusing on both technologies independently, 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. https://doi.org/10.1016/j.aap.2016.06.017
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. https://doi.org/10.1016/j.aap.2019.02.003
Description
Timeline
Strategic Description / RD&T
Section left blank until USDOT’s new priorities and RD&T strategic goals are available in Spring 2026.
Deployment Plan
Quarter 1: Begin compiling safety data and meet with PennDOT on AV and EV road safety issues and needs.
Quarter 2: Crash identification and regression analysis framework outlined and meet with City of Pittsburgh on AV and EV road safety issues and needs.
Quarter 3: Compile initial results of analysis. Present initial results to deployment partners (i.e., PennDOT and City of Pittsburgh). 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 contributing factors of crashes involving EVs.
Expected Outcomes/Impacts
This project will have a variety of outcomes and impacts:
1. Designing the framework to assess the contributing factors for crashes involving AVs and EVs lays the foundation for state and local organizations to better understand the infrastructure improvements and policies that are needed to enhance AV and EV 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 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 and frameworks for state and local agencies 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
First, a search with the keywords ‘automated vehicle crashes contributing factors’ was done using the TRID database which yielded 57 results. Out of the 57 results there were ten projects sponsored by USDOT. The most relevant research project was by Harper and Hendrickson (2023), which assessed the contributing factors in crashes involving automated driving systems (i.e., Level 3 and above) (Harper and Hendrickson, 2023). While Harper and Hendrickson’s (2023) project assesses the contributing factors for crashes involving automated vehicles (AVs), their study utilizes national crash data and focuses on higher level AV features. We contribute to the literature by focusing on warning systems and Level 1 features and using state level crash data.
Finally, we conducted a search with the keywords ‘electric vehicle crashes contributing factors’ using the TRID database and yielded 12 results. Out of the 12 results there were 2 projects sponsored by USDOT. The most relevant research project was by Harper and Hendrickson (2023), which assessed the contributing factors in crashes involving electric vehicles (EVs) (Harper and Hendrickson, 2023). While Harper and Hendrickson’s (2023) project assesses the contributing factors for crashes involving EVs, their study utilizes national crash data. We contribute to the literature by using state level crash data in order to motivate state and local policy.
References
Harper, C. D., Hendrickson, C. (2023), Analysis of Contributing Factors in Crashes Involving Electric Vehicles and Vehicles with Automated Features, USDOT UTC Safety 21 Project.
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| cdharper@andrew.cmu.edu |
Harper, Corey |
Carnegie Mellon University |
PI |
Faculty - Untenured, Tenure Track |
| cth@andrew.cmu.edu |
Hendrickson, Chris |
Carnegie Mellon School of Engineering |
Co-PI |
Faculty - Tenured |
Budget
Amount of UTC Funds Awarded
$100000.00
Total Project Budget (from all funding sources)
$200000.00
Documents
Match Sources
No match sources!
Partners
| Name |
Type |
| City of Pittsburgh |
Deployment Partner_ Deployment Partner_ |
| PennDOT |
Deployment Partner_ Deployment Partner_ |
