Connected and Automated Vehicles and Safety of Vulnerable Road Users: A Systems Approach

McDonald, Noreen C; Khattak, Asad J.; Combs, Tabitha S; Shay, Elizabeth; University of North Carolina at Chapel Hill. Dept. of City and Regional Planning; University of Tennessee, Knoxville. Center for Transportation Research; Appalachian State University

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Connected and Automated Vehicles and Safety of Vulnerable Road Users: A Systems Approach

2018-11-30
By McDonald, Noreen C ; Khattak, Asad J. ; Combs, Tabitha S ; ...

[PDF-3.11 MB]

English

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Details:

Creators:

McDonald, Noreen C ; Khattak, Asad J. ; Combs, Tabitha S ; Shay, Elizabeth

McDonald, Noreen C ; Khattak, Asad J. ; Combs, Tabitha S ; Shay, Elizabeth Less -
Corporate Creators:

University of North Carolina at Chapel Hill. Dept. of City and Regional Planning ; University of Tennessee, Knoxville. Center for Transportation Research ; Appalachian State University

University of North Carolina at Chapel Hill. Dept. of City and Regional Planning ; University of Tennessee, Knoxville. Center for Transportation Research ; Appalachian State University Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; Collaborative Sciences Center for Road Safety ; University of North Carolina at Chapel Hill

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; Collaborative Sciences Center for Road Safety ; University of North Carolina at Chapel Hill Less -
Subject/TRT Terms:

[+]

Autonomous Vehicles Behavior Connected Vehicles Crash Risk Forecasting Data Analysis Evaluation And Assessment Literature Reviews Pedestrian Safety Pedestrian Vehicle Crashes Risk Analysis Spatial Analysis System Safety Text Analysis Vulnerable Road Users
Publication/ Report Number:

CSCRS-R6
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report (March 2017 – November
Corporate Publisher:

Collaborative Sciences Center for Road Safety ; University of North Carolina, Chapel Hill

Collaborative Sciences Center for Road Safety ; University of North Carolina, Chapel Hill Less -
Abstract:

Connected and automated vehicle (CAV) technologies can dramatically improve safety by reducing human errors, which contribute substantially (an estimated 94 percent) to roadway crashes. CAVs can eventually operate effectively on roadways without experiencing decreased performance due to distraction or fatigue. However, technological advances will not uniformly decrease crash risks. Some environments, crash types, and user groups will continue to experience elevated risks, particularly vulnerable road users such as pedestrians. This project addresses these critical safety issues by: 1) Assessing the current and future landscape of pedestrian and vehicle conflicts; 2) Identifying how vehicle technology, planning policies, and data analytics can provide systemic solutions to pedestrian-vehicle conflicts; and 3) Using data analytics to identify dangerous pre-crash behaviors. This trans-disciplinary and multimodal approach is critical because solutions require insights from multiple fields. The information presented includes literature reviews on current patterns of pedestrian-vehicle conflicts, assessment of how planning and physical design strategies can reduce pedestrian-CAV conflicts. Furthermore, risk analysis was conducted based on Fatality Accident Reporting System (FARS) data, and SHRP2 Naturalistic Driving Study data. An assessment of how automated vehicle technology will impact crash risk and how future countermeasures may change with the adoption of emerging technologies is provided. The team has analyzed safety data from diverse sources and propose a framework to link automation technology to human error/crash typologies. Overall, the study applies innovative statistical, artificial intelligence, and visualization tools to extract valuable information from studies and data, with the purpose of improving safety across modes, especially for vulnerable road users.
Format:

PDF
Funding:

69A3551747113
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha-512:157df86b39e501c318446d40db6f979778b81fda3aa189d67dbb91d07f86c0c06640d2dd2681e57146424d5ad70b5215dd8ee50ca0dc12667b25ef3413f165c7
Download URL:

https://rosap.ntl.bts.gov/view/dot/62321/dot_62321_DS1.pdf
File Type:

	Brief	pdf
	Slides	pdf

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