Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis
The need to improve road safety performance for all road users is clear, particularly for vulnerable road users (such as pedestrians and cyclists), and users of micro-mobility services (such as e-scooters). The optimization of investment by local and state agencies to maximize lives saved and injuries reduced takes on even greater importance when financial resources are constrained. Unlocking the broader sustainable benefits that come from active transportation modes also requires an understanding of the safety performance of infrastructure. The absence of low-cost data, safety performance metrics, and prioritized investment options make it difficult for agencies to understand the business case for safer roads and to measure progress. The objective of this research was to advance the use of artificial intelligence (AI) and machine learning (ML) in analyzing Big Data (BD) and unconventional data and assessing their effectiveness to support safe system and modal priority decision-making as well as performance tracking. This research investigated the use of AI, ML and BD to provide the information needed to power key data-driven, public, and proprietary safety analysis tools as well as predictive and other systemic safety tools.
Language
- English
Project
- Status: Completed
- Funding: $650000
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Contract Numbers:
Project 17-100
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Sponsor Organizations:
National Cooperative Highway Research Program
Transportation Research Board
500 Fifth Street, NW
Washington, DC United States 20001American Association of State Highway and Transportation Officials (AASHTO)
444 North Capitol Street, NW
Washington, DC United States 20001Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Project Managers:
Barcena, Roberto
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Performing Organizations:
University of Washington, Seattle
1107 NE 45th Street, Suite 535
Seattle, WA United States 98105 -
Principal Investigators:
Wang, Yinhai
- Start Date: 20220203
- Expected Completion Date: 20240802
- Actual Completion Date: 20240802
Subject/Index Terms
- TRT Terms: Algorithms; Artificial intelligence; Data collection; Databases; Decision making; Highway safety; Machine learning; Safety analysis; Strategic planning; Vehicle occupants; Vulnerable road users
- Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01767991
- Record Type: Research project
- Source Agency: Transportation Research Board
- Contract Numbers: Project 17-100
- Files: TRB, RIP
- Created Date: Mar 24 2021 6:02PM