Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis: A Guide
2025

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The availability of large-scale, high-resolution, and consistently collected road data will improve the safety of all road users. The use of artificial intelligence (AI), machine learning (ML), and big data is key in providing the information needed to power data-driven, public, and proprietary safety analysis tools, as well as predictive and other systemic safety tools.

NCHRP Research Report 1152: Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis: A Guide, a publication from TRB's National Cooperative Highway Research Program, outlines the limitations of traditional safety evaluations and demonstrates how new data sources— like connected vehicles, LiDAR, video analytics, and edge devices—can enhance safety insights and predictive capabilities. Case studies include successful pilots with the Oregon Department of Transportation, showcasing ML models for inventorying streetlight luminaires to inform crash risk analysis, and the City of Bellevue, WA, analyzing vehicle turning behavior to improve safety at signalized intersections.

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110 pages · 8.5 x 11 · paperback
ISBN Paperback: 0-309-59960-1
ISBN Ebook: 0-309-59961-X
DOI: https://doi.org/10.17226/29098

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National Academies of Sciences, Engineering, and Medicine. 2025. Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis: A Guide. Washington, DC: The National Academies Press.

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Front Matter i-x
1 Introduction 1-3
2 Traditional Safety Evaluations 4-9
3 How AI, ML, and Big Data Can Improve Safety Evaluations 10-19
4 Applying AI, ML, and Big Data to Safety Analysis 20-38
5 Additional AI, ML, and Big Data Applications 39-93
References and Bibliography 94-95
Abbreviations and Acronyms 96-100

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Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis: A Guide (2025)

Chapter: 1 Introduction

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Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis: A Guide (2025)

Chapter: 1 Introduction