BACKGROUND

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.

OBJECTIVE

The objective of this research was to advance the use of AI and ML in analyzing 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 artificial intelligence (AI), machine learning (ML) and Big Data (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. 

STATUS: Publication decision pending.