Best Practice Guide to Understanding Near Miss Conflict Analytics
Separating fact from fiction: The best practice guide to understanding near miss conflict analytics and why the right machine-learning models and methodologies matter.
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Separating fact from fiction: The best practice guide to understanding near miss conflict analytics and why the right machine-learning models and methodologies matter.
To transform the way road safety is understood, AMAG leverages sophisticated video and Lidar analytics technologies, cloud and edge computing, vision engineering, artificial intelligence, and advanced econometric and statistical techniques to identify and diagnose safety concerns well before accidents occur—delivered through a Software as-a-Service platform called SMART. “We are a company focused on social good. Our success directly translates to saving lives, preventing injuries, making transport systems more efficient, and making society more livable,” expresses Simon Washington, managing director and CEO of AMAG.
what are critical conflicts? And why do we use these to inform road safety? It has been suggested that “For every passage of a motorist within the domain of a traffic facility, there is a chance set up for a collision to take place.” – Hauer. Building on this, each passage taken by a road user can be seen as a trial with a non-zero underlying chance of failure. It means that, no matter what, there is always a chance of a collision — even if the incident hasn’t happened yet.
AMAG CMO Darren Needham-Walker advocating for change from legacy reactive methods to proven proactive methods of managing risk on our nation’s roads.
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