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Media Release

Advanced Mobility Analytics Group Recognized as Australian Top 10 Startup for 2021

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 Softwareas-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. The company helps governments overcome the challenges of legacy systems and methods for assessing and actioning road user safety improvements by delivering technology that identifies infrastructure and operational deficiencies before they contribute to serious crashes. AMAG’s SMART delivers solutions to local, regional, and state governments to better manage and operate transport systems for improved safety for all road users.

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Analytics

A Bivariate Extreme Value Model for Estimating Crash Frequency by Severity using Traffic Conflicts

Despite more than 50 years of research and advancements in road safety evaluations using traffic conflict methods, a major lacuna that hampered their widespread application was their inability to accurately predict the severity component of crashes in addition to their frequency. Ignoring crash severity in road safety evaluations could be counterproductive as the safety treatments being implemented could decrease the number of crashes occurring at a location, but, possibly, increase their

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Analytics

Are all traffic conflict studies equal? – How to ensure reliability

Well-known limitations with the quality of crash data and crash-based safety analysis methods is giving rise to the use of alternate measures of safety such as the presence of ‘critical’ traffic conflicts, or near misses. The traffic conflict technique has gained considerable popularity as a proactive surrogate tool for road safety analysis . Compared to crashes, conflicts occur much more frequently, and as such are

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Media Release

Advanced Mobility Analytics Group Partners with Boulder AI

AMAG has partnered with Boulder AI to deliver world-leading video and predictive analytics for road safety management and operations. Boulder AI’s patented ultra high definition, high dynamic range, edge-enabled camera (DNN Cam), with IoT and app store capability, will unleash the technology that AMAG is delivering through its SMART Platform. Together, Boulder AI and AMAG are poised to deliver world-leading technology for managing road safety

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Analytics

Can the measurement of critical conflicts using video analytics revolutionise the way we manage road safety and operations?

For more than 25 years, the modern road safety profession has relied on police reported crashes to evaluate crash risk, implement countermeasures, and evaluate their effectiveness. While a great deal of excellent methodologies have been developed to make the best use of crash data, their limitations are no longer acceptable in light of the massive technological advances available today.

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A systematic mapping review of surrogate safety assessment using traffic conflict techniques

Safety assessment of road sections and networks have historically relied on police-reported crash data. These data have several noteworthy and significant shortcomings, including under-reporting, subjectivism, post hoc assessment of crash causes and contributing factors, limited behavioural information, and omitted potential important crash-related factors resulting in an omitted variable bias. Moreover, crashes are relatively rare events and require long observation periods to justify expenditures. The rarity

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