These proactive solutions offer the promise of using surrogate measures of safety, rather than crashes, to inform and guide safety investment and management decisions, with the intent to deliver safer operations.
This project is funded by Road Safety and Innovation Fund Round 2 that is currently conducted by Dr. Shamsi Shekari (leader) and Prof Simon Smith (Chief Investigator) at the Institute for Social Science Research, the University of Queensland. This is the first study in Australia that assesses schoolchildren’s safety via a vision-based tracking platform.
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
Widespread adoption of automated video-based traffic management systems can revolutionise the way we manage transport networks .
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
AMAG has been awarded $750,000 to accelerate their AI-Enabled Predictive Analytics Platform for Improved Road Safety.
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
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.
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
Advanced Mobility Analytics Group (AMAG) (www.amagroup.io) is looking to expand our amazing development team to further advance our world-leading Safe Mobility Alert Real Time (SMART) Digital Platform—delivered via Software-as-a-Service (SaaS). The SMART Platform supports Vision Zero and Safe Systems policy goals of local, regional, and state departments of transport globally. Using video analytics, artificial intelligence (AI), deep learning, and advanced econometrics techniques, AMAG has solved
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