Principal Investigator: Sanjay Ranka

Sponsor: Florida Department of Transportation

Start Date: March 19, 2018

End Date: June 15, 2020

Amount: $299,717

Abstract

The focus of this work is on development of machine learning techniques that use historical and real-time data for fusion of information and detection of incidents and accidents on arterial networks. This research seeks to investigate the application of machine learning in data relevance amplification and incident detection, explore potential challenges and propose innovative solutions. If machine learning can be successfully integrated with existing systems, it would potentially open an array of future applications and benefits not only in operations aspects but also in safety, reliability and planning. With automated vehicular technology around the corner, the integration of machine learning with signal systems would provide additional capabilities especially in the transition phase where there will be a mix of AV, CV and manual vehicles creating a complex heterogeneous environment.