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Enhancing Traffic Flow and Driving Safety via Artificial Intelligence



Project Details
Author(s) Amir Shahmoradi, University of Texas at Arlington
Co-Author(s) Shima Hamidi, University of Texas at Arlington
CTEDD Funding Year 2019 General RFP
Project Status In Progress
UTC Funding $34,996.50
End Date February 29, 2020

SummarySummary

Motivated by the priorities highlighted by Texas Department of Transportation TXDOT and following the guidelines in the recent presidential Executive Order on Maintaining American Leadership in Artificial Intelligence in 2019, this proposal aims to utilize the state-of-the-art tools and techniques in the field of Artificial Intelligence and Data Science to automatically identify and report traffic-related anomalies and hazards using live traffic camera footage across major highways and arterial roads in the State of Texas. Examples of such hazards that are the focus of this proposal include major vehicle-wildlife and vehicle-debris encounters VWEs and VDEs respectively . Our work builds on top of the existing massive body of literature and research at the intersection of Computer Vision and Traffic Engineering. However, to the extent of our knowledge, this proposal is the first attempt to study the development of an automated pipeline for the detection and reporting of VWEs and VDEs using live traffic camera data. To this aim, we outline and investigate the feasibility of our approach to set up a prototype of a pipeline for ...real-time collection of data, its reduction, segmentation, analysis, and finally, drawing traffic engineering conclusions and recommendations based on the detected patterns or anomalies in the analysis. This exploratory investigation aims to provide a comprehensive review and proof-of-concept to pave the way towards the next-step proposal and implementation of a commercial-scale version of such data-analytics pipeline in collaboration with the potential major stakeholders, in particular, within Dallas-Fort Worth Read more


Subjects#: Artificial Intelligence, Asset Management, Big Data, Traffic Engineering