Real-time surrogate safety analysis solution using Lidar technology powered by artificial intelligence

The safety of intersections, interchanges, and other traffic facilities is most often assessed by tracking and analyzing police-reported motor vehicle crashes over time. Given the infrequent and random nature of crashes, this process is slow to reveal the need for remediation of either the roadway design or the flow-control strategy. This process is also not applicable to assess the safety of roadway designs that have yet to be built or flow-control strategies that have yet to be applied in the field. In this project, we are building an automated surrogate safety analysis platform that uses real-time traffic data generated by BCT Lidar-based traffic monitoring system and converts them into safety metrics such as near-misses, time to collision, etc. These metrics will be available to the city planners using a dashboard in real-time. This project speeds up the development of BCT’s SaaS platform that generated a new stream of revenue along with its standalone sensor sale. Additionally, this analysis will automate the process that currently BCT is doing manually to generate safety reports for its customer.

Faculty Supervisor:

Yann-Gael Gueheneuc


Mohammad Nazemi


Blue City Technology


Computer science




Concordia University



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