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Using Big Spatiotemporal Data for Road Safety

Primary supervisor

Aamir Cheema


Research area

Data Engineering

On their own, traffic accidents cause 1.3 million fatalities every year – and improper situational awareness is often a major cause. This project aims to exploit big spatio-temporal data to design intelligent techniques for scheduling and offloading tasks to the cloud and peer vehicles. This will ultimately meet the Quality of Service (QoS) requirements of time-critical road safety applications and increase situational awareness by automatically identifying unsafe road conditions and risky driving behaviors – and sending alerts in real time to affected vehicles.

Required knowledge

Required knowledge


  • First class Honors or Masters degree including substantial research project, GPA 80%+ from a reputed university
  • Refereed publications including journal or conference of high repute


  • Background in Machine Learning, Algorithms and Data Structures

Learn more about minimum entry requirements.