DC3. AI for road safety in LMICs



  • Define a framework for exploiting available data sources and adapting existing road survey databases (e.g., SLAIN) inLMICs
  • To perform AI-supported knowledge discovery in databases utilising extraction of implicit, previously unknown, and potentially useful
  • information from data collected for other purposes
  • Facilitate transport modelling with AI deep learning to establish predictive crash models to support transport infrastructure investment
  • decisions in LMIC on road networks where traditional data is non-existing or unreliable

Expected results:

  • Identification and classification of potential structured and unstructured big data sources for the development of road safety
  • performance assessments and predictive crash modelling
  • Innovative and cost-effective data acquisition, processing and analysis methods to support road safety management in
  • LMIC (performance indicators, star rating, risk mapping, road safety audit and road safety inspection)
  • A taxonomy of data, methods and tools tailored for AI road safety support to less resourced countries

Planned secondment(s):

  • EIRA-SI, Supervisor: Purpose: to test the developed methods on datasets from LMICs