• To investigate the effect of spatial scale on road safety monitoring and crash prediction
• To develop a new art AI framework to observe and analyse road safety KPIs and predict crashes by achieving transition from smaller scales (e.g., on a segment, intersection or neighbourhood level) to larger ones (e.g., highway corridor or prefecture/county level), taking into account the time dimension
• To assess the effectiveness and scalability of microscopic road safety models for macroscopic crash prediction and vice versa
• To develop a new art AI framework to observe and analyse road safety KPIs and predict crashes by achieving transition from smaller scales (e.g., on a segment, intersection or neighbourhood level) to larger ones (e.g., highway corridor or prefecture/county level), taking into account the time dimension
• To assess the effectiveness and scalability of microscopic road safety models for macroscopic crash prediction and vice versa
• Evaluation of several scaling combinations that will also feature capabilities of ‘zooming in/zooming out’ of study areas using different levels of telematics (e.g., trip-based, driver-based or network-based using several drivers)
• Knowledge on comparable advantages and disadvantages for each analysis scale
• A case study utilising driver telematics in an urban area, with actionable results, compatible with the vision and activities of OSeven – showcasing the impact of using AI for micro-analysis based on driver telematics and integrating the findings to larger scales
| ID | at23 |
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