Abstract
The Safe System (SS) approach to road safety emphasizes safety‐by‐design through ensuring safe vehicles, road networks, and road users. With a strong motivation from the World Health Organization (WHO), this approach is increasingly adopted worldwide. Considerations in SS, however, are made for the medium‐to‐long term. Our interest in this work is to complement the approach with a short‐to‐medium term dynamic assessment of road safety. Toward this end, we introduce a novel, cost‐effective Internet of Things (IoT) architecture that facilitates the realization of a robust and dynamic computational core in assessing the safety of a road network and its elements. In doing so, we introduce a new, meaningful, and scalable metric for assessing road safety. We also showcase the use of machine learning in the design of the metric computation core through a novel application of Hidden Markov Models (HMMs). Finally, the impact of the proposed architecture is demonstrated through an application to safety‐based route planning.
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