  Recent research has found that navigation systems usually assume that all
roads are equally safe, directing drivers to dangerous routes, which led to
catastrophic consequences. To address this problem, this paper aims to begin
the process of adding road safety awareness to navigation systems. To do so, we
first created a definition for road safety that navigation systems can easily
understand by adapting well-established safety standards from transportation
studies. Based on this road safety definition, we then developed a machine
learning-based road safety classifier that predicts the safety level for road
segments using a diverse feature set constructed only from large-scale publicly
available geographic data. Evaluations in four different countries show that
our road safety classifier achieves satisfactory performance. Finally, we
discuss the factors to consider when extending our road safety classifier to
other regions and potential new safety designs enabled by our road safety
predictions.
