Modelling driver expectations for safe speeds on freeway curves using Bayesian belief networks.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to model driver expectations using measured speeds in 153 curves and data on the characteristics of the curve approaches. The probabilities were given in the referenced paper. The vertices are:
(A010-100, A100-200, A200-310);
(Present, Not Present);
(Left, Right);
(S060-069, S070-079, S080-089, S090-099, S100-109, S110-119, S120-129, S130-140);
(One, Two, Three, Four);
(S060-080, S080-100, S100-120, S120-140, Tangent);
(Connector Road, Deceleration Lane, Fork, Main Carriageway, Merge, Weaving Section);
(AdvSpeed50, AdvSpeed60, AdvSpeed70, AdvSpeed80, AdvSpeed90, SpeedLimit50, SpeedLimit60, SpeedLimit70, SpeedLimit80, SpeedLimit90, NoSpeedLimit);
(Present, Not Present);
Vos, J., Farah, H., & Hagenzieker, M. (2024). Modelling driver expectations for safe speeds on freeway curves using Bayesian belief networks. Transportation Research Interdisciplinary Perspectives, 27, 101178.