Modeling pipeline driving behaviors: Hidden Markov model approach

Driving behaviors at intersections are complex. At intersections, drivers face more traffic events than elsewhere and are thus exposed to more potential errors with safety consequences. Drivers make real-time responses in a stochastic manner. This study used hidden Markov models (HMMs) to model the driving behavior of through-going vehicles on major roads at intersections. Observed vehicle movement data were used to estimate the model. A single HMM was used to cluster movements when vehicles were close to the intersection. The reestimated clustered HMMs could more accurately predict vehicle movements compared with traditional car-following models.