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.
Modeling Pipeline Driving Behaviors
Hidden Markov Model Approach
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 1980 , 1 ; 16-23
2006-01-01
Article (Journal)
Electronic Resource
English
Modeling Pipeline Driving Behaviors: Hidden Markov Model Approach
British Library Conference Proceedings | 2006
|Modeling Pipeline Driving Behaviors: Hidden Markov Model Approach
Online Contents | 2006
|Modeling Pipeline Driving Behaviors: Hidden Markov Model Approach
Transportation Research Record | 2006
|Modeling of Human Behaviors in Real Driving Situations
British Library Conference Proceedings | 2001
|Modeling of human behaviors in real driving situations
IEEE | 2001
|