Research Article
Vehicle Driving Risk Prediction Based on Markov Chain Model
Algorithm 1
The description of MNL-based Markov chain risk state prediction algorithm.
Initialization: | (1) Calculate and obtain the feature variable vector for time window at . | (2) Calculate the initial state probability by Eqn. (5) | (3) Obtain the corresponding independent variable vector for the initially observed time window: | | MNL-based state transition probability estimation: | (4) For | (4.1) Calculate by Eqn. (7). | (4.2) Calculate the state probability distribution at by Markov property: | (4.3) Estimate the three-dimension feature variable vector by solving a set of three equations | according to Eqn. (5) | (4.4) Obtain the updated independent variable vector (assuming the driving mode remains unchanged). | (5) For | (5.1) Calculate by Eqn. (7). | (5.2) Calculate the state probability distribution at by Markov property: | (5.3) Estimate the three-dimension feature variable vector in the same way as step (4.3). | (5.4) Obtain the updated independent variable vector (same assumption as step (4.4)). | Outputs: | (6) Return the predicted state probability distribution for time window : | |
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