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Using a stochastic approach, this paper explores and models the basic stochastic characteristics of freeway traffic behavior under a wide range of traffic conditions during peak periods and then applies the models to short-term traffic speed prediction. The speed transition probabilities are estimated from real-world 30-s speed data over a six-year period at three different locations along the 38-mi corridor of Interstate 4 (I-4) in Orlando, FL. The cumulative negative/positive transition probabilities and expected values are derived from the transition probabilities and fitted using logistic and exponential models, respectively. The expected values associated with the most likely transition of speed are then derived from the fitted models and used for predicting speed. Each predicted speed is also associated with a probability value, indicating the chance of observing the occurrence of such transition. The prediction performance was compared for three methods using the root mean square errors (RMSEs). The weighted average method was very close to the higher probability method in most cases. For the two probabilistic methods, the performance was slightly better for the morning peak periods than the evening peak period or all data combined. While the prediction performance of the probabilistic models was comparable with those of other methods found in the literature, the probabilistic approach based on the higher probability provides estimates of the associated probability with each prediction. This provides a measure of confidence in the predicted values before such information is disseminated to the public by traffic agencies.
Intelligent Transportation Systems, IEEE Transactions on (Volume:PP , Issue: 99 )