Stochastic Approach for Short-Term Freeway Traffic Prediction During Peak Periods

Abstract

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.

On-road PHEV power management with hierarchical strategies in vehicular networks

“…A driver's future average speed on a road is approximated as the same as the road's average traffic speed prediction. It is assumed that next 30-minute traffic speed predictions are available for freeways and urban arterials in the form of expectations and distribution probabilities, which is reasonable according to recent research results [17], [18], [19]. For other roads without prediction, their historical average speeds and speed transition probabilities are used.…”

A New Approach to Regional Traffic Estimation for Intelligent Transport Systems

“…To mitigate the burden of the underlying road networks, efficient traffic management is of great importance, and metropolitanscale traffic estimation is valuable to traffic management and to build smart city. To obtain precise traffic information for future plans, many existing studies [1][2][3][4][5][6][7] have been focused on the future traffic estimation, which aim to predict the urban traffic situations for the nearby time periods.…”

An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division

“…With the increasingly rapid economic globalization and urbanization, traffic congestion becomes a critical problem and causes great concern among people and the governments in metropolises [1,2]. To alleviate traffic congestion, a large amount of money was spent on traffic planning and management, especially in traffic subarea division as this is crucial for ITSs.…”