The availability of traffic data and computers now makes it possible to build data-driven models that capture the evolution of the state of traffic along modeled stretches of road. These models are used for short-time prediction so that transportation facilities can be operated in an efficient way that guarantees a high level of service. In this project, we adopted Dynamic Linear Models (DLM) to speed evolution at one road stretch of the I-64. The proposed approach is expected to be a tool used in daily routines to enhance proactive decision support systems.

• Traffic Speed in one of segments was chosen as an input data.
• It was dynamically divided into training and testing set.
• Short-term Predictions were made on the testing set.
• 4 separate models were created namely,

Model 1 : Weather and Visibility as Regressor

Model 2 : Weather, Visibility as Regressor with 2nd Degree Polynomial Degree

Model 3 : Weather, Visibility and Speed in other segments as Regressor

Model 4 : Weather, Visibility and Speed in other segments as Regressor with 2nd Degree Polynomial Degree

• • E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, "Short-term traffic forecasting: Where we are and where we’re going," Transportation Research Part C: Emerging Technologies, vol. 43, pp. 3-19, 2014.
• • B. M. Williams and L. A. Hoel, "Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results," Journal of transportation engineering, vol. 129, pp. 664-672, 2003