Modeling Arterial Travel Time with Limited Traffic Variables using Conditional Independence Graphs & State-Space Neural Networks

This paper presents travel time prediction models for both congested and non-congested conditions on urban arterial using only limited basic traffic data. The state-space notion of traffic processes and State-Space Neural Network (SSSNNet) models are used on simulation generated traffic data. Conditional Independence (CI) graphs are used to identify independence and interaction between observable traffic parameters thus only relevant ones can be used to predict travel time. Even with limited data, the predictive performance and computational efficiency of Conditional Independence Graphs coupled with State-Space Neural Networks are practically accurate. They also outperformed a traditional Artificial Neural Network model.