Travel time estimation and short-term prediction in urban arterial networks using conditional independence graphs and state-space neural networks

An important component of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) is the travel time estimation and short-term prediction on urban arterial networks. This thesis develops robust and efficient average travel time estimation and short-term prediction model for both congested and non-congested conditions appearing throughout a day on a network. A State-Space Neural Network model is proposed. An innovative implementation of Conditional Independence graph is used to identify the independence and interaction between observable traffic parameters that are used to estimate and predict the travel time. This led to the selection of relevant variables from a set of independent variables for travel time prediction. The predictive and computational performance of the Conditional Independence graph coupled with State-Space Neural Network outperformed the traditional State-Space Neural Network model in this study. The travel time estimation and prediction models are developed for links and routes in an arterial network.